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Colicodendron scabridum, commonly known as “sapote”, is one of the most common woody species in seasonally dry neotropical forests, and an important food for numerous animals. We investigated sapote’s reproductive phenology and evaluated its relationship to temperature and precipitation. To do so, we installed seven 2 m x 300 m transects, each with fifteen focal individual sapote trees, at seven sites from 257 – 353 m at Cerro Venado, Lambayeque, Peru. The duration of flowering and fruiting varied among individuals, sites, and years. On average, 85-97 % of individuals with flowers bore fruit. The average fruiting period was shorter than the average flowering period, which it followed three months later. We used a cross correlation test to evaluate the relationship between flowering, and fruiting, with the monthly average temperature and precipitation. Sapote flowering changed in opposition to monthly average temperature, with a strong inverse correlation between flowering and temperature. In contrast, the frequency of individuals with fruits was positively correlated with temperature. There was no relationship between monthly precipitation with either flowering or fruiting. Our results suggest that temperature may determine when the species reproduces, thus affecting food availability for wildlife. The phenological variations we recorded are relevant to decisions for the conservation of this species.

In this study, data of the monthly average of daily global solar radiation falling on a horizontal surface, relative humidity, maximum temperature, and duration of sunshine for the city of Baghdad were collected through two sources. First, from the Iraqi Meteorological Authority (IMA) for a period extending from 1961 to 2016. The second is from NASA, for the period from 1984 to 2004. Then, four linear regression models, two single and two polynomials were formulated to calculate the values of the monthly average of daily global horizontal solar radiation (GHSR) incidents. The models calculated the monthly average of daily extraterrestrial radiation and day length, using some data provided by NASA and the IMA. To ensure the validity of the used models, a statistical test was performed for the performance of the proposed models, using the indicators mean bias Error (MBE), root mean square error (RMSE) as well as mean percentage error (MPE). The validation shows the relationship between the measured and computed values (through the analysis of the results), where a great convergence was found between the measured and calculated values. This means that the proposed models can be adapted to predict global solar radiation. The highest values of measured solar radiation were during the month of June, which were 28.555 and 27.280 MJ/m2/day from the IMA and NASA, respectively. The same applies to the radiation calculated using the four empirical models. The month of June was the highest in terms of solar radiation values. The radiation values were 28.947, 26.315, 29.699, and 26.716 MJ/m2/day for the first, second, third, and fourth models, respectively. The lowest values of measured and calculated radiation were during the month of December. Always, radiation measured by the IMA was greater than those of NASA, as well as the values of radiation calculated in the two IMA-based models were greater than the other two NSA-based models. In the absence of a method for measuring the diffuse and direct (beam) solar radiations, as well as the lack of such values by meteorological authorities, and its paramount importance, they were reported to mathematically calculate them in this study. The values of statistical indicators RMSE; MJ/m2/day, MBE; MJ/m2/day and MPE% were (0.4769, 0.0164, 0.2207), (0.8641, 0.1773, -0.9680), (0.6420, 0.3996, -1.1487), (0.9604, 0.218, -1.0225) for the first, second, third and fourth models, respectively. According to the results of the statistical test, it can be indicated that the single linear regression model, based on the IMA’s data (model No.1), is the most accurate to calculate global solar radiation for Baghdad City.

Understanding the variation regularity of water extent can provide insights into lake conservation and management. In this study, inter- and inner-annual variations of water extent during the period of 1987–2020 were analyzed to understand the temporal and spatial distribution characteristics of Dongting Lake. We applied the Multiple Index Water Detection Rule to extract the Dongting Lake water extent quickly and accurately based on Google Earth Engine platform, and then assessed the extraction accuracy. The water surface analysis results showed that (1) based on sustainable development goals (SDG) 6.6.1, the trend of water extent showed the downward fluctuating trend from 1987 to 2020, with the overall average water extent being 1894.48 km². (2) Among the monthly average water area, the largest extent was 2477.14km² (July) and the smallest was 848.14 km² (January). Among the seasonal mean water area, summer was the largest, with an area of 2438.06 km², and winter was the smallest at 967.34 km². (3) For the water inundation frequency, seasonal water bodies accounted for the largest proportion, with 1577.85 km²; the nonwater area was the smallest, with the area of 573.02 km²; and the permanent water area was 1086.21 km². Through the analysis of the historical water body extent of the long time series of Dongting Lake, this study reflected support for SDG, for which the research idea and design can help us understand the importance and feasibility of the SDG 6.6.1 indicator.

Accurate prediction of EI Nino-southern oscillation (ENSO) is of great significance to seasonal climate forecast. Recently, a convolutional neural network (CNN) has shown an optimal skill for ENSO prediction. However, it is difficult for the convolutional kernel to capture long-range precursors of ENSO due to its build-in local property. The transformer model has long been used in natural language processing (NLP) for its ability to focus on global features. Here, we introduce it to the ENSO research community and propose the ENSO transformer (ENSOTR). We show that using the ENSOTR model, the monthly average Nino3.4 index can be skillfully predicted up to one and a half years ahead. The model can also predict strong EI Nino cases more than a year ahead, such as 1997-1998. Experimental results show that our model achieves better skill than CNN for ENSO prediction.

The recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states’ governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people’s abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

The first ever (to our knowledge) long-term characteristics of aerosol absorption optical depth (AAOD) and then absorption Angstrom exponent (AAE) over the Tarim Basin during 2005–2018 are investigated based on the latest Ozone Monitoring Instrument (OMI) retrievals. This paper aims at evaluating the AAOD and AAE in terms of annual, seasonal, and monthly variations, and referential AAE values of dust aerosols over the Tarim Basin. Our results reveal that the AAOD increases by a factor of nearly 2 from 2015 to 2018, with yearly average values from 0.08 to 0.14 and from 0.04 to 0.07 at 388 and 500 nm, respectively, indicating that the situation of dust aerosol pollution in the Tarim Basin has become severe in recent years. The AAE based on the AAODs at wavelengths of 388 and 500 nm over the Tarim Basin exhibits large variations, with a range of 2.5–3.8. The seasonality of AAOD depicts maximum levels in spring, moderate levels in winter and autumn, and minimum levels in summer, whereas the AAE shows a distinctive pattern with high values in summer and low levels in winter. The monthly mean AAOD values vary by twofold, while monthly average AAE is in a range of 2.8–3.3, with the lowest value in February and the highest in June. The daily AAE between 388 and 500 nm for dust episodes is found to be in a range of 3.2–3.6, with a mean of 3.3, which hopefully provides typical AAE values of dust aerosols over the Tarim Basin.

China's shutdown due to COVID-19 in 2020 reduced air pollutant emissions, which is located on the windward side of South Korea. The positive temperature anomaly and negative zonal wind anomaly from northern Mongolia to South Korea through eastern China presented warm and stationary air masses during January and February 2020. Decreased concentrations of PM10, NO2, and O-3 were measured at Seokmo-ri and Pado-ri, located in the central-western region of South Korea, due to decreased emissions in China from January to March 2020. After China's shutdown from January to March 2020, in Pado-ri, the ratio of monthly average concentrations in that period with those of PM10 and O-3 in the last four years decreased by approximately 0.7-4.7% and 9.2-22.8%, respectively. In January 2020, during the Lunar New Year holidays in China, concentrations of PM10, NO2, and O-3 at Seokmo-ri and Pado-ri decreased just as much as it did during the same period in the last four years. However, average concentrations in January 2020 decreased before and after the Lunar New Year holidays in China when compared with those in January of the last four years. In Seokmori, ratios of actual and predicted values ((O) over bar (s)/M) for PM10, NO2, and O-3 concentrations were calculated as 70.8 to 89.7%, 70.5 to 87.1%, and 72.5 to 97.1%, respectively, during January and March 2020. Moreover, those of Pado-ri were 79.6 to 93.5%, 67.7 to 84.9%, and 83.7 to 94.6%, respectively. In January 2020, the aerosol optical depth (AOD) data showed a higher distribution than that of the last four years due to photochemical reactions in regions from northern Mongolia to eastern China and the Korean Peninsula. However, the decrease in AOD values compared with those of the last four years was attributed to the decrease in emissions of precursors that generate secondary aerosols in China during March 2020.

Temperature and relative humidity are the most important factors affecting on human comfort. This Research test the monthly average temperature and relative humidity for some selected stations in Iraq, three stations (Baghdad - Mosul - Basra) to find the pattern covariance of the annual average of temperatures and relative humidity for the period of time (2000-2014) By using the Welch T-test or paired T- test, We find that the calculated value is greater than the tabular value in both stations (Baghdad - Mosul) and (Baghdad - Basra) for both temperatures and relative humidity, and this indicates an increasing trend in the average of temperature and relative humidity in two stations (Baghdad - Mosul) and (Baghdad - Basra), and since the main hypothesis is that there is a relationship between the two racists and the calculations prove that, so we reject the null hypothesis, which proves the validity of the main hypothesis, i.e. there is a relationship between temperature and humidity in these selected stations.

Tingkat kenyamanan merupakan interaksi antara manusia dengan lingkungan yang berkaitan dengan cuaca dan iklim. Tingkat kenyamanan ini cukup mempengaruhi kegiatan manusia baik di dalam maupun di luar ruangan. Kenaikan suhu yang diakibatkan oleh pemanasan global menyebabkan potensi perubahan tingkat kenyamanan yang dirasakan oleh manusia. Kalimantan Barat yang berada di wilayah khatulistiwa umumnya memperoleh radiasi matahari yang intensif sehingga suhu udara rata-ratanya relatif tinggi. Penelitian ini dilakukan untuk mengetahui tren perubahan suhu dan perubahan tingkat kenyamanan termis akibat perubahan iklim dengan menggunakan humidex di wilayah Kalimantan Barat. Data yang digunakan adalah data suhu dan kelembaban rata-rata bulanan selama 30 tahun di enam stasiun, yaitu Stasiun Klimatologi Mempawah, Stasiun Meteorologi Supadio, Stasiun Meteorologi Sambas, Stasiun Meteorologi Melawi, Stasiun Meteorologi Sintang, dan Stasiun Meteorologi Ketapang. Hasil perhitungan humidex di wilayah Kalimantan Barat menunjukkan rata-rata humidex bulanan berkisar antara 37.5°C hingga 39.2°C. Nilai ini menunjukkan bahwa kondisi tidak nyaman akibat panas sangat umum dirasakan di Kalimantan Barat. Suhu di Kalimantan Barat mengalami peningkatan selama periode 1990-2019. Peningkatan ini mengindikasikan telah terjadi perubahan iklim di Kalimantan Barat. Humidex juga mengalami tren peningkatan pada periode 1990-2019. Namun, tren peningkatan tersebut masih pada rentang kategori “perasaan tidak nyaman akibat panas” (35⁰C - 39⁰C).

