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Background: Aripiprazole once-monthly (AOM) has proven effective in the treatment of schizophrenia, although little is known about its impact on global functioning and quality of life beyond 1 year. Here, we investigate the continued impact of AOM on the participants of the ReLiAM study during the second year of follow-up. Methods: The participants who were evaluated at ≥ 1 time point during the second year of the ReLiAM study (months 15, 18, 21, and 24; year 1 completers) were assessed via the GAF scale. Secondary outcomes were reported on the SOFAS, CGI-S, and QLS. Results: 109 (86%) completed at least 1 post-12-month visit and 33 (30.3%) patients completed the final assessment at month 24. The improvements observed in the year 1 completers in GAF total score were maintained through to year 2 completers. The improvements in CGI-S and SOFAS that were observed at the end of year 1 were also maintained through the end of the second year. Similar trends of sustained improvement in GAF total score, CGI-S score, and SOFAS were observed in the post-hoc analyses of the year 2 completers. Seventy-four percent (74.3%) of year 1 completers experienced mild treatment-emergent adverse events during the second year, the most frequently reported being weight gain, akathisia, and insomnia. Seventeen percent (17.4%) experienced serious adverse events. Similar findings regarding effectiveness and tolerability were reported in the year 1 completers and in year 2 completers. Conclusions: These findings suggest that the favorable effectiveness, including tolerability observed during the first year following AOM initiation, are maintained and may even continue to improve during the second year of treatment. Trial registration: ClinicalTrials.gov NCT02131415, first posted on May 6, 2014. Overall trial status: Terminated. [ABSTRACT FROM AUTHOR]

Abstract Background Aripiprazole once-monthly (AOM) has proven effective in the treatment of schizophrenia, although little is known about its impact on global functioning and quality of life beyond 1 year. Here, we investigate the continued impact of AOM on the participants of the ReLiAM study during the second year of follow-up. Methods The participants who were evaluated at ≥ 1 time point during the second year of the ReLiAM study (months 15, 18, 21, and 24; year 1 completers) were assessed via the GAF scale. Secondary outcomes were reported on the SOFAS, CGI-S, and QLS. Results 109 (86%) completed at least 1 post-12-month visit and 33 (30.3%) patients completed the final assessment at month 24. The improvements observed in the year 1 completers in GAF total score were maintained through to year 2 completers. The improvements in CGI-S and SOFAS that were observed at the end of year 1 were also maintained through the end of the second year. Similar trends of sustained improvement in GAF total score, CGI-S score, and SOFAS were observed in the post-hoc analyses of the year 2 completers. Seventy-four percent (74.3%) of year 1 completers experienced mild treatment-emergent adverse events during the second year, the most frequently reported being weight gain, akathisia, and insomnia. Seventeen percent (17.4%) experienced serious adverse events. Similar findings regarding effectiveness and tolerability were reported in the year 1 completers and in year 2 completers. Conclusions These findings suggest that the favorable effectiveness, including tolerability observed during the first year following AOM initiation, are maintained and may even continue to improve during the second year of treatment. Trial registration ClinicalTrials.gov NCT02131415, first posted on May 6, 2014. Overall trial status: Terminated.

Abstract Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance.

Global databases provide complete source of required precipitation information for users. The aim of this study is to investigate the accuracy of the TRMM, NCEP and MERRA global precipitation databases in estimating the daily and monthly precipitation in the Balkhaluchai catchment area of Ardabil province. Precipitation data of Nir station the three mentioned databases were extracted from 2002 to 2018. Mann-Kendall, Mann-Whitney and standard normal tests were used to check the trend and homogeneity of precipitation data. For three global precipitation databases, amount of R², RMSE, NSE, RE and GMER were determined. The results of homogeneity test showed that all P-values were higher than 0.05 and the precipitation data were completely stochastic and homogeneous and no trends were observed in them. In both TRMM and NCEP databases, the R² coefficient is good and is above 0.6. The MERRA database has had a weaker performance in this case. For the monthly period and the two TRMM and NCEP databases, the NSE index was higher than 0.5, which indicates the reliability of the precipitation data produced in these two stations. At the daily and monthly level, for all three global databases the GMER parameter values were obtained smaller than unity, which indicates that all global databases have overestimates. The overestimation of precipitation for the MERRA global database is much more severe than other two databases. MERRA database has a much weaker performance than NCEP and TRMM databases. In general, the TRMM database has a better efficiency than other databases. [ABSTRACT FROM AUTHOR]

