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Purpose: To explore how the change-point method can be used to analyze complex longitudinal data and detect when meaningful changes (change points) have occurred during rehabilitation. Method: This design is a prospective single-case observational study of a football player in a professional club who sustained an acute lower-limb muscle injury during high-speed running in training. The rehabilitation program was entirely completed in the football club under the supervision of the club's medical team. Four wellness metrics and 5 running-performance metrics were collected before the injury and until the player returned to play. Results: Data were collected over 130 days. In the univariate analysis, the change points for stress, sleep, mood, and soreness were located on days 30, 47, 50, and 50, respectively. The change points for total distance, acceleration, maximum speed, deceleration, and high-speed running were located on days 32, 34, 37, 41, and 41, respectively. The multivariate analysis resulted in a single change point for the wellness metrics and running-performance metrics, on days 50 and 67, respectively. Conclusions: The univariate approach provided information regarding the sequence and time point of the change points. The multivariate approach provided a common change point for multiple metrics, information that would benefit clinicians to have a broad overview of the changes in the rehabilitation process. Clinicians may consider the change-point method to integrate and visualize data from multiple sources to evaluate athletes' progression along the return-to-sport continuum. [ABSTRACT FROM AUTHOR]

Background: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets. Methods: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression. Results: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends. Conclusions: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia. [ABSTRACT FROM AUTHOR]

Background and Objective: Understanding the seasonal behaviours of respiratory viruses is crucial for preventing infections. We evaluated the seasonality of respiratory viruses using time‐series analyses. Methods: This study analysed prospectively collected nationwide surveillance data on eight respiratory viruses, gathered from the Korean Influenza and Respiratory Surveillance System. The data were collected on a weekly basis by 52 nationwide primary healthcare institutions between 2015 and 2019. We performed Spearman correlation analyses, similarity analyses via dynamic time warping (DTW) and seasonality analyses using seasonal autoregressive integrated moving average (SARIMA). Results: The prevalence of rhinovirus (RV, 23.6%–31.4%), adenovirus (AdV, 9.2%–16.6%), human coronavirus (HCoV, 3.0%–6.6%), respiratory syncytial virus (RSV, 11.7%–20.1%), influenza virus (IFV, 11.7%–21.5%), parainfluenza virus (PIV, 9.2%–12.6%), human metapneumovirus (HMPV, 5.6%–6.9%) and human bocavirus (HBoV, 5.0%–6.4%) were derived. Most of them exhibited a high positive correlation in Spearman analyses. In DTW analyses, all virus data from 2015 to 2019, except AdV, exhibited good alignments. In SARIMA, AdV and RV did not show seasonality. Other viruses showed 12‐month seasonality. We describe the viruses as winter viruses (HCoV, RSV and IFV), spring/summer viruses (PIV, HBoV), a spring virus (HMPV) and all‐year viruses with peak incidences during school periods (RV and AdV). Conclusion: This is the first study to comprehensively analyse the seasonal behaviours of the eight most common respiratory viruses using nationwide, prospectively collected, sentinel surveillance data. [ABSTRACT FROM AUTHOR]

A new automated algorithm for picking the arrival times of the global P -, SH - and SV -wave phases from multi-component seismic waveform data is presented. This picker is based on a sequential approach using autoregressive prediction of the filtered waveform in a sliding time window, the Akaike Information Criterion and the Hilbert transform of the original waveform. The quality of the individual picks is computed by combining signal-to-noise ratios and higher order statistics into a single measure. Synthetic tests are used to find values for high and low quality thresholds. The algorithm is applied to a global data set of waveforms from teleseismic events with magnitude 6 or higher that occurred between 1990 and 2019. This resulted in approximately 4 million P-phase arrival times as well as approximately 3 million SH- and SV-phase arrival times each. These automatic picks are compared to approximately |$830\, 000$| manual P-picks as well as approximately |$70\, 000$| manual S-picks from the ISC-EHB catalogue. An upper bound for the picking errors of the automatic picks is estimated by using high quality picks of neighbouring stations. This upper bound is found to be 0.55 s for the P-picks and 4.3 s for the S-picks. If only high quality picks are considered, this represents 50 per cent of the P-picks and 25 per cent of the S-picks, then these errors decrease to 0.35 s for the P-picks and 1.5 s for the S-picks, respectively. As a by-product of the picking, the dominant periods of the arriving signals are determined as well. [ABSTRACT FROM AUTHOR]

