Three-month Average Research Articles

Model-based single-month unemployment rate estimates for the Brazilian Labour Force Survey1

The Brazilian Labour Force Survey (BLFS) is a quarterly rotating panel survey with 80% sample overlap between two successive quarters. Monthly unemployment rate estimates are regularly produced based on a three-month average of direct estimates. Due to the unforeseen situation of COVID19 pandemic and its effects in the economy and labour market, there was a need to investigate model-based estimation procedures to obtain unemployment rate single-month estimates. We present structural time series models developed to produce model-based single month estimates at national level as well as small area (state-level) estimates at a higher frequency than those currently being published. Using the state-space framework, the models account for the autocorrelation due to sample overlap and the increased dynamics in the labour force series in 2020. In addition, bivariate models that combine claimant count and survey data are investigated. The models not only yield estimates with better precision than direct estimates, since the latter were affected by a rise in non-response, but they can deliver reliable state-level official statistics at a monthly frequency that are presently required. The new improved model-based estimates were proposed as experimental statistics for the Brazilian national statistical office (IBGE).

Pay Senedi Fiyat Yönünün Makine Öğrenmesi Yöntemleri ile Tahmini: Borsa İstanbul Örneği

In this study, stock price with the calculated next three-month average of five manufacturing industry companies in the Borsa Istanbul 30 Index and the Corporate Governance Index was predicted with the data of the 2010/3 and 2020/3 periods. The dataset consisted of quarterly nine financial statements and five macroeconomic variables with a three-month average of the sample companies. Artificial Neural Networks, Classification and Regression Tree, and K- Nearest Neighbor Algorithm were used as prediction methods. A 10-fold cross-validation method was used in all methods in the study. In Artificial Neural Networks, Classification and Regression Tree analysis, the models that gave the best results in line with the given parameter ranges were obtained by using the determining the best parameters and performance criteria function. According to the results of the analysis, general classification accuracy was achieved 98.05% for Artificial Neural Networks, 96.10% for Classification and Regression Tree, and 92.20% K-Nearest Neighbor Algorithm. “Net Profit Margin”, “Price/Earning”, “Profit Per Share”, “CDS Premium (3-month average)”, “Consumer Confidence Index” were found as important variables that divided the data into two in the creation of the Classification and Regression Tree (CART) analysis. This result shows that the models used in this study can be incorporated into the models used by investors.

Pharmacy workload benchmarking: Establishing a health-system outpatient infusion productivity metric.

Current productivity assessment models lack the ability to measure the quality of pharmacy services through workload validation. The goal of our efforts was to create a model to more accurately assess workload at multiple outpatient infusion centers. Current procedural terminology codes were identified as representative of the key drivers of pharmacy workload. Fourteen current procedural terminology codes representing medication orders were selected and categorized into eight distinctive groups associated with varying amounts of pharmacy workload. A three-month average of current procedural terminology volumes were calculated and used to create a workload baseline. Our study found a usable productivity assessment and coefficient to compare relevant clinical workload across outpatient oncology sites. The current procedural terminology codes were readily retrievable from our system electronic medical record. By assigning activities, e.g. clinical review, verification, barcoding, batch preparation, we were able to compute a workload and then adjust staffing to achieve a median coefficient across sites. This study evaluated the use of administration current procedural terminology codes for an outpatient oncology productivity model. Based upon our analysis, it can be successfully used to determine workload for pharmacists and technicians across variable locations. We believe it is the first study to demonstrate a productivity model for this setting.

The Madden-Julian Oscillation: A Tool for Regional Seasonal Precipitation Outlooks?

The Madden-Julian Oscillation (MJO) is an important intraseasonal climate signal which circles the global tropics, but also impacts extratropical weather regimes. Few studies have investigated whether the MJO is a source of regional seasonal climate predictability. The present objective is to determine the extent to which the season and phase (geographic location) of MJO contribute to the frequency of global rainfall anomalies in ensuing seasons. Indices of June-July-August and December-January-February MJO activity for each phase and the El Niño/Southern Oscillation (ENSO) were correlated to three-month averages of rainfall up to a six-month lead time. Field significance was calculated and patterns of the relationships were described. In general, MJO shows some skill in regional seasonal precipitation prediction, but to a lesser extent than ENSO. However, the presence of MJO in the western Indian Ocean and near the date line did reveal a persistent and significant relationship with regional seasonal rainfall, especially over Northern Hemisphere land areas.