A escassez da água vem sendo debatida e argumentada em vários lugares, mostrando a necessidade de ações voltadas ao uso eficiente deste recurso. Assim, o objetivo deste trabalho foi realizar a captação, armazenamento e reuso da água captada de telhado para residência do projeto “Minha Casa, Minha Vida” (70 m2) no município de Rio Verde, Goiás. Foram utilizados dados de precipitação média mensal de 19 anos (1996 - 2015). Simulou-se o armazenamento máximo para assegurar o período de estiagem. Estimou-se o consumo médio de água semanal considerando de 1 a 3 pessoas por residência. Considerou-se como consumo de água lavagem de carro, gramado ou jardim, descarga na bacia, máquina de lavar roupa e lavagem de piso. Nos meses de maior precipitação (novembro-março), conseguiu atender à necessidade das pessoas na residência, e ainda armazenar para aproveitar nos períodos de estiagem. Os meses de fevereiro, março, abril e dezembro, atingiu a capacidade de armazenamento do reservatório (20.000 L) apenas para a residência com 1 e 2 pessoas, para 3 pessoas apenas o mês de março a caixa d’água encheu completamente, sendo assim, supriu as necessidades nos meses de estiagem, onde a demanda não atingiu a capacidade do reservatório. Assim o reuso da água do telhado propicia na economia de água nobre e economia a médio prazo, além de assegurar picos de enchente no período chuvoso.

This study attempted to examine the future behaviour of monthly average rainfall and temperature of South Asian countries by using the Seasonal Autoregressive Moving Average model. Mann–Kendall trend test with Sen's Slope Estimator, to find the trending behaviour of all data series. The study has also been attempted to compare the above methods with the help of actual data. The monthly average rainfall and temperature of South Asian countries except Afghanistan and Maldives viz. Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka data from January, 1961 to December, 2016 have been collected from World Bank Group, Climate change knowledge portal. For estimating the trending behaviour, a non-parametric model such as the Mann–Kendall test was used with Sen's slope estimation to determine the magnitude of the trend. Box–Jenkins methodology was also used to develop the model and estimate the forecasting behaviour of rainfall and temperature in South Asian countries. Forecasting is carried out for both monthly rainfall and the average temperature of all the countries using best fitted models based on the data series. The monthly data from January, 1961 to December, 2010 are considered for validation of the model can be regarded as in-sample forecast and the data from January, 2011 to December, 2016 are used as out-sample forecast. The forecasting values with 95% confidence limit from January, 2011 to December, 2021 using best-fitted models for both rainfall and temperature. We conclude that climate change occurs for both rainfall and temperature in South Asian countries from the study period. The selected model can be used for forecasting both rainfall and temperature of respective countries from January, 2011 to December, 2021. As the climatic data analysis is valuable to understand the variation of global climatic change, this study may help for future research work on rainfall and temperature data.

With monthly average temperature series in the contiguous United States during the period from January 1895 to July 2018, the author uses wavelet analysis to investigate possible connection between the average temperature and solar activity. The results show obvious fluctuation characteristics in the temperature series. The modulation action from solar activity plays an important role in the oscillation of the contiguous United States average temperature, and there is a possible association existing in the temperature series and solar activity on decade time scales.

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method. The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

