Cognitive consequences of clumsy automation on high workload, high consequence human performance

The growth of computational power has fueled attempts to automate more of the human role in complex problem solving domains, especially those where system faults have high consequences and where periods of high workload may saturate the performance capacity of human operators. Examples of these domains include flightdecks, space stations, air traffic control, nuclear power operation, ground satellite control rooms, and surgical operating rooms. Automation efforts may have unanticipated effects on human performance, particularly if they increase the workload at peak workload times or change the practitioners' strategies for coping with workload. Smooth and effective changes in automation requires detailed understanding of the congnitive tasks confronting the user: it has been called user centered automation. The introduction of a new computerized technology in a group of hospital operating rooms used for heart surgery was observed. The study revealed how automation, especially 'clumsy automation', effects practitioner work patterns and suggest that clumsy automation constrains users in specific and significant ways. Users tailor both the new system and their tasks in order to accommodate the needs of process and production. The study of this tailoring may prove a powerful tool for exposing previously hidden patterns of user data processing, integration, and decision making which may, in turn, be useful in the design of more effective human-machine systems

Grounding explanations in evolving, diagnostic situations

Certain fields of practice involve the management and control of complex dynamic systems. These include flight deck operations in commercial aviation, control of space systems, anesthetic management during surgery or chemical or nuclear process control. Fault diagnosis of these dynamic systems generally must occur with the monitored process on-line and in conjunction with maintaining system integrity.This research seeks to understand in more detail what it means for an intelligent system to function cooperatively, or as a 'team player' in complex, dynamic environments. The approach taken was to study human practitioners engaged in the management of a complex, dynamic process: anesthesiologists during neurosurgical operations. The investigation focused on understanding how team members cooperate in management and fault diagnosis and comparing this interaction to the situation with an Artificial Intelligence(AI) system that provides diagnoses and explanations. Of particular concern was to study the ways in which practitioners support one another in keeping aware of relevant information concerning the state of the monitored process and of the problem solving process

Electrical anisotropy due to gas hydrate-filled fractures

In 2006, the Indian National Gas Hydrate Program Expedition 01, or NGHP-01, discovered gas hydrate as fill in near-vertical fractures in unconsolidated sediments at several drilling sites on the Indian continental margins. These gas hydrate-filled fractures were identified on logging-while-drilling resistivity images. The gas hydrate-filled fracture intervals coincide with high measured resistivity at the NGHP-01 sites. High measured resistivity translates into high hydrate saturations via Archie's equation; however, these high saturations contradict lower gas hydrate saturations determined from pressure core and chlorinity measurements. Also, in intervals with near-vertical gas hydrate-filled fractures, there is considerable separation between phase shift and attenuation resistivity logs, with 2-MHz resistivity measurements being significantly higher than 400-kHz resistivity measurements. We modeled the sensitivity of the propagation resistivity measurements in the gas hydrate-filled fracture intervals at NGHP-01 Sites 5 and 10. Near-vertical hydrate-filled fractures can cause the abnormally high resistivity measurements in vertical holes due to electrical anisotropy. The model suggests the gas hydrate saturations in situ are usually significantly lower than those calculated from Archie's equation. In addition, these modeled gas hydrate saturations generally agree with the lower gas hydrate saturations obtained from pressure core and chlorinity measurements at NGHP-01 Sites 5 and 10

North American megadroughts in the Common Era: reconstructions and simulations

During the Medieval Climate Anomaly (MCA), Western North America experienced episodes of intense aridity that persisted for multiple decades or longer. These megadroughts are well documented in many proxy records, but the causal mechanisms are poorly understood. General circulation models (GCMs) simulate megadroughts, but do not reproduce the temporal clustering of events during the MCA, suggesting they are not caused by the time history of volcanic or solar forcing. Instead, GCMs generate megadroughts through (1) internal atmospheric variability, (2) sea-surface temperatures, and (3) land surface and dust aerosol feedbacks. While no hypothesis has been definitively rejected, and no GCM has accurately reproduced all features (e.g., timing, duration, and extent) of any specific megadrought, their persistence suggests a role for processes that impart memory to the climate system (land surface and ocean dynamics). Over the 21st century, GCMs project an increase in the risk of megadrought occurrence through greenhouse gas forced reductions in precipitation and increases in evaporative demand. This drying is robust across models and multiple drought indicators, but major uncertainties still need to be resolved. These include the potential moderation of vegetation evaporative losses at higher atmospheric [CO₂], variations in land surface model complexity, and decadal to multidecadal modes of natural climate variability that could delay or advance onset of aridification over the the next several decades. Because future droughts will arise from both natural variability and greenhouse gas forced trends in hydroclimate, improving our understanding of the natural drivers of persistent multidecadal megadroughts should be a major research priority

Ferroelectricity in the xAg2Nb4O11–(1−x)Na2Nb4O11 solid solution

Compositions in the (AgxNa1-x)2Nb4O11 solid solution have been prepared by a conventional solid state method. Composites containing Ag2Nb4O11 have been shown to be ferroelectric and the Curie temperature shown to decrease from 149 °C at x = 1 to 62 °C at x = 0.7. Roomtemperature compositions with x ≤ 0.7 are monoclinic, while those with x ≥ 0.8 are rhombohedral with structures consistent with the relevant end-members. At x = 0.75, the structure was mainly rhombohedral but with coexistence of the monoclinic structure, indicating the proximity of a phase boundary