Processo logístico de faturamento: estudo de caso aplicado a uma distribuidora de alimentos

Organizations are increasingly interested in knowing the business they execute, in order to achieve cost and time reductions (MAGDALENO et al, 2007). Goncalves (2000) emphasizes that process management is capable of promoting greater flexibility in the management of organizations, enabling better results. The company object of o this research is a food distributor located in the State of Santa Catarina. The company suffers from a large number of returns of sales that are billed for cash payment (when the product is delivered at the point of sale). A survey pointed to a three-month average of R$ 6,436.00 of losses. The problem of this study is the high cost of return of goods due to non-payment at the time of delivery at the point of sale. The purpose of this article was to develop, through BPM, a new billing process for Beta distributor. Such process would reduce the level of returns. The case study was carried out through field mapping, tabulation, modeling and analysis of flow processes. The research concluded that there are possibilities to change the flow of processes to guarantee the delivery of the orders in sight without the risk of returns due to non-payment. The study also testifies to the effectiveness of the mapping and modeling tools (5W2H and BPMN) used to solve the research problem.

PADRÕES TEMPORAIS DO VENTO À SUPERFÍCIE EM MESORREGIÕES DO ESTADO DA BAHIA

The objective in this study was to determine the temporal variability patterns of the surface wind in mesoregions of the Bahia state. Multivariate analysis methods (principal component analysis and cluster analysis) were applied to three-month averages of the zonal and meridional components of the surface wind registered by five automatic stations operated by the Instituto do Meio Ambiente e Recursos Hidricos (INEMA). The years studied were 2000, 2002, 2005, 2006 and 2007. The first two principal components of the zonal wind component explained 99.4% of the total variance of the three-month averages, while the first two principal components of the meridional wind component explained 99.6%. The cluster analysis applied to the most significant temporal factor loadings by means of the Ward method and Euclidian distance resulted in three homogeneous groups for each wind component, with two groups being comprised by one station only. Higher wind speeds characterize these groups, a result that points toward the importance of local factors such as the aspect of the relief.

The probability of the impact of ENSO on precipitation and near-surface temperature

The widespread influence of El Niño Southern Oscillation (ENSO) events on regional climate is well known, and such events can have considerable socio-economic impact. Thus an aspect of practical importance is the chance of the occurrence of various climate categories given that ENSO conditions are predicted or underway. Using observational and re-analysis datasets for precipitation and near-surface temperature this aspect is analysed systematically in terms of three near-equiprobable categories (below normal, near normal, above normal) to quantify the probability of their occurrence when central-east equatorial Pacific sea surface temperatures are abnormal, as during El Niño and La Niña events, with emphasis on land areas and on seasonal (three-month average) values. By using recently available gridded datasets, this analysis complements and updates previous analyses of ENSO impact. Spatial maps of the rate of occurrence of various categories conditional on ENSO state are obtained by point-by-point analysis of the global datasets. Consistent with previous analyses, several regions with rates well above those expected by chance are identified, in both tropical and extratropical sectors. Regional averages are used to examine a selection of these in more detail, in particular to describe the strong seasonal dependencies of ENSO influences. These depend on the interplay between the seasonality of ENSO, of teleconnection pathways and of local climate: indeed in several locations opposite effects arise at different times of year.The methodology is based on contingency tables calculated from historical records, and effectively quantifies the probability that, for example, above-normal rainfall will occur in a given season and location conditional on the state of equatorial Pacific sea surface temperature. The rates of occurrence provide more directly relevant information for risk assessment and management than do statistics such as correlations or composites. Each ENSO event is different and occurs in conjunction with other climatic factors, so while the historical rates provide general guidance they should not be regarded as a prediction of impacts for a particular imminent or ongoing ENSO event.

