Trends In Series Research Articles

Evaluation of MERRA-2 land surface temperature dataset and its application in permafrost mapping over China

The second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) land surface temperature (LST) dataset has been widely used for permafrost mapping in specific areas; however, its accuracy and application need to be evaluated over China. In this study, the MERRA-2 LST was evaluated against meteorological observations and three other reanalysis datasets including the first-generation MERRA, Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), using multiple statistical methods over the period from 1980 to 2018. The results revealed that the MERRA-2 LST generally exhibited cold bias compared to meteorological observations while performing better than the JRA55, ERA-Interim, and MERRA datasets in China, particularly in high-altitude permafrost regions. The comparison indicated that the time series trends for the MERRA-2 LST was consistent with that observed until 2000, and noticeably amplified cold bias, particularly for the period after 2005, was observed. Moreover, two correction methods were proposed and compared to reduce the error range for the MERRA-2 dataset, which was caused by the difference in elevation and land cover types. Calibrated results demonstrated that the linear regression method (Method1) between the elevation difference and mean bias error (MBE) for the LST performed well with root mean square error (RMSE) ranged from 2.15 to 5.97 °C to 1.09–2.53 °C. Moreover, in comparison with the MODIS LST dataset, the results showed that the adjusted MERRA-2 LST was in good agreement with smaller RMSEs against the observations. The surface frost number model was used for mapping the permafrost distribution over China based on the daily adjusted MERRA-2 LST dataset. According to the simulation results, the permafrost extent had a slightly continued degradation trend with a rate of 3–5% per decade over the past 39 years. The simulated permafrost area over China for the years 2010–2018 was approximately 1.63 × 106 km2, which accounts for 16.9% of mainland China. Thus, the adjusted MERRA-2 LST with high spatial–temporal consistency is the optimal choice to investigate permafrost distribution on a large scale.

Mann–Kendall-Based Concrete Failure Trend Analysis and Its Implementation for Dynamic Building Monitoring

Analyzing monitoring data efficiently is a classic problem in structural health monitoring. A nonparametric test method, the Mann–Kendall (MK) method, was implemented in this study, which is commonly used to detect monotonic trends in a series of environmental data. Using the MK method, three types of time series were studied: the stress time series measured in the concrete prism compression test, the resultant force time series obtained from the pseudostatic test of a reinforced masonry shear wall, and the translation velocity time series detected in a high-rise building. The statistics calculated, as well as the intersections of curves, indicate the trend change in the time series. The results demonstrated that the MK method could efficiently analyze the trend in the engineering time series.

Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.

TREND ANALYSIS OF GROUND-WATER LEVEL TO ASSESS VULNERABILITY OF GROUNDWATER IN CHITTAR SUB BASIN OF TAMIRABHARANI RIVER BASIN IN TAMIL NADU STATE

In Chittar Sub basin, agricultural, domestic and industrial water use is highly dependent on groundwater extractions. For meeting the increasing demand of groundwater resources, it is highly important to realize about interrelation between agriculture, climate change impacts, groundwater levels and other water resources for agricultural purposes. Keeping this point, the study was under taken in Chittar sub basin of Tamirabharani river basin, Tamil Nadu for investigating the temporal trends in groundwater level using the Mann–Kendall test and the Sen’s slope estimator in the area during 1986–2020. The results of the tests indicated a mix of negative and positive trends in the groundwater level series. The positive trends were found to be higher than the negative trends. The statistical tests reported a significant increasing trend in 25 percent of the wells. Moreover, the highest number of wells with significant positive trends occurred in zone 2 and zone 3.

Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates

Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

Comparison of sub-series with different lengths using şen-innovative trend analysis

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to dependent series and cannot detect non-monotonic trends. Şen-innovative trend analysis method is launched into literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial independence and normal distribution and examines a given time series as equally divided into two sub-series. The Şen multiple innovative trend analysis methodology is improved to detect partial trends on different sub-series, again with equal lengths. Climate change strongly affects hydro-meteorological parameters today compared to the last twenty or thirty years and gives asymmetrical trend change points in hydro-meteorological time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, the Şen innovative trend analysis method is revised to satisfy these requirements (ITA_DL). The new approach compared with the traditional Mann-Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. The ITA_DL gives four monotonic trends on May, July, September, and October rainfall series of Oxford although the MK gives three monotonic trends in the May, July, and December and cannot detect trends on the September and October. In the ITA_DL visual inspection, the December rainfall series does not show an overall or partial trend. The ITA_DL trend results are consistent with the Şen_ITA except for the September rainfall series, although it has different trend slope amounts.

