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The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods. [ABSTRACT FROM AUTHOR]

The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods.

Background: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups. Methods: A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG). Results: We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations. Conclusions: This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.

Decomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and trend shifts. Numerical experiments on real-world and synthetic problem sets present the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We present a comprehensive methodological framework for structural deformation monitoring of critical infrastructure assets based on differential SAR interferometry. By employing persistent scatterer interferometry, deformation time series in line-of-sight are derived from freely available Sentinel-1 single look complex products. These raw time series are analysed and refined using an extensive post-processing chain to obtain daily rates for vertical and horizontal deformation components. The post-processing includes cleaning, smoothing and a temperature correction to account for different sensing times in ascending and descending orbits. Longitudinal clustering of time series is used to reveal spatial patterns in the single epoch deformation series. Seasonal trend decomposition of the aggregated time series is performed to separate deformation trends from seasonal deformations. The applicability of the framework is showcased at the example of an integral concrete bridge located in the port of Vienna. Results are validated against in situ deformation measurements. The presented framework constitutes a blueprint for the continuous monitoring of critical infrastructure assets using satellite interferometry, which may supplement conventional structural health monitoring. [ABSTRACT FROM AUTHOR]

Background: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups., Methods: A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG)., Results: We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations., Conclusions: This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.

Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not to mention the development of a time series model itself for prediction. Runoff forecasting is a time series prediction problem with a series of past runoff data (water level and discharge series data) as inputs and a fixed-length series of future runoff as output. Most previous work paid attention to the sufficiency of input data and the structural complexity of deep learning, while less effort has been put into the consideration of data quantity or the processing of original input data—such as time series decomposition, which can better capture the trend of runoff—or unleashing the effective potential of deep learning. Mutual information and seasonal trend decomposition are two useful time series methods in handling data quantity analysis and original data processing. Based on a former study, we proposed a deep learning model combined with time series analysis methods for daily runoff prediction in the middle Yangtze River and analyzed its feasibility and usability with frequently used counterpart models. Furthermore, this research also explored the data quality that affect the performance of the deep learning model. With the application of the time series method, we can effectively get some information about the data quality and data amount that we adopted in the deep learning model. The comparison experiment resulted in two different sites, implying that the proposed model improved the precision of runoff prediction and is much easier and more effective for practical application. In short, time series analysis methods can exert great potential of deep learning in daily runoff prediction and may unleash great potential of artificial intelligence in hydrology research.

The dense highland field area in the upstream region of the Lake Soyang watershed is subject to excessive soil erosion during the wet season. In this study, stormwater runoff from the Lake Soyang watershed was monitored during four rainfall events at 10 locations throughout 2016. The maximum SS concentration at Naedongcheon, which is located in the upper part of the Soyang River, reached 4598 mg/L. The event mean concentration (EMC) of SS loads in Naedongcheon ranged from 82.2 mg/L to 926.3 mg/L. We found that, although the first flush events were usually concentrated in highly paved urban areas, a first flush occurred in the agricultural area of the dense highland field region. The first flush phenomenon was identified by a dimensionless cumulative runoff mass and volume curve (M(V) curve), and the intensity of the first flush was analyzed by the coefficient of the nonlinear regression model and the FF30 and FF25 values (the fraction of pollution load transported by the first 30% and 25% of runoff, respectively). Nonlinear regression models using the power function were applied to fit the M(V) curve, the FF30 values were inversely proportional to the coefficient a of the regression model. A long-term seasonal trend decomposition for monthly turbidity and precipitation was performed for the Lake Soyang. Long-term turbidity trend was approximately coincident with the trend in long-term precipitation. In addition, the present status of the best management practices (BMPs) in the upper part of the Soyang River basin was investigated, and a survey of the management and operation of the BMPs was conducted for selected farmers. [ABSTRACT FROM AUTHOR]

Identification of the interactive responses of water quantity and quality to changes in nature and human stressors is important for the effective management of water resources. Many studies have been conducted to determine the influence of these stressors on river discharge and water quality. However, there is little information about whether sewage treatment plants can improve water quality in a region where river streamflow has decreased sharply. In this study, a seasonal trend decomposition method was used to analyze long-term (1996–2015) and seasonal trends in the streamflow and water quality of the Guanting Reservoir Basin, which is located in a semi-arid region of China. The results showed that the streamflow in the Guanting Reservoir Basin decreased sharply from 1996–2000 due to precipitation change and human activities (human use and reservoir regulation), while the streamflow decline over the longer period of time (1996–2015) could be attributed to human activities. During the same time, the river water quality improved significantly, having a positive relationship with the capacity of wastewater treatment facilities. The water quality in the Guanting Reservoir showed a deferred response to the reduced external loading, due to internal loading from sediments. These results implied that for rivers in which streamflow has declined sharply, the water quality could be improved significantly by actions to control water pollution control. This study not only provides useful information for water resource management in the Guanting Reservoir Basin, but also supports the implementation of water pollution control measures in other rivers with a sharp decline in streamflow.