O presente trabalho tem por objetivo analisar a estacionariedade e homogeneidade de series hidrologicas de chuva e vazao da bacia de contribuicao da Usina Hidreletrica (UHE) Castro Alves, localizada no Estado do Rio Grande do Sul. Foram obtidos dados de seis estacoes pluviometricas, no portal Hidroweb da Agencia Nacional de Aguas (ANA), e calculada a serie de precipitacoes medias para o periodo de 1945 a 2017. Os dados de vazao foram obtidos do Operador Nacional do Sistema Eletrico (ONS), para o mesmo periodo. A analise foi realizada nas series completas de precipitacoes totais anuais e vazoes medias mensais, e em dois periodos obtidos da mesma, referentes ao periodo anterior e posterior a 1970. Foram utilizados seis testes estatisticos; t-Student, F-Snedecor, Coeficiente de Correlacao de Spearman, Cox-Stuart, Mann-Whitney e Mann-Kendall. A verificacao do ponto de mudanca nas series de precipitacoes totais anuais, totais mensais e vazoes medias mensais foi realizado com aplicacao do teste de Pettitt. As analises realizadas permitiram concluir que as series de chuva e vazao sao nao-estacionarias. O periodo antecedente a 1970 foi caracterizado como nao-estacionario, enquanto o periodo posterior apresentou indicios significativos de estacionariedade e homogeneidade para ambas variaveis. Identificou-se uma mudanca na tendencia no ano de 1970 para a serie de precipitacoes totais anuais e vazoes medias anuais. Nas analises mensais, verificou-se que os meses em que foi mais acentuado o aumento do total precipitado foram os meses secos de maio e novembro, corroborando com o aumento das vazoes medias mensais, observadas nesses meses. Analysis of hydrological series in the contribution basin of Castro Alves Hydroelectric Power Plant, RS A B S T R A C T This study aims to analyze the stationariety and homogeneity of rainfall and streamflow series of the contribution basin of Castro Alves Hydroeletric Power Plant, in Southern Brazil. The rainfall data were acquired from the Agencia Nacional de Aguas, and the average rainfall series was calculated for the period from 1945 to 2017. The streamflow data were acquired from the Operador Nacional do Sistema Eletrico covering a period from 1945 to 2017. The analysis were performed in the complete series and in two periods obtained from the same, referring to the period before and after 1970. Six statistical tests were used to check the stationarity: t-Student, F-Snedecor, Spearman Correlation Coefficent, Cox-Stuart, Mann-Whitney and Mann-Kendall. The verification of the point of change in the series of total annual precipitations, monthly totals and monthly average flows was performed using Pettitt’s test. The analysis results made it possible to conclude that the hydrological series are non-stationary. The period prior to 1970 was characterized as non-stationary. The period after 1970 showed significant signs of stationarity and homogeneity for both variables. Using the Pettitt test, a trend change was identified in the year 1971 and 1970 for the series of annual total precipitations and monthly mean flows, respectively. In the monthly analyzes of the series, it was found that the increase in the precipitated total was more accentuated in the dry months of May and November. This corroborates the more pronounced increase in the average monthly streamflow observed in those same months. Keywords: Stationarity; Homogeneity; trends; hydrological series.

Employee Churn which is otherwise called representative turnover is an exorbitant issue for organizations. The genuine expense for supplanting a worker can frequently be very enormous. In this work, we aimed to understand why and when employees are most likely to leave a company i.e the probability of an active employee leaving the organization and the key factors of an employee leaving the organization. For this purpose, we created such standard dataset where we include those attributes that are helpful for our analysis to predict the factors that are responsible for an employee to leave a company. The attributes we used in the dataset are satisfaction level, last evaluation, a number of projects, monthly average hours, amount of time spend in the company, employees left the company, promotions in last 5years, departments, salary. Further, under these attributes, we include 603 data samples. It is also useful to the company to retain the employees' safety and secure without losing them in the organization for a long time. We applied various Machine Learning models such as, Logistic Regression Classifier, Random Forest Classifier, SVM to check that our dataset is resulting with accurate values or not and which model is predicting the best. Thus, after applying all the models to the dataset, the Random Forest Classifier is giving more accuracy that is about 97.2% when compared to all the other classification models. This Random Forest Classifier correctly depicts the factors responsible for an employee leaving the company.

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

Solar photovoltaic (PV) technologies are now considered viable options to fulfill the electricity demand for end-users worldwide. However, these PV technologies need to be technically analyzed in terms of pre-feasibility and performance assessment by utilizing the available solar radiation and ambient temperature of the PV system installation site. This paper presents the comparative analysis of three different types of 1 kWp PV technologies viz. polycrystalline silicon, monocrystalline silicon, and thin-film amorphous silicon in terms of monthly average PV power output, PV modules daily output profile, hourly-based PV modules year-round power output, PV output vs. incident solar radiation and the PV module cell temperature vs. PV module power. The geographical site considered for the study is Sikkim state, India (latitude: 27.33 N and longitude: 88.62 E). With solar radiation of 3.82–5.88 kWh/m2/day for the investigation site, the annual average PV module power output for poly-Si, mono-Si and thin-film a-Si are found to be 0.173225 kW, 0.174252 kW and 0.172861 kW, respectively.

The changes and fluctuations in climate variables, especially temperature, that subsequently affect human activities and natural environments are one of the critical topics in scientific societies. Therefore, time variability analysis of average temperature is an important concept in climate studies, particularly in environmental planning and management at various levels. The main purpose of the present study is to detect the temporal and spatial variability of average monthly temperature in Iran during the period 2015–2060 based on the RCP4.5 and RCP 8.5 scenarios simulated by the CMIP5 atmospheric general circulation models. For this purpose, the monthly average temperature data relative to four CMIP5 models, including CMCC-CM, CESMI-BGC, CCSM4, and MRI-CGCM3 models, for the period 1987–2060, and the observed monthly average temperature for the period 1987–2014 measured at 88 synoptic stations distributed all over Iran were used. The accuracy of the CMIP5 models in simulating the historical data for the period 1987–2005 was evaluated against the observed data at the synoptic stations using R, R2, RMSE, bias, EF, NARMSE, slope, and IA statistics. To study the temporal and spatial variation of temperature relative to the historical and future periods across Iran, the statistical spatial variance model was applied. The result showed that the historical temperatures estimated by all CMIP5 models are highly correlated with the observed temperatures all over Iran, but the accuracy of the MRI-CGCM3 model was found slightly higher than the other three models in most areas of Iran. In general, the results showed that the temperature estimated by the selected models and scenarios have a very high correlation with the observed data in most parts of Iran, especially in the mountainous areas of the western country. In the coastal areas of southern and northern Iran, the accuracies of the models somehow decreased which can be attributed to the complex topographical structure of these areas and/or the other effective local features that have not been incorporated in the models. The results showed that the highest temporal variability of temperature has occurred in winter months and partly in the autumn, and the spatial variability of temperature has been observed primarily in the mountainous areas of Iran. The investigation of the temperature variability in the future decades (2015–2059) was found in parallel with the temporal variations of temperature in the present period, considering that the highest temperature variability will occur in winter and somehow in the autumn, mostly in the mountainous areas of Iran. Generally, in most parts of the country, the air temperature in future decades will have an increasing tendency in all four seasons of the year.

