Detrending Research Articles

Тенденции распределения доходов между трудом и капиталом в странах с низкой долей труда в ВВП

The work is devoted to obtaining quantitative estimates of trends in the distribution of income between labor and capital in countries with a low share of labor in GDP. UN data was used for a set of European countries, post-Soviet countries, Israel, Canada, the USA and Turkey. The lowest labor share levels were observed in Ireland, Kyrgyzstan, Romania and Turkey. To assess trends in the share of labor in GDP on the rate of economic growth, linear econometric models of changes in the share of labor compensation in GDP by year in the period from 2012 to 2021 were built. Hungary, Ireland, Kazakhstan, Kyrgyzstan, Uzbekistan and Ukraine have seen a decline in the share of labor in GDP. In Uzbekistan, this trend is weakly expressed. Germany, Greece, Iceland, Luxembourg, the Czech Republic, Switzerland and Estonia have seen an increase in the labor share of GDP. In Germany and Switzerland this trend is weakly expressed. An increase in the share of labor in GDP is observed in countries such as Azerbaijan, Belarus, Bulgaria, Georgia, Israel, Cyprus, Latvia, Lithuania, Malta, Norway, Poland, the Russian Federation, Romania, Serbia, Slovakia, Turkmenistan and Turkey. Moreover, this trend is weakly expressed in Belarus and Turkey. There are no significant trends in the redistribution of income between labor and capital in countries such as Albania, Malta, North Macedonia, Tajikistan and Montenegro. Trends in the redistribution of income between labor and capital can be determined by institutional conditions in the country’s economy.

Quasi-likelihood analysis for Student-Lévy regression

We consider the quasi-likelihood analysis for a linear regression model driven by a Student-t Lévy process with constant scale and arbitrary degrees of freedom. The model is observed at high frequency over an extending period, under which we can quantify how the sampling frequency affects estimation accuracy. In that setting, joint estimation of trend, scale, and degrees of freedom is a non-trivial problem. The bottleneck is that the Student-t distribution is not closed under convolution, making it difficult to estimate all the parameters fully based on the high-frequency time scale. To efficiently deal with the intricate nature from both theoretical and computational points of view, we propose a two-step quasi-likelihood analysis: first, we make use of the Cauchy quasi-likelihood for estimating the regression-coefficient vector and the scale parameter; then, we construct the sequence of the unit-period cumulative residuals to estimate the remaining degrees of freedom. In particular, using full data in the first step causes a problem stemming from the small-time Cauchy approximation, showing the need for data thinning.

LLMDiff: Diffusion Model Using Frozen LLM Transformers for Precipitation Nowcasting.

Precipitation nowcasting, which involves the short-term, high-resolution prediction of rainfall, plays a crucial role in various real-world applications. In recent years, researchers have increasingly utilized deep learning-based methods in precipitation nowcasting. The exponential growth of spatiotemporal observation data has heightened interest in recent advancements such as denoising diffusion models, which offer appealing prospects due to their inherent probabilistic nature that aligns well with the complexities of weather forecasting. Successful application of diffusion models in rainfall prediction tasks requires relevant conditions and effective utilization to direct the forecasting process of the diffusion model. In this paper, we propose a probabilistic spatiotemporal model for precipitation nowcasting, named LLMDiff. The architecture of LLMDiff includes two networks: a conditional encoder-decoder network and a denoising network. The conditional network provides conditional information to guide the denoising network for high-quality predictions related to real-world earth systems. Additionally, we utilize a frozen transformer block from pre-trained large language models (LLMs) in the denoising network as a universal visual encoder layer, which enables the accurate estimation of motion trend by considering long-term temporal context information and capturing temporal dependencies within the frame sequence. Our experimental results demonstrate that LLMDiff outperforms state-of-the-art models on the SEVIR dataset.

Network traffic prediction based on PSO-LightGBM-TM

Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.

