Application of Digitalisation in Staffing Planning for Railway Infrastructure Maintenance

Existing and suggested approaches to development of staffing plans regarding infrastructure maintenance are analysed from the point of view of ensuring interests of a railway infrastructure company, using example of JSC Russian Railways.The existing methodology of planning workforce involved in maintenance of the railway infrastructure is based on the use of average values of parameters resulted from monitoring of the state of infrastructure structures, roadbed and track devices conducted monthly, as well as during autumn and spring continuous inspections and checks of the track. The received values of work to be performed only implicitly reflect the required number of workers, effective involvement in operation, and efficiency of each worker.The modern trend towards digitalisation and cybernation of all processes, when organising maintenance of the track infrastructure, provides for maximum social adaptation of production processes to requests of owners, tasks of organisers and interests of performers. Staff planning based only on the volume of work to be performed does not allow digitalisation and social cybernation of an individual performer.The syntagmatic component of such an algorithm is the monthly total worked time, which is determined by the actual engagement of each performer. The paradigmatic component of such models should be the same daily number of performers. The stage of preliminary assessment of application of the proposed algorithm for digitalisation and social cybernation of workforce planning supposed an increase in labour productivity, approximately by 20–25 %, an increase in quality of work and a more effective assessment of activities of each performer.

Characteristics of solar radiation at Xiaotang, in the northern marginal zone of the Taklimakan Desert

The characteristics of solar radiation and the influence of sand and dust on solar radiation in the northern margin of Taklimakan Desert were analyzed using radiation observation data from 2018. The results showed that the annual total radiation, direct radiation, and scattered radiation at Xiaotang were 5,781.8, 2,337.9, and 3,323.8 MJ m−2, respectively. The maximum monthly total radiation, direct radiation, and scattered radiation were observed in July (679.8 MJ m−2), August (317.3 MJ m−2), and May (455.7 MJ m−2), respectively. The aerosol optical depth corresponded well with the scattered radiation, and the maximum value was in May. Further analysis showed a significant correlation between the total radiation and solar height angle under different weather conditions. Under the same solar height angle, total radiation was higher during clear days but lower on sandstorm days. Calculation of atmospheric transmittance showed that the average atmospheric transmittance on a clear day was 0.67; on sand-and-dust days, it was 0.46. When the atmospheric transmittance was 0.5, the increase in scattering radiation by aerosol in the air began to decrease. Probability analysis of radiation indicated the following probabilities of total radiation <500 W m−2 occurring on clear, floating-dust, blowing-sand, and sandstorm days: 67.1%, 76.3%, 76.1%, and 91.8%, respectively. Dust had the greatest influence on direct radiation; the probabilities of direct radiation <200 W m−2occurring on clear, floating-dust, blowing-sand, and sandstorm days were 44.5%, 93.5%, 91.3%, and 100%, respectively, whereas those of scattered radiation <600 W m−2were 100%, 99.1%, 98.1%, and 100%, respectively. Therefore, the presence of dust in the air will reduce scattered radiation.

High NH3 deposition in the environs of a commercial fattening pig farm in central south China

Abstract Intensive livestock production has been increasing, and has resulted in the emission of more than seven teragram per year of ammonia (NH3) in China in recent years. However, little is known about the fate of the emitted NH3, especially the dry deposition of NH3 in the environs of intensive animal farms. In this study, the spatial and temporal variations of NH3 deposition in the environs of an intensive fattening pig farm were investigated in the central south of China. NH3 concentrations were measured at sites situated 50, 100, 200, 300, and 500 m in the downwind direction from the farm each month from July 2018 to June 2019. The NH3 deposition was calculated based on a bidirectional NH3 exchange model. The monthly NH3 emissions from the pig farm were estimated based on the breeding stock. The annual average NH3 concentrations ranged from 1,200 to 14 μg m-3 at the downwind sites within 500 m of the pig farm, exhibiting exponential decay as distance increased. Strong seasonality in NH3 deposition was observed, with the highest season being in the summer and lowest in the winter, and air temperature was found to be an important factor affecting this seasonal variation. The estimated monthly total dry deposition within 500 m of the pig farm ranged from 92 to 1,400 kg NH3-N mo-1, which accounted for 4.1 to 14% of the total monthly NH3 emissions from the pig farm. The estimated total NH3 emissions and NH3 deposition from the pig farm were 63,000 kg NH3-N yr-1 and 5,400 kg NH3-N yr-1, respectively, with the annual average ratio of NH3 deposition to NH3 emission being 8.6%. This study found NH3 deposition around intensive pig farms to be high, and determined it as a significant fate of the NH3 emitted from pig farms.