Terri Call,1,* Andrew D Pucker,1,* Gerald McGwin Jr,2 Quentin X Franklin,1 Amy Logan1 1Department of Optometry and Vision Science, University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA*These authors contributed equally to this workCorrespondence: Andrew D Pucker, University of Alabama at Birmingham, School of Optometry, 1716 University Blvd, Birmingham, AL, 35233, USA, Tel +1 920 579-2900, Email andrewpucker@hotmail.comPurpose: To map contact lens (CL) comfort over the full wear day and across 1 month’s wear in established, asymptomatic to minimally symptomatic, reusable, soft CL wearers.Methods: Adult, 18- to 45-year-old, participants were recruited and required to have 20/20 best-corrected visual acuity or better and must have been asymptomatic to minimally symptomatic CL wearers. Participants were required to be able to wear TOTAL30® sphere CLs and have minimal astigmatism. Participants were fit in the study CLs and asked to wear these CLs daily for the next month for 16 hours per day each day. Participants were asked to complete a visual analog scale (VAS) survey via text message at time of CL application and after 8, 10, 12, 14, and 16 hours of wear and at CL removal on days 1, 2, 3, 4, and 5 and at 2 weeks and 1 month. The utilized VAS had a ± 50 scale with positive scores being comfortable, negative scores being uncomfortable, and scores of 0 being neutral comfort.Results: Forty-eight participants were recruited who had a mean ± SD age of 26.2 ± 5.2 years (71% female). Mean initial VAS CL comfort scores at initial CL dispense were 45.56 ± 9.20 units. Mean CL wear times for any of the days evaluated were at least 14.80 ± 2.41 hours per day and did not differ across the study (p = 0.77). Mean comfort VAS scores significantly decreased over the wear day (all days p ≤ 0.02), yet there were no significant differences in VAS comfort scores across the same time of day for the duration of the study (all times p ≥ 0.06).Conclusion: This study determined that while CL wearers were slightly less comfortable at the end of the day compared to application, this comfort change was minimal given that the average participants had overwhelmingly good comfort at all time-points evaluated. Comfort scores were likewise consistent across 1 month of wear.Keywords: comfort, contact lenses, monthly, reusable, text message, visual analog scale

Few researches studied the effect of plant extracts on potato growth and yield performance. This study aimed to evaluate the effect of three application frequencies (weekly, bi-weekly, or monthly) of methanol and water extracts from tobacco, wild marigold or garlic at 50 mg mL-1 on potato growth and yield parameters under field conditions over two growing seasons (A and B) in Rwanda. The results revealed that the weekly application of methanol extract from tobacco or wild marigold at 50 mg mL-1 led to a delayed 50% potato sprouting when compared to methanol extract from garlic and water extracts from tobacco, wild marigold or garlic. Furthermore, the weekly application of methanol extract from tobacco showed a delay in 50% flowering similar to positive control (1% Copper oxychloride) than other treatments. The weekly and bi-weekly applications of methanol extract from tobacco or wild marigold at 50 mg mL-1 resulted in higher total yield than controls. No significant effect of growing season was observed in number of days to 50% sprouting and number of main stems per plant. Though, season B led to delayed 50% flowering, taller plants and higher total yield than season A. From this study, it is concluded that weekly or bi-weekly application of methanol extract from tobacco and wild marigold at 50 mg mL-1 can significantly improve potato growth and yield and can be recommended in sustainable horticultural production. [ABSTRACT FROM AUTHOR]

Abstract Background ReLiAM, Real-Life Assessment of Abilify Maintena, was the first reported long-term prospective non-interventional study for patients with schizophrenia treated with aripiprazole once-monthly injectable formulation (AOM) under real-life conditions. ReLiAM’s primary aim was to evaluate the evolution of global functional status in patients treated with AOM for 12 months in Canada. Methods The objective of this post hoc analysis of the ReLiAM study is to investigate the treatment effects of real-life use of AOM over a 1-year period in the subgroup of patients with reported substance use compared with patients without substance use. Results The results of this post hoc analysis demonstrate that treatment with AOM for 12 months in patients with schizophrenia was comparably effective in improving global functioning in subgroups of patients with and without concomitant substance use. Conclusions These results support the use of AOM for the treatment of schizophrenia in patients with or without concomitant substance use. Trial registration ClinicalTrials.gov NCT02131415, first posted on May 6, 2014. Overall trial status: Terminated.

