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Aiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analysis (SSA) and Long and Short-Term Memory Neural Network (LSTM) was proposed by mining the data features in the time-series data in the wind speed sensors. Firstly, SSA was used to pre-process the wind speed sensor monitoring data, and the wind speed data was decomposed into trend component, periodic component and noise component. The data noise generated by turbulent pulsation was removed via reorganizing the trend component and noise component. The LSTM parameters was then optimized, and the optimized LSTM model was used to predict the pre-processed data and obtain the reconstructed wind speed. Finally, the anomaly fraction of the monitored wind speed and reconstructed wind speed was calculated by using the logarithmic probability density function. Anomaly detection for monitoring wind speed was performed by calculating the threshold set value of training set data samples. The experimental results shown that, the removing effect for the data noise generated by turbulence pulsation via SSA was better. Removing the noise component without affecting the data fluctuation was helpful in improving the wind speed reconstruction effect and the anomaly detection accuracy. LSTM can correctly reconstruct the small amplitude wave due to turbulence pulsation without anomalous fluctuation and fits well with the actual data. The reconstruction of abnormal fluctuation segment based on historical fluctuation trend when there was abnormal fluctuation can effectively improve the accuracy of anomaly detection. Through comparative analysis, the reconstruction effect of proposed method in this paper was better than ARIMA, BP and CNN models, with an anomaly detection accuracy of 99.2% and an F1-Score of 0.97, which verified the reliability of the proposed method. The method proposed in the paper has important application value in detecting the abnormal fluctuation of wind speed caused by the opening and closing of dampers.

Abstract 2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.

Published studies on outdoor air pollution and tuberculosis risk have shown heterogeneous results. Discrepancies in prior studies may be partially explained by the limited geographic scope, diverse exposure times, and heterogeneous statistical methods. Thus, we conducted a multi-province, multi-city time-series study to comprehensively investigate this issue. We selected 67 districts or counties from all geographic regions of China as study sites. We extracted data on newly diagnosed pulmonary tuberculosis (PTB) cases, outdoor air pollutant concentrations, and meteorological factors in 67 sites from January 1, 2014 to December 31, 2019. We utilized a generalized additive model to evaluate the relationship between ambient air pollutants and PTB risk. Between 2014 and 2019, there were 172,160 newly diagnosed PTB cases reported in 67 sites. With every 10-μg/m3 increase in SO2, NO2, PM10, PM2.5, and 1-mg/m3 in CO, the PTB risk increased by 1.97% [lag 0 week, 95% confidence interval (CI): 1.26, 2.68], 1.30% (lag 0 week, 95% CI: 0.43, 2.19), 0.55% (lag 8 weeks, 95% CI: 0.24, 0.85), 0.59% (lag 10 weeks, 95% CI: 0.16, 1.03), and 5.80% (lag 15 weeks, 95% CI: 2.96, 8.72), respectively. Our results indicated that ambient air pollutants were positively correlated with PTB risk, suggesting that decreasing outdoor air pollutant concentrations may help to reduce the burden of tuberculosis in countries with a high burden of tuberculosis and air pollution.

This article investigates the time-series properties of cryptocurrency returns and compares them with currency and commodity returns. We perform and analyze the mean reversion, normality, unit root, high and low returns, correlation, Autoregressive Moving Average (ARMA) [2,2], Autoregressive (AR) [5], and long-run components in the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [1,1] estimates. We also perform regression analyses to evaluate two possible behavioral biases: familiarity and disposition effect. Our time series analysis documents that cryptocurrencies are neither currencies nor commodities. We also show that adding cryptocurrency to a portfolio increases market efficiency and uncertainty. We also document that cryptocurrency investors exhibit the same familiarity and disposition effect biases as commodity and currency investors. Overall, we conclude that investors in cryptocurrencies tend to underestimate risk and misestimate future prices, as they do in commodity and currency markets. This study makes at least three contributions to the literature. First, we evaluate whether cryptocurrencies tend to hedge or financialization. Second, our analysis includes both univariate and portfolio dimensions. Third, this is a pioneering study on using behavioral bias analysis to determine whether a cryptocurrency is a commodity or a currency.

Background The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies’ impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited. Objective To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia. Methods Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020–2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods. Results The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies. Conclusion This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures’ intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

The Raniganj coalfield, which is the oldest coal mine in India, is susceptible to ground subsidence. For the purpose of detecting surface deformation, this work makes use of field surveys, interferometric synthetic aperture radar (InSAR), and Differential GPS (DGPS). The study utilised Sentinel-1 InSAR data spanning from 2017 to 2023. PS-InSAR was used for both ascending and descending datasets, while SBAS-InSAR was used for descending datasets alone. Records show a maximum subsidence rate of −21.18 mm/year. Three surface deformation maps were generated from time-series assessments of individual locations, revealing regions undergoing significant changes. Known mining collapse locations and continuing deformation zones were designated with four differential GPS stations. By analysing the DGPS data with the GAMIT/GLOBK software, we were able to measure surfaces that were undergoing rapid deformation. Using Google Earth Engine (GEE), we generated thermal maps to delve deeper into the coal fire activity. We found coal bed methane (CBM) mining causing substantial subsidence in Kataberia and the surrounding areas. Shyamsundarpur and New Kenda are impacted by mining voids and coal fire-induced subsidence, respectively. The results of this study give the Raniganj region’s decision-making procedures more credibility in terms of minimising and controlling geological dangers.

