Publications of the Jet Propulsion Laboratory, July 1961 through June 1962

Jpl bibliography on space science, 1961-196

Pain State as a Mediator in the Psychological and Behavioral Assessment of Migraine and Tension Headache Sufferers

While research has suggested there is a possibility that headache assessment tools may be affected by the pain state of the individual, only one study to date has examined pain-state differences in assessment results for individuals diagnosed with a headache disorder. Holroyd, France, Nash & Hursey (1993) showed that most differences between headache sufferers and control groups on psychological symptom reports were an artifact of pain state. The present study examined the influence of headache pain state on self-reported psychological and behavioral variables. Undergraduate male and female subjects between the ages of 18 and 30 were selected based on their fulfillment of criteria for one of three groups: chronic tension-type headache sufferers (n = 37), migraine headache sufferers ( n = 31), or headache-free individuals (n = 30). Migraine and tension headache sufferers met the International Headache Society\u27s criteria for chronic tension-type headache and migraine with or without aura (IHS, 1988). The results of a repeated measures MANOVA using subscales of the Coping Strategies Inventory revealed significant group and pain-state effects, such that scores on wishful thinking and social withdrawal subscales were higher during pain state. Results of a repeated measures MANOVA for the Daily Hassles Scale showed a significant group effect, such that migraine, tension and control groups differed on all seven subscales. While significant group differences on inner concerns and time pressures on the Daily Hassles Scale replicated previous findings, group differences on all seven subscales had not been previously demonstrated. Significant correlations between headache subjects\u27 pain rating during assessment and symptom reports, as well as discriminant analyses conducted to examine redundancies in symptom measures, were discussed. Results were discussed in terms of the importance of pain-state in the assessment of headache disorders

People making deontological judgments in the Trapdoor dilemma are perceived to be more prosocial in economic games than they actually are

Why do people make deontological decisions, although they often lead to overall unfavorable outcomes? One account is receiving considerable attention: deontological judgments may signal commitment to prosociality and thus may increase people’s chances of being selected as social partners–which carries obvious long-term benefits. Here we test this framework by experimentally exploring whether people making deontological judgments are expected to be more prosocial than those making consequentialist judgments and whether they are actually so. In line with previous studies, we identified deontological choices using the Trapdoor dilemma. Using economic games, we take two measures of general prosociality towards strangers: trustworthiness and altruism. Our results procure converging evidence for a perception gap according to which Trapdoor-deontologists are believed to be more trustworthy and more altruistic towards strangers than Trapdoor-consequentialists, but actually they are not so. These results show that deontological judgments are not universal, reliable signals of prosociality

People making deontological judgments in the Trapdoor dilemma are perceived to be more prosocial in economic games than they actually are

Why do people make deontological decisions, although they often lead to overall unfavorable outcomes? One account is receiving considerable attention: deontological judgments may signal commitment to prosociality and thus may increase people’s chances of being selected as social partners–which carries obvious long-term benefits. Here we test this framework by experimentally exploring whether people making deontological judgments are expected to be more prosocial than those making consequentialist judgments and whether they are actually so. In line with previous studies, we identified deontological choices using the Trapdoor dilemma. Using economic games, we take two measures of general prosociality towards strangers: trustworthiness and altruism. Our results procure converging evidence for a perception gap according to which Trapdoor-deontologists are believed to be more trustworthy and more altruistic towards strangers than Trapdoor-consequentialists, but actually they are not so. These results show that deontological judgments are not universal, reliable signals of prosociality

Nitrogenase FeMoco investigated by spatially resolved anomalous dispersion refinement

The [Mo:7Fe:9S:C] iron-molybdenum cofactor (FeMoco) of nitrogenase is the largest known metal cluster and catalyses the 6-electron reduction of dinitrogen to ammonium in biological nitrogen fixation. Only recently its atomic structure was clarified, while its reactivity and electronic structure remain under debate. Here we show that for its resting S=3/2 state the common iron oxidation state assignments must be reconsidered. By a spatially resolved refinement of the anomalous scattering contributions of the 7 Fe atoms of FeMoco, we conclude that three irons (Fe1/3/7) are more reduced than the other four (Fe2/4/5/6). Our data are in agreement with the recently revised oxidation state assignment for the molybdenum ion, providing the first spatially resolved picture of the resting-state electron distribution within FeMoco. This might provide the long-sought experimental basis for a generally accepted theoretical description of the cluster that is in line with available spectroscopic and functional data

Reservoir characterization of the Ordovician Red River Formation in southwest Williston Basin Bowman County, ND and Harding County, SD

This topical report is a compilation of characterizations by different disciplines of the Red River Formation in the southwest portion of the Williston Basin and the oil reservoirs which it contains in an area which straddles the state line between North Dakota and South Dakota. Goals of the report are to increase understanding of the reservoir rocks, oil-in-place, heterogeneity, and methods for improved recovery. The report is divided by discipline into five major sections: (1) geology, (2) petrography-petrophysical, (3) engineering, (4) case studies and (5) geophysical. Interwoven in these sections are results from demonstration wells which were drilled or selected for special testing to evaluate important concepts for field development and enhanced recovery. The Red River study area has been successfully explored with two-dimensional (2D) seismic. Improved reservoir characterization utilizing 3-dimensional (3D) and has been investigated for identification of structural and stratigraphic reservoir compartments. These seismic characterization tools are integrated with geological and engineering studies. Targeted drilling from predictions using 3D seismic for porosity development were successful in developing significant reserves at close distances to old wells. Short-lateral and horizontal drilling technologies were tested for improved completion efficiency. Lateral completions should improve economics for both primary and secondary recovery where low permeability is a problem and higher density drilling is limited by drilling cost. Low water injectivity and widely spaced wells have restricted the application of waterflooding in the past. Water injection tests were performed in both a vertical and a horizontal well. Data from these tests were used to predict long-term injection and oil recovery

Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented with a steadily evolving body of mechanistic theory provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates "readable" models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-east England. We find that the GEP retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components; the identification of a "general" terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data rich era, complementing more traditional modelling approaches