Gurugram is emerging as one of India's most advanced cities. The combined impact of industrial and vehicular emissions makes the environment toxic. Recently, Gurugram has experienced severe air quality. In the present work, an assessment of seasonal, monthly, and diurnal variations of ambient air quality was carried out in Gurugram during the period of March 2021 to 2022 February. Seasonal and monthly concentrations of key air pollutants like particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) were examined at Vikas sadan, Gwal Pahari and Teri Gram in Gurugram city to study the most polluted seasons and months. Significantly higher mean concentrations of Particulate matter PM2.5 (406.94 μgm-3) and NO2 (353.96ppb) were seen during the colder months and seasons. O3 showed a consistent trend with variations during the year, with the highest concentration in winter (106.35µg/m³). PM2.5 and NO2 concentrations during the night were greater for all seasons when compared to diurnal values. O3 concentrations displayed diurnal tendencies that were the opposite of those of NO2 concentrations. The highest concentrations of ambient PM2.5, NO2, and CO were observed at the Vikas Sadan Monitoring Station. While the NISE Gwal Pahari station showed greater O3 values. The findings highlight the necessity of efficient air pollution control in Gurugram. To prevent public exposure to air pollutants, preventive measures like green spaces, using public transport, etc. must be adopted. The study contributes to a better understanding of air pollution by seasonal, monthly and diurnal assessment in the city of Gurugram. [ABSTRACT FROM AUTHOR]

The cyclone tracks from 1877 to 2020 were analyzed to detect the spatial and temporal intensity. The tracks were gathered from previously published works. The previous articles' tracks were digitized and converted to shape files for analysis in Arc-GIS. A total 126 cyclone tracks were used to detect monthly and seasonal cyclone intensity and spatial variations. The fluctuations were examined over a 30-year period, which is believed to be the climate of a particular location. Tropical cyclones hit the Bay of Bengal's coast starting in May and lasting until December. In May and October, the number of cyclones is at its peak (26 nos in each month). From June through September, the number of cyclones fell. In October and November, the number of cyclones increased dramatically. The number of cyclones substantially fell in December, and no cyclones were observed from January through March. From 1939 through 1969, the highest number of cyclones (36) was recorded. In the mid- and late-twentieth century, there were a higher number of cyclones. The coastal region of Bangladesh suffered the fewest cyclones in history over the recent era (2001–2020). The western shore was particularly vulnerable from 1877 to 1907, and the entire coastal region was dangerous from 1908 to 2000. In the Post-monsoon (October to December) season, the number of cyclones is lower than in the Monsoon period (May to September). In the pre-monsoon season, 71 cyclones strike, while in the Monsoon season, 53 cyclones strike.

Background: ReLiAM, Real-Life Assessment of Abilify Maintena, was the first reported long-term prospective non-interventional study for patients with schizophrenia treated with aripiprazole once-monthly injectable formulation (AOM) under real-life conditions. ReLiAM’s primary aim was to evaluate the evolution of global functional status in patients treated with AOM for 12 months in Canada. Methods: The objective of this post hoc analysis of the ReLiAM study is to investigate the treatment effects of real-life use of AOM over a 1-year period in the subgroup of patients with reported substance use compared with patients without substance use. Results: The results of this post hoc analysis demonstrate that treatment with AOM for 12 months in patients with schizophrenia was comparably effective in improving global functioning in subgroups of patients with and without concomitant substance use. Conclusions: These results support the use of AOM for the treatment of schizophrenia in patients with or without concomitant substance use. Trial registration: ClinicalTrials.gov NCT02131415, first posted on May 6, 2014. Overall trial status: Terminated. [ABSTRACT FROM AUTHOR]

In this contribution, we assess the capabilities of monthly pan evaporation estimation using two methods: i) the first is a purely stochastic model designed by ARIMA (autoregressive with integrated moving average), which is based only on endogenous variables, and ii) the second is derived from Dalton's physical approach based on exogenous variables. The areas of application of the two methods are two sites of two dams, Ain Zada and Beni Haroun, with distinct climates of Algeria: semiarid and subhumid. The actual evaporations are measured directly from the pans for seven years. The first six years of observations are used for the calibration of the two models, and the last year is reserved for the test. The performance of the models is evaluated using three commonly used metrics, namely, the correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE). According to the results of this research, it appears that ARIMA models perform better than models derived from Dalton's approach with significantly improved predictive capabilities. [ABSTRACT FROM AUTHOR]