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data. However, the availability of usable observations greatly varies spatially and temporally. Pixel-based compositing that generates temporally equidistant cloud-free synthetic images can mitigate temporal variability, by constructing uninterrupted time series using different compositing windows. Here, we evaluated the feasibility of using compositing windows ranging from five days to one year for 1984–2021 Landsat and 2015–2021 Sentinel 2 time series to derive uninterrupted time series across Europe. We considered separate and joint use of both data archives and analyzed the spatio-temporal availability of composites during each calendar year and pixel-specific growing season across a variety of time windows and hypothesizing data interpolation. Our results demonstrated opportunities and limitations in the available data records to support medium- and long-term analyses requiring uninterrupted time series of composites with sub-annual temporal resolution. Spatial disparities across different compositing windows provide guidance on the feasibility of workflows relying on different data densities and on the challenges in wall-to-wall analyses. The feasibility of consistent time series based on composites with sub-monthly aggregation periods was mostly limited to the combined Landsat and Sentinel-2 archives after 2015, yet in some geographies requires interpolation of up to 50% of data.

ABSTRACTPoverty continues to pose significant global challenges. Analyzing poverty distribution is pivotal for identifying spatial and demographic disparities, informing targeted policy interventions, and fostering inclusive and equitable development. The absence of a worldwide pixel-scale time-series poverty dataset has hampered effective policy formulation. To address this gap, we employ the international wealth index (IWI) derived from household survey data to represent poverty levels. Subsequently, a random forest regression model was constructed, with IWI serving as the dependent variable and representative features extracted from nighttime lights, land cover, digital elevation model, and World Bank statistical data serving as independent variables. This yielded a global map of the IWI for low- and middle-income nations at a 10-km resolution spanning 2005 to 2020. The model demonstrated robust performance with an R2 value of 0.74. Over the studied period, areas and populations with IWI ≤ 50 decreased by 8.85% and 16.17%, indicating a steady decrease in global poverty regions. Changes in the IWI at the pixel scale indicate that areas closer to cities have faster growth rates. Furthermore, our poverty estimation models present a novel method for real-time pixel-scale poverty assessments. This study provides valuable insights into the dynamics of poverty, both globally and nationally.

Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner. [ABSTRACT FROM AUTHOR]

COVID-19 is a deadly infection that causes severe acute respiratory syndrome. Although not particularly spreading rapidly as before due to the introduction of vaccines and other measures, its effect still portends grave danger to human lives in Nigeria and other countries. This study aimed to model and forecast Nigeria's COVID-19 (new) trend of confirmed cases, discharged (recovery) cases and deaths and also to examine the pattern of the infection and survival rate in the face of vaccine introduction. The Box-Jenkins methodology was employed in this study to model and forecast COVID-19 confirmed cases, discharged cases and deaths. The data used for this study was secondary data of weekly confirmed cases, recoveries (discharged) and deaths extracted from the weekly publication of the Nigeria Centre for Disease Control (NCDC). The mean survival rate of COVID-19 was found to be 0.7765 and the three series were found to be stationary after differencing. Also, from an array of candidate models obtained through Autocorrelation Function (ACF) and partial autocorrelation function (PACF) plots, the best-fitted models selected based on minimum Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were found to be ARIMA (4,1,0), (3,1,0) and (7,3,1) for newly confirmed cases, discharged cases and death, respectively. This implied that these models were adequate for forecasting future rates of infection, recovery and death as further diagnostic tests showed that the ARIMA models were the perfect fit for the three cases (since P>0.05). Finally, a 29-week out of sample forecast showed a steep downward trend in the three cases and in particular a drastic decline to zero of future COVID-19 deaths. Based on these results, it was recommended that existing vaccination strategies should be expanded to achieve near-zero new COVID-19 cases and deaths. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) energy, with its clean, local, and renewable features, is an effective complement to traditional energy sources today. However, the photovoltaic power system is highly weather-dependent and therefore has unstable and intermittent characteristics. Despite the negative impact of these features on solar sources, the increase in worldwide installed PV capacity has made solar energy prediction an important research topic. This study compares three encoder-decoder (ED) networks for day-ahead solar PV energy prediction: Long Short-Term Memory ED (LSTM-ED), Convolutional LSTM ED (Conv-LSTM-ED), and Convolutional Neural Network and LSTM ED (CNN-LSTM-ED). The models are tested using 1741-day-long datasets from 26 PV panels in Istanbul, Turkey, considering both power and energy output of the panels and meteorological features. The results show that the Conv-LSTM-ED with 50 iterations is the most successful model, achieving an average prediction score of up to 0.88 over R-square (R2). Evaluation of the iteration counts' effect reveals that the Conv-LSTM-ED with 50 iterations also yields the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, confirming its success. In addition, the fitness and effectiveness of the models are evaluated, with the Conv-LSTM-ED achieving the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for each iteration. The findings of this work can help researchers build the best data-driven methods for forecasting PV solar energy based on PV features and meteorological features. [ABSTRACT FROM AUTHOR]

