Exact and heuristic reactive planning procedures for multi-mode resource-constrained projects.

The multi-mode resource-constrained project scheduling problem (MRCPSP) involves the determination of a baseline schedule of the project activities, which can be executed in multiple modes, satisfying the precedence relations and resource constraints while minimizing the project duration. During the execution of the project, the baseline schedule may become infeasible due to activity duration and resource disruptions. We propose and evaluate a number of dedicated exact reactive scheduling procedures as well as a tabu search heuristic for repairing a disrupted schedule. We report on promising computational results obtained on a set of benchmark problems.Project scheduling; Uncertainty; Reactive scheduling; Multi-mode RCPSP;

RESCON: Educational project scheduling software.

In this article we discuss a freely downloadable educational software tool for illustrating project scheduling and project management concepts. The tool features exact and heuristic scheduling procedures and visualizes project networks, project schedules, resource profiles, activity slacks, and project duration distributions.Project scheduling; Project management; Educational software; Visualization; Scheduling algorithms;

Proactive resource allocation heuristics for robust project scheduling.

The well-known deterministic resource-constrained project scheduling problem (RCPSP) involves the determination of apredictive schedule (baseline schedule or pre-schedule)of the project activities that satisfies the finish-start precedence relations and the renewable resource constraints under the objective of minimizing the project duration. This pre-schedule serves as a baseline for the execution of the project. During execution, however, the project can be subject to several types of disruptions that may disturb the baseline schedule. Management must then rely on a reactive scheduling procedure for revising or reoptimizing the pre-schedule. The objective of our research is to develop procedures for allocating resources to the activities of a given baseline schedule in order to maximize its stability in the presence of activity duration variability. We propose three integer programming based heuristics and one constructive procedure for resource allocation. We derive lower bounds for schedule stability and report on computational results obtained on a set of benchmark problems.Research; Resource allocation; Project scheduling; Heuristics; Scheduling;

Characterizing microstructural alterations in a ratmodel of mild traumatic brain injury

1. INTRODUCTION Traumatic brain injury (TBI) is an acquired brain injury that contributes to a substantial number of deaths (mortality rate: 15 per 100 000 in Europe) and a high number of cases of permanent disability (incidence rate: 235 per 100 000 in Europe). Most of the TBI patients have mild TBI (mTBI), a condition that shows no abnormalities on conventional imaging but can result in persisting cognitive defects. Diffusion imaging is an MRI technique sensitive to diffusion of water molecules in the brain and can detect subtle changes in white matter organization. The aim of this study is to investigate whether advanced diffusion MRI scanning can be used to detect microstructural changes in a rat model of mTBI. 2. MATERIALS AND METHODS 2.1 Animal model Nine female Wistar rats weighing 250 ± 19.6 g obtained mTBI utilizing the Marmarou weight drop model [1]. In brief, in anesthetized rats a steel helmet was fixed on the skull 1/3 before and 2/3 behind bregma. The rat was positioned under a 450 g brass weight on a foam bed. The weight was dropped from a height of 1m guided through a plexiglass column. The foam bed together with the rat was rapidly removed away from the column to prevent a second injury. Rats were allowed to recover for one week. 2.2 Imaging and data analysis MRI data was acquired on a 7T MRI scanner (PharmaScan, Bruker, Ettlingen) before and 1 week after injury. T2-weighted images were acquired for anatomical reference. Multishell diffusion data was acquired with multiple directions (b=800, 1500 and 2000; 32, 46 and 64 directions, respectively). Diffusion weighted images were corrected for EPI, motion and eddy current distortions and quantitative maps were calculated for the diffusion tensor and diffusion kurtosis model in ExploreDTI [2]. Furthermore diffusion kurtosis tensor estimation was done using weighted linear least squares method and maps for white matter metrics were calculated using the model of Fieremans et al. [3]. The maps were co-registered in SPM12 with a template based on the local population and a volume-of-interest analysis was performed in the hippocampus, cingulum and corpus callosum using Amide toolbox [4]. Differences between the two time points were calculated for each map using the Wilcoxon signed-rank test in SPSS. P < 0.05 was considered significant. 3. RESULTS AND DISCUSSION The DTI and DKI metrics were not significantly different between the two time points. The axonal water fraction (AWF) was significantly increased in the cingulum, corpus callosum and hippocampus after mTBI and could be explained by axonal swelling. To verify this hypothesis, histological analysis is currently ongoing. Sections will be stained for synapses, astrocytes, neurons and myelin. References Marmarou, A. et al. A new model of diffuse brain injury in rats: Part I. J Neuroscience, 80, 291-300, 1994. Leemans, A. et al. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of Intl Soc Mag Reson Med, p. 3537, Hawaii, USA, 2009 Fieremans, E. et al. White matter characterization with diffusional kurtosis imaging, Neuroimage 58(1): 177-188, 2011. Loening, AM. et al. AMIDE: A Free Software Tool for Multimodality Medical Image Analysis. Molecular Imaging, 2(3):131-137, 2003

Automated MRI based pipeline for glioma segmentation and prediction of grade, IDH mutation and 1p19q co-deletion

In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Cancer Imaging Archive and BraTS 2019 databases. Additionally, the network was validated on an independent dataset of 110 patients retrospectively acquired at the Ghent University Hospital (GUH). Classification AUC scores are 0.93, 0.94 and 0.82 on the TCIA test data and 0.94, 0.86 and 0.87 on the GUH data for grade, IDH and 1p19q status respectively

Computer aided FCD lesion detection based on T1 MRI data

Focal cortical dysplasia (FCD) is a frequent cause of epilepsy and can be detected using brain magnetic resonance imaging (MRI). The FCD lesions in MRI images are characterized by blurring of the gray matter/white matter (GM/WM) junction, cortical thickening and hyper-intensity signal within lesional region compared with other cortical regions. However, detecting FCD lesions by means of visual inspection can be a very difficult task for radiologists because the lesions are very subtle. To assist physicians in detecting the FCD lesions more efficiently and reduce the false positive regions resulted from the existing methods, we propose an algorithm for automated FCD detection based on T1 MRI data