The main objective of this study is to develop a new adjusted Turc equation through introduction of the monthly adaptation coefficients implicitly deduced from the influence of wind and air humidity; and on the other hand, to highlight the wind speed influence on the estimation of the potential evapotranspiration (PET) as well as the synthetic modelling of the wind's influence coefficient (WIC). The monthly data also includes the characteristics of seven climatological synoptics in the humid or semi-humid of northern Algeria. For every studied station, the data of PET stemming from the application of the initial Turc relation and after adjustment was statistically compared with the classic relation of Penman considered as a reference. The adjusted Turc relation gives a good concordance and a rather reliable evaluation towards the NAHR's relation based on some well-known statistical indices like: coefficient of determination R2 = 0.9895 with root-mean-square error (RMSE) = 5.44 and R2 = 0.9712 with RMSE = 12.98 respectively. The modelling of the WIC was successfully completed. The principal component analysis (PCA) allows to improve the relation of the power type model by considering only the data of the humid season, and consequently we were able to propose the new adjusted relation argued by a sufficiently high correlation coefficient (R = 0.92). [ABSTRACT FROM AUTHOR]

From 2021 to 2024, the number of pupae found in storm sewer catch basins in the North Shore suburbs of Chicago were assessed as part of routine quality control and improvement efforts. Over the course of these 4 yr, pupae were collected at least once during all calendar months, allowing for some insight into monthly mosquito production in catch basins. Thus, the intent of this study was to use those data to describe the number of pupae found in these structures across calendar months. In total, 1,993 unique catch basins were sampled. Pupae were found during the months of April through December, with the highest levels seen during June through November. Results suggest that basins may remain productive a month or more after local abatement districts end their routine Culex adult surveillance with gravid traps in late September and early October each year. These observations can help with the timing and intensity of larvicide treatments in Chicago area catch basins and may serve as a baseline for comparison to future seasonal changes., (Copyright © 2024 by The American Mosquito Control Association, Inc.)

Over the last few years, decadal prediction has been paid much attention for its potential applications in agriculture, hydrology and for assessing the climate impact on the various aspects of human life. Though the fidelity of decadal prediction specifically the hindcasts experiments through Coupled Model Inter-comparison Project Phase 5 (CMIP5) has been examined for many climate variables and at different temporal and spatial scales, the drift in CMIP5 decadal precipitation at a local scale remains unknown. Drift is the long-term time varying systematic bias generated by GCMs while they revert to their equilibrium state from the forced initialized state. This study used seven general circulation models (GCMs) from five different modelling groups to examine the drift in monthly and seasonal mean precipitation from the CMIP5 decadal hindcasts for Brisbane river catchment, Australia. Drifts of individual model's ensemble mean and multi-model ensembles' mean (MMEM) at monthly and seasonal time scales were quantified and examined using four different skill tests. Results revealed that the magnitude of drifts are higher in monthly precipitation than the seasonal mean precipitation. Next, the drift in hindcast precipitation was corrected using mean drift correction method and found that the mean drift correction method is not sufficient to alleviate the drift in CMIP5 decadal precipitation. This suggests further research for an appropriate drift correction method for decadal precipitation. Comparing the drift and skill test results over the entire catchment, this study finds, MMEM showing the lowest drifts and outperformed in all models in skill tests. [ABSTRACT FROM AUTHOR]