Dengue, a disease with multifactorial determinants, is linked to population susceptibility to circulating viruses and the extent of vector infestation. This study aimed to analyze the temporal trends of dengue cases and deaths in Belo Horizonte, Minas Gerais, Brazil, from 2007 to 2020. Data from the Notifiable Diseases Information System (Sinan) were utilized for the investigation. To assess the disease's progression over the study period and predict its future incidence, time series analyses were conducted using a generalized additive model (GAM) and a seasonal autoregressive integrated moving average (SARIMA) model. Over the study period, a total of 463,566 dengue cases and 125 deaths were reported. Notably, there was an increase in severe cases and deaths, marking hyperendemics characterized by simultaneous virus circulation (79.17% in 2016–50% in 2019). The generalized additive model revealed a non-linear pattern with epidemic peaks in 2010, 2013, 2016, and 2019, indicating an explosive pattern of dengue incidence. The SARIMA (3,1,1) (0,0,0)12 model was validated for each year (2015 to 2019). Comparing the actual and predicted numbers of dengue cases, the model demonstrated its effectiveness for predicting cases in the municipality. The rising number of dengue cases emphasizes the importance of vector surveillance and control. Enhanced models and predictions by local health services will aid in anticipating necessary control measures to combat future epidemics. [ABSTRACT FROM AUTHOR]

Railway infrastructure maintenance faces a challenge posed by the laborious task of monitoring widespread deformation, which is critical for ensuring safety. This study utilized the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique with high-resolution TerraSAR-X satellite images to measure and analyze deformation at the operating track. Tailored through parameter optimization to fit regional topography and validated against field measurements, PS-InSAR was applied across a 30km by 50km area over two years. The analysis demonstrated that PS-InSAR could efficiently generate high-accuracy time-series data, capable of detecting both the uplift and subsidence processes. It highlighted the importance of augmenting single-point subsidence values with comprehensive time-series analysis for a complete deformation assessment. The study concluded that PS-InSAR is an accurate and cost-effective tool for large-scale linear infrastructure monitoring, despite technological constraints such as radar imaging frequency and the lack of high-resolution sources. Consideringthese constraints, future research will prioritize developing an enhanced algorithm capable of analyzing both urban and suburban areas, accommodating varying numbers of point scatterers. [ABSTRACT FROM AUTHOR]

In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification. [ABSTRACT FROM AUTHOR]

We develop two models for the temporal evolution of extreme events of multivariate k$$ k $$th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to classify and evaluate defects in concrete structures automatically. To overcome the limitations of defect detection methods that traditionally relied on expert visual observation, the reflection signal of electromagnetic pulses is extracted as time-series data and used to analyze the propagation characteristics of each defect. This study uses deep learning models to analyze these time-series data and classify defects. Since anomaly detection data has more normal data than anomaly data, data augmentation methods such as Time Warping, Noise Injection, Smoothing, Trend Shifting, etc., were applied to solve the problem of data imbalance and overfitting. Among them, Noise Injection showed the best performance. The generalization performance of the proposed method was evaluated through performance evaluation using LSTM, GRU, and TCN models, and LSTM models showed the highest performance. The study results show that the proposed method effectively classifies defect types in concrete structures and can solve the limitations of existing methods by automatic classification through deep learning models. In addition, it was confirmed that the model's performance could be improved by improving the amount and diversity of data by selecting and applying appropriate data augmentation methods. The contribution of the research is to present a new approach that automates the defect detection and classification task of concrete structures and provides high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making. [ABSTRACT FROM AUTHOR]