As the world is facing an infectious disease pandemic caused by coronavirus disease 2019 (COVID-19), medical institutions are experiencing significant financial losses. we attempted to estimate such financial losses through a survey of clinics that reported temporary closure due to COVID-19 to the Korean Medical Association. As a result, the data of 56 clinics were obtained. The average closure period was 6.7 days, and the overall number of related health insurance claims and loss of sales reached 13,408,000 Korean won (KRW) and 20,911,000 KRW, respectively, while daily losses reached 1,343,000 and 2,202,000 KRW, respectively. In addition, the monthly average health insurance claims and sales were compared to those of the same month of the previous year. In January, they slightly increased by 4.4% and 7.8%, respectively, whereas in February, they slightly decreased by 10.2% and 12.4%, respectively. In March, they significantly decreased by 46.8% and 49.8%, respectively, as COVID-19 was at its peak. Considering the financial losses of clinics that played a pivotal role in quarantine at the forefront of infectious diseases, the government should prepare a reasonable compensation plan for such losses.

The results of analysis of the content of benzo(a)pyrene and other high-molecular polycyclic aromatic hydrocarbons (PAH) in atmospheric air samples collected in 2018 in industrial cities of the Krasnoyarsk region at the state monitoring network of Roshydromet are presented. The PAH measurements were performed by the high-performance liquid chromatography method, using a fluorometric detector. The monthly average concentrations of benzo(a)pyrene in the atmospheric air exceeded 10 MPC in the cities of Minusinsk, Lesosibirsk, Krasnoyarsk, and Achinsk. The maximum monthly average content of benzo(a)pyrene during the observation period in 2018 was found in Minusinsk (January, 90 MPC) and Lesosibirsk (January, 59 MPC). The main reasons for the increased levels of air pollution with PAH are investigated. Seasonal variability in the content of benzo(a)pyrene and other PAH in the atmospheric air of the Krasnoyarsk region cities with a maximum in winter and a minimum in summer is shown. The annual average concentration of benzo(a)pyrene in the atmospheric air in 2018 decreased in the following series: Minusinsk (17.2 ng/m3), Lesosibirsk (12.4 ng/m3), Krasnoyarsk (6.5 ng/m3), Achinsk (3.6 ng/m3), and Norilsk (0.6 ng/m3). A similar decreasing concentration trend in this series of cities is typical for other PAH. The level of air pollution with benzo(a)pyrene in the cities of Minusinsk, Lesosibirsk, Krasnoyarsk in 2018 is characterized as “very high.” The pyrogenic nature of PAH in the atmosphere of the surveyed cities of the Krasnoyarsk region is shown using molecular ratios.

In this study, we model and forecast monthly average temperature and monthly average precipitation of Turkey by employing periodogram-based time series methodology. We compare autoregressive integrated moving average methodology and harmonic regression. We show that harmonic regression performs better than the classical methodology in both time series. Also, we find that the monthly average temperature and monthly average precipitation have two different periodic structures of 6 months and 12 months which coincide with the seasonal pattern of the time series.

Monthly average daily global solar radiation is estimated using several mathematical models available in the literature. Various input parameters and their different combinations are explored to fo...

We compare the time variations in monthly average concentrations of black carbon in the surface air layer obtained from field measurements ([BC]) and from MERRA-2 reanalysis ([BC]M) at four monitoring sites located in the northern part of Russia (Tiksi Observatory, Pechora-Ilych Biosphere Reserve), Alaska (Barrow station), and Greenland (Summit station). It is shown that the MERRA-2 reanalysis data for the regions of Tiksi and Barrow do not completely reflect the [BC] variations during the year, in contrast to the Pechora-Ilych Biosphere Reserve, where the discrepancies are within 30–50%. The [BC]M reanalysis products for the Summit monitoring site qualitatively agree with the measurements characterizing the BC content in the free troposphere, but they underestimate [BC] by more than a factor of two. On the whole, our analysis showed that the results of the MERRA-2 reanalysis of the monthly average indices of near-surface concentration of atmospheric black carbon can be used for climate assessments for hard-to-reach northern regions in the warm season with an error of about 30%. Possible causes for the discrepancies between [BC] and [BC]M are discussed for different times of the year and observation sites.