Human Paleodemography and Paleoecology of the North Pacific Rim from the Mid to Late Holocene.

Using 14 proxy human population time series from around the North Pacific (Alaska, Hokkaido and the Kuril Islands), we evaluate the possibility that the North Pacific climate and marine ecosystem includes a millennial-scale regime shift cycle affecting subsistence and migration. We develop both visual and statistical methods for addressing questions about relative population growth and movement in the past. We introduce and explore the use of a Time Iterative Moran I (TIMI) spatial autocorrelation method to compare time series trends quantitatively - a method that could prove useful in other paleoecological analyses. Results reveal considerable population dynamism around the North Pacific in the last 5000 years and strengthen a previously reported inverse correlation between Northeast and Northwest Pacific proxy population indices. Visual and TIMI analyses suggest multiple, overlapping explanations for the variability, including the potential that oscillating ecological regime shifts affect the North Pacific basin. These results provide an opening for coordinated research to unpack the interrelated social, cultural and environmental dynamics around the subarctic and arctic North Pacific at different spatial and temporal scales by international teams of archaeologists, historians, paleoecologists, paleoceanographers, paleoclimatologists, modelers and data management specialists.

Improving the methodological approach to determining the number of postpostgraduates planned for admission

The process of managing a higher institution in determining the number of postgraduates who are planned for admission is the object of this study. The problem that was addressed is the inability to reasonably predict the need for the number of postgraduates to offset the negative consequences of staff turnover in a higher education institution. An improved method of forecasting the trend of the time series of indicators that form the need for specialists of higher qualification has been proposed. Special feature of the proposed method is the ability to reasonably determine the number of full-time positions of higher qualification specialists; those dismissed; postgraduates who successfully defended their thesis over a certain period. Due to this feature of this method, it is possible to form reasonable initial data for predicting the negative consequences of staff turnover in a higher education institution. The scope of practical use of this method for determining the projected indicators is the stage of planning the educational process in a higher education institution. A comprehensive methodology for determining the number of postgraduates who are planned for admission to postgraduate studies in specialties is proposed. Applying the suggested procedure can make it possible to obtain the projected data with sufficient accuracy and prevent excessive recruitment, or under-recruitment of postgraduates, which will affect the expenditure of finances and the quality of higher education organization. The projected inaccuracy of the existing approach to determining the number of postgraduates compared to the proposed one is about 35 %. The peculiarity of the methodology is the ability to justify the number of postgraduates that need to be recruited to graduate school, taking into consideration regional and sectoral contexts. Additionally, a special feature of this procedure is its versatility for any specialty and any institution of higher education. The scope and conditions for the practical use of this set of techniques for determining the projected indicators is the process of managing a higher education institution when determining the need for postgraduates

Analytical time series alignment liquefaction number of corn starch mixture

The statistical description of the development of dynamic processes in time is carried out using time series. To eliminate random fluctuations and build an analytical function of the trend of the time series, an analytical alignment procedure is used. The choice of the type of the trend function is carried out by the method of finite differences, the calculation of the trend parameters ‒ by the method of least squares. The purpose of this work was the analytical alignment of the time series of the liquefaction number of the corn starch mixture obtained in the experiment on the PChP-99 device. The mechanism of starch gelatinization with a given liquefaction rate under such conditions requires additional theoretical and experimental study. Calculations have shown that the process of liquefaction of starch gel corresponds to the exponential trend equation: y = a • ebt, which is a particular case of an exponential trend. It has been experimentally established that an increase in the proportion of amylopectin starch in a corn starch mixture leads to an increase in the maximum viscosity of the resulting gel when the swelling water-starch suspension is heated. In the process of further gelatinization of corn starch mixture with an increase in the proportion of amylopectin starch, the strength of the gel decreases due to the preservation of the mobility of water molecules during the transition in the sol-gel system, which contributes to an increase in liquefaction number. Experimental data do not contain anomalous values; the error in approximating the regression equation for the trend of the time series is less than 5 %. The statistical significance of the coefficients of the linearized trend equation is proved in favor of the hypothesis of the existence of a time series. The obtained estimates of the regression equation make it possible to use it for predictive purposes, providing an accuracy of up to 95.42 % of the total variability of the liquefaction number in the absence of autocorrelation of first-order residues. Checking the normality of the distribution of the residual component according to the RS test showed the adequacy of the trend model, the hypothesis of the absence of heteroscedasticity according to the Spearman and Goldfeld-Quandt tests is accepted.