Intermediate-depth earthquakes, accommodating intraslab deformation, typically occur within subduction zone settings at depths between 60–300 km. These events are in a unique position to inform us about the geodynamics of the subducting slab, specifically the geometry of the slab and the stress state of the host material. Improvements in the density and quality of recorded seismic data enhance our ability to determine precise locations of intermediate-depth earthquakes, in order to establish connections between event nucleation and the tectonic setting. Depth phases (near-source surface reflections, e.g. pP and sP) are crucial for the accurate determination of earthquake source depth using global seismic data. However, they suffer from poor signal-to-noise ratios in the P wave coda. This reduces the ability to systematically measure differential traveltimes to the direct P arrival, particularly for the frequent lower magnitude seismicity which highlights considerable seismogenic regions of the subducted slabs. To address this limitation, we have developed an automated approach to group globally distributed stations at teleseismic distances into ad-hoc arrays with apertures of 2.5 |$^\circ$|⁠ , before optimizing and applying phase-weighted beamforming techniques to each array. Resultant vespagrams allow automated picking algorithms to determine differential arrival times between the depth phases and their corresponding direct P arrival. Using these differential times we can then determine the depths of earthquakes, which in turn can be used to create a catalogue of relocated events. This will allow new comparisons and insights into the governing controls on the distribution of earthquakes in subducted slabs. We demonstrate this method by relocating intermediate-depth events associated with northern Chile and the Peruvian flat slab regions of the subducting Nazca plate. The relocated Chilean catalogue contains comparable event depths to an established catalogue, calculated using a semi-automated global methodology, which serves to validate our fully automatic methodology. The new Peruvian catalogue we generate indicates three broad zones of seismicity approximately between latitudes 1–7 |$^\circ$| S, 7–13 |$^\circ$| S and 13–19 |$^\circ$| S. These align with flat to steep slab dip transitions and the previously identified Pucallpa Nest. We also find a regionally deeper slab top than indicated by recent slab models, with intraslab events concentrated at points where the slab bends, suggesting a link between slab flexure and intermediate-depth earthquake nucleation. [ABSTRACT FROM AUTHOR]

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases. [ABSTRACT FROM AUTHOR]

A strategy to assist visualization and analysis of large and complex datasets is dimensionality reduction, with which one maps each data point into a low-dimensional manifold. However, various dimensionality reduction techniques are computationally infeasible for large data. Out-of-sample techniques aim to resolve this difficulty; they only apply the dimensionality reduction technique on a small portion of data, referred to as landmarks, and determine the embedding coordinates of the other points using landmarks as references. Out-of-sample techniques have been applied to online settings, or when data arrive as time series. However, existing online out-of-sample techniques use either all the previous data points as landmarks or the fixed set of landmarks and therefore are potentially not good at capturing the geometry of the entire dataset when the time series is non-stationary. To address this problem, we propose an online landmark replacement algorithm for out-of-sample techniques using geometric graphs and the minimal dominating set on them. We mathematically analyse some properties of the proposed algorithm, particularly focusing on the case of landmark multi-dimensional scaling as the out-of-sample technique, and test its performance on synthetic and empirical time-series data. [ABSTRACT FROM AUTHOR]

In order to ensure food security, it is crucial to collect agricultural information efficiently and accurately. Remote sensing has become increasingly important in obtaining crop distribution information on a large scale. However, current research based on satellite platforms struggles to meet the requirements of high-precision and large-scale crop monitoring simultaneously. To address this challenge, we propose a method for achieving fine-scale crop classification by integrating remote-sensing data from various satellite platforms by constructing temporal-scale crop features within the parcels using Sentinel-2A, Landsat-8, and Gaofen-6. We adopt a feature-matching method to fill in missing values in the time-series feature construction process, to avoid issues with unidentifiable crops. The classification results of the Yellow River basin of the Ningxia region show that our method can achieve a wide range of crop discrimination on a fine scale, with an overall accuracy of 80%. Our proposed method demonstrates the potential of integrating multi-platform remote-sensing data to achieve fine-scale crop classification, which can aid decision-making for farmers, government agencies, and other stakeholders involved in the agricultural sector. [ABSTRACT FROM AUTHOR]

Background: The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID‐19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID‐19‐related national lockdowns on alcohol‐related traffic collisions, injuries, and fatalities in Lithuania. Methods: Using monthly data from the Lithuanian Road Police Service for January 2004 to December 2022, we performed interrupted time‐series analyses using a generalized additive model to evaluate the impact of COVID‐19‐related national lockdowns on alcohol‐related traffic collisions, injuries, and fatalities. In Lithuania, the COVID‐19‐related lockdowns occurred from March 2020 to June 2020 and from November 2020 to June 2021. Results: Although overall rates for traffic collisions and injuries decreased during the COVID‐19‐related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol‐related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol‐related traffic injuries. The association between the lockdowns and alcohol‐related traffic fatalities was not statistically significant. Conclusion: The COVID‐19‐related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol‐related traffic collisions and injuries. [ABSTRACT FROM AUTHOR]