Reconciled estimation of Antarctic ice sheet mass balance and contribution to global sea level change from 1996 to 2021

AbstractThe Antarctic Ice Sheet (AIS) has been losing ice mass and contributing to global sea level rise (GSLR). Given its mass that is enough to cause ∼58 m of GSLR, accurate estimation of mass balance trend is critical for AIS mass loss monitoring and sea level rise forecasting. Here, we present an improved approach to reconciled solutions of mass balance in AIS and its regions from multiple contributing solutions using the input-out, altimetric, and gravimetric methods. In comparison to previous methods, such as IMBIE 2018, this approach utilizes an adaptive data aggregation window to handle the heterogeneity of the contributing solutions, including the number of solutions, temporal distributions, uncertainties, and estimation techniques. We improved the regression-based method by using a two-step procedure that establishes ensembled solutions within each method (input-output, altimetry, or gravimetry) and then estimates the method-independent reconciled solutions. For the first time, 16 contributing solutions from 8 Chinese institutions are used to estimate the reconciled mass balance of AIS and its regions from 1996 to 2021. Our results show that AIS has lost a total ice mass of ∼3213±253 Gt during the period, an equivalent of ∼8.9±0.7 mm of GSLR. There is a sustained mass loss acceleration since 2006, from 88.1±3.6 Gt yr−1 during 1996–2005 to 130.7±8.4 Gt yr−1 during 2006–2013 and further to 157.0±9.0 Gt yr−1 during 2014–2021. The mass loss signal in the West Antarctica and Antarctic Peninsula is dominant and clearly presented in the reconciled estimation and contributing solutions, regardless of estimation methods used and fluctuation of surface mass balance. Uncertainty and challenges remain in mass balance estimation in East Antarctica. This reconciled estimation approach can be extended and applied for improved mass balance estimation in the Greenland Ice Sheet and mountain glacier regions.

Data-Driven Radial Compressor Design Space Mapping

Abstract Estimates of turbomachinery performance trends inform system-level compromises during preliminary design. Existing empirical correlations for efficiency use limited experimental data, while analytical loss models require calibration to yield predictive results. From a set of 3708 radial compressor computations, this paper maps efficiency as a function of mean-line aerodynamics, and determines the governing loss mechanisms. An open-source turbomachinery design code creates annulus and blade geometry, then runs a Reynolds-averaged Navier–Stokes simulation for compressors sampled from the mean-line design space. Polynomial surface fits yield a continuous eight-dimensional representation of the design space for analysis, predicting efficiency with a root-mean-square error of 1.2% points. The results show a balance between surface dissipation in boundary layers and mixing loss due to casing separations sets optimum values for inlet Mach number, hub-to-tip ratio, de Haller number, and backsweep angle. Surface dissipation drives the effect of flow coefficient, with high surface areas at low values, and high velocities at high values. Compact compressor designs are achieved by increasing inlet Mach number, reducing hub-to-tip ratio, and minimizing the radial loading coefficient—all of which reduce efficiency approaching design space boundaries. An interactive web-based tool makes the results available to practising engineers, demonstrating large ensembles of automated designs and simulations as a higher-fidelity replacement for legacy empirical correlations in preliminary design.

Changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the Tibetan Plateau

AbstractChanges in the soil freeze–thaw status will inevitably affect the thermal conditions and properties of the Tibetan Plateau (TP), thereby affecting its upper atmosphere, and further afield in East Asia and even globally. In this study, using the soil temperature simulated by the Community Land Model Version 5.0 (CLM5.0), the timing and duration of the soil freeze–thaw status were divided into freeze start‐date, freeze end‐date and freeze duration. Then, using linear trend estimation, correlation analysis and other methods, the changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the TP were analysed, and the relationships between them and surface temperature, altitude and latitude were analysed. The results obtained were as follows: (1) The soil temperature simulated by CLM5.0 can reasonably reproduce the seasonal changes in multilayer soil temperature, and correlated well with observations. Simulated by CLM5.0 of the time and duration of the soil freeze–thaw status also correlated well with observations. (2) The spatial distribution of the soil freeze–thaw status is characterized by a trend of delayed freezing, advanced thawing and shortened freeze duration from northwest to southeast over the TP. From 1979 to 2018, the freeze start‐date postponed by 7.3 days and became delayed at a rate of 1.9 days per decade, while the freeze end‐date advanced by 6.4 days at a rate of 1.7 days per decade, and the freeze duration shortened by 13.7 days at a rate of 3.6 days per decade. The timing and duration of the soil freeze–thaw status vary across different regions of the TP. The freeze start‐date in all areas of the TP has been delayed in the past 39 years. Except for the subcold zone and arid regions of the TP, the freeze end‐date has occurred earlier and the freeze duration has shortened, with the most significant changes in the subcold zone and humid regions, while the freeze end‐date has advanced at a rate of 3.6 days per decade and the freeze duration has shortened at a rate of 6.3 days per decade. (3) The timing and duration of the soil freeze–thaw status are significantly correlated with surface air temperature, elevation and latitude, exceeding the 99% confidence level. The correlation between the timing and duration of the soil freeze–thaw status and surface temperature is strongest, followed by altitude, and correlation with latitude is weaker. The correlation between surface air temperature and the timing and duration of the soil freeze–thaw status in the western TP is stronger than that in the eastern TP. The rate of change in the soil freeze–thaw status increases with altitude to 3000 m above sea level, while this rate decreases with elevation above 3000 m. The rate of change in the soil freeze–thaw status is greatest at 29°N, while the rate of delay in the freeze start‐date is minimal at 33°N and the rates of advancement in the freeze end‐date and shortening of the freeze duration are minimal at 35°N.

Measures of stimulant medications: A population-based study in Alberta, Canada

ObjectivesStimulants are a class of drugs approved for the treatment of attention-deficit hyperactivity disorder (ADHD) and narcolepsy. However, they are also often used “off-label” as adjunct therapies for the treatment of obesity and depression. The objective of this study is to summarize how stimulant use is globally measured in the literature and to explore rates of stimulant use in Alberta, Canada. MethodsA traditional narrative literature review was conducted to summarize global methods of stimulant assessment. Then using definitions guided by the literature and current regulatory bodies in Alberta, we conducted a series of descriptive analyses to assess how frequent stimulant use was in Alberta patients from 2019 to 2021: 1) number of dispenses by year; 2) average days of drug supply; 3) proportion of days covered (PDC); and 4) defined daily dose (DDDs). ResultsIn the literature review, the most frequently used measures of stimulant drug use were trends over time (prevalence), types of drug use, and dispensations of prescriptions. In all, there is a global trend of increased use of stimulants among both adults and children. In Alberta, 173,789 patients were prescribed stimulant medication in 2019–2021, representing approximately 4 % of the entire Alberta population. Overall, 61.1 % were between the ages of 10–34 and 46.8 % were female. The number of dispensations rose from 713,896 in 2019 to 973,930 in 2021 – with up to 43 % being lisdexamfetamine stimulant dispenses. ConclusionsAlthough stimulant use in AB was measured using similar trend estimates as the literature, there is a lack of research to support whether these measures are accurate and effective at the population-level. Future steps to standardize both medical and nonmedical use of prescription stimulants are warranted in efforts to fully quantify both benefits and risks associated with stimulant use.

