Search

We analyze the distributional effects of rent‐seeking via the financial sector in a model calibrated to US data. Rent‐seeking implies a misallocation of resources that increases wealth inequality among non‐rent‐seekers and for the whole economy. A deterioration in institutional quality implying more rent‐seeking leads to welfare losses for non‐rent‐seekers, especially for those with higher earnings and initial wealth, because they are most affected by the deterioration of the aggregate economy. On the other hand, welfare gains are larger for rent seekers with higher earnings and wealth, who have an increased resource extraction capacity. [ABSTRACT FROM AUTHOR]

Purpose: In view of the need for dose-validation procedures on each individual intensity-modulated radiation therapy (IMRT) plan, dose-verification measurements by film, by ionization chamber, and by polymer gel–MRI dosimetry were performed for a prostate-treatment plan configuration. Treatment planning system (TPS) calculations were evaluated against dose measurements.Methods and materials: Intensity-modulated radiation therapy (IMRT) treatments were planned on a commercial TPS. Kodak EDR-2 films were used for the verification of two-dimensional (2D) dose distributions at 1 coronal and 5 axial planes in a water-equivalent phantom. Full three-dimensional (3D) dose distributions were measured by use of a novel polymer gel formulation and a 3D magnetic resonance imaging (MRI) readout technique. Calculations were compared against measurements by means of isocontour maps, γ-index maps (3% dose difference, 3-mm distance to agreement) and dose–volume histograms.Results: A good agreement was found between film measurements and TPS predictions for points within the 60% isocontour, for all the examined plans (γ-index <1 for 96% of pixels). Three-dimensional dose distributions obtained with the polymer gel–MRI method were adequately matched with corresponding TPS calculations, for measurements in a gel phantom covering the planning-target volume (PTV).Conclusions: Measured 2D and 3D dose distributions suggest that, for the investigated prostate IMRT plan configuration, TPS calculations provide clinically acceptable accuracy. [Copyright &y& Elsevier]

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the accuracy of clinical dosimetry planning using commercially available treatment planning systems in 192Ir high-dose-rate (HDR) breast brachytherapy, with emphasis on skin dose, in view of potential uncertainties owing to the patient finite dimensions and the presence of the lung. Methods and materials: A patient-equivalent mathematical phantom was constructed on the basis of the patient computed tomography scan used in the clinical treatment planning procedure. The actual treatment plan delivered to the patient, involving an implant of six plastic catheters and 26 programmed source dwell positions, was simulated by means of the Monte Carlo method. Results are compared with corresponding dose calculations of a commercially available treatment planning system in the form of prescribed dose percentage isodose contours and cumulative dose—volume histograms. Results: The comparison of Monte Carlo results and treatment planning system calculations revealed that all percentage isodose contours greater than 60% of the prescribed dose are not affected by the finite breast dimensions or the presence of the lung. Treatment planning system calculations overestimate dose in the lung as well as lower isodose contours at points lying both close to the breast or lung surface and relatively away from the implant. In particular, skin dose is overestimated by 5% in the central breast region and within 10% at all other points. Conclusions: Dose—volume histogram and all other relevant planning quality indices for the planning target volume calculated by the treatment planning system are credible. Skin and lung dose calculations by the treatment planning system can be thought of as a conservative approach in view of the reported dose overestimation. [Copyright &y& Elsevier]

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented. [ABSTRACT FROM AUTHOR]