Detection and separation of the subtle post-seismic deformation signals associated with moderate magnitude earthquakes from interferometric synthetic aperture radar (InSAR) time-series is often challenging. Singular spectrum analysis (SSA) is a statistical non‐parametric technique used to decompose and reconstruct signals from complex time-series data. We show that the SSA analysis effectively distinguished the post-seismic signal associated with the 2019 M w 6 Mirpur earthquake from periodic and noise components. The SSA-derived post-seismic deformation signal is smoother and fits better to an exponential model with a decay time of 34 d. The post-seismic deformation is confined to the southeast of the rupture area and lasted for ∼90 d following the main shock. Inversion of the post-seismic deformation suggests an afterslip mechanism with a maximum slip of ∼0.07 m on the shallow, updip portions of the Main Himalayan Thrust. The 2019 Mirpur earthquake and afterslip together released less than 12 per cent of the accumulated strain energy since the 1555 Kashmir earthquake and implies continued seismic hazard in the future. [ABSTRACT FROM AUTHOR]

We present a computational technique to model hydroacoustic waveforms from teleseismic earthquakes recorded by mid-column Mermaid floats deployed in the Pacific, taking into consideration bathymetric effects that modify seismo-acoustic conversions at the ocean bottom and acoustic wave propagation in the ocean layer, including reverberations. Our approach couples axisymmetric spectral-element simulations performed for moment-tensor earthquakes in a 1-D solid Earth to a 2-D Cartesian fluid–solid coupled spectral-element simulation that captures the conversion from displacement to acoustic pressure at an ocean-bottom interface with accurate bathymetry. We applied our workflow to 1129 seismograms for 682 earthquakes from 16 Mermaid s (short for Mobile Earthquake Recording in Marine Areas by Independent Divers) owned by Princeton University that were deployed in the Southern Pacific as part of the South Pacific Plume Imaging and Modeling (SPPIM) project. We compare the modelled synthetic waveforms to the observed records in individually selected frequency bands aimed at reducing local noise levels while maximizing earthquake-generated signal content. The modelled waveforms match the observations very well, with a median correlation coefficient of 0.72, and some as high as 0.95. We compare our correlation-based traveltime measurements to measurements made on the same data set determined by automated arrival-time picking and ray- traced traveltime predictions, with the aim of opening up the use of Mermaid records for global seismic tomography via full-waveform inversion. [ABSTRACT FROM AUTHOR]

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored. [ABSTRACT FROM AUTHOR]

The science and technology (S&T) workforce plays a crucial role in social development by promoting technological innovation and economic growth, as well as serving as a key indicator of research and development productivity and measure of innovation capability. Therefore, effective S&T workforce policies must be established to enhance national competitiveness. This study proposes a time-series forecasting methodology to predict the scale and structural trends of South Korea's doctorate-level S&T workforce. Based on earlier research and case data, we applied both the traditional time-series model exponential smoothing and the latest model Prophet, developed by Meta, in this study. Further, public data from South Korea were used to apply the proposed models. To ensure robust model evaluation, we considered multiple metrics. With respect to both forecasting accuracy and sensitivity to data variability, Prophet was found to be the most suitable for predicting the S&T doctorate workforce's scale. The scenarios derived from the Prophet model can help the government formulate policies based on scientific evidence in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Method Result Conclusion Public health and social measures (PHSMs) are considered the most effective approaches for controlling epidemic diseases. This study aimed to explore variations in the time‐dependent characteristics of and public preferences for cosmetic treatments during and after the implementation of PHSMs during the COVID‐19 pandemic.Medical records from six medical institutions were extracted retrospectively. Time–series analyses were conducted to reveal the variations in characteristics in volume and proportion of cosmetic treatments according to PHSMs. A cross‐sectional study was conducted with online questionnaire designed for the general population during and after the implementation of PHSMs.A total of 141 033 records were included in this retrospective study. The implementation of PHSMs led to extremely low treatment volumes; compared with the increases in private hospitals, the treatment volumes in public hospitals exhibited earlier and more significant increases, even higher than pre‐PHSM levels (p < 0.05), which mainly contributed to the increase in plastic surgery volumes during and after the implementation of PHSMs. The differences in the anxiety state, self‐perceived appearance, and cosmetic treatment intentions of the participants were illustrated during and after PHSMs. We further demonstrated the participants' decisions on cosmetic treatments after the implementation of PHSMs during the COVID‐19 pandemic.The immediate effects and aftereffects of PHSMs on cosmetic treatments were different in public and private hospitals. Furthermore, as PHSMs guided the adjustment of cosmetic treatments in the post‐COVID‐19 era, the intention to undergo cosmetic treatment during PHSMs was associated with the anxiety states and preferences of the population. [ABSTRACT FROM AUTHOR]

Background: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission. [ABSTRACT FROM AUTHOR]

We present the analysis of the spatio-temporal changes in absolute extreme temperatures of the coldest night (TNn), coldest day (TXn), and the hottest night (TNx) and, hottest day (TXx) as defined by the Expert Team on Climate Change Detection and Indices in the Mediterranean Region (MedR) for the period 1950–2023 using ERA5-Land reanalysis gridded dataset. Results show that the annual and seasonal frequencies of TNn and TXn have significantly decreased, while the frequencies of TNx and TXx have increased over the last 74 years in the MedR particularly during hot periods of the year. Since 1950, the magnitude of change in the annual TNn is higher than all absolute extreme temperature indices in the MedR, with more pronounced trends in winter in the western MedR. The hottest year in the MedR since 1950 was 2023, when 35% of the highest absolute maximum temperatures were recorded. According to the results of Pettitt's test, the most significant change point for MedR was in the late 1980s for the absolute extreme cold temperature indices and in the late 1990s for the absolute extreme warm temperature indices. Spatial differences in warming rates are observed for all absolute extreme temperature indices in the MedR. The increase in temperatures, particularly TXx and TNn, is much more pronounced in Western Mediterranean (WMed) during the annual and summer season than in the eastern Mediterranean (EMed). [ABSTRACT FROM AUTHOR]