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23

Key population HIV programmes in sub-Saharan Africa require epidemiological information to ensure equitable and universal access to effective services. We aimed to consolidate and harmonise survey data among female sex workers, men who have sex with men, people who inject drugs, and transgender people to estimate key population size, HIV prevalence, and antiretroviral therapy (ART) coverage for countries in mainland sub-Saharan Africa. Key population size estimates, HIV prevalence, and ART coverage data from 39 sub-Saharan Africa countries between 2010 and 2023 were collated from existing databases and verified against source documents. We used Bayesian mixed-effects spatial regression to model urban key population size estimates as a proportion of the gender-matched, year-matched, and area-matched population aged 15-49 years. We modelled subnational key population HIV prevalence and ART coverage with age-matched, gender-matched, year-matched, and province-matched total population estimates as predictors. We extracted 2065 key population size data points, 1183 HIV prevalence data points, and 259 ART coverage data points. Across national urban populations, a median of 1·65% (IQR 1·35-1·91) of adult cisgender women were female sex workers, 0·89% (0·77-0·95) were men who have sex with men, 0·32% (0·31-0·34) were men who injected drugs, and 0·10% (0·06-0·12) were women who were transgender. HIV prevalence among key populations was, on average, four to six times higher than matched total population prevalence, and ART coverage was correlated with, but lower than, the total population ART coverage with wide heterogeneity in relative ART coverage across studies. Across sub-Saharan Africa, key populations were estimated as comprising 1·2% (95% credible interval 0·9-1·6) of the total population aged 15-49 years but 6·1% (4·5-8·2) of people living with HIV. Key populations in sub-Saharan Africa experience higher HIV prevalence and lower ART coverage, underscoring the need for focused prevention and treatment services. In 2024, limited data availability and heterogeneity constrain precise estimates for programming and monitoring trends. Strengthening key population surveys and routine data within national HIV strategic information systems would support more precise estimates. UNAIDS, Bill & Melinda Gates Foundation, and US National Institutes of Health.

Cycle Fluctuations in the U.S. Real GDP and National Income and Product Accounts Aggregates

This article provides a new set of empirical regularities describing the U.S. macroeconomy, focusing on business cycle fluctuations in the real GDP and eleven major aggregates from the U.S. National Income and Product Accounts (NIPAs). Patterns of the cyclical fluctuations are assessed using filtering methodologies that adapt to the properties of the series being studied. We employ a recent dataset that includes the Great Recession caused by the 2008 financial crisis and the ensuing recovery. We aim to (1) examine the lead-lag relations via cross-correlations between the aggregate cycle in the U.S. real GDP and the cyclical movements in the eleven major NIPA aggregates; (2) investigate econometrically the inter-linkage between the cyclical component of real GDP and that of each NIPA aggregate by way of the Granger-causality test; and (3) evaluate the capability or the predictive power of each NIPA aggregate to forecast real GDP growth via three forecasting models. Comparisons are made with popular nonparametric HP and Baxter-King (BK) filters. The basic conclusion from the empirical analysis is that the adaptive model-based filters have demonstrated advantages over the commonly used HP and BK filters for business cycle analysis across very diverse economic series from the U.S. national accounts, and the structural time series model using smoothed trend and cycle estimates produced more accurate forecasts than the AR(p) model that forecasts directly using the unfiltered time series.

Recursive identification of kernel-based hammerstein model for online energy efficiency estimation of large-scale chemical plants

Accurate energy efficiency prediction is crucial for decision-making in large-scale chemical production, especially considering energy management and environmental concerns. The complexity of chemical processes, characterized by strong nonlinearities, dynamics, and uncertainties, challenges traditional prediction methods. This paper introduces KHSM (Kernel-based Hammerstein Subspace Model), which models nonlinear static and linear dynamic processes in series by embedding kernel function-based nonlinear mapping into subspace modeling for energy efficiency estimation. KHSM identifies nonlinear process models without explicit nonlinear mapping functions, constructing dynamic models that encompass both energy input and product output variables. An adaptive KHSM variant dynamically updates the energy efficiency model, improving accuracy by iteratively selecting and excluding samples. Comparative analysis shows KHSM outperforms existing methods, achieving a coefficient of determination (R2) closest to 1 and reducing the root-mean-square error (RMSE) by up to an order of magnitude, with the smallest mean and standard deviation. Additionally, modeling efficiency increases by an order of magnitude. The developed model enables ongoing energy efficiency estimation and proactive identification of efficiency trends. Validation through mathematical and real-world ethylene process cases demonstrates KHSM's efficacy and applicability. These estimates facilitate informed adjustments to energy management strategies and optimization of production approaches in energy-intensive chemical processes.