Hybrid solar PVT has the potential to become a major player in the renewable energy sector, but one of the main barriers is the lack of reliable data due to limited number of monitored PVT hybrid systems with proven thermal performance results. In order to address this issue, a statistical study was realized to analyze in-field performances of twenty-eight hybrid solar domestic hot water installations equipped with innovative non-overglazed PVT collectors in Western Europe (France, Switzerland and Portugal). The results show that PVT technology, more than PV production, is particularly relevant on domestic hot water preheating, with a monthly average of daily maximal temperature (Tmean) in the panels reaching above 45 °C during 5 months in the year. The results also demonstrate the robustness of the non-overglazed PVT collector, as the highest stagnation temperature was always measured below 74.5 °C, which is very safe in terms of risk of overheating and effect on PV performance. The twenty-eight hybrid solar domestic hot water data base allows the quantify the main deviation compared to the classical simplified production simulations.

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

Objective To evaluate the effect of COVID-19 on the working of a tertiary care hospital. Study design Audit study. Place and duration of study Services Hospital, Lahore from 1st March to 30th June 2020. Methodology We calculated and compared various parameters of hospital working. Two time periods were selected. The study period was defined as the time after first confirmed case of COVID-19 in Pakistan. The control period was defined as one year prior to the first case being reported, taken from 1st March 2019 to 29th February, 2020. The parameters we studied included were number of number of hospital admissions, emergency patients, OPD patients, major surgeries, total surgeries, radiological investigations done, laboratory investigations done, births and mortalities. All parameters were calculated by taking monthly average during each time period and then compared. Results A decrease in almost all parameters was seen when the cases of the two time periods were compared. There was a decrease in the average hospital admission by 51%, while the patients seen in OPD fell by almost 60%. A slight decrease of 25% was seen in the cases presenting to the emergency. The most marked decrease was in the elective surgeries, which was 66% closely followed by average monthly mortality which decreased by 64%. Average monthly minor surgeries and births were decreased by 33% and 35%, respectively. Conclusion There had been a decrease in the number of patients presenting to the hospital across the board with the most marked increase being in elective surgeries. Key Words: COVID-19, Hospital working, Patient load, Elective surgery.

Objetivo: Determinar las características del consumo de Filgrastim en el Servicio De Oncología del Hospital Ramiro Priale Priale de EsSalud. Material y métodos: Corresponde a un estudio descriptivo, retrospectivo y de corte transversal, empleándose una muestra de 144 casos clínicos del consumo de Filgrastim; empleándose como técnica de análisis documental y el instrumento la ficha de recolección de datos. Resultados: La prevalencia de utilización del filgrastim en la práctica clínica en el Servicio de Oncología del Hospital Ramiro Priale Priale de EsSalud, fue del 100%, con un consumo según edad, entre los rangos de 68 a 77 años, mayormente en el sexo femenino, con promedio mensual de consumo de 52.31 unidades y costo promedio anual de 94,221.6 y mensual 7851.80 nuevos soles. Conclusiones: El consumo de Filgrastim 300mg, fue mayor en mujeres y pacientes de la tercera edad, resaltando el diagnóstico Linfoma No Hodgkin difuso para su consumo y se requiere se implemente guías de manejo para su prescripción.

Rhegmatogenous retinal detachment (RRD) frequency was observed to be higher with an increase in the daily temperature range. This showed that a wide daily range of temperature, rather than the absolute value of the temperature, is associated with the occurrence of RRD.Purpose. To investigate the association between the frequency of rhegmatogenous retinal detachment (RRD) and the atmospheric temperature.Method. A retrospective review of consecutive eyes that had undergone primary RRD surgery from 1996 to 2016 at Chungbuk National University Hospital was conducted. Temperature data (highest, lowest, and mean daily temperatures and daily temperature range) in Chungbuk Province were obtained from the Korean Meteorological Administration database. We investigated the relationship between the daily temperature range and the frequency of RRD surgery. We also analyzed the association between various temperature data and the frequency of RRD surgery.Result. There were 1,394 RRD surgeries from 1996 to 2016. Among them, 974 eyes were included in this study. The monthly average number of RRD operations showed a bimodal peak (in April and October) throughout the year. With the same tendency as the frequency of RRD, the monthly average of the daily temperature range over 1 year also showed a bimodal peak in April and October. There was a significant positive correlation between the monthly average of the daily temperature range and the number of RRD surgeries (r = 0.297,P<0.001). However, there were no associations between RRD frequency and the mean temperature, highest temperature, and lowest temperature.Conclusion. The higher the daily temperature range, the higher was the RRD frequency observed. We speculated that dynamic changes in temperature during the day may affect degrees in chorioretinal adhesion and liquefaction of the vitreous, which may eventually result in retinal detachment. Therefore, further experimental studies on the correlation between temperature changes and retinal detachment are needed.

In September 2003, Mexico City introduced "Conduce sin Alcohol" (CSA)-drive without alcohol-a program that monitors breath alcohol concentration limits among drivers to reduce road traffic crashes. To our knowledge, no study has evaluated the impact of this program on mortality. We estimated the effect of CSA on the monthly rate of traffic-related deaths (deaths per one million people) in Mexico City. We applied interrupted time series analyses (ITSA) using monthly data from 1998 to 2016, adjusting for number of people covered by a public health insurance, monthly number of public health care facilities in the city, monthly average rain precipitation in milliliters, and number of vehicles registered. Our results show a statistically significant average reduction in the monthly trend of traffic-related deaths of 0.08 per 1 million people/per month after the program was implemented relative to the pre-intervention trend. The relative difference comparing pre- and post-intervention predicted values from the ITSA model shows that there was a 23.2% reduction in the fatality rate. Findings from this study can be used to scale up programs to monitor alcohol concentration limits among drivers in cities with high alcohol-related crashes and deaths where the program has not been implemented.

