Content Analysis of Drug Offenders\u27 Sketches on the Draw-an-Event Test for Risky Sexual Situations

Objectives: To evaluate the utility of the Draw-an-Event Test for risky sexual situations (DET-RS), a nonverbal memory-based assessment tool used for productions of spontaneous content associated with risky sex. Methods: Traditional holistic coding analysis of 298 drug offenders\u27 content productions. Results: Content analyses of DET-RS sketches provided increased understanding of substance use and other context preceding risky sexual situations with different types of sex partners. None of the sketches including drugs depleted condoms, only one of the sketches with alcohol included a condom, and only 2 sketches mentioned sexually transmitted diseases. Conclusions: The DET-RS is a useful research tool for generating nonverbal context-specific stimuli associated with risky sexual situations

Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control

This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and potentially nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a generic, spatial branch-and-bound algorithm. A new operation, called lifting, is introduced, which refines the control parameterization via a Gram-Schmidt orthogonalization process, while simultaneously eliminating control subregions that are either infeasible or that provably cannot contain any global optima. Conditions are given under which the image of the control parameterization error in the state space contracts exponentially as the parameterization order is increased, thereby making the lifting operation efficient. A computational technique based on ellipsoidal calculus is also developed that satisfies these conditions. The practical applicability of branch-and-lift is illustrated in a numerical example. © 2013 Springer Science+Business Media New York

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures – for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil–plant interface. We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten–Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 106 model runs randomly drawn from a uniform distribution. The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model

Initiatives to reduce energy use in cold stores

The cold chain is believed to be responsible for approximately 2.5% of global greenhouse gas emissions through direct and indirect (energy consumption) effects. Cold storage rooms consume considerable amounts of energy. Within cold storage facilities 60-70% of the electrical energy can be used for refrigeration. Therefore cold store users have considerable incentive to reduce energy consumption

Specific energy consumption values for various refrigerated food cold stores

Two benchmarking surveys were created to collect data on the performance of chilled, frozen and mixed (chilled and frozen stores operated from a single refrigeration system) food cold stores with the aim of identifying the major factors influencing energy consumption. The volume of the cold store was found to have the greatest relationship with energy use with none of the other factors collected having any significant impact on energy use. For chilled cold stores, 93% of the variation in energy was related to store volume. For frozen stores, 56% and for mixed stores, 67% of the variation in energy consumption was related to store volume. The results also demonstrated the large variability in performance of cold stores. This was investigated using a mathematical model to predict energy use under typical cold store construction, usage and efficiency scenarios. The model demonstrated that store shape factor (which had a major impact on surface area of the stores), usage and to a lesser degree ambient temperature all had an impact on energy consumption. The work provides an initial basis to compare energy performance of cold stores and indicates the areas where considerable energy saving are achievable in food cold stores. © 2013 Elsevier B.V

Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data

Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.</p