Trend Analysis Of Time Series Research Articles

Making medical education great again: analysing trainee feedback to identify key drivers of trainee satisfaction and improve experience

ObjectivesThe London School of Paediatrics (LSP) surveys trainees annually regarding their training placements. There is a wide variation in satisfaction. We explored the last 5 years of data to identify trends and determine predictors of overall trainee satisfaction.MethodsThe survey is distributed annually to all London paediatric trainees with response rates of 86–90%. It explores overall placement satisfaction, followed by key questions covering staffing, supervision, teaching, facilities and other facets. The survey blends both quantitative and qualitative feedback.Satisfaction levels were analysed using time series trend analysis. Overall levels were compared using t-tests. Associations between satisfaction and components of the survey were identified using a convergence of multiple regression and other multivariate analyses.ResultsOverall trainee satisfaction increased steadily until 2020. There was a considerable variation across sites, some having consistently higher or lower scores than the median, while others showed an upward or downward trend, or very variable pattern.By training levels, ST3 trainees consistently rated their placement below average and ST7 trainees above average. There was also a considerable variation in responses by training level with regard to perceived staffing levels and teaching.To understand the influences on overall satisfaction, regression coefficients were derived between specific metrics and overall satisfaction using multiple regression and then normalised to 100%. The explanatory power of this list to explain satisfaction overall is high at 82% R2.Trainee satisfaction was most strongly correlated with workplace atmosphere and consultant support. Staffing, educational supervision, facilities and wider MDT (Multidisciplinary Team) support are associated with overall satisfaction, but to a lesser degree.ConclusionsTrainee satisfaction is crucial, affecting patient safety, retention and trainee mental health. Our analysis identified significant associations with satisfaction including modifiable elements such as consultant engagement and team morale. Staffing levels were less related to satisfaction than expected.Experimental data exploring the components of clinical training in real-world situations are lacking. These data use the natural experiment of placing trainees in different training locations to explore the factors that relate to their overall experience.

Clip versus coil shift for aneurysm treatment in Brazil: an exploratory analysis of trends in a 10-year time-series.

Regarding intracranial aneurysm treatment, the clip versus coil debate remains inconclusive and lacking studies in Brazil. To examine trends in the management of intracranial aneurysms in Brazil over time, both ruptured and unruptured. A descriptive and exploratory study was conducted based on data of neurovascular procedures for aneurysm treatment using the Brazilian Public Health System database (DATASUS). The variables analyzed were the number of procedures, mortality rates, length of hospital stays, and global costs of hospitalization, from 2010 to 2019. Temporal trend analysis and statistical comparisons were conducted to assess changes over time and differences between the treatment options. The mean annual number of aneurysm treatments with endovascular embolization was 2206.30 (± 309.5), with a non-significant increasing trend (B = 55.66; p = 0.104). Conversely, microsurgical clipping exhibited a significant decreasing trend (B = -69.97; p < 0.001) with a mean of 1133.1 (± 223.12) procedures. The mortality rate associated with clipping procedure was higher in the period, with a mean difference of 5.23 (± 0.39); ([CI95%: 4.36; 6.10]; p < 0.001) and showed an increase trend, while embolization showed a stable trend. The length of in-hospital stay remained stable for clipping but increased for embolization. Costs associated with clipping increased over time, whereas costs for embolization decreased. This study highlights a significant shift in the treatment of aneurysm towards Endovascular Embolization. Despite higher costs, endovascular procedures were associated with lower mortality rates and shorter hospital stays. These findings provide valuable insights into aneurysm treatment patterns and indicators in a middle-income country's Public Health System.

Long-term trends in cycle threshold values: a comprehensive analysis of COVID-19 dynamics, viral load, and reproduction number in South Korea.