A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating of financial indicators

The main purpose of this study is to develop a new discrete time stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation which follows bouncing autoregressive AR1, with fixed variance and trend for modeling and simulating of international financial time series forecasting.This study offers the four discrete time stochastic models. The first model is determined as autoregressive integrated moving average-Arima1,1,1, and the parameter estimates of this model are found using the maximum likelihood method (MLM). Monte Carlo simulation is applied to the obtained parameters. This model is named MoCArima for short. Second, the MoCArima model whose parameters are estimated with particle swarm optimization (PSO) is determined as Proposed Monte Carlo (MC) Arima1,1,1, and is briefly named as ProMoCArima. The third model is determined as Fixed Variance Stochastic Model which has a bouncing autoregressive AR1 structure with constant variance and trend component. This model is briefly named as FiVaS. The parameter estimates are found using the MLM. The fourth model is determined as the Proposed Fixed Variance Stochastic Model. This model is briefly named as ProFiVaS. Structurally the same as the third model. The parameter estimates of both trend and stochastic parts of the FiVaS model are found by the PSO.Proposed models are applied to forecasting international different financial indicators to show its superiority and applicability. The performance and usage efficiency of the proposed four stochastic models are examined on a total of six different international financial data types, five of which are stock index data BIST100 (Türkiye), S&P500 (USA), DAX (Germany), FTSE100 (UK), NIKKEI225 (Japan), and one of which is the exchange rate EURO/USD. The ProFiVaS among all models provides the best performance on the all indices. The ProFiVaS can easily be extended to any real-world time series prediction requirements.The proposed models can be extended to all stationary and non-stationary linear or nonlinear autoregression models opening the door to modeling data types obtained from different sector fields. A part of the future plan of this work is to design a decision support system software that assigns a value of 1 when an investor needs to invest in a financial indicator, and 0 otherwise.

Burden of unintentional drowning in Indonesia: insights from the Global Burden of Disease Study 2019

IntroductionA high burden of unintentional fatal drowning has been reported in low- and middle-income countries. However, little is known about unintentional drowning in Indonesia.MethodsThis population-based retrospective cohort study analysed unintentional...

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

A detection method of Auricularia auricula mycelium storage period based on near-infrared spectral characteristics and deep learning model

The storage time of Auricularia auricula mycelium was an important factor influencing its growth activity. The rapid detection of storage time had important theoretical significance and practical value for evaluating, cultivating, and scientifically managing Auricularia auricula germplasm resources. Due to the problem of mapping the relationship between the complex near-infrared (NIR) spectral characteristics of Auricularia auricula mycelium and its storage period, it was difficult to characterize using mathematical models accurately. In this paper, a method for detecting the storage period of Auricularia auricula mycelium based on NIR spectral characteristics and deep learning was proposed. First, five different storage periods of Auricularia auricula mycelium were taken as the research object, and the de-trending (DT) algorithm was used to pre-process the spectral data. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was applied to extract the NIR spectral characteristics of the mycelium. The reduction rate of the spectral variables reached 94.64 %. Further, a one-dimensional convolutional neural network (T-CNN) was constructed, and the SGD-Adam algorithm was used to calculate the optimal network parameters. Finally, a method for detecting the storage period of Auricularia auricula mycelium was implemented, achieving a simulation accuracy of 98.33 %. The experimental results revealed that this method outperformed traditional machine learning models such as partial least squares-discriminant analysis (PLS-DA), back propagation (BP), support vector machine (SVM), and k-nearest neighbors (KNN) by an average of 19.56 % in detection accuracy. Additionally, the detection speed was only 0.07 s. This achievement enabled rapid and accurate detection of the storage time of Auricularia auricula mycelium, providing a dependable foundation for identifying the growth activity of Auricularia auricula mycelium. It provided technical support for the scientific management of the edible fungus mycelium, including quality monitoring, evaluation, and screening.