Adaptive application of machine learning models on separate segments of a data sample in regression and classification problems

Introduction: Achievement of specified qualitative indicators in machine learning solutions depends not only on the efficiency of algorithms, but also on data properties. One of the lines for the development of classification and regression models is the specification of local properties of data. Purpose: To improve the qualitative predictors when solving classification and regression problems based on the adaptive selection of various machine learning models on separate local segments of data sample. Results: We propose a method that uses a combination of different models and machine learning algorithms on subsamples in regression and classification problems. The method is based on the calculation of qualitative predictors and the selection of the best models on the local segments of data sample. The finding of transformations of data and time series allows to create sample sets, with the data having different properties (for example, variance, sampling fraction, data range, etc.). We consider the data segmentation based on the change point detection algorithm in time series trends and on analytical information. On the example of the real dataset, we show the experimental values of the loss function for the proposed method with different classifiers on separate segments and on the whole sample. Practical relevance: The results can be used in classification and regression problems for the development of machine learning models and methods. The proposed method allows to improve classification and regression qualitative predictors by assigning models that have the best performance on separate segments.

Graphical approaches for cleaner production and sustainability in process systems

This work provides an overview of recent graphical approaches which can provide beneficial support for various applications enhancing cleaner production and, consequently, sustainability. Graphical tools, mostly based on a physical inside, play an important role in the decision-making process in engineering and can also be in cleaner production and contribute to sustainability. They have the advantage of illustrating the process by providing clear insight. This paper reviews the graphical approaches to show the developing timeline and trend of some tool series and the method of implementing these tools for specific issues. The review focuses on the energy, water, material, pollution and greenhouse gas emission reduction and also the safety issues that may affect human health and the normal production process. A total of 48 visual tools, especially some classic and widely used diagrams, are illustrated, and their characteristics are analysed. The future direction and potential of graphical tools to include the consideration of flexibility and reliability in cleaner production to reduce energy/water/material consumption and emissions in a longer period and to avoid harm to human health and living conditions have been assessed. The development of efficient concepts and visualisations for Circular Economy implementations is also an important area to be tackled.

Variability and time series trend analysis of rainfall in the mid-hill sub humid zone: a case study of Nauni.

The spatiotemporal variability of rainfall, particularly in the context of climate change, has been imperative for examining the cropping patterns, farming sustainable crop production, and food security in rainfed areas. To that end, trend analysis was done to study the change in rainfall patterns in the mid-hills of Himachal Pradesh. The study investigated the historical rainfall data from 1971 to 2020 on a monthly, annual, seasonal, and decadal basis by using the variability analysis methods,viz., standard deviation (SD), coefficient of variance (CV), and transformed annual precipitation departure (Z). The trend analysis was also done by Mann-Kendall (MK) and Sen's slope estimator (SSE) test and linear regression model. The annual rainfall in the region was 1115.1mm, which showed a decreasing trend (Z = - 0.79mm/year). Based on the linear regression model, the decrease in annual rainfall was about - 2.28mm/year. The monthly and seasonal variability of rainfall exhibited a sensitivity to change. The months of January, April, July, and September showed an increasing trend, whereas the rest of the other months showed a decreasing trend. The seasonal rainfall (summer, monsoon, and post-monsoon) showed a decreasing trend, whereas the winter season depicted an increasing trend. During the entire study period, 1988 recorded as the wettest year, with highest annual rainfall of about 2205.0mm and monsoon rainfall of about 1653.0mm. The highest annual (2205.0mm) and monsoon (1653.0mm) rainfall was recorded in the year 1988. The decadal analysis of the rainfall on an annual basis revealed a decrease in rainfall during the periods 1971-1980, 2001-2010, and 2011-2020 as compared to 1981-1990 and 1991-2000. The rainfall over the study region confirms the strength of the change in trend.Thus, the erratic rainfall pattern makes the cropping calendar shorter and affects the agricultural productivity.

