Classical deterrence theory revisited: An empirical analysis of Police Force Areas in England and Wales

The severity, certainty and celerity (swiftness) of punishment are theorised to influence offending through deterrence. Yet celerity is rarely included in empirical studies of criminal activity and the three deterrence factors have never been analysed in one empirical model. We address this gap with an analysis using unique panel data of recorded theft, burglary and violence against the person for 41 Police Force Areas in England and Wales using variables that capture these three theorised factors of deterrence. We find that the three factors affect crime in different ways. Increased detection by the police (certainty) is associated with reduced theft and burglary but not violence. We find that variation in the celerity of sanction has a significant impact on theft offences but not on burglary or violence offences. Increased average prison sentences (severity) reduce burglary only. We account for these results in terms of data challenges and the likely different motivations underlying violent and acquisitive crime.

Integration of Semi-Empirical and Artificial Neural Network (ANN) for Modeling Lithium-Ion Electrolyte Systems Dynamic Viscosity

Dynamic viscosity is a key characteristic of electrolyte performance in lithium-ion battery. This work introduces a one parameter semi-empirical model and artificial neural network (ANN) to predict the viscosity of salt-free solvent mixtures and relative viscosity of Li-ion electrolyte solutions (lithium salt + solvent mixture), respectively. Data used in this study were obtained experimentally, in addition to data extracted from literature. There are seven inputs of the ANN model: salt concentration, electrolyte temperature, salt anion size, solvent melting and boiling temperatures, solvent dielectric constant, and solvent dipole moment. Different configuration of the ANN was tested and the configuration with least error was chosen. The results show the capability of the semi-empirical model to predict the viscosity with an overall mean absolute percentage error (MAPE) of 2.05% and 3.17% for binary and tertiary mixtures, respectively. The ANN model predicted the relative viscosity of electrolyte solutions with MAPE of 4.86%. The application of both models in series, resulted predicted the viscosity with MAPE 2.3%, although the ANN MAPE alone is higher than this value. Thus, this work highlights the promise of using predictive models to complement physical approaches and to provide an effective way to perform initial screening on Li-ion electrolytes.

Improved Model for Predicting Total Dissolved Gas Generation With the Residence Time of the Water in the Stilling Phase

Quantitative predictions of total dissolved gas (TDG) super-saturation are essential for developing operation schemes for high dams. Most TDG generation prediction models have various shortcomings that affect the accuracy of TDG super-saturation estimation, such as oversimplification of influencing factors and uncertainty in parameter values. In this study, the TDG generation process was divided into three parts, gas-liquid mass transfer process in the stilling phase, dilution resulting from the water jet plunging into the stilling phase, and outflow of TDG–super-saturated water from the stilling phase, while considering the water body and bubbles in the stilling phase as a whole. The residence time of the water in the stilling phase (Tr) was introduced to estimate mass transfer time, along with dimensional analysis methods. The properties of TDG generation were evaluated experimentally under varying Tr values. Based on the theoretical analysis and experimental results, a basic water renewal model was proposed and was validated using experimental data. Furthermore, prediction results of this model were compared with those of a classical empirical model and mechanical model based on observed data from a field survey at Xiluodu Dam. The results show that the relative errors between the predicted and experimental measurements were all less than 5%, indicating that the developed prediction model has a good performance. Compared with the mechanism model, the developed model could reduce the standard error (SE), normalized mean error (NME), and error of maximum (REMAX) by 60, 96, and 15%, respectively. Meanwhile, the developed model could reduce the SE, NME, REMAX by 17.4, 36, and 23%, respectively, compared with the empirical model. Considering all the error indexes, it can be concluded that the prediction performance of the water renewal model is the best among the three models. The proposed model was also more generically versatile than the existing models. Prediction results of water regeneration model for TDG could aid the drafting of governing strategies to minimize the risk of super-saturated TDG.

Development of a Semi-Empirical Model for Droplet Size Determination of a Three-Channel Spray Nozzle for Pellet Coating Based on the Optical Method Concept

The purpose of this study was to investigate the droplet size obtained with a three-channel spray nozzle typically used in fluid bed devices and to construct a semi-empirical model for prediction of droplet size. With the aid of a custom-made optical method concept, the impact of the type of polymer and solvents used through dispersion properties (viscosity, density, and surface tension), dispersion flow rate, atomization pressure, and microclimate pressure on droplet size was investigated. A semi-empirical model with adequate predictability for calculating the average droplet size (R2 = 0.90, Q2 = 0.73) and its distribution (R2 = 0.84, Q2 = 0.61) was constructed by employing dimensional analysis and design of experiments. Newtonian and non-Newtonian dispersion and process parameters on laboratory and on production scale were included, thereby enabling constant droplet size irrespective of the scale. Based on the model results, it would be possible to scale-up the atomization process (e.g., coating process) from laboratory to production scale in a systematic fashion, regardless of the type of solvent or polymer used. For the system investigated, this can be performed by understanding the dispersion properties, such as viscosity, density, and surface tension, as well as the following process parameters: dispersion flow rate, atomization, and microclimate pressure.

Toward Real-Time Giga-Voxel Optoacoustic/Photoacoustic Microscopy: GPU-Accelerated Fourier Reconstruction with Quasi-3D Implementation

We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) employing truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time as compared to CPU-based SAFT. We provide an empirical model that can be used to predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system.