Novel genetic markers improve measures of atrial fibrillation risk prediction

Aims Atrial fibrillation (AF) is associated with adverse outcome. Whether recently discovered genetic risk markers improve AF risk prediction is unknown. Methods and results We derived and validated a novel AF risk prediction model from 32 possible predictors in the Women's Health Study (WHS), a cohort of 20 822 women without cardiovascular disease (CVD) at baseline followed prospectively for incident AF (median: 14.5 years). We then created a genetic risk score (GRS) comprised of 12 risk alleles in nine loci and assessed model performance in the validation cohort with and without the GRS. The newly derived WHS AF risk algorithm included terms for age, weight, height, systolic blood pressure, alcohol use, and smoking (current and past). In the validation cohort, this model was well calibrated with good discrimination [C-index (95% CI) = 0.718 (0.684–0.753)] and improved all reclassification indices when compared with age alone. The addition of the genetic score to the WHS AF risk algorithm model improved the C-index [0.741 (0.709–0.774); P = 0.001], the category-less net reclassification [0.490 (0.301–0.670); P < 0.0001], and the integrated discrimination improvement [0.00526 (0.0033–0.0076); P < 0.0001]. However, there was no improvement in net reclassification into 10-year risk categories of <1, 1–5, and 5+% [0.041 (−0.044–0.12); P = 0.33]. Conclusion: Among women without CVD, a simple risk prediction model utilizing readily available risk markers identified women at higher risk for AF. The addition of genetic information resulted in modest improvements in predictive accuracy that did not translate into improved reclassification into discrete AF risk categories

Novel genetic markers improve measures of atrial fibrillation risk prediction

Aims Atrial fibrillation (AF) is associated with adverse outcome. Whether recently discovered genetic risk markers improve AF risk prediction is unknown. Methods and results We derived and validated a novel AF risk prediction model from 32 possible predictors in the Women's Health Study (WHS), a cohort of 20 822 women without cardiovascular disease (CVD) at baseline followed prospectively for incident AF (median: 14.5 years). We then created a genetic risk score (GRS) comprised of 12 risk alleles in nine loci and assessed model performance in the validation cohort with and without the GRS. The newly derived WHS AF risk algorithm included terms for age, weight, height, systolic blood pressure, alcohol use, and smoking (current and past). In the validation cohort, this model was well calibrated with good discrimination [C-index (95% CI) = 0.718 (0.684-0.753)] and improved all reclassification indices when compared with age alone. The addition of the genetic score to the WHS AF risk algorithm model improved the C-index [0.741 (0.709-0.774); P = 0.001], the category-less net reclassification [0.490 (0.301-0.670); P < 0.0001], and the integrated discrimination improvement [0.00526 (0.0033-0.0076); P < 0.0001]. However, there was no improvement in net reclassification into 10-year risk categories of <1, 1-5, and 5+% [0.041 (−0.044-0.12); P = 0.33]. Conclusion Among women without CVD, a simple risk prediction model utilizing readily available risk markers identified women at higher risk for AF. The addition of genetic information resulted in modest improvements in predictive accuracy that did not translate into improved reclassification into discrete AF risk categorie

Electrical anisotropy of gas hydrate-bearing sand reservoirs in the Gulf of Mexico

We present new results and interpretations of the electrical anisotropy and reservoir architecture in gas hydrate-bearing sands using logging data collected during the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II. We focus specifically on sand reservoirs in Hole Alaminos Canyon 21 A (AC21-A), Hole Green Canyon 955 H (GC955-H) and Hole Walker Ridge 313 H (WR313-H). Using a new logging-while-drilling directional resistivity tool and a one-dimensional inversion developed by Schlumberger, we resolve the resistivity of the current flowing parallel to the bedding, R‖ and the resistivity of the current flowing perpendicular to the bedding, R⊥. We find the sand reservoir in Hole AC21-A to be relatively isotropic, with R‖ and R⊥ values close to 2 Ω m. In contrast, the gas hydrate-bearing sand reservoirs in Holes GC955-H and WR313-H are highly anisotropic. In these reservoirs, R‖ is between 2 and 30 Ω m, and R⊥ is generally an order of magnitude higher. Using Schlumberger’s WebMI models, we were able to replicate multiple resistivity measurements and determine the formation resistivity the gas hydrate-bearing sand reservoir in Hole WR313-H. The results showed that gas hydrate saturations within a single reservoir unit are highly variable. For example, the sand units in Hole WR313-H contain thin layers (on the order of 10–100 cm) with varying gas hydrate saturations between 15 and 95%. Our combined modeling results clearly indicate that the gas hydrate-bearing sand reservoirs in Holes GC955-H and WR313-H are highly anisotropic due to varying saturations of gas hydrate forming in thin layers within larger sand units