Solar radiation is the external driving force of the Earth's climate system. In different spatial and temporal scales, meteorological elements have different responses and lag periods to solar activity (SA), climatic oscillation (CO), geographic factors (GF) and other influencing factors. However, such studies are not abundant and in-depth in the world. To further understand the "solar-climate-water resource" system, this study considers China as the study area and investigates the monthly data of temperature (T) and precipitation (P) during 1900–2020 that were obtained from 3836 grid stations. The strong interaction and lag distribution between T or P with SA and CO were studied and influence weights of SA, CO, and geographical factors (GF) of each grid station were calculated. A multivariate hysteretic decomposition model was established to simulate and quantitatively decompose the periodic lag considering the factors of the earth's revolution. It is found that the strong interaction/lag periods obtained in a long-time scale can be decomposed into several periods shorter than the SA period. The distribution of strong interaction/lag periods is nested with topography and echoes with cities. The underlying surface conditions and urbanization are also important factors affecting the T and P lag. There are two distinct dividing lines in the lag period and influencing factor pattern of T and P. The T dividing line moves through valleys where water or mountain ranges meet, where the gap facilitates monsoon movement across regions, while the P dividing line is a zone of dramatic terrain, where tall mountains block water vapor transport. In the lag trend of T, the northern region of China has the longest lag period, and the lag period of surrounding regions tends to converge to the northern region. The lag period caused by SN in southwest China is larger than that in northwest China, while the lag effect of CO is opposite in the above two regions. The lag trend of P also has the above characteristics, but the difference is that the lag period in central China is the longest. • Two distinct dividing lines in the hysteresis distribution of T and P respectively. • MHDM descripts how lag of SA to T & P decomposed into CO, SY & GF. • Weight distribution of SA, GF and CO in RGB false color synthesis. [ABSTRACT FROM AUTHOR]

Marek's disease, a disease primarily affecting immature chickens, is a worldwide problem that has on at least three occasions threatened the poultry industry in the United States. A rich dataset to study the epidemiology of this disease is available because the United States Department of Agriculture has required mandatory inspections of all commercially sold poultry of significant scale since the mid-20th century with over 99% of all chickens inspected. This dataset includes monthly totals aggregated by state since 1961 of the number of “young chickens” inspected and the number with “leukosis”, a condemnation category that is almost always associated with Marek's disease in this category of birds. The objective of this study was to analyze temporal and spatial patterns in this condemnation data to gain insight into the ecology and epidemiology of the causative virus. We extracted visual patterns in the data using seasonal trend decomposition, and we tested for statistical significance using extended linear modeling techniques. The analysis confirmed previous findings that there are differences in leukosis condemnation rates between states, across years, and within years. The analysis also revealed several patterns not previously highlighted, including spatial and temporal autocorrelations in leukosis condemnation, changes to the amplitude of seasonality over time, and increasing within-year variation in condemnation rate over time. These patterns suggest that locally shared farm practices, virus transmission between farms, or viral persistence may be important to understanding the dynamics of the disease. We also discuss the plausibility of other potential explanations for these patterns. [ABSTRACT FROM AUTHOR]

This study aims at characterizing the diversity and temporal changes of species richness and composition of fungi in an ecotone of a forest border and a meadow in the Taunus mountain range in Germany. All macroscopically visible, epigeous fungi and vascular plants were sampled monthly over three years, together with climatic variables like humidity and temperature that influence fungal diversity and composition as shown by previous studies. In this mosaic landscape, a total of 855 fungal species were collected and identified based on morphological features, the majority of which belonged to Ascomycota (51 %) and Basidiomycota (45 %). Records of fungal species and plant species (218) for this area yielded a fungus to plant species ratio of 4:1, with a plant species accumulation curve that reached saturation. The three years of monitoring, however, were not sufficient to reveal the total fungal species richness and estimation factors showed that a fungus to plant species ratio of 6:1 may be reached by further sampling efforts. The effect of climatic conditions on fungal species richness differed depending on the taxonomic and ecological group, with temporal patterns of occurrence of Basidiomycota and mycorrhizal fungi being strongly associated with temperature and humidity, whereas the other fungal groups were only weakly related to abiotic conditions. In conclusion, long-term, monthly surveys over several years yield a higher diversity of macroscopically visible fungi than standard samplings of fungi in autumn. The association of environmental variables with the occurrence of specific fungal guilds may help to improve estimators of fungal richness in temperate regions.

Spatial and temporal analyses are critical to understand the pattern of myocardial infarction (MI) hospitalizations over space and time, and to identify their underlying determinants. In this paper, we analyze MI hospitalizations in Calgary from 2004 to 2013, stratified by age and gender. First, a seasonal trend decomposition analyzes the seasonality; then a linear regression models the trend component. Moran's I and hot spot analyses explore the spatial pattern. Though exploratory, results show that most age and gender groups feature a statistically significant decline over the 10 years, consistent with previous studies in Canada. Decline rates vary across ages and genders, with the slowest decline observed for younger males. Each gender exhibits a seasonal pattern with peaks in both winter and summer. Spatially, MI hot spots are identified in older communities, and in socioeconomically and environmentally disadvantaged communities. In the older communities, higher MI rates appear to be more highly associated with demographics. Conversely, worse air quality appears to be locally associated with higher MI incidence in younger age groups. The study helps identify areas of concern, where MI hot spots are identified for younger age groups, suggesting the need for localized public health policies to target local risk factors., Competing Interests: The authors declare no conflict of interest.