Emission sources sensitivity study for ground-level ozone and PM2.5 due to oil sands development using air quality modeling system: Part I- model evaluation for current year base case simulation

The Community Multiscale Air Quality (CMAQ) and the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling systems were used to simulate emissions and air quality in North Eastern Alberta where a rapid rise in oil sands development has caused air quality concerns over the last decade. The models were run on a 36/12/4 km domain for the four month period of May through August 2002. A model performance evaluation was conducted by comparing the CMAQ model estimates against ambient air quality measurements. In the Alberta oil sands region, the model tended to achieve or nearly achieve ozone model performance goals, albeit with an underestimation bias. The magnitudes of the observed PM2.5 concentrations were matched by the modeling system, except when the observed PM2.5 concentrations were influenced by emissions from forest fires in which case the model underestimated the observed PM2.5 concentrations. The CMAQ-estimated 4th highest daily maximum 8-hour ozone concentrations in the oil sands region were below the 65 ppb Canada Wide Standard (CWS) as well as the 58 ppb Alberta Management Plan Trigger Level. The highest estimated ozone concentrations occurred near the oil sands development area just north of Fort McMurray with values approaching, but below, the 58 ppb Management Plan Trigger Level; estimated ozone concentrations are much lower in the farther northern portions of the oil sands region. The acute (i.e., maximum 3-day value) SUM60 vegetative ozone exposure metric was mostly less than 100 ppb h, which is below the threshold of concern for crops. However, just north of Fort McMurray there were small areas where the acute SUM60 metric exceeded the 500–700 ppb h threshold of concern for crops with maximum values in plumes from sources in the oil sands mine area of ∼900 ppb h. The maximum chronic (three-month average) SUM60 ozone exposure metric was below the thresholds of concern. The CMAQ-estimated maximum 98th percentile 24-hour average PM2.5 concentration occurs in the mine area north of Fort McMurray and was below the CWS threshold (30 μg m−3), however it was above the Alberta Management Plan Trigger Level (20 μg m−3). The estimated 98th percentile 24- hour average PM2.5 concentrations were mostly in the 2–6 μg m−3 range with regions of higher PM2.5 concentrations predicted just north of Fort McMurray in the oil sands development region.

Single month Labour Force Survey estimates: November 2011 (Not designated as National Statistics)

This report contains charts that compare the Labour Force Survey (LFS) headline three-month average rates for employment, unemployment and economic inactivity with their equivalent single month estimates. The single month estimates are derived from the same data source as the headline three-month figures but are not designated as National Statistics and their use is restricted to helping to understand the movements in the headline three-month averages.

Influence of planetary waves on total ozone column distribution in northern and southern high latitudes

Results of a comparative analysis of quasi-stationary planetary wave (QSW) influences on total ozone column (TOC) distribution at 60° N and 60° S latitudes are presented. Total Ozone Mapping Spectrometer (TOMS) version 8 total ozone satellite data for years 1979–2003 were used to examine the longitudinal variation of ozone distribution. Three-month averages for each year were used to obtain the spatial distribution and spectral components of the QSW patterns. Long-term changes of QSW characteristics are compared for the Arctic and Antarctic regions. Quasi-stationary wave 1 dominates in the Southern hemisphere (SH) during spring for all analysed years. The amplitude of wave 1 is also largest, although wave 2 reaches a close level in several years in the Northern hemisphere (NH). The maximal TOC disturbances by planetary waves are observed in the winter–spring period both in the NH (January–March) and SH (August–October). During the development of the Antarctic ozone hole (from mid-1980s to the present), the QSW phase at 60° N does not show longitudinal motion observed at 60° S. Both hemispheres show strong inter-annual variability in both position and amplitude of the QSW pattern.