The forest structure of Canada's northern unmanaged boreal forests are not well characterized despite their critical importance for ecosystem services such as carbon storage and caribou habitat, as well as their vulnerability to climate change and increasing disturbance rates, especially wildfire. Spaceborne lidar observations acquired from the Advanced Topographic Laser Altimeter System (ATLAS) instrument onboard the Ice, Cloud and Elevation Satellite-2 (ICESat-2), enables monitoring of forest structure in these remote northern ecosystems and characterization of long-term post-fire recovery; however, ICESat-2 is a sampling instrument, acquiring data transects rather than wall-to-wall data coverage. By imputing ICESat-2 estimates of canopy height using annual time-series (1984–2021) of spectral indices, change metrics, and land cover metrics from Landsat surface reflectance composites, we derived annual, spatially explicit, wall-to-wall estimates of canopy height for the period 1984–2021 across a 19.6 Mha area of boreal forests in northwestern Ontario and a 630,000 ha area of managed boreal forest further south that had coincident airborne laser scanning (ALS) data for validation. The accuracy of the imputation of derived canopy height estimates in the northwestern site was assessed using a reserved set of ICESat-2 observations (r = 0.76; RMSD = 3.04 m, pRMSD = 33.9%, MD = −0.10 m, and pMD = −1.0%). Using coincident ALS data, the accuracy of the imputed canopy heights in the southern study site were also assessed (r = 0.81; RMSD = 3.65 m, pRMSD = 25.87%), MD = −0.70 m, pMD = 4.96%). Examination of height dynamics post fire indicated that canopy height decreased 8–10 years post fire and recovered between 60% and 85% of pre-fire canopy height within 30 years post fire. The approach presented could readily be extended to similar northern boreal forest areas to provide broad-scale characterizations of fire impacts on forest structure and subsequent recovery. [ABSTRACT FROM AUTHOR]

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario. [ABSTRACT FROM AUTHOR]

The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral bands. In the case of supervised mapping, time series based on classical vegetation indices (e.g., NDVI, GNDVI, ...) are usually input characteristics, but the selection of the best index or set of indices (which guarantees the best performance) is still based on human experience and is also influenced by the study area. In this work, several different time series, based on Sentinel-2 images, were created exploring new combinations of bands that extend the classic basic formulas as the normalized difference index. Multivariate Functional Principal Component Analysis (MFPCA) was used to contemporarily decompose the multiple time series. The principal multivariate seasonal spectral variations identified (MFPCA scores) were classified by using a Random Forest (RF) model. The MFPCA and RF classifications were nested into a forward selection strategy to identify the proper and minimum set of indices' (dense) time series that produced the most accurate supervised classification of plant communities and habitat. The results we obtained can be summarized as follows: (i) the selection of the best set of time series is specific to the study area and the habitats involved; (ii) well-known and widely used indices such as the NDVI are not selected as the indices with the best performance; instead, time series based on original indices (in terms of formula or combination of bands) or underused indices (such as those derivable with the visible bands) are selected; (iii) MFPCA efficiently reduces the dimensionality of the data (multiple dense time series) providing ecologically interpretable results representing an important tool for habitat modelling outperforming conventional approaches that consider only discrete time series. [ABSTRACT FROM AUTHOR]

The recent launch of Landsat-9 satellite enriches the opportunities to work with dense time series of multispectral medium-resolution images. The integration of Landsat-9 in a multi-constellation series with Landsat-8 and Sentinel-2 requires a harmonization of the surface reflectance values that can be obtained from the official Level-2 products. This paper proposes the coefficients of the optimal linear transformations for the European continent, which allow to integrate Landsat-9 with the similar operating missions. These coefficients are based on a regression over 30 independent random extractions of 240,000 samples from images of the same areas but acquired by different sensors within two days. The coefficients were validated on an independent dataset. Furthermore, the effects of the proposed harmonization were tested on four popular vegetation indices, by evaluating the distributions of the differences in values obtained from each sensor pair. Finally, a test on a local scale was carried out with a spectroradiometer survey on 16 locations to collect some reference spectra to be compared with the reflectance values provided by the images. The results demonstrate the interoperability of Landsat and Sentinel-2 missions, since reflectance differences are in most cases within the accuracy specifications of the sensors. However, some discrepancies are observed in the blue and SWIR bands, probably due to inconsistencies in the atmospheric correction processes. [ABSTRACT FROM AUTHOR]

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications. [ABSTRACT FROM AUTHOR]

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Groundwater exploitation, soft ground, and urban development are the main causes of land subsidence in Vietnam. This study focuses on identifying, zoning, monitoring, and evaluating land subsidence using PSI technique with IBI method applied to Ho Chi Minh City. The results show diverse land subsidence trends from 2014 to 2021 with different levels in varying locations 98,278 km2 had subsided by over 10 cm, 214,593 km2 had subsided by 5 to 10 cm, and 1.377,897 km2 had subsided by less than 5 cm. Notably, certain areas have higher subsidence funnel centres. To ensure accuracy when using PSI techniques, both natural and human-induced factors must be considered during data collection, which influences the actual subsidence rate. Though difficult, this technique provides reliable insights into this complex phenomenon, e.g. in the geodetic precise levelling method to detect millimetre-level deformations in urban areas. The average PS density in urban areas is between 0,5% and 2,5% of the original number of pixels. The positioning accuracy of a PS is within 1 m in all three directions if a large number of SAR scenes are used. Therefore, the results have been documented with 1 mm precision. [ABSTRACT FROM AUTHOR]

