Evaluating Integrated Treatment on Recidivism for Female Offenders in Criminal Justice System

The burden of co-occurring disorders (CODs) among offenders in the criminal justice system (CJS) in the United States, particularly among the female population, is threatening the communities. About 80% of women in the CJS were diagnosed and treated for CODs, and 63% tend to be rearrested. The study examined the possible influence of CODs, integrated treatment of CODs, and gender, on recidivism while controlling for other demographic factors. The study was based on the conceptual framework of integrated dual disorder treatment (IDDT) and feminist criminology theory. Cross-sectional quantitative study design was applied on a secondary dataset from the 2017 Treatment Episode Data Set - Discharge (TEDS-D). All the eligible records, based on the study inclusion and exclusion criteria, were analyzed. Frequency distribution tables, chi-square test, and multivariable logistic regression model were used to describe the participants and determine the associations between the independent variables and the dependent variable (recidivism). A total of 442,905 participants were analyzed. Most (38%) of them were between 25 to 34 years old and majority (71.4%) were men. The associations between prevalence of COD (Odds Ratio [OR] = 0.81; Confidence Interval [CI] 0.79, 0.84), previous treatment episode (OR = 1.3; CI 1.30, 1.28) and recidivism were statistically significant. Women appear to be at higher risks (8.7%) of recidivism than men (7.8%). In conclusion, COD and previous treatment episode are associated with recidivism. The social implications of these findings are the potential to promote individualized and gender-sensitive treatment, which may reduce recidivism, reduce incidence of crimes, and promote safer and healthier communities

Defect-based testing of LTS digital circuits

A Defect-Based Test (DBT) methodology for Superconductor Electronics (SCE) is presented in this thesis, so that commercial production and efficient testing of systems can be implemented in this technology in the future. In the first chapter, the features and prospects for SCE have been presented. The motivation for this research and the outline of the thesis were also described in Chapter 1. It has been shown that high-end applications such as Software-Defined Radio (SDR) and petaflop computers which are extremely difficult to implement in top-of-the-art semiconductor technologies can be realised using SCE. But, a systematic structural test methodology had yet to be developed for SCE and has been addressed in this thesis. A detailed introduction to Rapid Single-Flux Quantum (RSFQ) circuits was presented in Chapter 2. A Josephson Junction (JJ) was described with associated theory behind its operation. The JJ model used in the simulator used in this research work was also presented. RSFQ logic with logic protocols as well as the design and implementation of an example D-type flip-flop (DFF) was also introduced. Finally, advantages and disadvantages of RSFQ circuits have been discussed with focus on the latest developments in the field. Various techniques for testing RSFQ circuits were discussed in Chapter 3. A Process Defect Monitor (PDM) approach was presented for fabrication process analysis. The presented defect-monitor structures were used to gather measurement data, to find the probability of the occurrence of defects in the process which forms the first step for Inductive Fault Analysis (IFA). Results from measurements on these structures were used to create a database for defects. This information can be used as input for performing IFA. "Defect-sprinkling" over a fault-free circuit can be carried out according to the measured defect densities over various layers. After layout extraction and extensive fault simulation, the resulting information will indicate realistic faults. In addition, possible Design-for-Testability (DfT) schemes for monitoring Single-Flux Quantum (SFQ) pulses within an RSFQ circuit has also been discussed in Chapter 3. The requirement for a DfT scheme is inevitable for RSFQ circuits because of their very high frequency of operation and very low operating temperature. It was demonstrated how SFQ pulses can be monitored at an internal node of an SCE circuit, introducing observability using Test-Point Insertion (TPI). Various techniques were discussed for the introduction of DfT and to avoid the delay introduced by the DfT structure if it is required. The available features in the proposed design for customising the detector make it attractive for a detailed DBT of RSFQ circuits. The control of internal nodes has also been illustrated using TPI. The test structures that were designed and implemented to determine the occurrence of defects in the processes can also be used to locate the position for the insertion of the above mentioned DfT structures

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Prognostics and Health Management of Electronics by Utilizing Environmental and Usage Loads

Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. Thus by determining the advent of failure, procedures can be developed to mitigate, manage and maintain the system. Since, electronic systems control most systems today and their reliability is usually critical for system reliability, PHM techniques are needed for electronics. To enable prognostics, a methodology was developed to extract load-parameters required for damage assessment from irregular time-load data. As a part of the methodology an algorithm that extracts cyclic range and means, ramp-rates, dwell-times, dwell-loads and correlation between load parameters was developed. The algorithm enables significant reduction of the time-load data without compromising features that are essential for damage estimation. The load-parameters are stored in bins with a-priori calculated (optimal) bin-width. The binned data is then used with Gaussian kernel function for density estimation of the load-parameter for use in damage assessment and prognostics. The method was shown to accurately extract the desired load-parameters and enable condensed storage of load histories, thus improving resource efficiency of the sensor nodes. An approach was developed to assess the impact of uncertainties in measurement, model-input, and damage-models on prognostics. The approach utilizes sensitivity analysis to identify the dominant input variables that influence the model-output, and uses the distribution of measured load-parameters and input variables in a Monte-Carlo simulation to provide a distribution of accumulated damage. Using regression analysis of the accumulated damage distributions, the remaining life is then predicted with confidence intervals. The proposed method was demonstrated using an experimental setup for predicting interconnect failures on electronic board subjected to field conditions. A failure precursor based approach was developed for remaining life prognostics by analyzing resistance data in conjunction with usage temperature loads. Using the data from the PHM experiment, a model was developed to estimate the resistance based on measured temperature values. The difference between actual and estimated resistance value in time-domain were analyzed to predict the onset and progress of interconnect degradation. Remaining life was predicted by trending several features including mean-peaks, kurtosis, and 95% cumulative-values of the resistance-drift distributions

Field weakening and sensorless control solutions for synchronous machines applied to electric vehicles.

184 p.La polución es uno de los mayores problemas en los países industrializados. Por ello, la electrificación del transporte por carretera está en pleno auge, favoreciendo la investigación y el desarrollo industrial. El desarrollo de sistemas de propulsión eficientes, fiables, compactos y económicos juega un papel fundamental para la introducción del vehículo eléctrico en el mercado.Las máquinas síncronas de imanes permanentes son, a día de hoy la tecnología más empleada en vehículos eléctricos e híbridos por sus características. Sin embargo, al depender del uso de tierras raras, se están investigando alternativas a este tipo de máquina, tales como las máquinas de reluctancia síncrona asistidas por imanes. Para este tipo de máquinas síncronas es necesario desarrollar estrategias de control eficientes y robustas. Las desviaciones de parámetros son comunes en estas máquinas debido a la saturación magnética y a otra serie de factores, tales como tolerancias de fabricación, dependencias en función de la temperatura de operación o envejecimiento. Las técnicas de control convencionales, especialmente las estrategias de debilitamiento de campo dependen, en general, del conocimiento previo de dichos parámetros. Si no son lo suficientemente robustos, pueden producir problemas de control en las regiones de debilitamiento de campo y debilitamiento de campo profundo. En este sentido, esta tesis presenta dos nuevas estrategias de control de debilitamiento de campo híbridas basadas en LUTs y reguladores VCT.Por otro lado, otro requisito indispensable para la industria de la automoción es la detección de faltas y la tolerancia a fallos. En este sentido, se presenta una nueva estrategia de control sensorless basada en una estructura PLL/HFI híbrida que permite al vehículo continuar operando de forma pseudo-óptima ante roturas en el sensor de posición y velocidad de la máquina eléctrica. En esta tesis, ambas propuestas se validan experimentalmente en un sistema de propulsión real para vehículo eléctrico que cuenta con una máquina de reluctancia síncrona asistidas por imanes de 51 kW

Real-time fault identification for developmental turbine engine testing

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing.An automated data validation and fault identification approach employing enginecycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady state and transient engine operation. Automated steady-state, transient, and real-time model calibration processes are also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in real-time.The result is a capability to detect data and engine anomalies in real-time during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance. The component-level model-based engine performance and fault identification technique of the present research is capable of: identifying measurement errors on the order of 0.5 percent (e.g., sensor bias, drift,level shift, noise, or poor response) in facility fuel flow, airflow, and thrust measurements; identifying measurement errors in engine aerothermodynamic measurements (rotorspeeds, gas path pressures and temperatures); identifying measurement errors in engine control sensors (e.g., leaking/biased pressure sensor, slowly responding pressure measurement) and variable geometry rigging (e.g., misset guide vanes or nozzle area) that would invalidate a test or series of tests; identifying abrupt faults (e.g., faults due to domestic object damage, foreign object damage, and control anomalies); identifying slow faults (e.g., component or overall engine degradation, and sensor drift). Specifically, the technique is capable of identifying small changes in compressor (or fan) performance on the order of 0.5 percent; and being easily extended to diagnose secondary failure modes and to verify any modeling assumptions that may arise for developmental engine tests (e.g., increase in turbine flow capacity, inaccurate measurement of facility bleed flows, horsepower extraction, etc.).The component-level model-based engine performance and fault identification method developed in the present work brings together features which individually and collectively advance the state-of-the-art. These features are separated into three categories: advancements to effectively quantify off-nominal behavior, advancements to provide a fault detection capability that is practical from the viewpoint of the analysis,implementation, tuning, and design, and advancements to provide a real-time fault detection capability that is reliable and efficient