With the emergence of COVID-19 cases, governments quickly responded with aggressive testing, contact tracing, isolation and quarantine measures. South Korea's testing strategy primarily relied on real-time reverse-transcriptase polymerase chain reaction (real-time RT-PCR), focusing on cycle threshold (Ct) values, indicative of viral load, to determine COVID-19 positivity. This study examined the long-term time series distribution of Ct values measured in the same laboratory using a nationally standardized testing type and sampling method in South Korea. It aimed to link Ct values, new COVID-19 cases, and the reproduction number (Rt), setting the stage for using Ct values effectively. This study analyzed nationally collected 296,347 samples Ct values from February 2020 to January 2022 and examined their associations with the number of new cases and Rt trends. The data were categorized into four COVID-19 periods for in-depth analysis. Statistical methods included time series trend analysis, local regression for smoothing, linear regression for association analysis, and calculation of correlation coefficients. The median Ct values across four COVID-19 periods decreased gradually from 31.71 in the initial period to 21.27 in the fourth period, indicating higher viral load. The comparison of trends between Ct values and the number of new cases revealed that the decline in Ct values preceded the surge in new cases, particularly evident during the initial stages when new cases did not undergo a significant increase. Also, during variant emergence and vaccination rollout, marked shifts in Ct values were observed. Results from linear regression analysis revealed a significant negative relationship between Ct values and new cases (β = -0.33, p < 0.001, R 2 = 0.67). This implies that as Ct values decrease, new case numbers increase. This study demonstrates the potential of Ct values as early indicators for predicting confirmed COVID-19 cases during the initial stages of the epidemic and suggests their relevance in large-scale epidemic monitoring, even when case numbers are similar.

TIME SERIES MODELLING OF ROAD TRAFFIC ACCIDENTS IN WEST ARSI, ETHIOPIA

The aim of this research is time series modeling of road traffic accidents in the west Arsi zone, Ethiopia, it focused on monthly traffic accidents from January 2016 to December 2020. The goal of this study was to explore the number of traffic accidents to fit a time series model for the monthly number of road traffic accidents and to forecast a tow year ahead of the number of road traffic accidents. The analysis was done by using statistical software packages using this software and knowledge of time series analysis, trend, ACF, PACF, and Box-Jenkins analysis were computed. From the trend plot, the road traffic accidents was fluctuate from month to month as well as from year to year. There was total accident fluctuation from month to month (not stationary) a total of 1010 RTA were observed. The mean of 16.83 and standard deviation of 5.764 for the total accident was served the minimum and maximum record of road traffic accidents is 4 and 33 respectively. By differencing data one time, (2, 1, 3) model was fitted for making a two-year ahead forecast. Proper model adequacy checking was done. Two-year ahead forecasts showed that October, January, and April 2021 are the months with the most prominent values. Even if the trend in total accidents was decreasing there is still a need to pay more attention in order to prevent the occurrence of accidents related to road traffic accidents.

Irregularity and time series trend analysis of rainfall in Johor, Malaysia

Due to its effect on weather and its propensity to cause catastrophic incidents, climate change has garnered considerable global attention. Depending on the area, the effects of climate change may vary. Rainfall is among the most significant meteorological factors associated with climate change. In Malaysia, changes in rainfall distribution pattern have led to many floods and droughts events which lead to La Nina and El Nino where Johor is one of the states in southern part that usually affected. Thus, rainfall trend analysis is important to identify changes in rainfall pattern as it gives an initial overview for future analysis. This research aims to evaluate historical rainfall data of Johor between 1991 and 2020. Normality and homogeneity tests were used to ensure the quality of data followed by Mann-Kendall and Sen's slope analysis to determine rainfall trend as the rainfall data is not normally distributed (p > 0.05). Standardized precipitation anomaly, coefficient of variation, precipitation concentration index and rainfall anomaly index were used to identify rainfall variability and intensity while standard precipitation index was used to evaluate drought severity. The lowest annual rainfall recorded was 1725.07 mm in 2016 and the highest was 2993.19 mm in 2007. Annual rainfall and seasonal rainfall showed a declining trend although it is not statistically significant (p > 0.05). Results reveal that Johor experienced extreme wet and dry years, leading to drought and flood incidents. Major floods arose in 2006, 2007, 2008, 2010 and 2011 while driest years occurred in 1997, 1998 and 2016 which led to El Nino phenomenon. March and April were identified as the driest months among all. Thus, the findings from this study would assist researchers and decision-makers in the development of applicable adaptation and mitigation strategies to reduce climate change impact. It is recommended that more data analysis from more stations should be done in the future research study to obtain a clearer view and more comprehensive results.