Wildfires are a widespread phenomenon that affects every corner of the world with the warming climate. Wildfires burn tens of thousands of square kilometres of forests and vegetation every year in the United States alone with the past decade witnessing a dramatic increase in the number of wildfire incidents. This research aims to understand the regions of forests and vegetation across the US that are susceptible to wildfires using spatiotemporal kernel heat maps and, forecast these wildfires across the United States at country-wide and state levels on a weekly and monthly basis in an attempt to reduce the reaction time of the suppression operations and effectively design resource maps to mitigate wildfires. We employed the state-of-the-art Neural Basis Expansion Analysis for Time Series (N-BEATS) model to predict the total area burned by wildfires by several weeks and months into the future. The model was evaluated based on forecasting metrics including mean-squared error (MSE)., and mean average error (MAE). The N-BEATS model demonstrates improved performance compared to other state-of-the-art (SOTA) models, obtaining MSE values of 116.3, 38.2, and 19.0 for yearly, monthly, and weekly forecasting, respectively. [ABSTRACT FROM AUTHOR]

2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. [ABSTRACT FROM AUTHOR]

The magnesium isotopic composition (δ26Mg) of river water is a promising indicator of continental chemical weathering. While many studies have investigated the factors that influence riverine δ26Mg, the impact of hydrology remains unclear. In this study, we collected eighty-four samples of stream water with nearly diurnal resolution in 2018 from a well-monitored, carbonate-rich catchment on the Chinese Loess Plateau. Our results demonstrate that δ26Mg in stream water increases (0.14 ± 0.05 ‰) from dry to wet seasons, but decreases (0.21 ± 0.05 ‰) during rainfall events. These variations closely link to the dissolution and deposition of carbonates (i.e., source-related processes), and the adsorption and desorption of the exchangeable pool. Carbonate dissolution during rainfalls lowers the δ26Mg in stream water, while during rainfall-free periods in wet seasons carbonate deposition elevates the δ26Mg. Conversely, the exchangeable pool, reflecting carbonate weathering in the geological past, cannot be a source of Mg in stream waters, but act as a transfer Mg-pool. At an instantaneous picture, it releases the majority of Mg (>80 %) to stream water, and thereby has buffering effect on riverine Mg isotope. This highlights the significance of considering the buffering effects when studying riverine δ26Mg variations. Overall, our findings suggest that the response of δ26Mg to hydrology is typically associated with extreme hydrologic events and has important implications for tracing continental weathering. [ABSTRACT FROM AUTHOR]

Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented. [ABSTRACT FROM AUTHOR]

Primary producers form the base of lake ecosystems and, due to their often short lifecycles, respond rapidly to changing conditions. As the Arctic is warming nearly four times faster than the global average, we see major shifts in environmental conditions, which impacts lake ecosystem functioning. Previous studies have found a general increase in primary productivity due to climate warming. However, few long‐term studies have included changes in phytoplankton community composition and biomass in relation to warming in Arctic lakes and it therefore remains unclear how different algal taxa and thus the community respond.We investigated how climate warming affects phytoplankton community composition, taxon richness and biomass in High Arctic lakes, using a unique 23‐year data series on phytoplankton in two shallow lakes at Zackenberg, north‐east Greenland, one with Arctic charr (Salvelinus alpinus) and one without fish. We further elucidated the role of physico‐chemical variables and zooplankton grazers in the changes observed.Few major changes were observed in phytoplankton community composition over time, but the year‐to‐year variation was large. Taxon richness did, however, increase throughout the monitoring period, and in both lakes there was a significant increase in diatom biomass coinciding with increasing conductivity. Additionally, phytoplankton biomass was greater during warmer years with earlier ice melt. We further found that nutrient levels were positively associated with the total phytoplankton biomass in both lakes, indicating that expected increased nutrient levels, due to climate change, may lead to a greater phytoplankton biomass in High Arctic lakes in the future.The large year‐to‐year variability, in both climate and environmental conditions, makes it difficult to predict weather patterns and their consequences for lake ecosystems in the Arctic region. This underlines the importance of long‐term monitoring programmes across the circumpolar Arctic and collaboration across regions and institutes within large scale studies. [ABSTRACT FROM AUTHOR]

The advent of Large Language Models (LLMs) has sparked significant interest in their application across various domains, including time-series forecasting. This paper introduces an encoding strategy designed to bridge the gap between the inherently quantitative nature of time-series data and the primarily textual processing capabilities of LLMs. By leveraging an innovative combination of adaptive segmentation and tokenization, inspired by the fast Brownian bridge-based aggregation (fABBA) algorithm, our method transforms time series data into a format conducive to LLM analysis. Through evaluation on diverse datasets (DARTS series), we demonstrate that our approach, on average, improves time-series forecasting accuracy. [ABSTRACT FROM AUTHOR]

