Event- and time-based design of operation sequences with uncertainties in execution times

In this paper, we introduce a complete framework for integrating the design of the manufacturing process and control system. We show how operation sequences can be designed in a modeling tool, Sequence Planner (SP), and how relations between operations may be expressed using logical conditions. An approach to convert the SP model into a constraint programming model for optimization is presented. The time-based solution is transformed to an event-based description. Due to uncertainties in execution times, some logical restrictions based on the optimal schedule are relaxed to avoid unnecessary delays. The control logics to achieve the desired operation sequences are added to the SP model. Hence, the process designer can revise the sequences if necessary, and the control designer retrieves a logical description of the optimized process that can be automatically converted to control code

Optimization of Hybrid Systems with Known Paths

In this paper we study a subset of hybrid systems and present a generalized method for their optimization. We outline Hybrid Cost Automata (HCA), an extension to Hybrid Automata, where discrete and continuous cost expressions are added. The class of hybrid systems with known spatial paths is dened in the context of HCA. This type of system is common in industry where for example AGVs transport goods from one location to another, or manipulators move between joint coordinates. The optimization is performed using Dynamic Programming as a preprocessing step, whereafter Mixed Integer Nonlinear Programming is used for scheduling. A case study of a four robot cell is presented with energy consumption used as a minimization criterion

Optimization of operation sequences using constraint programming

In this paper, we connect the dots: design and optimization of production systems. A possible link between these two areas, is a previously presented modeling language, Sequence Planner Language (SPL). It has been demonstrated how relevant information can be extracted from production systems modeling applications, and converted into SPL. We show how the SPL model can be converted into a constraint programming model for optimization. Also, a useful abstraction concept, work-equivalence, is introduced to enable alternative model formulations. A case study consisting of an aero engine structure assembly plant is presented, in which the efficiency of the resulting constraint programs is investigated. The formulations enabled by abstraction are shown to perform better than the standard formulation

Planning transport sequences for flexible manufacturing systems

When designing a manufacturing system it is important to plan what the system should do. One important activity in most manufacturing systems is to transport products or resources between different positions. In a flexible manufacturing system it can be challenging to design and plan these transport operations due to their complex logical behavior. This paper presents a method that identifies, creates and visualizes these transport operations based on inputs from a standard virtual manufacturing tool and a high level product operation recipe. The planning of the created transport operations is transformed into a problem of finding a non-blocking solution for a discrete model of the product refinement

Molecular differences between stromal cell populations from deciduous and permanent human teeth

Introduction: Deciduous and permanent human teeth represent an excellent model system to study aging of stromal populations. Aging is tightly connected to self-renewal and proliferation and thus, mapping potential molecular differences in these characteristics between populations constitutes an important task. Methods: Using specifically designed microarray panels, Real-Time Quantitative Polymerase Chain Reaction (RT q-PCR), Western blot, immunohistochemistry and siRNA-mediated knock down experiments, we have detected a number of molecules that were differentially expressed in dental pulp from deciduous and permanent teeth extracted from young children and adults, respectively. Results: Among the differentially regulated genes, high-mobility group AT-hook 2 (HMGA2), a stem cell-associated marker, stood out as a remarkable example with a robust expression in deciduous pulp cells. siRNA-mediated knock down of HMGA2 expression in cultured deciduous pulp cells caused a down-regulated expression of the pluripotency marker NANOG. This finding indicates that HMGA2 is a pulpal stem cell regulatory factor. In addition to this, we discovered that several proliferation-related genes, including CDC2A and CDK4, were up-regulated in deciduous pulp cells, while matrix genes COL1A1, fibronectin and several signaling molecules, such as VEGF, FGFr-1 and IGFr-1 were up-regulated in the pulp cells from permanent teeth. Conclusions: Taken together, our data suggest that deciduous pulp cells are more robust in self- renewal and proliferation, whereas adult dental pulp cells are more capable of signaling and matrix synthesis

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Low-resolution pressure reactivity index and its derived optimal cerebral perfusion pressure in adult traumatic brain injury: a CENTER-TBI study

Abstract: Background: After traumatic brain injury (TBI), brain tissue can be further damaged when cerebral autoregulation is impaired. Managing cerebral perfusion pressure (CPP) according to computed “optimal CPP” values based on cerebrovascular reactivity indices might contribute to preventing such secondary injuries. In this study, we examined the discriminative value of a low-resolution long pressure reactivity index (LPRx) and its derived “optimal CPP” in comparison to the well-established high-resolution pressure reactivity index (PRx). Methods: Using the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study dataset, the association of LPRx (correlation between 1-min averages of intracranial pressure and arterial blood pressure over a moving time frame of 20 min) and PRx (correlation between 10-s averages of intracranial pressure and arterial blood pressure over a moving time frame of 5 min) to outcome was assessed and compared using univariate and multivariate regression analysis. “Optimal CPP” values were calculated using a multi-window algorithm that was based on either LPRx or PRx, and their discriminative ability was compared. Results: LPRx and PRx were both significant predictors of mortality in univariate and multivariate regression analysis, but PRx displayed a higher discriminative ability. Similarly, deviations of actual CPP from “optimal CPP” values calculated from each index were significantly associated with outcome in univariate and multivariate analysis. “Optimal CPP” based on PRx, however, trended towards more precise predictions. Conclusions: LPRx and its derived “optimal CPP” which are based on low-resolution data were significantly associated with outcome after TBI. However, they did not reach the discriminative ability of the high-resolution PRx and its derived “optimal CPP.” Nevertheless, LPRx might still be an interesting tool to assess cerebrovascular reactivity in centers without high-resolution signal monitoring. Trial registration: ClinicalTrials.gov Identifier: NCT02210221. First submitted July 29, 2014. First posted August 6, 2014

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations