Temporal Aggregation and the Stock Adjustment Model of Inventories

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.

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Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽

10.3390/atmos12020218 ◽

2021 ◽

Vol 12 (2) ◽

pp. 218

Author(s):

Changjun Wan ◽

Changxiu Cheng ◽

Sijing Ye ◽

Shi Shen ◽

Ting Zhang

Keyword(s):

Spatial Autocorrelation ◽

Extreme Precipitation ◽

Eastern China ◽

Temporal Aggregation ◽

Global Changes ◽

Percentile Method ◽

Extreme Precipitation Events ◽

Spatio Temporal ◽

Precipitation Events ◽

Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

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Adjusting Client-Level Risks Impacts on Home Care Organization Ranking

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115502 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5502

Author(s):

Aylin Wagner ◽

René Schaffert ◽

Julia Dratva

Keyword(s):

Risk Factors ◽

Home Care ◽

Risk Adjustment ◽

Care Quality ◽

Assessment Instrument ◽

Care Organization ◽

Discrimination Power ◽

Adjustment Model ◽

Risk Adjustment Model ◽

The Impact

Quality indicators (QIs) based on the Resident Assessment Instrument-Home Care (RAI-HC) offer the opportunity to assess home care quality and compare home care organizations’ (HCOs) performance. For fair comparisons, providers’ QI rates must be risk-adjusted to control for different case-mix. The study’s objectives were to develop a risk adjustment model for worsening or onset of urinary incontinence (UI), measured with the RAI-HC QI bladder incontinence, using the database HomeCareData and to assess the impact of risk adjustment on quality rankings of HCOs. Risk factors of UI were identified in the scientific literature, and multivariable logistic regression was used to develop the risk adjustment model. The observed and risk-adjusted QI rates were calculated on organization level, uncertainty addressed by nonparametric bootstrapping. The differences between observed and risk-adjusted QI rates were graphically assessed with a Bland-Altman plot and the impact of risk adjustment examined by HCOs tertile ranking changes. 12,652 clients from 76 Swiss HCOs aged 18 years and older receiving home care between 1 January 2017, and 31 December 2018, were included. Eight risk factors were significantly associated with worsening or onset of UI: older age, female sex, obesity, impairment in cognition, impairment in hygiene, impairment in bathing, unsteady gait, and hospitalization. The adjustment model showed fair discrimination power and had a considerable effect on tertile ranking: 14 (20%) of 70 HCOs shifted to another tertile after risk adjustment. The study showed the importance of risk adjustment for fair comparisons of the quality of UI care between HCOs in Switzerland.

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Optimization of Preloaded Coefficient Based on the Adapted CBR

Materials Science Forum ◽

10.4028/www.scientific.net/msf.628-629.13 ◽

2009 ◽

Vol 628-629 ◽

pp. 13-18

Author(s):

H.L. Li ◽

Li Hui Lang ◽

W. Jiao ◽

H.Z. Su

Keyword(s):

Genetic Algorithm ◽

Optimum Design ◽

Prediction Error ◽

Selection Model ◽

Knowledge Based Systems ◽

Estimation Model ◽

Knowledge Based ◽

Adjustment Model ◽

Successful Technique

Selecting an appropriate preloaded coefficient has always been a challenge in wire- winding prestressed structure optimum design. Cased-based reasoning (CBR) has become a successful technique for knowledge-based systems in many domains. However, hardly any research has addressed the issue of how to generate the adaptation solution when the case has been retrieved. The present paper investigates the adoption of genetic algorithm(GA) to explore the suitable adjustment model. Two adapted model were presented and assessed in terms of their mean relative prediction error rates.The experiment results shown that applying GA to adjust the preloaded coefficient selection model is a feasible approach to largely improve the accuracy of estimation model. It also demonstrate that the adapted CBR presents better estimate accuracy than the results ontained by other unadapted CBR methods.

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