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Plastic greenhouses (PGs) are integral to modern agricultural practices, enhancing crop yields but also raising environmental concerns. Consequently, comprehending their widespread distribution is essential. Although deep learning has been extensively used for land use/cover classification and extraction with satellite data, the large number of labels limits its application due to the time-consuming and labor-intensive nature of manual labeling. This study introduces a novel approach coupling Prior Knowledge and Deep Learning methods for PG Mapping (PGMPK+DL) using Sentinel-1/2 data. We use an automatic labeling strategy guided by prior knowledge from Sentinel-2 optical data to construct PG labels in six small regions. Moreover, to overcome the cloud contamination issue of optical data, the potential of Sentinel-1 time-series SAR data for PG extraction is analyzed. Deep learning methods are further utilized to capture more abstract and generalized temporal and spatial features from time-series radar data to accommodate complex scenes for large-scale PG extraction. The U-Net model emerges as superior from rigorous comparisons among FCN, SegNet, U-Net, DeepLabV3 + and U-Net3 + deep learning models. Finally, the U-Net model harnessed prior knowledge-based PG labels and Sentinel-1 time-series SAR data to generate a precise map depicting PG distribution across Shandong province, China. Remarkably, it accurately identifies approximately 238,000 ha of PG areas. This PGMPK+DL approach presents a groundbreaking solution for label construction, enabling the achievement of large-scale PG extraction. Beyond enhancing PG extraction, it also holds broader implications for advancing deep learning applications within remote sensing.

Accurately classifying and mapping winter wheat is important for agricultural development. It is difficult to meet the requirement of high accuracy when using single models to identify winter wheat; thus, model fusion methods have been used to improve classification accuracy. However, complex model fusion methods are challenging for winter wheat classification because of model complexity and data processing efficiency problems. Therefore, we propose an efficient and simple multimodel fusion (MMF) method to improve winter wheat classification accuracy at 10-m resolution and map the planting area of winter wheat. Two common supervised classification models, random forest (RF) and gradient boosting decision tree (GBDT), and a similarity matching algorithm, time-weighted dynamic time warping (TWDTW), were used to initially classify the main planting area of winter wheat in Shandong Province. Subsequently, the proposed MMF method with five fusion strategies based on the preliminary classification results was used to subclassify winter wheat. The evaluation results showed that the MMF method with each fusion strategy could effectively improve the overall accuracy (OA), with an optimal OA of 95.7%, compared to RF, GBDT, and TWDTW (OA =91.5%, 92.1%, and 93.3%, respectively). In addition, the coefficient of determination (R2) and the root mean square error (RMSE) of statistical vs. mapped areas obtained using the optimal MMF method were 0.92 and 62.72 km2 respectively, under county level area statistics, which are higher and lower than those obtained using RF, GBDT, and TWDTW (R $^{2} =0.81$ , 0.86, and 0.90, respectively; RMSE =107.1, 89.67, and 72.48 km2, respectively). The results of this study can serve as a scientific basis for improving the accuracy and method selection for classifying and mapping the fine resolution of winter wheat.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals’ features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

Ensuring the privacy of personal data is crucial in the era of big data, especially in the transportation industry where sensitive data needs to be processed to develop intelligent vehicle technologies. In particular, collecting and analyzing anomalous data is essential for improving vehicle safety and performance, but accessing such data is often difficult and costly. To address this problem, we propose a novel approach to generating synthetic anomalous data using Generative Adversarial Networks (GANs) and Federated Learning (FL). The proposed learning strategy is decentralized, utilizing data generated by each vehicle to locally train individual discriminators. These discriminators then share only the loss values and weights with a centralized generator. Consequently, the GAN model is trained without exchanging raw data, thereby ensuring the privacy of personal information. Our approach involves a Convolutional Neural Network (CNN)-based architecture for both the generator and discriminator, with the generator residing on the server and a separate discriminator at each vehicle. This design reduces the computational demand on edge devices and enables us to train the GANs using FL. We experiment with different FL strategies and find that the best performer favored the least forgiving discriminator considering data from a pool of vehicles. Our results demonstrate the feasibility of using FL with CNN-based GANs to generate synthetic time-series data for training models in a privacy-preserving manner. This approach has potential applications in the transportation industry, particularly in the context of intelligent vehicles and automated driving systems.