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality N D V I time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality N D V I time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original N D V I time series and producing high-quality N D V I profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an R M S E of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality N D V I time series data could produce timely and spatially explicit phenological information at the field and crop levels. [ABSTRACT FROM AUTHOR]

The effective monitoring of railway facilities is crucial for safety and operational efficiency. This study proposes an enhanced remote monitoring technique for railway facilities, specifically bridges, using satellite radar InSAR (Interferometric Synthetic Aperture Radar) technology. Previous studies faced limitations such as insufficient data points and challenges with topographical and structural variations. Our approach addresses these issues by analysing displacements from 30 images captured by the X-band SAR satellite, TerraSAR-X, over two years. We tested each InSAR parameter to develop an optimal set of parameters, applying the technique to a post-tensioned PSC (pre-stressed concrete) box bridge. Our findings revealed a recurring arch-shaped elevation along the bridge, attributed to temporal changes and long-term deformation. Further analysis showed a strong correlation between this deformation pattern and average surrounding temperature. This indicates that our technique can effectively identify micro-displacements due to temperature changes and structural deformation. Thus, the technique provides a theoretical foundation for improved SAR monitoring of large-scale social overhead capital (SOC) facilities, ensuring efficient maintenance and management. [ABSTRACT FROM AUTHOR]

SUMMARY: Mode conversion of P waves at the boundary between Earth's crust and upper mantle, when analysed using receiver functions (RFs), allows characterization of Earth structure where seismic station density is high and earthquake sources are favourably distributed. We applied two ensemble decision tree algorithms—Random Forest (RanFor) and eXtreme Gradient Boost (XGBoost)—to synthetic and real RF data to assess these machine learning techniques' potential for crustal imaging when available data are sparse. The synthetic RFs, entailing both sharp increases in seismic velocity across the Moho and gradational Moho structures, calculated with and without added random noise, correspond to idealized crustal structures: a dipping Moho, Moho offset by crustal-scale faults, anti- and synform Moho structures and combinations of these. The RanFor/XGBoost algorithm recovers input structures well regardless of event-station distributions. Useful crustal and upper mantle seismic velocities can also be determined using RanFor and XGBoost, making it possible to image crustal thickness and P- and S-wave velocities simultaneously from RFs alone. We applied the trained RanFor/XGBoost to RFs determined from real seismic data recorded in the contiguous United States, producing a map of the Moho and P- and S-wave velocities of the lowermost crust and uppermost mantle. Use of XGBoost, which evaluates residuals between input RFs and ground-truth to update the decision tree using the gradient of a penalty function, improves the crustal thickness estimates. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID‐19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID‐19 pandemic changed the ratio of severe illness. In addition, the best‐fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non‐pharmacological interventions implemented during the COVID‐19 pandemic have led to a reduction in the incidence of severe illness. [ABSTRACT FROM AUTHOR]

Multiple actors may harness social media to promote their own stances and influence others on climate change. Our investigation focused on second-level agenda-setting, determining which actors drove climate change conversations by attracting public attention to various topics. We examined the primary topics and actors involved in climate change discussions on Weibo (N = 1,090,420 posts) from 2010 to 2020. Our findings indicate that most discussions centered around environmental impact rather than culture, politics and government, or action advocacy. Celebrities, ordinary users, and civil society organizations were found to be influential for many topics, challenging the notion that governments and media lead the online climate change discourse. This study contributes to a deeper understanding of second-level agenda-setting in the digital age and highlights the importance of leveraging different social actors, especially celebrities, in China to achieve global climate change mitigation goals. [ABSTRACT FROM AUTHOR]

Abstract Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R 2 : 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

Abstract Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P

Abstract Background Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

The issue of volatile food prices is a consistent problem for American consumers, as rising prices make it challenging to afford nutritious food that meets dietary standards. Various complex factors influence this price volatility, including economic conditions, weather patterns, global trade, energy prices, and more. Notably, the impact of food price increases is not equal for everyone. Low-income individuals and those in rural areas are disproportionately affected. A comprehensive understanding of the driving factors is essential to tackle this issue effectively. We employ advanced time-series techniques such as Vector Error Correction Models (VECM) and modern causal inference methods such as probabilistic graphical models implemented via machine learning and artificial intelligence approaches on monthly data from 2000 to 2021 to investigate the U.S. food price inflation issue. These methods help unravel the intricate dynamics among key variables driving food price inflation. The study aims to achieve several objectives. It intends to (1) clarify how factors influencing food price inflation in the U.S. change over time using VECM models, (2) establish causal relationships among interconnected variables to develop probabilistic graphical models using innovative search algorithms, and (3) create and validate forecasts related to U.S. food price inflation. The end goal is to provide actionable insights for policy design. Results show that food price inflation is heavily tied to commodity pricing and pricing for medical services. Additionally, historical decompositions for COVID-19 show ties between food price inflation and energy inflation. [ABSTRACT FROM AUTHOR]