ENSO induced monthly oscillations of precipitation: the unique case of the south tropical Indian Ocean in austral summer

Seasonal (three-month average) climate forecasts have advanced due in large part to improved modeling of the ENSO phenomenon. Long-range monthly forecasts are more problematic because of internal atmospheric variability. Further, it is often assumed that monthly precipitation anomalies are representative of the overall seasonal anomaly. This is not always the case as, according to the Global Precipitation Climatology Project Version 2.1 data set, up to 20% of areas demonstrating some significant teleconnection to ENSO show El Nino minus La Nina differences of one sign in the middle month and the opposite sign in the adjacent months. Most interestingly, this maximum percentage occurs in December–January–February (DJF), a time when the ENSO boundary forcing is strongest. These oscillatory DJF seasons also cluster in space—with significant positive–negative-positive differences in the western South Tropical Indian Ocean (STIO) and negative–positive–negative differences in the far eastern STIO. Representative gauges confirm that these precipitation patterns have been associated with ENSO events since 1951, and pentad precipitation data confirm that they are confined to DJF and evolve at the monthly scale. The abrupt end of the Indian Ocean Dipole mode in January, an increase in the importance of local SST anomalies in February, and an ENSO-induced mid-latitude Rossby wave during austral summer combine to generate the cross-basin precipitation gradient around 15°S.

Studying psychosocial adaptation to end-stage renal disease: The proximal–distal model of health-related outcomes as a base model

Objective Studying psychosocial adaptation in end-stage renal disease (ESRD) is increasingly important, as it may explain the variability in health outcomes unaccounted for by clinical factors. The Brenner et al. proximal–distal model of health-related outcomes provides a theoretical foundation for understanding psychosocial adaptation and integrating health outcomes, clinical, and psychosocial factors (Brenner MH, Curbow B, Legro MW. The proximal–distal continuum of multiple health outcome measures: the case of cataract surgery. Med Care. 1995;33(4 Suppl):AS236-44). This study aims to empirically validate the proximal–distal model in the dialysis population and examine the impact of psychosocial factors on the model. Methods A cross-sectional observational study was conducted with a sample of long-term dialysis patients ( n=201). Eleven factors: quality of life (QoL), depression, positive affect, comorbidity, symptoms, physical functioning, disease accommodation, loss, self-efficacy, illness acceptance, and social support were measured by standardized psychometric scales. A three-month average of hemoglobin was used. Latent composite structural equation modeling was used to examine the models. Results The proximal–distal model with slight modification was supported by fit statistics [χ 2=16.04, df=13, P=.25, root mean square error of approximation (RMSEA)=0.024], indicating that the impact of clinical factors on QoL is mediated through a range of functional and psychological factors, except for hemoglobin which impacts directly on QoL. The model with additional psychosocial factors was also supported by fit statistics (χ 2=43.59, df=41, P=.36, RMSEA=0.018). These additional factors mainly impact on symptom status, psychological states, and QoL components of the model. Conclusion The present study supported the proximal–distal model in the dialysis population and demonstrated the considerable impact of psychosocial factors on the model. The proximal–distal model plus psychosocial factors as a biopsychosocial model can be applied to studying psychosocial adaptation in ESRD.

An investigation of the solar cycle impact on the lower thermosphere O( 1S) nightglow emission as observed by WINDII/UARS

The yearly variation of the integrated emission rate of the O( 1S) nightglow in the lower thermosphere is studied and the solar cycle impact is examined from the observations of the Wind Imaging Interferometer (WINDII) operated on the Upper Atmosphere Research Satellite (UARS). More than 300,000 volume emission rate profiles of the O( 1S) nightglow observed by WINDII for 40°S–40°N latitudes during November 1991–August 1997 over half of a solar cycle are utilized. These profiles are vertically integrated for the altitude range of 80–100 km and the equivalent column integrated emission rates are then zonally averaged for bins with 10° latitude and 3 month intervals. It is found that for each latitude the O( 1S) nightglow emission rate appears to increase with increasing solar F10.7 cm flux, following a linear relationship. This characterizes the solar cycle impact on the O( 1S) nightglow, while the solar influence is modulated by a seasonal variation. Based on these variations, an empirical formula is derived for predicting the three-month averages of the O( 1S) nightglow integrated emission rate. The standard error of the estimated values from the formula is smaller than 30 Rayleigh.