Structural health monitoring (SHM) is key in civil engineering because of the importance and the aging of the infrastructure. We argue that applying leading-edge, data-driven methods of large-scale complex industrial systems may be beneficial, particularly for accuracy and responsiveness. A fundamental step concerns the identification of the best tools to extract meaningful information from the vibrational raw signals. To this end, we study the application of two convolutional neural network architectures that have emerged in the literature for efficient feature extraction from time series, namely WaveNet and MINImally RandOm Convolutional KErnel Transform (MiniRocket). The test bench is the Z24 bridge progressive damage test classification dataset. Results show that a model based on WaveNet reaches state-of-the-art performance, also reducing model size and computational complexity. WaveNet proves perfectly suited to interpret the bridge vibration waveforms directly in the time domain, without any specific preprocessing. On the other hand, MiniRocket excels for ease of configuration (only two hyperparameters are to be tweaked), overall training efficiency, and model size, lending itself as a valuable agile alternative (e.g., for rapid prototyping). Our main advancement is, thus, the identification and characterization of highly effective feature extraction methods, employable in different SHM tasks. We have assessed the performance of the models on two embedded platforms, proposing a smart sensor system where a local hub collects the signals from a constellation of inertial sensors and infers damage assessment onsite, allowing the bridge to self-assess its health state without resorting to connectivity nor cloud resources.

In the contemporary world, where the escalating demand for energy and the imperative for sustainable sources, notably solar energy, have taken precedence, the investigation into solar radiation (SR) has become indispensable. Characterized by its intermittency and volatility, SR may experience considerable fluctuations, exerting a significant influence on energy supply security. Consequently, the precise prediction of SR has become imperative, particularly in the context of the potential proliferation of photovoltaic panels and the need for optimized energy management. Several works in the existing literature review the state of the art in SR prediction, focusing on trends identified using machine learning (ML) or deep learning (DL) techniques. However, there is a gap in the literature regarding the integration of optimization algorithms with ML and DL techniques for SR prediction. This systematic review addresses this gap by studying prediction models for SR that leverage metaheuristic optimization algorithms alongside artificial intelligence (AI) techniques, aiming primarily for maximum prediction accuracy. Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have featured in 29% and 12.1% of the analyzed articles, respectively, while intelligent approaches like Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), and Multilayer Perceptron (MLP) have emerged as the predominant choices, collectively accounting for 43.9% of the studies. Analysis has encompassed studies examining SR across hourly, daily, and monthly intervals, with daily intervals representing 48.7% of the focus. Noteworthy variables including temperature, humidity, wind speed, and atmospheric pressure have surfaced, capturing proportions of 90%, 68.2%, 56%, and 41.4%, respectively, within the reviewed literature.

The necessity of combining multi-source remote sensing sensors for multi-temporal observation of the Earth, arises from the influence of cloud cover and the limited operational period of single satellite. However, significant differences exist among satellites and among sensors within the same satellite system. In this case, conducting a cross-comparison analysis is crucial for reliable change detection. The Jilin-1 satellite constellation has a total of 108 satellites in orbit, forming the largest commercial remote sensing satellites constellation in China. Firstly, the top of atmosphere (TOA) data from the Jilin-1GF03B series satellites (Jilin-1GF03B04, Jilin-1GF03B05, and Jilin-1GF03B06), following radiometric calibration processing, were selected to evaluate their performance in a single phase by comparison to Sentinel-2 satellites. Subsequently, we compared four spectrum bands and four commonly used biophysical indices, and the results indicated a higher radiation consistency between Jilin-1GF03B and Sentinel-2 satellites, suggested by correlation coefficient (over 0.953), and R2 (over 0.907). Moreover, we assessed the band sensitivity of the two satellites across different land use classes. It was found that Sentinel-2 exhibited greater sensitivity in bare land, vegetation, and forest, whereas the near-infrared band of Jilin-1GF03B proved more sensitive in water areas. Finally, to mitigate the risk of randomness in a single temporal phase, we constructed two long-term vegetation index datasets for phenological analysis. The close performance alignment observed for both single-phase and phenological variations indicated that Jilin-1GF03B satellites and Sentinel-2 can be effectively utilized together for long-term monitoring of Earth surface attributes.

Abstract Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

Abstract Background Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval. Methods This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation. Results We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching. Conclusion The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

In the current context, the focus on optimizing energy distribution is intensified by high-impact events such as resource scarcity, import dependence, war, inflation, or environmental threats. As a consequence, international agencies have built extensive policies and targets that will reflect directly into the general energy distribution. One of the most crucial would be the 65% share of electricity that is bound to be generated from renewables until 2030. To study the feasibility of that goal, we will analyze the evolution of the energy mix worldwide in the last two decades, identifying the shift towards alternative energy sources, while also pinpointing the agents of change in terms of the low-carbon transition. The methodology will consist of a bootstrap clustering algorithm that has been computed for 3 moments: 2000, 2010, and 2020, generating clear differences in terms of energy distribution. In the second part of the paper, we have employed an ARIMA model that aims to predict the share of renewable energy by 2030, with the worrying conclusion that if the current rhythm continues, the goal will not be met and the climate could face severe consequences. Therefore, the paper significantly contributes to the methodological void in terms of bootstrap clustering applicability, while also illustrating a complete energy picture from a geographical and time perspective.

