Do state holding companies facilitate private participation in the water sector? evidence from Cote d'Ivoire, the Gambia, Guinea, and Senegal

When the Gambia, Guinea, and Senegal decided to involve the private sector in the provision of water services, they also established state holding companies - state-owned entities with exclusive or partial responsibilities for: a) owning infrastructure assets; b) planning and financing investments (replacing assets and expanding networks); c) regulating the activities of the private sector; and d) promoting public acceptance of private participation in the sector. In Cote d'Ivoire, by contrast, when private participation was introduced (in 1960), no state holding company was established. To determine whether state holding companies help private participation in the water sector succeed, the author reviews the four functions these entities are expected to perform in the Gambia, Guinea, and Senegal. In light of experience in all four countries, he examines whether, and under what circumstances, state holding companies might be the entities best suited for carrying out such functions. He concludes that creating a state holding company is often not the best solution. A state holding company might be better suited than other entities for planning and financing investments when (and only when): a) investment responsibilities cannot be transferred to the private operator; b) tariffs are insufficient, at least for a time, to cover investment needs, so it is crucial that a public entity has access to other sources of finance; and c) the holding company's financial strength and accountability, or its incentives and ability to promote the gradual adoption of cost-covering tariffs, are superior to those of a ministerial department. When one or more of these conditions are not met, the main investment responsibilities should be transferred to the private operator or, if that is not possible, left to the government itself. The other three functions should not, as a general rule, be performed by a state holding company.Town Water Supply and Sanitation,Environmental Economics&Policies,Water Supply and Sanitation Governance and Institutions,Water Conservation,Water and Industry

The interpolation method of Sprague-Karup

AbstractThe usual interpolation method is that of Lagrange. The disadvantage of the method is that in the given points the derivatives of the interpolating polynomials are not equal one to the other. In the method of Hermite, polynomials of a higher degree are used, whose derivatives in the given points are supposed to be equal to the derivatives of the function at the given points. This means that those derivatives must be known.If those derivatives are not known, then in the given points the derivatives may be replaced by approximative values, e.g. based on the interpolating polynomials of Lagrange. Such a method has been described by T. B. Sprague (1880) and in a simplified form by J. Karup (1898). In this paper the formulae are derived. Both methods are illustrated with an example. Some properties and theorems are stated. Tables to simplify the computational work are given. Subroutines for these interpolation methods will be published in a next article

Automated Radiation Therapy Patient Scheduling: A Case Study at a Belgian Hospital

The predicted increase in the number of patients receiving radiation therapy (RT) to treat cancer calls for an optimized use of resources. To manually schedule patients on the linear accelerators delivering RT is a time-consuming and challenging task. Operations research (OR), a discipline in applied mathematics, uses a variety of analytical methods to improve decision-making. In this paper, we study the implementation of an OR method that automatically generates RT patient schedules at an RT center with ten linear accelerators. The OR method is designed to produce schedules that mimic the objectives used in the clinical scheduling while following the medical and technical constraints. The resulting schedules are clinically validated and compared to manually constructed, historical schedules for a time period of one year. It is shown that the use of OR to generate schedules decreases the average patient waiting time by 80%, improves the consistency in treatment times between appointments by 80%, and increases the number of treatments scheduled the machine best suited for the treatment by more than 90% compared to the manually constructed clinical schedules, without loss of performance in other quality metrics. Furthermore, automatically creating patient schedules can save the clinic many hours of administrative work every week.Comment: 11 page

Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning

We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test set of patients shows that the predicted modes follow their respective ground truths well both spatially and in terms of their dose-volume histograms. A special dose mimicking method based on the MDN output is used to produce deliverable plans and thereby showcase the usability of voxel-wise predictive densities. Thus, this type of MDN may serve to support clinicians in managing clinical tradeoffs and has the potential to improve quality of plans produced by an automated treatment planning pipeline.Comment: 14 pages, 11 figures. To be submitted to Physics in Medicine & Biolog

Hunting for the New Symmetries in Calabi-Yau Jungles

It was proposed that the Calabi-Yau geometry can be intrinsically connected with some new symmetries, some new algebras. In order to do this it has been analyzed the graphs constructed from K3-fibre CY_d (d \geq 3) reflexive polyhedra. The graphs can be naturally get in the frames of Universal Calabi-Yau algebra (UCYA) and may be decode by universal way with changing of some restrictions on the generalized Cartan matrices associated with the Dynkin diagrams that characterize affine Kac-Moody algebras. We propose that these new Berger graphs can be directly connected with the generalizations of Lie and Kac-Moody algebras.Comment: 29 pages, 15 figure

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm

Objective. The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned. Therefore, an automated quality assurance framework was developed with the aim to detect in advance aberrant auto-segmentations. Approach. Five organs (prostate, bladder, anorectum, femoral head left and right) were auto-delineated on CT acquisitions for 48 prostate patients by an in-house trained primary DL model. An experienced radiation oncologist assessed the correctness of the model output and categorised the auto-segmentations into two classes whether minor or major adaptations were needed. Subsequently, an independent, secondary DL model was implemented to delineate the same structures as the primary model. Quantitative comparison metrics were calculated using both models' segmentations and used as input features for a machine learning classification model to predict the output quality of the primary model. Main results. For every organ, the approach of independent validation by the secondary model was able to detect primary auto-segmentations that needed major adaptation with high sensitivity (recall = 1) based on the calculated quantitative metrics. The surface DSC and APL were found to be the most indicated parameters in comparison to standard quantitative metrics for the time needed to adapt auto-segmentations. Significance. This proposed method includes a proof of concept for the use of an independent DL segmentation model in combination with a ML classifier to improve time saving during QA of auto-segmentations. The integration of such system into current automatic segmentation pipelines can increase the efficiency of the radiotherapy contouring workflow