170. Antimicrobial Use Before and During COVID-19 – Data from 108 VA Facilities

Background Increased antibiotic prescribing rates during the early phases of the COVID-19 pandemic have been widely reported. We previously reported that while both antibiotic days of therapy (DOT) and total days present (DP) declined in the first 5 months of 2020 at Veterans Affairs (VA) acute care facilities nationwide relative to the comparable period in 2019, antibiotic DOT per 1000 DP increased by 11.3%, largely reversing declines in VA antimicrobial utilization from 2015 – 2019. We now evaluate whether these changes in antibiotic use persisted throughout the COVID-19 pandemic. Methods Data on antibacterial use, patient days present, and COVID-19 care for acute inpatient care units in 108 VA level 1 and 2 facilities were extracted through the VA Informatics and Computing Infrastructure; level 3 facilities which provide limited acute inpatient services were excluded. DOT per 1000 DP were calculated and stratified by CDC-defined antibiotic classes. Results From 1/2020 to 2/2021, care for 34,096 COVID-19 patients accounted for 13% of all acute inpatient days of care in the VA. Following the onset of COVID-19 pandemic, monthly total acute care antibiotic use increased from 533 DOT/1000 DP in 1/2020 to a peak of 583 DOT/1000 DP in 4/2020; during that month COVID-19 patients accounted for 13% of all DP (Figure). In subsequent months, total antibiotic use declined such that for the full year the change of antibiotic use from 2019 to 2020 (a decrease of 18 DOT/1000 DP) was similar to the rate of decline from 2015 to 2019 (mean decrease of 13 DOT/1000 DP; Table). The decreased DOT/1000 DP from 5/2020 to 2/2021 occurred even as the percentage of all DP due to COVID-19 peaked at 14 - 24% from 11/2020 to 2/2021. Conclusion Although rates of antibiotic use increased within the VA during the early phases of the COVID-19 pandemic, rates subsequently decreased to below previous baseline levels even as the proportion of COVID-19 DP spiked between 11/2020 and 02/2021. Although the degree to which the initial increase in antibiotic use is attributable to concerns of bacterial superinfection versus changes in case-mix (e.g., decreased elective admission) remains to be assessed, these data support the continued effectiveness of antimicrobial stewardship programs in the VA. Disclosures Matthew B. Goetz, MD, Nothing to disclose

Assessment and monitoring of drought in Chitradurga district of Karnataka using different drought indices

Drought is a natural disaster due to less precipitation than the normal that can occur irrespective of climate regimes. Impact assessment of drought and monitoring are the most important mitigation stratregies to combat the drought effects. As the single index cannot assess all the drought conditions, in the present study multi-variate indices approach has been used to assess and monitor drought. Five indices were assessed using precipitation data such as deciles index (DI), percent normal (PN), China-Z index (CZI), Z-Score index (ZSI) and standardized precipitation index (SPI). Monthly total precipitation data was used to calculate drought events occurred during the period 1967–2017 in different talukas of Chitradurga district of Karnataka, India. The assessment revealed that SPI, CZI and ZSI performance was similar in identifying drought. PN was very much responsive for the rainfall events that occurred during the particular year however, it exhibited variations in dry conditions. DI was not that much satisfactory in identifying drought conditions. Among the five indices assessed, SPI seems to be the best indicator to predict the drought onset than the other four drought indices. Therefore SPI can be recommended for assessing and monitoring the drought in Chitradurga district of Karnataka, India.

MODIS-derived fire spatial and temporal distribution during haze season in Southeast Asia using empirical orthogonal function

One of the efforts to control the forest and land fire disasters which affect on the biomass burning haze is fire hotspots monitoring. Biomass burning haze in Southeast Asia (SEA) has become a recurring annual issue. This study aims to determine the spatial and temporal distribution of fire hotspots along SEA, so that it can serve as guidance for efforts to control them. The hotspot data used is derived from NASA’s Fire Information for Resource Management System (FIRMS) MODIS sensors which is collected from 2001-2020. Spatial analysis of the re-gridded data shows the highest burning activities over SEA occurred in Feb-Apr, with >2000 fire events in the Indo-China area and >1000 fire events in Sumatra and Borneo. Empirical Orthogonal Function (EOF) was performed on monthly total hotspot data for 228 months for determining dominant patterns spatially and temporally. Based on the EOF analysis results, the three major modes have achieved a total variance of 71 %. The first mode (EOF1) explains 65 % of the total variance. The second (EOF2) and third (EOF3) modes account for 3.60 % and 2.97 % of the total variance respectively. The first and the third principal component identified high loadings over the Indo-China and Sumatra-Borneo regions respectively. Whereas the second principal component separates the fire areas into China and Indo-China region. Inter-annual pattern is dominant in the EOF1, while the inter-seasonal pattern is dominant in EOF2 and EOF3. ENSO, IOD, and MJO are factors that influence the pattern of the determined principal components. The result of this study provides general understanding on how the fire events varied over the past two decades in SEA.