Study of trends in the time series of maximum water discharges in the Tisza basin rivers within Ukraine

Study of trends in the time series of maximum water discharges in the Tisza basin rivers within Ukraine

Occupational injuries in categories of personnel of Federal Fire Service of EMERCOM of Russia (2006–2020)

Relevance. Occupational injuries - a set of injuries received by workers in the workplace and caused by non-compliance with working conditions for a certain period of time, for example, for 1 year. Industrial injuries - a controlled process and an indicator of occupational safety. Intention. To analyze occupational injuries among the personnel of the Federal Fire Service (FFS) of the EMERCOM of Russia by category for 15 years from 2006 to 2020. Methodology. The injury rates for personnel (with special ranks and employees) of the FFS of the EMERCOM of Russia were obtained from the statistical data bank on morbidity, injuries, disability and death of personnel of the EMERCOM of Russia units during the performance of their duties. Injury rates per 10,000 firefighters were calculated. The received injuries were correlated with the categories of personnel (operational personnel, preventive, technical and managerial personnel) and activities (fire fighting, training / sports and everyday activities). The circumstances of receiving injuries were generalized by causes: technical, organizational, psycho-physiological and dangerous fire-related factors. The results were checked for normality of distribution. Arithmetic mean values and their errors are presented. The dynamics of receiving injuries was assessed using time series and 2nd order polynomial trends. Consistency (congruence) of the studied trends was assessed using Pearson's correlation coefficients. Results and Discussion. There is a decrease in occupational injuries in all categories of personnel of the EMERCOM of Russia FFS. Average annual level of occupational injuries in 2006-2020 was (14.66 ± 2.01) per 10,000 firefighters. Russian workers overall tended to have higher levels (17.87 ± 1.56) per 10,000 Russian workforce (nonsignificant difference). The consistency of the trends is strong positive and statistically significant (r = 0.833; p &lt; 0.001), which may indicate the influence of unidirectional factors in recieving injuries. The proportion of injuries among operational personnel was 67.1% of the structure of all occupational injuries, the average annual injury rate was (14.47 ± 2.06) per 10 thousand firefighters; among preventive personnel - 6.6 % and (12.33 ? 2.33) per 10 thousand, respectively; among technical personnel - 6.5 % and (10.86 ± 1.58) per 10 thousand, respectively; among managerial personnel - 19.8 % and (18.65 ± 2.49) per 10 thousand firefighters, respectively. The highest level of occupational injuries and the proportion of injuries among the operational staff were due to psychophysiological causes during daily activities - (4.31 ± 0.86) per 10 thousand firefighters and 19.5 % of the structure of all injuries and fire hazards during fire extinguishing - (4.03 ± 0.45) per 10 thousand and 18.2 %, respectively. For preventive, technical and managerial personnel, the leading causes of injuries were psychophysiological factors during daily activities - (7.21 ± 1.43) per 10 thousand and 3.8 %; (6.19 ± 0.95) per 10 thousand and 3.7 %; (6.55 ± 0.81) per 10 thousand firefighters and 7.2 %, respectively. The average annual risk of receiving injury during firefighting among the personnel of the EMERCOM of Russia FFS in 2009-2020 amounted to (0.18 ± 0.01) x 10-3 injuries/(fire x year), for the operational staff -(0.15 ± 0.01) x 10-3 injuries/(fire x year). There is a tendency to reducing risks of injury. Risks of injury turned out not to depend on the number of fires; the congruence of trends for fires and risks in the personnel and operational staff is weak positive and statistically nonsignificant (r = 0.208 and r = 0.201, respectively; p &gt; 0.05 for both coefficients). Conclusion. Occupational injuries can become manageable only if all injuries are taken into account and a wide range of concerned professionals (firefighters, managers, engineers, doctors, etc.) are involved in the analysis of their causes.

Machine learning-based assessment of long-term climate variability of Kerala.

Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann-Kendall test (MK), three modified Mann-Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70mm and 28.30mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20°C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons.