Abstract Background The interplay between root-knot nematode (RKN) parasitism and the complex web of host-associated microbiota has been recognized as pivotal for effective management of the pest. However, studies assessing this relationship have focussed on the bacterial and fungal communities, neglecting the unicellular eukaryotic members. Here, we employed amplicon sequencing analysis of the bacterial 16S rRNA, fungal ITS and eukaryotic 18S rRNA genes, and comprehensively examined how the microbiome composition, diversity and networking developed with time in the rhizospheres and roots of RKN-inoculated and non-inoculated tomato plants. Results As expected, infection with the RKN Meloidogyne incognita decreased plant growth. At individual timepoints, we found distinct bacterial, fungal and eukaryote community structures in the RKN-inoculated and non-inoculated rhizospheres and roots, and RKN inoculation affected several taxa in the root-associated microbiome differentially. Correlation analysis revealed several bacterial and fungal and few protist taxa that correlated negatively or positively with M. incognita. Moreover, network analysis using bacterial, fungal and eukaryotic data revealed more dynamic networks with higher robustness to disturbances in the RKN-inoculated than in the non-inoculated rhizospheres/roots. Hub taxa displayed a noticeable successional pattern that coincided with different phases of M. incognita parasitism. We found that fungal hubs had strong negative correlations with bacteria and eukaryotes, while positive correlations characterized hub members within individual kingdoms. Conclusion Our results reveal dynamic tomato-associated microbiomes that develop along different trajectories in plants suffering M. incognita infestation and non-infested plants. Overall, the results identify stronger associations between RKN and bacterial and fungal taxa than between eukaryotic taxa and RKN, suggesting that fungal and bacterial communities could play a larger role in the regulation of RKN. The study identifies several putative RKN-antagonistic bacterial and fungal taxa and confirms the antagonistic potential previously identified in other taxa.

Abstract Objectives Acute upper respiratory tract infections (AURTIs) are prevalent in the general population. However, studies on the association of short-term exposure to air pollution with the risk of hospital visits for AURTIs in adults are limited. This study aimed to explore the short-term exposure to air pollutants among Chinese adults living in Ningbo. Methods Quasi-Poisson time serious regressions with distributed lag non-linear models (DLNM) were applied to explore the association between ambient air pollution and AURTIs cases. Patients ≥ 18 years who visit three hospitals, being representative for urban, urban–rural junction and rural were included in this retrospective study. Results In total, 104,441 cases with AURTIs were enrolled in hospital during 2015–2019. The main results showed that particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), nitrogen dioxide (NO2) and nitrogen dioxide (SO2), were positively associated to hospital visits for AURTIs, except for nitrogen dioxide (O3), which was not statistically significant. The largest single-lag effect for PM2.5 at lag 8 days (RR = 1.02, 95%CI: 1.08–1.40), for NO2 at lag 13 days (RR = 1.03, 95%CI: 1.00–1.06) and for SO2 at lag 5 days (RR = 1.27, 95%CI: 1.08–1.48), respectively. In the stratified analysis, females, and young adults (18–60 years) were more vulnerable to PM2.5 and SO2 and the effect was greater in rural areas and urban–rural junction. Conclusions Exposure to ambient air pollution was significantly associated with hospital visits for AURTIs. This study provides epidemiological evidence for policymakers to control better air quality and establish an enhanced system of air pollution alerts.

Recommendation Systems (RS) are systems that propose products for customers to view. Since the turn of the millennium, these kinds of technologies have been increasingly common, and now almost all online apps use them to make suggestions to their users in an effort to keep and increase their engagement with the apps. The challenge of idea generation is tackled in a variety of ways by the various RS kinds. These strategies have progressed to the point where both complicated and straightforward algorithms can be used to implement them. In spite of the availability of numerous recommendation algorithms, some may be more suitable than others for specific tasks. This paper discusses a variety of recommendation algorithms, some of which are quite complex.