On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa

Abstract This study is an investigation of the relation between the Normalized Difference Vegetation Index (NDVI) and rainfall in East Africa. The temporal and spatial patterns of both variables and their inter-relationships are evaluated. The NDVI and rain-fall associations are analysed for ten vegetation formations of East Africa: four forest zones, two woodland zones, and four drier bush, thicket and grassland types. There is a strong similarity between temporal and spatial patterns of NDVI when annual rainfall is below about 1000 mm and monthly rainfall does not exceed ∼ 200 mm. In this range, NDVI is also a sensitive indicator of the inter-annual variability of rainfall. The overall relation between NDVI and rainfall is log-linear and the correlation between annually-integrated NDVI and the log of annual rainfall at 65 stations is 0–89. The best correlation on a monthly time scale is between the monthly composite NDVI and a three-month average of rainfall in the concurrent and two previous months. Considerable differences in water-use efficiency are apparent among the ten vegetation formations, the highest being in the bush and thicket zones, the lowest in the coastal forest with exceedingly high rainfall.

Modeling sulfur concentrations and depositions in the United States during ANATEX

Abstract The Hybrid Single-Particle Lagrangian Integrated Trajectory (HY-SPLIT) long-range transport, personal-computer-based model was incorporated with a non-linear chemistry module that includes gas- and aqueous-phase oxidation of sulfur dioxide (SO2) and dry and wet removal of SO2 and sulfate (SO42−) particles. The model considers multiple area and point sources (∼ 500) and calculates simultaneously 24-h averages of several species: air concentrations of SO2 and SO42− particles and SO42− wet deposition on each grid cell of the meteorological domain. Three-month averages of modeled air concentrations of SO2 and SO42− were compared with measurements from five National Oceanic and Atmospheric Administration/Air Resources Laboratory dry deposition sites. The model tended to underpredict air concentrations of SO2 (ratios of model-predicted to measured concentrations between 0.6 and 1.2) and overpredict air concentrations of SO42− (ratios between 1.0 and 1.6). Modeled SO42− wet deposition values were calculated, averaged for the 3 months, and compared with the measured values at 137 combined UAPSP, MAP3S, and NADP precipitation chemistry sites. For the majority of these sites, the agreement was good; the ratios between modeled and measured depositions were between 0.5 and 1.6. However, a consistent underprediction was observed across the southern and extreme eastern U.S. This underprediction was primarily attributed to the proximity of the edge of the model domain to the eastern and southern U.S., which caused trajectories from the continent that would have curved back into the south-eastern U.S. to terminate prematurely. When SO2 emissions were reduced by 50% over the entire model domain, approximately 50–55% reductions were observed in SO2 air concentrations over the entire model domain and at all levels. However, sulfate air concentrations and depositions were only reduced 35–50% due to non-linear reactions involving SO2. When emissions of SO2 were reduced by 50% only in the Ohio Valley, reductions in SO2 and SO42− air concentrations to the north and south of the Ohio Valley were generally less than 15%. These small reductions are attributed to local emissions of SO2 and long-range transport of SO42− from other regions not affected by the reduction in emissions. SO42− wet deposition, however, was reduced by 15–23% to the north of the Ohio Valley, indicating some long-range transport from the Ohio Valley. In the Ohio Valley, reductions of SO2 air concentrations were between 10 and 42%, depending on the proximity to major sources, and reductions of SO42− air concentrations were between 10 and 30%. However, SO42− wet deposition was reduced less than 10%, consistent with the assumption that its primary contribution is from long-range transport.

A Heat Budget Analysis of the Polar Troposphere in and around Alaska during the Abnormal Winter of 1988/89

Abstract In this study, we carried out a quantitative heat budget analysis of the polar troposphere in and around Alaska for the winter of 1988/89. The winter was characterized by drastic temperature variations. The surface minimum temperatures in the interior of Alaska were lower than −40°C during two weeks in late January. This cold period was followed by extremely warm weather during February, especially in northern Alaska. The results of the heat budget analysis for the three-month average (December to February) show that the heat energy of the continental air mass in and around Alaska is maintained by a balance of warm advection, adiabatic warming, and radiative cooling. The analysis of the long-term variation during this period shows that the severe cold in late January was caused by an anomalous reduction in warm advection of sensible heat. The abnormally warm weather in February was caused by enhanced warm advection associated with a blocking formation over the North Pacific. We find that strong a...