A radically simple way to monitor life expectancy

NOTE: this is an early registration of the research idea and findings in form of slides for a talk presented at EAPS Mort workshop on 2021-09-22 (video: https://youtu.be/rOndHnuajH4?t=2370)Period Life Expectancy is the key summary measure of current mortality. Elimination of the direct influence of population age structure allows to meaningfully compare mortality levels and changes across the populations and over time. Calculation of life expectancy demands high quality detailed data on death and population counts disaggregated by sex and age. Such data is only available for the more developed countries. Moreover, even in the most developed countries, it becomes available with a considerable time lag. And for the majority of countries across the world timely and high quality deaths statistics is not available. In situations of mortality shocks such as the COVID–19 pandemic near real time mortality level comparisons are crucial.Building on the studied regularities of human mortality, we offer a method of reliable life expectancy short-casting based only on the time series of its previous values and the time series of total deaths counts observed in the population, not disaggregated by sex and age. The radical simplicity of the method allows to monitor changes in life expectancy in near real time, if time disaggregated (daily, weekly, or monthly) total death counts are available.

Winter Precipitations of Northern Part of Farming-pastoral Zone and Hulunbuir Grassland

In January 2018 a high record of monthly total precipitation in northern China drew our attention. This number is 4 times more than that in normal winters over the past 30 years, and its location is in northern China. Thus our research region is composed by the northern part of the farming-pastoral zone and the Hulunbuir Grassland. We target our research at understanding the phenomena and causes of such high precipitations. We explore the heavy precipitation locations, and use dynamical analyses on different pressure levels to find out the cause of the high score. We analyze wind fields, geopotential heights and relative humidity for the pressure levels of 200 hPa, 500 hPa, 700 hPa and 850 hPa. We find that the location of the highest monthly total precipitation in January 2018 is on the mountain, whereas the spots of heavy precipitations during one event are not located on the mountain. Zooming in January 2018, it is the precipitation frequency that drastically increased, not the number of heavy precipitation events. The dynamical analyses show that the heavy precipitation events in January 2018 are mainly caused by appearance of cyclones either in or near the research region at high geopotential heights.

Time Series Analysis of Trend and Variability of Monthly Total Rainfall

The long-term variation in rainfall, one of the most important conditions for the climate in a particular region. The purpose of this study was to analysis the total monthly rainfall in the Maychew, which is located in the Tigray region of Ethiopia. The monthly rainfall is on the Maychew meteorological station has been calculated for the period from 2007-2018. The data were analyzed with the help of Minitab-14, R-3.3.1 an Overview of the descriptive statistics and unvaried, Box-Jenkins method, The seasonal ARIMA model was built to analyze the observed data and forecast the total rainfall, after the detection of nonstationarity using the Augmented Dickey-Fuller Test is a Test, and time plot. Some of the main findings of the study indicated that the monthly total rainfall tends to increase. In addition, it was found that, on the basis of the data contained in the history of the last twelve years of age. In addition, the descriptive statistics show that the average amount of rainfall in the Maychew is 58.82. After non-seasonal the first-order differentiation and once seasonal series, differentiation, they will be moved. A time series model for the Maychew Station and was adapted to be processed, diagnostically tested, and ultimately, to be obtained by SARIMA (3, 2, 2)*(0, 2, 2)12 a model has been created, and this model was used to Forecast the two years monthly values of the total rainfall. The forecasted accumulated rainfall values showed a similar pattern to the previous reports.

Multi-scale temporal variation in bird-window collisions in the central United States

Expansion of urbanization and infrastructure associated with human activities has numerous impacts on wildlife including causing wildlife-structure collisions. Collisions with building windows represent a top bird mortality source, but a lack of research into timing of these collisions hampers efforts to predict them and mitigate effects on avian populations. In Stillwater, Oklahoma, USA, we investigated patterns of bird-window collisions at multiple temporal scales, from within-day to monthly and seasonal variation. We found that collisions peaked during overnight and early morning hours, a pattern that was consistent across seasons. Further, temporal variation in fatal collisions was explained by an interaction between season and avian residency status. This interaction illustrated the expected pattern that more migrant individuals than residents collided in fall, but we also documented unexpected patterns. For example, the highest monthly total of collisions occurred in spring migration during May. We also found similarly high numbers of resident and migrant collisions in spring, and a roughly similar amount of migrant mortality in spring and fall migration. These findings, which provide unprecedented quantitative information regarding temporal variation in bird-window collisions, have important implications for understanding mechanisms by which birds collide and improving timing of measures to reduce this major bird mortality source.