Palaeolimnological records provide valuable information about how phytoplankton respond to long‐term drivers of environmental change. Traditional palaeolimnological tools such as microfossils and pigments are restricted to taxa that leave sub‐fossil remains, and a method that can be applied to the wider community is required. Sedimentary DNA (sedDNA), extracted from lake sediment cores, shows promise in palaeolimnology, but validation against data from long‐term monitoring of lake water is necessary to enable its development as a reliable record of past phytoplankton communities. To address this need, 18S rRNA gene amplicon sequencing was carried out on lake sediments from a core collected from Esthwaite Water (English Lake District) spanning ~105 years. This sedDNA record was compared with concurrent long‐term microscopy‐based monitoring of phytoplankton in the surface water. Broadly comparable trends were observed between the datasets, with respect to the diversity and relative abundance and occurrence of chlorophytes, dinoflagellates, ochrophytes and bacillariophytes. Up to 20% of genera were successfully captured using both methods, and sedDNA revealed a previously undetected community of phytoplankton. These results suggest that sedDNA can be used as an effective record of past phytoplankton communities, at least over timescales of <100 years. However, a substantial proportion of genera identified by microscopy were not detected using sedDNA, highlighting the current limitations of the technique that require further development such as reference database coverage. The taphonomic processes which may affect its reliability, such as the extent and rate of deposition and DNA degradation, also require further research. [ABSTRACT FROM AUTHOR]

Here, we employ natural time analysis of seismicity together with non-extensive statistical mechanics aiming at shortening the occurrence time window of the Kahramanmaraş-Gazientep M7.8 earthquake. The results obtained are in the positive direction pointing to the fact that after 3 February 2023 at 11:05:58 UTC, a strong earthquake was imminent. Natural time analysis also reveals a minimum fluctuation of the order parameter of seismicity almost three and a half months before the M7.8 earthquake, pointing to the initiation of seismic electrical activity. Moreover, before this earthquake occurrence, the detrended fluctuation analysis of the earthquake magnitude time-series reveals random behavior. Finally, when applying earthquake nowcasting, we find average earthquake potential score values which are compatible with those previously observed before strong ( M ≥ 7.1 ) earthquakes. The results obtained may improve our understanding of the physics of crustal phenomena that lead to strong earthquakes. [ABSTRACT FROM AUTHOR]

The characterization of the socioeconomic profile of COVID-19 mortality is limited. Likewise, the mapping of potential indirect adverse outcomes of the pandemic, such as suicide and alcohol abuse, along socioeconomic lines is still meagre. The main aim of this paper is to (i) depict SES-differences in COVID-19 mortality, and (ii) to assess the impact of the COVID-19 pandemic on suicide and alcohol mortality across socioeconomic groups. We used Swedish monthly data spanning the period January 2016–December 2021. We chose education as indicator of socioeconomic status (SES). The following causes of deaths were included in the analysis: COVID-19, all-cause mortality excluding COVID-19, suicide and a composite index of alcohol-specific deaths. SARIMA-modelling was used to assess the impact of the pandemic on suicide and alcohol-specific mortality. Two alternative measures of the pandemic were used: (1) a dummy that was coded 1 during the pandemic (March 2020 and onwards), and 0 otherwise, and (2) the Oxford COVID-19 Government Response Tracker's Stringency Index. There was a marked SES-gradient in COVID-19 mortality in the working-age population (25–64) which was larger than for other causes of death. A SES-gradient was also found in the old-age population, but this gradient did not differ from the gradient for other causes of death. The outcome from the SARIMA time-series analyses suggested that the pandemic did not have any impact on suicide or alcohol-specific mortality in any of the educational and gender groups. [ABSTRACT FROM AUTHOR]

The giant grouper (Epinephelus lanceolatus) is the largest reef fish in the Indo‐Pacific (~2.5 m TL, >400 kg), and it is highly susceptible to overfishing. Despite regional protections and documented population declines, the species is listed by IUCN as Data Deficient due to minimal long‐term population data and a paucity of life history information. This study used historical fishing records derived from newspaper articles, fishing magazines, grey literature and naturalists' descriptions to collate life history information and reconstruct giant grouper population trends from 1854 to 1958 in Queensland, Australia. Historical recreational catch trends of four biologically distinct grouper size classes demonstrated that over 92 years, fishing disproportionately affected two size classes: immature (fish below reproductive size) and mature individuals. Changes in the probability of capturing a grouper within a recreational fishery were examined as a proxy of relative abundance. The probability of catching a giant grouper within a popular recreational fishery significantly declined from 81% in 1860 to 2% in 1958. Further analysis based on a non‐probabilistic method of giant grouper sighting records showed fluctuations in the giant grouper population trajectory, from a steady decline during the early 20th century to an increase during WWII (1939–1945) followed by a reduction in the last half of the 20th century. This study highlights the importance of archival sources to uncover population trends of rare species by combining quantitative assessments and biological inferences to determine the timing and occurrence of population declines and recoveries and inform how vulnerable fish species respond to the cumulative effects of fishing over time. [ABSTRACT FROM AUTHOR]