Examining the Factor Structure of a Risk Assessment Inventory in Young Offenders: FER-R, Risk and Resource Assessment Form

The FER-R, Risk and Resource Assessment Form, is a multidimensional inventory of structured professional judgment that assesses criminogenic risks and resources for the design and management of individualized intervention plans with criminally sanctioned adolescents. The aim of this study was to examine the psychometric properties of the FER-R, reviewing its factorial structure to contribute evidence of convergent and discriminant construct validity in a sample of adolescents sentenced for crimes in Chile. For each domain (risks and resources) with its respective facets, a unidimensional bifactor structure (CFA-BF) was obtained, with adequate indices of fit that confirmed its construct validity, while the convergent validity was demonstrated with the YLS/CMI and the divergent validity with two MACI scales. The FER-R adds factorial validity to the evidence of the previously reported predictive validity, making it a robust inventory for the evaluation of young offenders, and a relevant tool to manage differentiated interventions in Chile, with a high potential for use in Latin America. The importance of finding a suitable balance in assessing risks and protective factors is discussed, in order to manage interventions adjusted to the needs of the adolescents to promote their criminal desistance.

Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

The two-parameter Weibull distribution has garnered much attention in the assessment of windenergy potential. The estimation of the shape and scale parameters of the distribution has broughtforth a successful tool for the wind energy industry. However, it may be inappropriate to use thetwo-parameter Weibull distribution to assess energy at every location, especially at sites wherelow wind speeds are frequent, such as the Equatorial region. In this work, a robust technique inwind resource assessment using a Bayesian approach for estimating Weibull parameters is firstproposed. Secondly, the wind resource assessment techniques using a two-parameter Weibulldistribution and a three-parameter Weibull distribution which is a generalized form of twoparameterWeibull distribution are compared. Simulation studies confirm that the Bayesianapproach seems a more robust technique for accurate estimation of Weibull parameters. Theresearch is conducted using data from seven sites in Equatorial region from 1o N of Equator to 19oSouth of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shiftparameter is a better fit for wind data having a higher percentage of low wind speeds (0-1 m/s) andlow skewness. However, wind data with a smaller percentage of low wind speeds and highskewness showed better results with a two-parameter distribution that is a special case of threeparameterWeibull distribution with zero shift parameter. The results also demonstrate that theproposed Bayesian approach and application of a three-parameter Weibull distribution areextremely useful in accurate estimate of wind power and annual energy production.

Offshore wind resource assessment using reanalysis data

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest.

Nature-Inspired Approach Using Seasonal Comparison of Wind Speed With Spectral and Statistical Analysis

Seasonal analysis of wind speed includes elements of its evaluation and analysis for wind energy production in complex geographical areas. These analyses require wind energy systems to be set up, integrated, operated, and designed according to seasonal differences. Istanbul wind speed data were collected hourly and analyzed seasonally. When the results of the analysis are examined, no significant increase in seasonal transitions was observed, while certain changes were observed between summer and winter. Here, statistical analysis, Weibull distribution function, and signal processing-based PSD analysis for wind speed is performed. In addition, correlation analysis was made between the seasons. Although significant results were obtained in signal-based analyses, results were obtained for seasonal transitions in correlation analyses. Seasonal spectral densities were calculated in the spectral analysis of wind speed data. This study has important implications in terms of extraction of seasonal characteristics of wind speed, resource assessment, operation, investment, and feasibility.

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer time scales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting Model (WRF) 4.2.1 to two different SST products—a daily, spatially coarser resolution data set (the Operational Sea Surface Temperature and Ice Analysis, or OSTIA), and an hourly, spatially finer resolution product (SSTs from the Geostationary Operational Environmental Satellite 16, or GOES-16). We find that in the Mid-Atlantic, although OSTIA SSTs validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height wind characterization performance on monthly time scales. Additionally, during flagged events that show statistically significant wind speed deviations between the two simulations, the GOES-16-forced simulation outperforms that forced by OSTIA.