Following the accelerated development of urbanization and industrialization, atmospheric particulate matter has become a significant threat to public health globally. Environmental health studies usually use the mass concentration of fine particles (PM2.5) as a base data to predict the health risks of particulate exposure. However, PM2.5 data from ground monitoring stations in China has not been provided until January 2013 by the Ministry of Environmental Protection of China. Hence, an alternative dataset of PM2.5 spatiotemporal distributions extending to years earlier than 2013 is urgently needed, which is of great significance to atmospheric environment assessment and pollution prevention and control. Atmospheric aerosol products by the moderate-resolution imaging spectroradiometer (MODIS) have been released since 2000, which provides the possibility to reconstruct historical PM2.5. However, most current methods do not have the ability to estimate PM2.5 mass concentration independently of ground observations. The PM2.5 mass concentration data set produced by PM2.5 remote sensing (PMRS) model based on physical processes does not depend on the ground observations, and also is not affected by the uncertainty of model emission sources or the completeness of chemical reaction mechanism. These ensure that the point-by-point validation for PM2.5 mass concentration data is more convincing, and the dataset can also be further used for model assimilation and artificial intelligence training to improve their predictions. In this study, we calculate the monthly PM2.5 mass concentration near the ground over land of China using aerosol inversion products (aerosol optical depth and fine-mode fraction) of MODIS and meteorological data (boundary layer height & relative humidity) provided by the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data set. The results show that, in China, 6 pollution centers mainly concentrated in the central and eastern regions. The highest PM2.5 mass concentration occurred in winter, whereas the pollution range was larger in summer. There are 63.4% of validation sites with biases within ±20 μg m−3, and the expected error is as ±(15 μg m−3 + 30%) enveloped by the monthly mean PM2.5 mass concentrations. The monthly PM2.5 is stored as NETCDF format, with a spatial resolution of 1°×1°. The published data is available in http://www.dx.doi.org/10.11922/sciencedb.j00076.00061.

Soil respiration is one of the main fluxes in the global carbon cycle and has high temporal and spatial variability. Respiration of a soddy-weakly-podzolic soil (Entic Podzol Arenic) in forest and meadow coenoses of the south-taiga zone was continuously monitored on a year-round basis for 21 years; the collected data allows us to assess the temporal variability of CO2 fluxes from soils at different temporal levels: monthly, seasonal, and annual. The total respiration of the soddy-weakly-podzolic soil varies most significantly at the monthly averaging level (coefficient of variation, CV = 25–56%); while the annual respiration variability is 20–22%. At the seasonal averaging level, winter CO2 fluxes from soils have the highest variability (CV = 39–44%); in contrast, during other calendar seasons, soil respiration variability remains roughly at the same level: 26–29%. The effect of the coenosis type on total CO2 fluxes from the soddy-weakly-podzolic soil is statistically significant both at the annual averaging level and in all calendar seasons of the year, except for winter. The most stable parameter that characterizes the seasonal distribution of CO2 fluxes throughout the year is the contribution of the warm period (May–October) to the total annual CO2 flux from soils (73–77% on average). In both studied coenoses, its variability over the 21-year observation period was 8%. This parameter can be used to estimate annual CO2 fluxes from soils based on field measurements performed during the growing season. Results of numerical experiments show that continuous measurements of CO2 emissions from soils for 5 consecutive years reduce the variability of annual and seasonal CO2 fluxes from soils by more than half compared with 1‑ or 2-year measurement periods. Accordingly, a 5-year observation period can be recommended as the optimal one: its duration is sufficient to obtain adequate values of annual and seasonal CO2 fluxes from soils in the south-taiga zone. [ABSTRACT FROM AUTHOR]

The cyclone tracks from 1877 to 2020 were analyzed to detect the spatial and temporal intensity. The tracks were gathered from previously published works. The previous articles' tracks were digitized and converted to shape files for analysis in Arc-GIS. A total 126 cyclone tracks were used to detect monthly and seasonal cyclone intensity and spatial variations. The fluctuations were examined over a 30-year period, which is believed to be the climate of a particular location. Tropical cyclones hit the Bay of Bengal's coast starting in May and lasting until December. In May and October, the number of cyclones is at its peak (26 nos in each month). From June through September, the number of cyclones fell. In October and November, the number of cyclones increased dramatically. The number of cyclones substantially fell in December, and no cyclones were observed from January through March. From 1939 through 1969, the highest number of cyclones (36) was recorded. In the mid- and late- twentieth century, there were a higher number of cyclones. The coastal region of Bangladesh suffered the fewest cyclones in history over the recent era (2001-2020). The western shore was particularly vulnerable from 1877 to 1907, and the entire coastal region was dangerous from 1908 to 2000. In the Post-monsoon (October to December) season, the number of cyclones is lower than in the Monsoon period (May to September). In the pre-monsoon season, 71 cyclones strike, while in the Monsoon season, 53 cyclones strike. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is one of the energy and water exchange main drivers between the atmosphere and land surface. The study aims to analyze the soil moisture characteristics in Indonesia on monthly and seasonal time scales. The analysis uses mapping of monthly and seasonal ESA CCI SM satellite products of mean daily from 1979 to 2016. The results showed the spatial and temporal variability of SM in Indonesia. Sumatera has SM values > 0.3 m³/m³ almost throughout the year. Other than that, Java has SM values > 0.3 m³/m³ from January to April and October to December while 0.2-0.3 m³/m³ from May to September. In Borneo, the SM value > 0.3 m³/m³ from February to June and November to December, while from July to September are 0.2-0.3 m³/m³. Sulawesi has SM values > 0.3 m³/m³ from January to July, on December, and 0.2-0.3 m³/m³ from august to November. Bali to Nusa Tenggara have SM values between 0.2-0.3 m³/m³ throughout the year, except <0.2 m³/m³ in Sumba, Timor Island, and Central Lombok from June to November. Maluku has SM values between 0.2-0.3 m³/m³ throughout the year, while Papua has SM values >0.3 m³/m³ throughout the year, except in Jayawijaya Mountain and South Papua. The ESA CCI SM product is essential for monitoring SM in Indonesia. [ABSTRACT FROM AUTHOR]