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decision-makers to foster sustainable urban development. Traditional mapping methods heavily depend on field surveys and subjective questionnaires, yielding less objective, reliable, and timely data. Recent advancements in Geographic Information Systems (GIS) and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations. Nonetheless, challenges persist, particularly concerning accuracy and significant temporal delays. This study introduces a novel approach to modeling urban redevelopment, leveraging machine learning algorithms and remote-sensing data. This methodology can facilitate the accurate and timely identification of urban redevelopment activities. The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment. The model is thoroughly evaluated, and the results indicate that it can accurately capture the time-series patterns of urban redevelopment. This research’s findings are useful for evaluating urban demographic and economic changes, informing policymaking and urban planning, and contributing to sustainable urban development. The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Introduction: Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD. Methods: This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging. Results: We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging. Discussion: The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis. [ABSTRACT FROM AUTHOR]

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic-to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data. [ABSTRACT FROM AUTHOR]

Tropical forests are currently under pressure from increasing threats. These threats are mostly related to human activities. Earth observations (EO) are increasingly used for monitoring forest cover, especially synthetic aperture radar (SAR), that is less affected than optical sensors by atmospheric conditions. Since the launch of the Sentinel-1 satellites, numerous methods for forest disturbance monitoring have been developed, including near realtime (NRT) operational algorithms as systems providing early warnings on deforestation. These systems include Radar for Detecting Deforestation (RADD), Global Land Analysis and Discovery (GLAD), Real Time Deforestation Detection System (DETER), and Jica-Jaxa Forest Early Warning System (JJ-FAST). These algorithms provide online disturbance maps and are applied at continental/global scales with a Minimum Mapping Unit (MMU) ranging from 0.1 ha to 6.25 ha. For local operators, these algorithms are hard to customize to meet users' specific needs. Recently, the Cumulative sum change detection (CuSum) method has been developed for the monitoring of forest disturbances from long time series of Sentinel-1 images. Here, we present the development of a NRT version of CuSum with a MMU of 0.03 ha. The values of the different parameters of this NRT CuSum algorithm were determined to optimize the detection of changes using the F1-score. In the best configuration, 68% precision, 72% recall, 93% accuracy and 0.71 F1-score were obtained. [ABSTRACT FROM AUTHOR]

Objective: Despite the importance for understanding mechanisms of change, little is known about the order of change in daily life emotions, cognitions, and behaviors during treatment of depression. This study examined the within-person temporal order of emotional, cognitive, and behavioral improvements using ecological momentary assessment data. Method: Thirty-two individuals with diagnosed depression completed ecological momentary assessment questions on emotions (sad mood, happy mood), behaviors (social interaction, number of activities), and cognitive variables (worrying, negative self-thoughts) 5 times a day during a 4-month period in which they underwent psychotherapy for depression. Nonparametric change-point analyses were used to determine the timing of gains (i.e., improvements in the mean of each variable) for each individual. We then established whether the first (i.e., earliest) gains in emotions preceded, followed, or occurred in the same week as cognitive and behavioral gains for each individual. Results: Contrary to our hypotheses, first gains in behaviors did not precede first emotional gains (3 times, 8%) more often than they followed them (26 times, 70%). Cognitive gains often occurred in the same week as first emotional gains (43 times, 58%) and less often preceded (13 times, 18%) or followed emotional gains (18 times, 24%). Conclusion: The first improvements in behaviors did not tend to precede the first improvements in emotions likely because fewer behavioral gains were found. The finding that cognitive variables tend to improve around the same time as sad mood may explain why many studies failed to find that cognitive change predicts later change in depressive symptoms. What is the public health significance of this article?: This study suggests that happy mood, sad mood, and negative ways of thinking often start to improve around the same time during treatment of depression. Fewer improvements were found in activities and interactions, which explains why activities and interactions often improved after individuals started to feel better. [ABSTRACT FROM AUTHOR]

Software reliability is a critical factor in assessing the health of software and identifying defects. Software reliability growth models (SRGM) are used to estimate the occurrence of software faults. There are various parameterized and non‐parameterized models of SRGM. These models effectively predict fault occurrence for limited testing conditions. To resolve this problem various neural and artificial neural network (ANN) models are proposed. A problem while using ANN is over‐fitting and under‐fitting. Non‐autoregressive time series models, including ANN variants, offer promising solutions to address under‐fitting issues in SRGM, providing enhanced predictive capabilities for fault occurrence across diverse testing conditions. This study proposes a modified version with a Bayesian regularization technique to address over‐fitting. This modification aims to enhance the suitability of the Bayesian regularization framework for nonlinear autoregressive (NAR) models by carefully adjusting regularization parameters. Comprehensive testing with real‐world software failure datasets is conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that our modified approach improved generalization capabilities and increased prediction accuracy. The NAR‐ANN model exhibits a lower mean squared error of 0.12935 and a higher R2$R^2$ value of 0.99853. [ABSTRACT FROM AUTHOR]

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture. [ABSTRACT FROM AUTHOR]

