We investigated the relationship between the intracranial pulse pressure (ICP/sub PP/) and the mean intracranial pressure (ICP/sub M/). In adult patients, several research groups have described a linear relationship b...
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We investigated the relationship between the intracranial pulse pressure (ICP/sub PP/) and the mean intracranial pressure (ICP/sub M/). In adult patients, several research groups have described a linear relationship between ICP/sub PP/ and ICP/sub M/ within the range of cerebral autoregulation. Current monitoring and therapy are mainly based on the mean ICP/sub M/, since it is believed that the ICP/sub M/ contains most of the information provided by the other pulse morphology metrics. In this paper we attempt to answer whether there is further information within the ICP morphology not explained by ICP/sub M/ that might be of prognostic significance. We screened ICP records of 42 patients admitted to the Pediatric Intensive Care Unit at Doernbecher Children's Hospital for segments in which the ICP/sub M/ varied at least 5 mmHg during a 1-hour period. We found 54 segments in 9 different pediatric TBI patients (ages 0.2-17.8 years, mean=9.9 years). ICP/sub PP/ and ICP/sub M/ were calculated for each pulse using an automatic pressure detection algorithm. The coefficient of linear correlation r was > 0.70 in 43/54 segments (p < 0.001), which indicates that there exists a linear relationship between ICP/sub PP/ and ICP/sub M/. However, we found r > 0.90 only in 16/54 segments (p=NS) . This result and visual inspection of ICP/sub PP/ vs. ICP/sub M/ density plots suggest that ICP pulse pressure is not fully explained by the ICP M.
We describe a computer model of intracranial pressure (ICP) dynamics that evaluates clinical treatment options for elevated ICP during traumatic brain injury (TBI). The model uses fluid volumes as primary state variab...
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We describe a computer model of intracranial pressure (ICP) dynamics that evaluates clinical treatment options for elevated ICP during traumatic brain injury (TBI). The model uses fluid volumes as primary state variables and explicitly models fluid flows as well as the resistance, compliance, and pressure associated with each intra- and extracranial compartment (arteries and arterioles, capillary bed, veins, venous sinus, ventricles, and brain parenchyma). The model evaluates clinical events and therapies such as intraand extra-parenchymal hemorrhage, cerebral edema, cerebrospinal fluid drainage, mannitol administration, head elevation, and mild hyperventilation. The model is able to replicate observed clinical behavior in many cases, including elevated ICP associated with severe cerebral edema following subdural, epidural, or intraparenchymal hematoma. The model also mimics cerebrovascular regulatory mechanisms that are activated during TBI.
The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated proc...
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The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated processing elements. The muon trigger algorithm identifies candidate tracks, and is sensitive to the muon charge (sign);candidate dimuon events are identified by complementary charge track-pairs. To insure that the trigger is operating effectively, the trigger development team is actively collaborating in an independent multi-university research program for reliable, self-aware, fault adaptive behavior in real-time embedded systems (RTES). Key elements of the architecture, algorithm, performance, and engineered reliability are presented.
Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems ...
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Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central Control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.
In this paper, we assess the performance of DSMIO cachecoherence algorithm implemented in a parallel object-based database management system (ODBMS). The distinguishing feature of DSMIO is its use of the lazy release ...
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The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It i...
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The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It is shown that AUTOWISARD is simple, fast and stable, whilst keeping compatibility with the original WISARD architecture. Experimental test results over binary patterns benchmarks have shown that, although both unsupervised learning models are remarkably simple, AUTOWISARD consistently exhibits better classification skills than fuzzy ART. It is also shown that such superiority happens thanks to AU-TOWISARD's richer internal representation of the trained patterns and the training methods employed by the algorithm, such as the learning window and partial training strategies.
We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
We present two new versions of the fuzzy Markov predictor (FMP) with different dependences among the inputs: first-order and second-order dependences. The FMP is a modification of the hidden Markov model in order to e...
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We present two new versions of the fuzzy Markov predictor (FMP) with different dependences among the inputs: first-order and second-order dependences. The FMP is a modification of the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor. These hybrid systems are applied to the task of monthly electric load forecasting and successfully compared with one fuzzy system, and two traditional forecasting methods: Box-Jenkins and Winters exponential smoothing.
In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron ...
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In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron injection to add or remove charge on a floating-gate of an auto-zeroing amplifier. We designed, fabricated and successfully tested a chip with the circuit. Test results show that the circuit reduces the offsets by more than an order of magnitude.
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