Air Force Web Preventive Health Assessment (AF WEBPHA) mental health screening effectiveness

The U.S. Air Force assesses Active Duty Air Force (ADAF) health annually using the Air Force Web-based Preventative Health Assessment (AF WebPHA). The assessment is based on a self-administered survey used to determine the overall Air Force health and readiness, as well as, the individual health of each airman. Individual survey responses as well as groups of responses generate further computer generated assessment and result in a classification of 'Critical', 'Priority', or 'Routine', depending on the need and urgency for further evaluation by a health care provider. The importance of the 'Priority' and 'Critical' classifications is to provide timely intervention to prevent or limit unfavorable outcomes that may threaten an airman. Though the USAF has been transitioning from a paper form to the online WebPHA survey for the last three years it was not made mandatory for all airmen until 2009. The survey covers many health aspects including family history, tobacco use, exercise, alcohol use, and mental health. ^ Military stressors such as deployment, change of station, and the trauma of war can aggravate and intensify the common baseline worries experienced by the general population and place airmen at additional risks for mental health concerns and illness. This study assesses the effectiveness of the AF WebPHA mental health screening questions in predicting a mental health disorder diagnosis according to International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes generated by physicians or their surrogates. In order to assess the sensitivity, specificity, and positive predictive value of the AF WebPHA as a screening tool for mental health, survey results were compared to ascertain if they generated any mental health disorder related diagnosis for the period from January 1, 2009 to March 31, 2010. ^ Statistical analysis of the AF WebPHA mental health responses when compared with matching ICD-9-CM codes found that the sensitivity for 'Critical' or 'Priority' responses was only 3.4% and that it would correctly predict those who had the selected mental health diagnosis 9% of the time.^

The Open Door Mission: Measuring and predicting outcomes of one community-based substance abuse treatment program

The objectives of this study were to identify and measure the average outcomes of the Open Door Mission's nine-month community-based substance abuse treatment program, identify predictors of successful outcomes, and make recommendations to the Open Door Mission for improving its treatment program.^ The Mission's program is exclusive to adult men who have limited financial resources: most of which were homeless or dependent on parents or other family members for basic living needs. Many, but not all, of these men are either chemically dependent or have a history of substance abuse.^ This study tracked a cohort of the Mission's graduates throughout this one-year study and identified various indicators of success at short-term intervals, which may be predictive of longer-term outcomes. We tracked various levels of 12-step program involvement, as well as other social and spiritual activities, such as church affiliation and recovery support.^ Twenty-four of the 66 subjects, or 36% met the Mission's requirements for success. Specific to this success criteria; Fifty-four, or 82% reported affiliation with a home church; Twenty-six, or 39% reported full-time employment; Sixty-one, or 92% did not report or were not identified as having any post-treatment arrests or incarceration, and; Forty, or 61% reported continuous abstinence from both drugs and alcohol.^ Five research-based hypotheses were developed and tested. The primary analysis tool was the web-based non-parametric dependency modeling tool, B-Course, which revealed some strong associations with certain variables, and helped the researchers generate and test several data-driven hypotheses. Full-time employment is the greatest predictor of abstinence: 95% of those who reported full time employment also reported continuous post-treatment abstinence, while 50% of those working part-time were abstinent and 29% of those with no employment were abstinent. Working with a 12-step sponsor, attending aftercare, and service with others were identified as predictors of abstinence.^ This study demonstrates that associations with abstinence and the ODM success criteria are not simply based on one social or behavioral factor. Rather, these relationships are interdependent, and show that abstinence is achieved and maintained through a combination of several 12-step recovery activities. This study used a simple assessment methodology, which demonstrated strong associations across variables and outcomes, which have practical applicability to the Open Door Mission for improving its treatment program. By leveraging the predictive capability of the various success determination methodologies discussed and developed throughout this study, we can identify accurate outcomes with both validity and reliability. This assessment instrument can also be used as an intervention that, if operationalized to the Mission’s clients during the primary treatment program, may measurably improve the effectiveness and outcomes of the Open Door Mission.^

Orbital-resolution Pliocene-Pleistocene simulations of CO2, sea level, global temperature and benthic d18O, and d11B-based proxy-CO2 data

During the past five million yrs, benthic d18O records indicate a large range of climates, from warmer than today during the Pliocene Warm Period to considerably colder during glacials. Antarctic ice cores have revealed Pleistocene glacial-interglacial CO2 variability of 60-100 ppm, while sea level fluctuations of typically 125 m are documented by proxy data. However, in the pre-ice core period, CO2 and sea level proxy data are scarce and there is disagreement between different proxies and different records of the same proxy. This hampers comprehensive understanding of the long-term relations between CO2, sea level and climate. Here, we drive a coupled climate-ice sheet model over the past five million years, inversely forced by a stacked benthic d18O record. We obtain continuous simulations of benthic d18O, sea level and CO2 that are mutually consistent. Our model shows CO2 concentrations of 300 to 470 ppm during the Early Pliocene. Furthermore, we simulate strong CO2 variability during the Pliocene and Early Pleistocene. These features are broadly supported by existing and new d11B-based proxy CO2 data, but less by alkenone-based records. The simulated concentrations and variations therein are larger than expected from global mean temperature changes. Our findings thus suggest a smaller Earth System Sensitivity than previously thought. This is explained by a more restricted role of land ice variability in the Pliocene. The largest uncertainty in our simulation arises from the mass balance formulation of East Antarctica, which governs the variability in sea level, but only modestly affects the modeled CO2 concentrations.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Thermodynamical Phase Noise in Oscillators Based on L-C Resonators

Using a new Admittance-based model for electrical noise able to handle Fluctuations and Dissipations of electrical energy, we explain the phase noise of oscillators that use feedback around L-C resonators. We show that Fluctuations produce the Line Broadening of their output spectrum around its mean frequency f0 and that the Pedestal of phase noise far from f0 comes from Dissipations modified by the feedback electronics. The charge noise power 4FkT/R C2/s that disturbs the otherwise periodic fluctuation of charge these oscillators aim to sustain in their L-C-R resonator, is what creates their phase noise proportional to Leeson’s noise figure F and to the charge noise power 4kT/R C2/s of their capacitance C that today’s modelling would consider as the current noise density in A2/Hz of their resistance R. Linked with this (A2/Hz?C2/s) equivalence, R becomes a random series in time of discrete chances to Dissipate energy in Thermal Equilibrium (TE) giving a similar series of discrete Conversions of electrical energy into heat when the resonator is out of TE due to the Signal power it handles. Therefore, phase noise reflects the way oscillators sense thermal exchanges of energy with their environment.