Variability and time series trend analysis of streamflow in response to climatic and anthropogenic factors over Narmada River basin, India

Variability and time series trend analysis of streamflow in response to climatic and anthropogenic factors over Narmada River basin, India

Spatiotemporal distribution of carbon monoxide dynamics and probabilistic health risk assessment: A comprehensive sensitivity analysis across India's state capitals

Carbon monoxide (CO) is a harmful trace gas produced from the incomplete combustion of fossil fuels. Carbon monoxide (CO) poses a substantial threat to human health. The objectives of this study were to investigate carbon monoxide (CO) annual and seasonal variations with meteorological factors, study the marginal effects of weather factors on CO, analyze the impact of independent variables (meteorological factors) importance on CO distribution, time series trend analysis (Mann-Kendall and Sen's slope, additive decomposition model, and linear trend model), study the spatial distribution of CO in the capital cities and non-carcinogenic health risk in India from 2019 to 2022. The study showed that CO concentrations varied, notably across different years (from 2019 to 2022), months, and seasons. The maximum annual CO (0.91 ± 0.17 mg/m3) concentration was recorded in 2019, while the lowest was (0.67 ± 0.12 mg/m3) in 2022. Seasonally, the winter season had the highest load of CO (ranging from 0.17 to 2.17 mg/m3) in the ambient atmosphere compared to the monsoon (0.08 to 1.38 mg/m3) and summer (0.10 to 1.50 mg/m3) seasons. The analysis of the importance of the independent variables (meteorological factors) on CO indicated that temperature had the most substantial impact on CO variation, followed by wind direction, relative humidity, and wind speed. The trend analysis using different models showed a declining trend in CO concentrations over the study period. The non-carcinogenic health risk HQ values were less than 1, suggesting no notable risk. The Monte Carlo simulation revealed that the 5th and 95th percentile range of HQ risk was 0.02 to 0.07 and the sensitivity analysis of non-carcinogenic risk at the state capital level was conducted, and revealed that Delhi was at a higher risk than Chandigarh, Patna, Gandhinagar, Bengaluru, Mumbai, and others, respectively. The results of this study on CO emissions will be beneficial in creating focused plans and policies to reduce CO pollution in seasonal influences and its effects on ambient air quality and public health in the capital cities of Indian states.

Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran.

Vegetation plays a crucial role in providing organic matter and regulating energy exchange on the Earth's surface. This study investigates the changes in vegetation cover, temperature, and precipitation in northeastern Iran during 2001-2020. MODIS-NDVI time series data and climatic data from 11 synoptic stations were utilized. The nonparametric Mann-Kendall method was employed to detect trends in vegetation cover and climatic variables. Additionally, the correlation between climatic parameters and vegetation was examined. Trend analysis revealed significant increases/decreases in vegetation cover in 32%/26% of the region, respectively. The increasing trend in vegetation cover was predominantly observed in highlands, suggesting that a warmer climate has enhanced the living conditions for plants in these regions. The vegetation trend map indicates an expansion of vegetation cover in the northern and central parts of Iran during the past 20years, whereas the southern and eastern portions experienced declines. The relationship between vegetation and elevation revealed that vegetation increased above 1,850m and decreased below 850m. Trend analysis showed no significant trend in precipitation data since the beginning of the twenty-first century, but an increasing trend in temperature was observed in 82% of the region's area, excluding the western strip. Correlation coefficients between temperature, precipitation, and vegetation indicated that declining temperatures are the limiting factors for vegetation in the highlands, while in lowland areas, the decrease in precipitation significantly diminishes vegetation growth.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

ИЗМЕНЕНИЯ КЛИМАТА НА ТЕРРИТОРИИ РЕСПУБЛИКИ ТАТАРСТАН

The paper looks at the dynamics of air temperature changes in Kazan in the 19th-21st centuries against the background of climatic processes occurring in the Northern hemisphere. The study used proven statistical methods of time series processing, correlation and trend analysis, and a Potter filter to isolate the low-frequency component. The fastest rate of warming in the region was established to occur in the winter-spring period. At the same time, the three decades between 1991 and 2021 turned out to be warmer in each month than the 5 previous three-decade periods. The contribution of the entire Northern Hemisphere to the fluctuations in the air temperature in Kazan was estimated (42% in winter and 27% in summer). It has been established that the average annual temperatures in Kazan and the entire Northern Hemisphere changed in the opposite phase until 1970. Since 1870, the winter temperature in Kazan has increased by 4.7°C. During the summer period, a ‘hump’ (warming) was detected on the low–frequency temperature curve in Kazan in the period 1920–1960, which can be explained by the fact that during this period the atmosphere in the Northern Hemisphere was more transparent (by 2%) than in the other periods. The consequences of climate change in the region in recent decades have had an effect on the dynamics of agro-climatic resources of Tatarstan. Several agrometeorological indicators were calculated, including the M.I. Budyko dryness index, the Selyaninov hydrothermal coefficient (HTC), and the Sapozhnikova moisture coefficient. According to the estimates made, there has been an increase in the duration of the growing season and the sum of effective temperatures, and also a weak trend toward increasing aridity in the summer period has emerged. Using ensemble calculations based on 40 climate models from the CMIP6 project, the paper provides an assessment of future climate changes in the region: the annual temperature in Kazan is expected to increase by 2.4°C by the end of the 21st century according to the ssp245 scenario. Under the ‘toughest’ ssp585 scenario, this increase will be 5.5°C.