SUMMARY: The solution of the remote reference method, a frequently used technique in magnetotelluric (MT) data processing, can be viewed as a product of the two-input–multiple-output relationship between the local electromagnetic field and the reference field at a remote station. By applying a robust estimator to the two-input–multiple-output system, one can suppress the influence of outliers in the local magnetic field as well as those in the local electric field based on regression residuals. Therefore, this study develops a new robust remote reference estimator with the aid of robust multivariate linear regression. By applying the robust multivariate regression S-estimator to the multiple-output system, the present work derives a set of equations for robust estimates of the transfer function, noise variances, and the scale of the Mahalanobis distance simultaneously. The noise variances are necessary for the multivariate analysis to normalize the residuals of dependent variables. The Mahalanobis distance, a distance measure for multivariate data, is a commonly used indicator of outliers in multivariate statistics. By updating those robust estimates iteratively, the new robust remote reference estimator seeks the transfer function that minimizes the robust scale estimate of the Mahalanobis distance. The developed estimator can avoid bias in the MT transfer function even if there are significant noises in the reference magnetic field and handle outlying data more robustly than previously proposed robust remote reference estimators. The authors applied the developed method to a synthetic data set and real-world data. The test results demonstrate that the developed method downweights outliers in the local electric and magnetic fields and gives an unbiased transfer function. [ABSTRACT FROM AUTHOR]

SUMMARY: The successful recovery of hydrogeophysical parameters through surface-NMR measurements depends on the quality of the signal, which can be significantly degraded by harmonics from multiple noise sources with different fundamental frequencies in urban areas. Accurate estimation of the fundamental frequencies of harmonics is the main step in harmonic noise cancellation-based methods. The existing 1-D and 2-D model-based approaches involve a computationally expensive process that sets limits for processing of large surface-NMR data sets. In addition, the classical Nyman, Gaiser and Saucier estimation (NGSE) algorithm, despite its fast implementation, may not accurately recover harmonic components when there is no prior knowledge of the expected value of the frequency offset between the true fundamental frequencies and their nominal values. This lack of knowledge can make it difficult to accurately estimate the maximum number of harmonics and, consequently, result in an incorrect recovery of the fundamental frequency. To surmount these limitations, we propose an enhanced version of the NGSE approach based on an efficient maximum number of harmonics search approach to process surface-NMR signals corrupted by powerline harmonics with both single and multiple frequency content. We verify the efficiency of our algorithm on a synthetic data set embedded in simulated powerline harmonic signals, and real electromagnetic noise recordings, as well as a real surface-NMR data set. Our numerical experiments confirm that the proposed algorithm can retrieve the multiple fundamental frequencies simultaneously with a significant speedup ranging from 4 to 87 times, depending on whether the signal has single, dual, or triple frequency content, in the overall computation time compared to the model-based methods. [ABSTRACT FROM AUTHOR]

The rapid expansion of Uber Technologies, Inc.'s ride-sharing, courier service, and food delivery system and e-hailing applications has been transforming the logistics network and urban mobility around the world. We aimed to evaluate the impact of the Uber system on traffic injury (TI) mortality during its implementation in Brazilian capital cities. A quasiexperimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analyzed. The date of availability of the Uber app, specific to each capital, was considered the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis, from an interrupted time-series design, autoregressive integrated moving average (ARIMA) models with a transfer function were fitted. In 92.6% (n  = 25) of Brazilian capitals, there was no impact of Uber system implementation, 12 months after the start of its activities, on TI mortality. A reduction in mortality from this cause was observed after the system was implemented in Belo Horizonte and Rio de Janeiro. The impact on TI mortality was progressive and continuous in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of Uber system implementation between different locations and the heterogeneity of effects. [ABSTRACT FROM AUTHOR]

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products. [ABSTRACT FROM AUTHOR]

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Background: The interplay between root-knot nematode (RKN) parasitism and the complex web of host-associated microbiota has been recognized as pivotal for effective management of the pest. However, studies assessing this relationship have focussed on the bacterial and fungal communities, neglecting the unicellular eukaryotic members. Here, we employed amplicon sequencing analysis of the bacterial 16S rRNA, fungal ITS and eukaryotic 18S rRNA genes, and comprehensively examined how the microbiome composition, diversity and networking developed with time in the rhizospheres and roots of RKN-inoculated and non-inoculated tomato plants. Results: As expected, infection with the RKN Meloidogyne incognita decreased plant growth. At individual timepoints, we found distinct bacterial, fungal and eukaryote community structures in the RKN-inoculated and non-inoculated rhizospheres and roots, and RKN inoculation affected several taxa in the root-associated microbiome differentially. Correlation analysis revealed several bacterial and fungal and few protist taxa that correlated negatively or positively with M. incognita. Moreover, network analysis using bacterial, fungal and eukaryotic data revealed more dynamic networks with higher robustness to disturbances in the RKN-inoculated than in the non-inoculated rhizospheres/roots. Hub taxa displayed a noticeable successional pattern that coincided with different phases of M. incognita parasitism. We found that fungal hubs had strong negative correlations with bacteria and eukaryotes, while positive correlations characterized hub members within individual kingdoms. Conclusion: Our results reveal dynamic tomato-associated microbiomes that develop along different trajectories in plants suffering M. incognita infestation and non-infested plants. Overall, the results identify stronger associations between RKN and bacterial and fungal taxa than between eukaryotic taxa and RKN, suggesting that fungal and bacterial communities could play a larger role in the regulation of RKN. The study identifies several putative RKN-antagonistic bacterial and fungal taxa and confirms the antagonistic potential previously identified in other taxa. [ABSTRACT FROM AUTHOR]

Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship. [ABSTRACT FROM AUTHOR]

Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nanjing, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River. [ABSTRACT FROM AUTHOR]

As deep-seated ore deposits become increasingly relevant for mineral exploration, the demand for time-efficient and powerful deep-sounding exploration methods rises. A suitable method for efficiently sensing ores at great depth is airborne electromagnetics (EM) using natural signal of atmospheric origin. The method relates airborne magnetic field recordings in the audio-frequency range to reference magnetic field recordings measured at a ground-based site and can achieve greater penetration depths when compared to controlled source airborne EM techniques. However, airborne natural source EM data are prone to noise caused by platform vibrations especially deteriorating data quality at low frequencies and thus narrowing the depth of investigation. Motional noise manifests as coherent noise on all airborne magnetic field components demanding for a powerful processing tool to remove such kind of noise. Unlike the bivariate approach, which is widely used in natural source EM, the multivariate approach is capable of detecting and reducing the effect of coherent noise. We introduce a robust multivariate processing for airborne natural source EM data and present the code implementation. The code was applied to a large-scale data set from the Kalahari–Copper–Belt in Namibia covering over 1000 km2. We obtained spatially consistent and smooth sounding curves in a frequency range of 10 to 1000 Hz including frequencies with prominent motional noise. Transfer functions are in good agreement with other geophysical and geological information. [ABSTRACT FROM AUTHOR]

COVID-19 caused serious personal and societal damage. It also created, however, a unique natural experiment. How did the pandemic affect crime levels? In this article, we analyse unique empirical data for the Netherlands, covering January 2016–March 2021, to assess if and how crime patterns changed during the first 12 months of COVID-19-related measures. Domestic violence, which was expected to rise, did not increase, nor did distraction burglary. Residential burglary, bicycle theft and pickpocketing, however, decreased considerably, especially during (some of the) weeks when lockdown measures were into force. Online crime rates on the other hand increased. An opportunity theoretical approach regarding crime offers a good explanation for these changes. [ABSTRACT FROM AUTHOR]

Mapping coastal wetlands' spatial distribution and spatiotemporal dynamics is crucial for ecological conservation and restoration efforts. However, the high hydrological dynamics and steep environmental gradients pose challenges for precise mapping. This study developed a new method for mapping coastal wetlands using time-series remote sensing images and a deep learning model. Precise mapping and change analysis were conducted in the Liaohe Estuary Reserve in 2017 and 2022. The results demonstrated the superiority of Temporal Optimize Features (TOFs) in feature importance and classification accuracy. Incorporating TOFs into the ResNet model effectively combined temporal and spatial information, enhancing coastal wetland mapping accuracy. Comparative analysis revealed ecological restoration trends, emphasizing artificial restoration's predominant role in salt marsh vegetation rehabilitation. These findings provide essential technical support for coastal wetland ecosystem monitoring and contribute to the study of sustainability under global climate change. [ABSTRACT FROM AUTHOR]

Air pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise. [ABSTRACT FROM AUTHOR]

Background and aims: Identifying effective opioid treatment options during pregnancy is a high priority due to the growing prevalence of opioid use disorder across North America. We assessed the temporal impact of three population‐level interventions on the use of opioid agonist treatment (OAT) during pregnancy in Ontario, Canada. Design: This was a population‐based time‐series analysis to identify trends in the monthly prevalence of pregnant people dispensed methadone and buprenorphine. The impact of adding buprenorphine/naloxone to the public drug formulary, the release of pregnancy‐specific guidance and the start of the COVID‐19 pandemic were assessed. Setting and participants: The study was conducted in Ontario, Canada between 1 July 2013 and 31 March 2022, comprising people who delivered a live or stillbirth in any Ontario hospital during the study period. Measurements We identified any prescription for methadone or buprenorphine dispensed between the estimated conception date and delivery date and calculated the monthly prevalence of OAT‐exposed pregnancies among all pregnant people in Ontario. Findings Overall, rates of OAT during pregnancy have declined since mid‐2018. Methadone‐exposed pregnancies decreased from 0.46% of all pregnancies in Ontario in 2015 to a low of 0.16% in 2022. In the primary analysis, none of the interventions had a statistically significant impact on overall OAT rates; however, in the stratified analyses, there was a small increase in buprenorphine after the formulary change [0.006%, 95% confidence interval (CI) = 0.0032−0.0081, P < 0.0001] and a decrease in buprenorphine after the release of the 2017 guidelines (−0.005%, 95% CI = −0.0080 to −0.0020, P = 0.001) and the start of the COVID‐19 pandemic (−0.003%, 95% CI = −0.0054 to −0.0006, P = 0.015). Conclusion: Despite changes in guidance and funding, opioid agonist treatment during pregnancy has been declining in Ontario, Canada since 2018. [ABSTRACT FROM AUTHOR]