Using length frequency information on Tenualosa ilisha catch in the coastal waters of Khouzestan Province (Abadan and Hendijan), we estimated growth parameters of the species. The data were composed of the total length measurement of around 10071 specimens caught in Abadan and 4032 specimens caught in Hendijan waters. The catches conducted monthly during the year 2005 using gill net with a mesh size of 8-9.5 centimeters.....

Objective: Venous thromboembolism, including deep vein thrombosis and pulmonary embolism, is likely to cause the death of both medical and surgical patients. Despite some evidence of seasonal variation in the incidence of venous thromboembolism, the existing studies obtain contradictory results. A temporal pattern for pulmonary embolism is known, but data on deep vein thrombosis are inconclusive. The purpose of this study is to make a meta-analysis and systematically review the literature about seasonal variations of pulmonary embolism and/or deep vein thrombosis in order to objectively diagnose venous thromboembolism. Methods: According to dichotomous data, risk ratios (RRs) and 95% confidence intervals (CIs) were used to compare the incidence of venous thromboembolism in different seasons. The research was classified according to pulmonary embolism mortality, pulmonary embolism/deep vein thrombosis incidence, latitude/elevation/climatic types, and monthly incidence for four subgroup comparisons. There were a total of 23 eligible studies, in which 40,309 patients with venous thromboembolism were compared. Results: The pooled total venous thromboembolism incidence was 27.2% in winter, 23.1% in spring, 24.6% in summer, and 25.1% in autumn. According to the results of pooled analysis, the incidence of venous thromboembolism in winter was much higher than that in summer (RR = 1.12, 95% CI: 1.01–1.24, adjusted P =.04), especially deep vein thrombosis. Moreover, the incidence of venous thromboembolism in summer and autumn was lower than that in winter in low-latitude (<200 m) areas and median low-latitude (0–50°-N) areas. Interestingly, the frequency of pulmonary embolism mortality was the largest in spring and smallest in summer (spring > winter ≈ autumn > summer). For monthly data, a statistically significantly lower incidence of venous thromboembolism was observed in May and July than in October. Conclusions: The study revealed a significantly higher incidence of venous thromboembolism and deep vein thrombosis in winter than in summer. Pulmonary embolism mortality occurred more frequently in spring than during other seasons. A statistically significantly lower incidence of venous thromboembolism was observed in May and July compared with that in October. [ABSTRACT FROM AUTHOR]

Water demand prediction is an essential prerequisite for urban water resource allocation and pipeline network management. This article uses a hybrid model to improve the accuracy of predicting water demand. The model combines a simulated annealing (SA) algorithm, genetic cross factor (GCF), fruit fly optimization algorithm (FFOA), and support vector machine (SVM) to predict monthly water demand. The monthly water consumption data from 2012 to 2017 in Austin, Texas, are used to develop and test the model. The prediction results are compared with other intelligent algorithms, such as the hybrid model combination particle swarm optimizer with SVM and back-propagation neural network. The results show that the mean absolute percentage error of FFOA-GCFSA-SVM is the smallest, and it is reduced by 16% and 51.17%, respectively, compared with the other two models. Moreover, the influences of kernel function and input variables on prediction results are analyzed, and the effects of sliding window length, data classification, and forecasting period on prediction results are discussed. We concluded that (1) for the city of Austin, the model with a Gaussian kernel function and historical data period of 6 months has higher prediction accuracy; (2) the monthly water demand in Austin is mainly influenced by the local population; and (3) data classification by season can reduce the interference of temperature on prediction. [ABSTRACT FROM AUTHOR]