Trend Analysis, Modeling And Interpolation of Reference Evapotranspiration Time Series in The Semi Arid Region of Duhok Governorate

Trend Analysis, Modeling And Interpolation of Reference Evapotranspiration Time Series in The Semi Arid Region of Duhok Governorate

Trend Analysis and Change Points in Time Series of Water Level of Shallow and Deep Wells in Gorganrood Watershed

Trend Analysis and Change Points in Time Series of Water Level of Shallow and Deep Wells in Gorganrood Watershed

Study of Ecosystem Degradation Dynamics in the Peruvian Highlands: Landsat Time-Series Trend Analysis (1985–2022) with ARVI for Different Vegetation Cover Types

The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation (p &lt; 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized.

Statistical forecast of the grain production development in Kemerovo region

Relevance. The growth of geopolitical tensions and climate change lead to a decrease in stability in the world grain market, which ensures the relevance of further grain production growth in our country. Such a task is also set for industrial regions that have the necessary capabilities for this, which should increase the level of self-sufficiency. Despite certain successes in recent years, significant reserves remain for further growth in grain production.Methods. In the course of the study, there were used methods of comparison, economic-statistical, trend analysis of dynamic series, calculation-constructive, abstract-logical and other methods.Results. Based on the official statistics, an assessment was made of the existing patterns and trends in the dynamics of grain production, incl. on the main crops in the Kemerovo region. The influence of the main factors on the gross harvest of grain crops is determined. A forecast was made for the yield of grain crops based on a trend analysis of time series. The reserves for the growth of sown areas and the gross harvest of grain crops until 2025 were estimated.

Wavelet-based de-noising approach for trend analysis and coherency relation in hydrological time series (case study: Ardabil plain)

Wavelet-based de-noising approach for trend analysis and coherency relation in hydrological time series (case study: Ardabil plain)

Driving Force Exploration for Flash Flood Based on Mann–Kendall Test and Geographical Detector: A Case Study of Hainan Island, China

Flash floods are among the deadliest hazards in China and have led to substantial casualties and losses, especially on Hainan Island. Therefore, it is of great significance to explore the main driving force behind them. Nevertheless, research on the driving force of flash floods is limited here. This study explores the driving force of flash floods on Hainan Island from 14 factors involving three categories: natural, social, and rainfall factors. Two quantitative methods, like the Mann–Kendall test and the geographical detector method, are applied. The Mann–Kendall test is usually used for time series trend analysis and is introduced to divide the flash flood periods into D95 (years from 1980 to 1995) and D14 (years from 1996 to 2014) through the results of reported flash flood trend analysis. The geographical detector is applied to analyze the driving force of flash floods. There are several key findings from this study that help better understand the driving force about flash floods. Firstly, the results show that the main driving forces of flash floods are natural factors like Elevation and Soil in both periods, and they are on the rise. Secondly, the influence of short-term heavy rainfall on flash floods is becoming more and more serious. Thirdly, even though the driving forces from social factors to flash floods are small, the impact of population density on that is significantly increasing.