Due to the scarcity of weather or solarimetric stations, and in the absence of observed solar irradiation data (Hg), one alternative for obtaining continuous and quality data distributed in space is the use of spatial interpolation. The aim of this study was to assess different methods of spatial interpolation applied to the monthly average daily Hg in the state of Rio de Janeiro, in the southeast of Brazil. Inverse distance weighting (IDW), tension spline (SpT) and ordinary Kriging (OKr) were assessed. Hg time series (2000–2013) were used, measured at 20 automatic weather stations located in the state of Rio de Janeiro and the states of Sao Paulo, Minas Gerais and Espirito Santo. The IDW and SpT methods showed satisfactory performance, and result in precision (coefficient of determination—r2 > 0.90 for IDW and > 0.80 for SpT) and accuracy (refined Willmott index—dref > 0.55 for IDW and SpT) for most months, while OKr showed inadequate precision (r2 0. 50) similar to that of IDW and SpT. The RMSE was less than 10% (1.64 MJ m−2 day−1) for all methods. Under conditions of heterogeneous station distribution, low station density and complex terrain, the OKr method does not produce a precise spatial interpolation of monthly Hg.

This paper evaluates the calibration effectiveness for estimating monthly average flow using the SWAT model in a watershed located at Espirito Santo state in Brazil. Six methodologies were tested, ...

Respiratory syncytial virus (RSV) infects young children and causes influenza-like illness. RSV circulation and prevalence differ among countries and climates. To better understand whether climate factors influence the seasonality of RSV in Thailand, we examined RSV data from children ≤ 5 years-old who presented with respiratory symptoms from January 2012–December 2018. From a total of 8,209 nasopharyngeal samples, 13.2% (1,082/8,209) was RSV-positive, of which 37.5% (406/1,082) were RSV-A and 36.4% (394/1,082) were RSV-B. The annual unimodal RSV activity from July–November overlaps with the rainy season. Association between meteorological data including monthly average temperature, relative humidity, rainfall, and wind speed for central Thailand and the incidence of RSV over 7-years was analyzed using Spearman’s rank and partial correlation. Multivariate time-series analysis with an autoregressive integrated moving average (ARIMA) model showed that RSV activity correlated positively with rainfall (r = 0.41) and relative humidity (r = 0.25), but negatively with mean temperature (r = − 0.27). The best-fitting ARIMA (1,0,0)(2,1,0)12 model suggests that peak RSV activity lags the hottest month of the year by 4 months. Our results enable possible prediction of RSV activity based on the climate and could help to anticipate the yearly upsurge of RSV in this region.

The common silver biddy Gerres oyena is one of the most commercial fish species in the Gulf of Suez. The reproductive characteristics of G. oyenawere investigated in the Gulf of Suez during the period from 2015 to 2016. The overall sex ratio of males to females was estimated at 1: 2.1. Males predominated the small length groups from 10.5 to 13.5 cm, while females dominated the larger lengths >13 cm. The monthly average values of gonad weight percentages and monthly different maturity stages showed that the species spawns during late spring with intensive spawning in May. The length at first sexual maturity was estimated at 13.5 cm for males and 14.1 cm for females which indicates that all individuals over two years of age were sexually mature. The results of this study can be used as a key figure for the conservation and maintenance of this commercially valuable stock in the Gulf of Suez.

espanolLa estimacion precisa de la evapotranspiracion de referencia permite obtener valores mas reales sobre las necesidades hidricas de los cultivos. El objetivo del estudio consistio en evaluar nueve metodos a fin de estimar la evapotranspiracion de referencia para las condiciones ambientales del Valle de Tumbaco, ubicado en la provincia de Pichincha (Ecuador). Se utilizo informacion climatologica mensual de la estacion La Tola para determinar ETo. Los metodos analizados fueron: FAO56, Tanque evaporimetro, Thornthwaite modificado, Hargreaves, Jensen-Haise, Makkink, Priestley-Taylor, Turc y FAO Radiacion. Se calcularon los siguientes parametros inferenciales para el analisis de los resultados, tomando como base FAO56: error medio absoluto, coeficiente de determinacion, coeficiente de concordancia y coeficiente de confiabilidad. Los resultados principales fueron: i) el metodo FAO Radiacion presento una precision buena; ii) los metodos Jensen and Haise, Makkink y Priestley and Taylor, presentaron indices tolerables; iii) los metodos del tanque evaporimetro, Thornthwaite modificado, Hargreaves y Turc proporcionaron indices inferenciales muy pobres. En conclusion, el metodo que debe utilizarse para determinar ETo mensual para el entorno agroecologico del Valle de Tumbaco es FAO56. EnglishThe precise determination of the reference evapotranspiration ensures computing real values of the crop water requirements. The objective of the study was to evaluate nine methods to determine the reference evapotranspiration for the environmental conditions of the Tumbaco Valley located in the province of Pichincha (Ecuador). Monthly climatological data from La Tola station was used to estimate ETo. The methods used to determine ETO were FAO56, Class A pan, modified Thornthwaite, Hargreaves, Jensen - Haise, Makkink, Priestley - Taylor, Turc and FAO Radiation. The following inferential parameters were used in the analysis, considering FAO56 as base: the absolute mean error, the coefficient of determination, the concordance coefficient and the confident coefficient. The results for monthly average climatological data showed that: i) the FAO Radiation method presented good statistical indexes; ii) the methods Jensen and Haise, Makkink and Priestley - Taylor presented poor indexes; iii) the methods Class A pan, modified Thornthwaite, Hargreaves and Turc presented the lowest inferential values. In conclusion, the method that should be used to estimate monthly ETo for the agro ecological environment of the Tumbaco Valley with good accuracy is FAO56.