Nondestructive detection of lead content in oilseed rape leaves under silicon action using hyperspectral image

This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89–1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with Rp2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.

Mortality Surveillance for the COVID-19 Pandemic: Review of the Centers for Disease Control and Prevention's Multiple System Strategy.

Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response-line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System-to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. Published online ahead of print July 25, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307743).

Machine learning models applied to altimetry era tide gauge and grid altimetry data for comparative long-term trend estimation: A study from Shikoku Island, Japan

Estimation of sea level trends is essential for understanding sea level rise dynamics. In this study, the performance of traditional Ordinary Least Squares (OLS) linear trend forecasting is compared with modern machine learning techniques, specifically Random Forests (RF) and Least Squares Support Vector Machines (LS-SVM).These methods are applied to 50 years of long-term tide gauge (TG) data from six tide gauge stations off the coast of Shikoku Island, Japan, and CMEMS Grid Altimetry data from 1993 to the present. The analysis uses OLS, RF, and LS-SVM to estimate trends from both data sets and compares the results. The objective is to determine the consistency and accuracy of RF and LS-SVM methods compared to the OLS method. The results indicate that machine learning algorithms (LS-SVM) effectively estimate sea level trends, offering potential improvements in precision for both long-term and medium-term analyses. Shikoku Island's coastal sea level trend is determined as 2.91±1.44 mm/yr using TG data and 3.00±1.52 mm/yr using CMEMS Grid Altimeter data with the OLS approach. Using the LS-SVM approach, the trend is found as 2.96±1.58 mm/yr with TG data and 3.02±1.60 mm/yr with CMEMS Grid Altimetry data. The novelty of this study lies in its thorough comparison of traditional and machine learning approaches for sea level trend estimation, providing valuable insights for future predictions of the sea level rise.

Changes in South American surface ozone trends: exploring the influences of precursors and extreme events

Abstract. In this study, trends of 21st-century ground-level ozone and ozone precursors were examined across South America, a less-studied region where trend estimates have rarely been comprehensively addressed. Therefore, we provided an updated regional analysis based on validated surface observations. We tested the hypothesis that the recent increasing ozone trends, mostly in urban environments, resulted from intense wildfires driven by extreme meteorological events impacting cities where preexisting volatile organic compound (VOC)-limited regimes dominate. We applied the quantile regression method based on monthly anomalies to estimate trends, quantify their uncertainties and detect trend change points. Additionally, the maximum daily 8 h average (MDA8) and peak-season metrics were used to assess short- and long-term exposure levels, respectively, for the present day (2017–2021). Our results showed lower levels in tropical cities (Bogotá and Quito), varying between 39 and 43 nmol mol−1 for short-term exposure and between 26 and 27 nmol mol−1 for long-term exposure. In contrast, ozone mixing ratios were higher in extratropical cities (Santiago and São Paulo), with a short-term exposure level of 61 nmol mol−1 and long-term exposure levels varying between 40 and 41 nmol mol−1. Santiago (since 2017) and São Paulo (since 2008) exhibited positive trends of 0.6 and 0.3 nmol mol−1 yr−1, respectively, with very high certainty. We attributed these upward trends, or no evidence of variation, such as in Bogotá and Quito, to a well-established VOC-limited regime. However, we attributed the greater increase in the extreme percentile trends (≥ 90th) to heat waves and, in the case of southwestern South America, to wildfires associated with extreme meteorological events.

Assessing the Potential Impacts of Climate Change on Drought in Uzbekistan: Findings from RCP and SSP Scenarios

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world’s largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan’s heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country.