The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin. [ABSTRACT FROM AUTHOR]

This article proposes an algorithm for solving the modeling problem with numerous variables in the MATLAB programming language. According to the developed algorithm, the MATLAB model calculates the values of solar insolation and ambient temperature of a particular location depending on five variables simultaneously: (1) time of day (τ), (2) day of the year (n), (3) tilt angle (β), (4) azimuth angle (γ) and (5) albedo (ρ). Meanwhile, the calculation results form a multidimensional database of solar insolation and ambient temperature of a given location for any values of the above variables and their combinations.An example of using the created database is given for calculating the annual total photovoltaic panel productivity and productivity dynamics on days of the year for latitude 41°2995′, which corresponds to a location in the city of Tashkent. The developed algorithm can be used to analyze/forecast the efficiency of solar plants using parameters from the database and other variables, such as temperature coefficient, dust content, nominal efficiency, wind speed, humidity, etc., as input data. The availability of data makes it possible to use data for both one-way and two-way reception of radiation as for bifacial photovoltaic panels. The database created based on the proposed program model can serve as a basis for comparison with other calculations or measurement databases and for studying or determining other parameters that affect possible differences in the compared data and creating advanced modeling algorithms for the solar irradiance. [ABSTRACT FROM AUTHOR]

The use of high-dose vitamin D supplementation has increased in recent years. However, relatively little is known about the safety of long-term high doses. To investigate the safety of a monthly high-dose of vitamin D 3 supplementation taken for up to 4 years. Data were collected in a randomized, double blind, placebo-controlled trial of 5108 adults aged 50–84 years old from Auckland, New Zealand. Participants were given monthly doses of 100,000 IU vitamin D 3 or placebo, for a median of 3.3 years (range 2.5–4.2 years). They answered an open-ended question in a monthly questionnaire about any adverse events they attributed to the study capsules, which were coded blindly. Incidence rates per person months were calculated for categories of adverse events. Cox regression model used to calculate hazard ratio of time to first adverse-event. In total, 419 (16.5%) participants taking vitamin D and 399 (15.8%) taking placebo reported ≥1 adverse event. Compared to placebo, the hazard ratio (HR) of reporting first adverse event in the vitamin D group was 1.03 (95% CI: 0.90, 1.18; p = 0.63). Despite a slightly higher incidence of recurrent adverse events in vitamin D arm, the incidence rate ratio (1.17) was not significantly higher in vitamin D (95% CI: 0.97, 1.41; p = 0.10). All regression results were adjusted for age, sex, and ethnicity. There was no difference between study arms in terms of participants' allocation perception (p = 0.52). Monthly supplementation of 100,000 IU vitamin D3 for a median of 3.3 years did not affect participant-reported adverse events. clinicaltrials.gov Identifier: ACTRN12611000402943; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=ACTRN12611000402943. [ABSTRACT FROM AUTHOR]

• An Automatic Threshold–based Multi-source data Combination (ATMC) method is proposed. • Generate a new product of Surface Water Dynamics in the Yangtze River Basin (SWDYRB). • ATMC has high accuracy and few parameters without manual intervention. • ATMC outperforms existing products and other algorithms in different environments. • SWDYRB has better representation of mountain and small water bodies. Surface water bodies are irreplaceable in the global climate and ecological system. However, quantifying the extent and change of these water bodies has been challenging due to environmental complexity, climatic diversity, and their highly dynamic nature in seasons and interannual periods. Most established remote-sensing water extraction methods involve a practical trade-off between spatial and temporal resolution. To remedy these deficiencies, we proposed a simple and robust Automatic Threshold–based Multi-source data Combination algorithm to improve the efficiency and accuracy of water mapping, achieving monthly stable periodic monitoring. The algorithm fully considered the principles and advantages of Sentinel-1/2 images for water detection and performed adaptive optimal threshold calculation based on the Edge Otsu algorithm. A new product of Surface Water Dynamics in the Yangtze River Basin was generated on a monthly temporal scale and 10-m spatial scale from 2017 to 2020 using the algorithm based on the Google Earth Engine platform. The confusion matrix-based metrics based on sample points were 95.85 % (producer's accuracy), 99.16 % (user's accuracy), 97.41 % (overall accuracy), 0.95 (Matthews correlation coefficient), and 0.95 (Kappa coefficient). Furthermore, the overall correct extraction rate of water bodies can reach 95.85 % and 91.87 % for small water bodies, with commission and omission errors of 0.04 % and 0.03 % at the sub-pixel scale, respectively. The distribution of water bodies within the basin is highly uneven, and their changes exhibit a distinct seasonal trend. The maximum inundation extent occurred in August 2020 (summer, 53,372.88 km2), while the minimum inundation extent was observed in November 2019 (winter, 42,491.74 km2). The successful application of the algorithm in the Yangtze River Basin established a good benchmark for surface water monitoring of multi-source satellite observations and showed great potential for global-scale applications. It will assist the monitoring and evaluation services of the UN Sustainable Development Goals and provide solutions to more global and regional issues. [ABSTRACT FROM AUTHOR]

