The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Photovoltaic differential power converter trade-offs as a consequence of panel variation

Photovoltaic (PV) elements have inherent variation between cells and panels due to manufacturing tolerance, degradation, and situational differences. This variation increases over system lifetime and creates maximum power point current mismatch that reduces output power when PV elements are strung in series. Traditionally, mismatch loss is addressed using cascaded converters. However, this research examines a differential converter architecture that achieves higher efficiency by processing a fraction of the total power. The effect of PV maximum power point (MPP) current variance on output power is modeled and examined using Monte Carlo simulation for the series string architecture with and without bypass diodes, and the PV-to-Bus and PV-to-PV differential power processing (DPP) architectures at various power ratings. Hot spotting can be a problem that significantly reduces output power. PV elements at fault can be bypassed, passively or actively, to reduce power loss. Simulation results show that both DPP architectures employing active bypass are able to compensate mismatch over the 25-year lifetime of a PV system with converters sized at approximately 10-20% of the panel ratings

Comparative analysis of differential power conversion architectures and controls for solar photovoltaics

Conventional solar photovoltaic (PV) energy conversion architectures are often forced to trade off efficiency and cost for increased power production. The differential power processing approach overcomes this limitation by enabling each PV element to operate at its maximum power point (MPP) while only processing a small fraction of the total power produced. This paper analyzes several differential energy conversion architectures and the associated local controls. Models are developed to describe operation of PV-to-PV and PV-to-bus differential converters. The overall power output of each system under various environmental conditions is compared. A Monte Carlo approach is used to compare three differential conversion implementations over a range of MPP conditions. Experimental results are included for a PV-to-PV, buck-boost differential converter to demonstrate the potential for increased energy production

Converter Rating Analysis for Photovoltaic Differential Power Processing Systems

When photovoltaic (PV) cells are connected in series, they experience internal and external mismatch that reduces output power. Differential power processing (DPP) architectures achieve high system efficiency by processing a fraction of the total power while maintaining distributed local maximum power point operation. This paper details the computationalmethods and analysis used to determine the operation of PV-to-bus and PV-to-PV DPP architectures with rating-limited converters. Simulations for both DPP architectures are used to evaluate system performance over 25 years of operation. Based on data from field studies, a PV power coefficient of variation can be estimated as 0.086 after 25 years. An improvement figure of merit reflecting the ratio of energy produced to that delivered in a conventional system is introduced to evaluate comparative performance. Converter ratings of 15-17% for PV-to-bus and 23-33% for PV-to-PV architectures are identified as appropriate ratings for a 15-submodule system (five PV panels in series). Both DPP architectures with these ratings are shown to deliver up to 2.8% more power compared to a conventional series-string architecture based on the expected panel variation over 25 years of operation. DPP converters also outperform dc optimizers in terms of lifetime performance.close1

Bayesian Prediction and Evaluation in the Anterior Cingulate Cortex

The dorsal anterior cingulate cortex (dACC) has been implicated in a variety of cognitive control functions, among them the monitoring of conflict, error, and volatility, error anticipation, reward learning, and reward prediction errors. In this work, we used a Bayesian ideal observer model, which predicts trial-by-trial probabilistic expectation of stop trials and response errors in the stop-signal task, to differentiate these proposed functions quantitatively. We found that dACC hemodynamic response, as measured by functional magnetic resonance imaging, encodes both the absolute prediction error between stimulus expectation and outcome, and the signed prediction error related to response outcome. After accounting for these factors, dACC has no residual correlation with conflict or error likelihood in the stop-signal task. Consistent with recent monkey neural recording studies, and in contrast with other neuroimaging studies, our work demonstrates that dACC reports at least two different types of prediction errors, and beyond contexts that are limited to reward processing

Bayesian Prediction and Evaluation in the Anterior Cingulate Cortex

The dorsal anterior cingulate cortex (dACC) has been implicated in a variety of cognitive control functions, among them the monitoring of conflict, error, and volatility, error anticipation, reward learning, and reward prediction errors. In this work, we used a Bayesian ideal observer model, which predicts trial-by-trial probabilistic expectation of stop trials and response errors in the stop signal task, to differentiate these proposed functions quantitatively. We found that dACC hemodynamic response, as measured by functional magnetic resonance imaging, encodes both the absolute prediction error between stimulus expectation and outcome, and the signed prediction error related to response outcome. After accounting for these factors, dACC has no residual correlation with conflict or error likelihood in the stop-signal task. Consistent with recent monkey neural recording studies, and in contrast with other neuroimaging studies, our work demonstrates that dACC reports at least two different types of prediction errors, and beyond contexts that are limited to reward processing

Differential Power Processing for Increased Energy Production and Reliability of Photovoltaic Systems

Conventional energy conversion architectures in photovoltaic (PV) systems are often forced to tradeoff conversion efficiency and power production. This paper introduces an energy conversion approach that enables each PV element to operate at its maximum power point (MPP) while processing only a small fraction of the total power produced. This is accomplished by providing only the mismatch in the MPP current of a set of series-connected PV elements. Differential power processing increases overall conversion efficiency and overcomes the challenges associated with unmatched MPPs (due to partial shading, damage, manufacturing tolerances, etc.). Several differential power processing architectures are analyzed and compared with Monte Carlo simulations. Local control of the differential converters enables distributed protection and monitoring. Reliability analysis shows significantly increased overall system reliability. Simulation and experimental results are included to demonstrate the benefits of this approach at both the panel and subpanel level.close58