Inventory Calculation System by Predicting The Amount of Sales of The Eye Shop Using The Moving Average Method

For a company, inventory is an asset that is quite large in value. The existence of inventory in a company implies whether there is an inventory or not. Business is an activity in the field of trade with the intention of making a profit. There are various types of businesses that are around us. Inventory is one of the current assets that must be managed properly, especially for the type of business that has an inventory of merchandise. Inventories that are owned by this type of business will be able to be determined at the cost of the inventory and the value of the inventories will be presented on the balance sheet. In calculating the value of the company's inventory, the company can use three methods, namely the FIFO, LIFO and Average methods. The moving average method is used if the past data is data that does not have an element of trend or seasonal factors. Moving average forecasting is widely used to determine the trend of a time series. The main purpose of using moving averages is to eliminate or reduce randomness in the time series. So that it can provide information on incoming and outgoing goods based on transaction reports so that it can predict the number of sales in the next period. It is hoped that this application will always be up to date so that this application follows the development of sales information technology based on Android Flutter.

Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

BackgroundGuizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies.MethodsData of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial–temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial–temporal scan statistic.ResultsGuizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014–2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial–temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020.ConclusionThis study identified the upward trend with seasonality and spatial−temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.

Assessing the consistency of trends in Indian summer monsoon rainfall

Adequate and consistent monsoonal rainfall is essential for the economic and societal well-being of India. Even though most past studies analysed trends in Indian summer monsoon rainfall (ISMR) to capture any changes, the nature and consistency (stability) of those trends remained unexplored. Therefore, this study investigates the monotonic, non-monotonic, and trendless nature as well as the stability of ISMR trends from 1897 to 2016 using both graphical and statistical approaches at different meteorological sub-divisions of India. Also, the trend stability is examined considering high, medium, and low clusters of ISMR. Results show a predominance of monotonic trends in the long-term series, and maximum eastern sub-divisions exhibit a decreasing trend and vice versa. However, further analysis revealed instabilities in the ISMR trend and its respective clusters. The possible mechanisms behind ISMR instabilities and the spatio-temporal changes are discussed, along with certain methodological challenges, which might aid in formulating climate action plans.

Effects of ozone therapy on hematological and serum biochemical parameters in dogs affected by visceral leishmaniasis

Visceral leishmaniasis (VL) is a zoonosis and chronic disease caused by the protozoan Leishmania spp. which is of great concern to public health. Currently, the drugs used to treat the disease are costly and present undesirable side effects. The use of ozone to treat chronic diseases in both humans and dogs has shown satisfactory results. The aim of this study was to evaluate the ozone therapy in dogs with VL (n=6), administered by intra-abdominal application (IAA), to evaluate hematological and serum biochemical parameters. The protocol involves one gas dose per week for four consecutive weeks. The immunomodulatory action of ozone was determined in the laboratory analyses, confirmed by the increase trend in the white series and maintenance of the renal and hepatic function. Ozone therapy was found to be well tolerated and no adverse effects were observed. Further studies are required to elucidate the mechanisms of action on the infected host.

Correlation between climate and flood indices in Northwestern Italy at different temporal scales

Abstract The occurrence of river floods is strongly related to specific climatic conditions that favor extreme precipitation events leading to catchment saturation. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the relationship between peak discharges and standard Climate Change Indices (ETCCDI) of precipitation and temperature, widely used in climate research. It is of interest to evaluate whether these indices are relevant for characterizing and predicting floods in the Alpine area. In this study, a correlation analysis of the ETCCDI indices annual time series and annual maximum flows is presented for the Piedmont Region, in North-Western Italy. Spearman’s rank correlation is used to determine which ETCCDI indices are temporally correlated with maximum discharges, allowing to hypothesize which climate drivers better explain the interannual variability of floods. Moreover, the influence of climate (decadal) variability on the tendency of annual maximum discharges is examined by spatially correlating temporal trends of climate indices with temporal trends of the discharge series in the last twenty years, calculated using the Theil-Sen slope estimator. Results highlight that, while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, with different indices that are consistent with catchment size, the decadal tendencies of extreme discharges may be better explained by the decadal tendencies of the total annual precipitation over the study area. This suggests that future projections of the annual precipitation available from climate models simulations, whose reliability is higher compared to precipitation extremes, may be used as covariates for non-stationary flood frequency analysis.