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.

The main role and significance of salary as stimulated factor in development of production of competitive and export oriented agricultural products and whole agrarian sector has been affirmed according to the strategic aim and goal of strategic road map about production and processing of agricultural products in Azerbaijan Republic in this article, comparative analysis of it has been conducted with other sectors of economics and factors, which have an effect on it, have been estimated. In this case after generalization, grouping, system approach, economic and econometric analysis, influence of gross output, intermediate consumption added value, labour cost, other production taxes, gross profit, consumption of main funds and net profit to average monthly nominal salary in separate sectors of economy has been valued and it has been identified dim connection level of salary with a lot of main macroeconomic indices. As a result it is noted the necessity to define average monthly nominal salary according to the role and share in real economy of each field for development of production of competitive and export oriented agricultural products and whole agrarian sector. Besides it, as competition factor-mutual relation of producer price indices of agricultural product with salary is agenda as well. The article covers dependence of producer price indices of agricultural product on salary, salary fund, net weight of salary pay in expenses spent on production of agricultural product, establishment of production account and profit in agricultural sector, dynamics of net weight of share of agricultural sector in economics, level of average monthly nominal salary on economic activity types, place of separate sectors of economics on macroeconomic indices in economic activity types, models of dependence of average monthly nominal salary on main macroeconomic indices annually in economics and average monthly nominal salary in economics have been made up. [ABSTRACT FROM AUTHOR]

Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. [ABSTRACT FROM AUTHOR]

The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain . [ABSTRACT FROM AUTHOR]

The study on Moringa oleifera was conducted over twelve months during 2014-2015 to evaluate the impact of the growing season and varying planting densities on biomass yield and physiological attributes under dryland conditions. Trial was established at densities of 5000, 2500, 1667 and 1250 plants ha-1, with eight replicates. The increase in planting density led to an increase in biomass production. The monthly and seasonal data collected showed significant differences in net photosynthetic rate, transpiration, sub-stomatal CO2 and stomatal conductance. However, planting densities of M. oleifera had no significant effect on all the gaseous exchange parameters measured. The results further revealed that the amount of carbon dioxide assimilated by the tree is not attributable to photosynthetic and transpiration rates as well as stomatal conductance. Under water shortage condition and high temperature, M. oleifera used an adaptation strategy by reducing stomatal conductance and transpiration and hence increasing water use efficiency. Moringa oleifera thus has the ability to sequester carbon even under water stress conditions. The tree can therefore be recommended for planting at a relatively high density of 5000 plants ha-1 in many parts of Limpopo province where temperatures are favorable for improved farmers’ livelihoods as well as for climate change mitigation. [ABSTRACT FROM AUTHOR]

Pinus taeda L. (loblolly pine), a commercial timber three of the Southeastern United States, provides a major component of the region's forest resources. We cultured loblolly ovules for in vitro fertilization. This procedure was assisted by quantifying time-dependent alterations of in situ ovular RNA, DNA and total protein (Jan-Aug, 1985) contents for ovules (yr 2 of reproductive cycle). Cold-hot, TCA extracted macromolecules were quantified by colorimetry and UV spectroscopy. Total protein was about 0.4 µg per 100 ovules x 104 (Jan-Apr) and except for July increased to 3.6 µg per 100 ovules x 104 by August. In contrast, RNA "dropped" from 3 to about I µg per 100 ovules x 102 (Jan-Mar) and then rose to 7 µg per 100 ovules x 102 by July. The DNA climbed from about I to above 8 µg per 100 ovules x 102 (Jan-Mar) and then plummeted to I µg per 100 ovules x 102 (Apr-June). The observed alterations may reflect ovule morphogenesis.