The analysis of the influence of climatic factors on the interannual changes of the gonadosomatic index (GSI) of the red mullet Mullus barbatus ponticus, which inhabits the coastal waters of Crimea, was carried out. The dependence of the development of the reproductive system of the red mullet, determined by the gonadosomatic index, on temperature was revealed. It was found that the correlation coefficient between changes in the average temperature during the spawning and the GSI index is r = 0.98 for females and r = 0.97 for males. The maximum monthly average GSI values were observed in females and males of the M. barbatus ponticus, accordingly, in June, 4.29 and 3.76% and in July – 4.83 and 4.17%. An analysis of the size composition of the fish showed that the females are larger than males. For females, the predominant size group is 10.0–11.0 cm, and for males 9.0–10.0 cm. It was found that the red mullet, living in the coastal waters of the Crimea, has a slight positive allometric growth. A comparison in the developmental features of the red mullet that lives in different regions of the Black Sea-Mediterranean basin was made and the differences in the change in the GSI and other parameters are analyzed.

The trend towards an increase in the West Nile fever incidence among the population in the Russian Federation, recorded in the season of 2018, continued and led to a significant increase in the incidence in 2019 (the indicator was 2 times higher than the long-term average). The features of manifestations of the epidemiological process of WNF in 2019 were identified: early registration of cases of the disease, activation of natural and natural-anthropourgic foci in the Southern Federal District (90 % of the total incidence in the Russian Federation), an increase in the share of neuro-invasive forms, dominance of patients aged 50 and older in the structure of the incidence, late epidemic season ending. It was established that in the season of 2019, the lineage 2 of WNV circulated in the European part of Russia. In the Volgograd Region, simultaneous presence of the West Nile virus and Sindbis virus in mosquitoes Culex pipiens and Culex modestus was identified. It was shown that the most significant factors for predicting the epidemiological situation on West Nile fever in the Volgograd Region are the average seasonal summer air temperature and monthly average indicators of relative humidity in the spring and summer periods, and the average monthly air temperatures in the spring and summer in the Rostov Region. In the Astrakhan Region, a significant correlation dependence of the influence of the considered factors on the incidence of the population has not been established. The forecast of the development of epidemic situation in 2020 does not exclude a possible increase in the incidence in the territories of the European part of Russia, endemic for West Nile fever, and the occurrence of local outbreaks in individual constituent entities, if the complex of climatic conditions and social factors favorable for West Nile virus coincide.

The study of PM10 temporal variation for two successive years (2009 and 2010), carried out at the proximity of a crossroad (≈ 41,390 vehicles per day) located in an urban centre in Sfax, Tunisia, showed comparable annual average concentrations (≈ 80 μg/m3). However, monthly and daily average concentration levels have been significantly variable. Moreover, the monthly average concentration levels have revealed almost similar tendencies to those observed in other Mediterranean regions, signalling the highest ones between winter and spring periods. High PM10 concentration levels, including the winter-spring peak, have been more significant due to the Saharan wind circulation. Complementary statistical approaches, concerning the simple linear regression, the factorial analysis of correspondences, the quality of representation, and the hierarchical cluster analysis, have revealed the simultaneous impact of local sources and extreme meteorological conditions on highly registered PM10 concentrations, particularly in the presence of an obstacle such as buildings. The investigation of Air Quality Health Index (AQHI) has exhibited that more than 60% of the period in focus has been characterized by good-to-fair air quality for the ordinary people. The remainder has been characterized by poor to very poor air quality for the same kind of people. Furthermore, more than 70% of the whole period has been found unhealthy for sensitive groups, hindering them from any outdoor activities.

The solar energy is the major source of power for the future and an important source of renewable energy in Iraq and the world. Suitable climate conditions for solar energy are available in Iraq, especially the high temperature in the summer season which extends for more than six months in the year. Hence, the global solar radiation is abundant with high intensity, which is very essential in applicable models for researchers and solar applications. Therefore, nine first-order regression empirical equations of Angstrom-type correlations were used to estimate the more appropriate global solar radiation model for Baghdad city. Two equations were developed empirically in this work, using the most available and easy to get meteorological data, which is the temperature value in various forms. The results of the comparison between the real and calculated values showed reasonable agreement for most equations (including the development of R2- at 99%) as well as the least values of types of errors RMSE and MBE. As an exception, two equations of models failed in this study because of inability to apply to such climate conditions of Baghdad city.