Meteorological Drought Assessment and Trend Analysis in Puntland Region of Somalia

Drought assessment and trend analysis of precipitation and temperature time series are essential in the planning and management of water resources. Long-term precipitation and temperature historical records (monthly for 41 years, from 1980 to 2020) are used to investigate annual drought characteristics and trend analysis in Somalia’s northern region. Six drought indices of the normal Standardized Precipitation Index (normal-SPI), the log normal Standardized Precipitation Index (log-SPI), the Standardized Precipitation Index using the gamma distribution (Gamma-SPI), the Percent of Normal Index (PNI), the Discrepancy Precipitation Index (DPI), and the Deciles Index (DI) are used in this study for the annual drought assessment. The log-SPI, the gamma-SPI, the PNI, and the DPI could capture historical extreme and severe droughts that occurred in the early 1980s and over the last two decades. The results indicate that Somalia has gone through extended drought periods over the past quarter century, exacerbating the existing humanitarian situation. The normal-SPI, gamma-SPI, and PNI indicate less and moderate drought conditions, whereas log-SPI, DPI, and DI accurately capture historical extreme and severe drought periods; thus, these methods are recommended as annual drought assessment tools in the studied region. Not only are the PNI and DPI less correlated to each other, but their correlation coefficient (CC) with SPI-based drought indices are not as high as SPI-based indices which are close to unity. For the purpose of the trend analysis, the Mann Kendall (MK) test, the Spearman’s rho (SR) test, and the Şen test are used. Furthermore, the Pettitt test is implemented to detect the change points and the Thiel-Sen approach is used to estimate the magnitude of trend in the precipitation and temperature time series. The results indicate that there is overall warming in the region which has experienced a significant shift in trend direction since 2000. The trend analysis of annual precipitation data time series shows that Bossaso and Garowe stations have significant positive trends, while the Qardho station has no trend. In 1997 and 1998, respectively, abrupt changes in annual precipitation are detected at Qardho and Garowe stations. Due to the civil war of more than three decades in Somalia and the non-institutionalized governance to inform historical drought conditions in the country, determining the most appropriate meteorological drought index would help to develop a drought monitoring system for states and the entire country.

Research on the Effect of Knowledge Stock on Technological Advance and Economic Growth in Republic of Korea

The Republic of Korea’s R&amp;D investment has been rapidly increasing over the long term. This R&amp;D investment is accumulated as knowledge stock, which means the amount of knowledge held by individual countries. However, is the rapid increase in R&amp;D investment contributing to the advancement of domestic science and technology and economic growth? In general, technological advance will lead to a country’s sustainable economic growth. If so, how much has it contributed? This is a very important question for a neoclassical economist. This study reviewed previous studies on “knowledge stock”, “TFP estimation”, and “R&amp;D investment, TFP and economic growth relationship”, and sequentially analyzed the relationship among knowledge stock, TFP, and economic growth through a three-step process. The analysis procedure first performs data processing and time series trend analysis, then time series characteristics analysis, and finally Orderful Least Squares (OLS). The analysis results showed that all three models had a positive (+) relationship and most of them were derived to be statistically significant. On this basis, three policy implications are presented. First, the knowledge stock is estimated to have had a positive (+) effect on economic growth, and the continuous increase in domestic R&amp;D investment is believed to have acted as a growth engine. In particular, financial of the government by financial resources and the mid-to-high tech industry groups by industrial characteristics are statistically positively significant. Next, domestic knowledge stocks have had a positive effect on TFP but the efficiency is judged to be decreasing. It is implied that high-tech industries need to form knowledge stocks above the threshold through continuous R&amp;D investment. Finally, TFP was analyzed to have had a high impact on economic growth, and this confirmed that innovation-led growth was important. It is still relevant that innovation-led sustainable growth is more important than factor led in the Republic of Korea.

Forecasting of local tax realization with least square method in Tanggamus Regency

Purpose: This study aims to identify the contribution of local taxes to regional revenues in Tanggamus Regency and predict the pattern of realization of regional tax revenues in the coming period. Research methodology: This research is a quantitative study with a time series trend analysis according to the type of regional tax and an overview of the target and its realization. The sample used in this study was data from seven types of regional taxes obtained from the Regional Financial Management Agency of Tanggamus Regency. The data analysis process uses the least squares method, often used to forecast, and the application uses Minitab software with fitted line plots and descriptive statistics features. Results: This study's results showed that the seven tax subjects seemed to go through changes and tended to grow in the future. Limitations: This research is limited to the specific scope of taxes selected and does not cover all other types of taxes and levies. Contributions: This study's ability to predict the future helps make plans for regional tax revenue, especially when it comes to planning for policies that might change in the near future. Novelty: This research is new because it has a new update on how to use predictive methods with Minitab and an analysis of how local taxes have changed over time.