An efficient null space inexact Newton method for hydraulic simulation of water distribution networks

Null space Newton algorithms are efficient in solving the nonlinear equations arising in hydraulic analysis of water distribution networks. In this article, we propose and evaluate an inexact Newton method that relies on partial updates of the network pipes' frictional headloss computations to solve the linear systems more efficiently and with numerical reliability. The update set parameters are studied to propose appropriate values. Different null space basis generation schemes are analysed to choose methods for sparse and well-conditioned null space bases resulting in a smaller update set. The Newton steps are computed in the null space by solving sparse, symmetric positive definite systems with sparse Cholesky factorizations. By using the constant structure of the null space system matrices, a single symbolic factorization in the Cholesky decomposition is used multiple times, reducing the computational cost of linear solves. The algorithms and analyses are validated using medium to large-scale water network models.Comment: 15 pages, 9 figures, Preprint extension of Abraham and Stoianov, 2015 (https://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001089), September 2015. Includes extended exposition, additional case studies and new simulations and analysi

Graph-theoretic Surrogate Measures for Analysing the Resilience of Water Distribution Networks

AbstractHydraulic resilience can be formulated as a measure of the ability of a water distribution network to maintain a minimum level of service under operational and failure conditions. This paper explores a hybrid approach to bridge the gap between graph-theoretic and hydraulic measures of resilience. We extend the concept of geodesic distance of a pipeline by taking into account energy losses associated with flow. New random-walk algorithms evaluate hydraulically feasible routes and identify nodes with different levels of hydraulic resilience. The nodes with the lowest scores are further analysed by considering the availability and capacity of their supply routes

Non-LTE, Relativistic Accretion Disk Fits to 3C~273 and the Origin of the Lyman Limit Spectral Break

We fit general relativistic, geometrically thin accretion disk models with non-LTE atmospheres to near simultaneous multiwavelength data of 3C~273, extending from the optical to the far ultraviolet. Our model fits show no flux discontinuity associated with a hydrogen Lyman edge, but they do exhibit a spectral break which qualitatively resembles that seen in the data. This break arises from relativistic smearing of Lyman emission edges which are produced locally at tens of gravitational radii in the disk. We discuss the possible effects of metal line blanketing on the model spectra, as well as the substantial Comptonization required to explain the observed soft X-ray excess. Our best fit accretion disk model underpredicts the near ultraviolet emission in this source, and also has an optical spectrum which is too red. We discuss some of the remaining physical uncertainties, and suggest in particular that an extension of our models to the slim disk regime and/or including nonzero magnetic torques across the innermost stable circular orbit may help resolve these discrepancies.Comment: Accepted for publication in Ap

Localise to segment: crop to improve organ at risk segmentation accuracy

Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest. We investigate the accuracy improvements brought about by such a localisation stage by comparing to a single-stage baseline network trained on full resolution images. We find that localisation approaches can improve both training time and stability and a two stage process involving both a localisation and organ segmentation network provides a significant increase in segmentation accuracy for the spleen, pancreas and heart from the Medical Segmentation Decathlon dataset. We also observe increased benefits of localisation for smaller organs. Source code that recreates the main results is available at \href{https://github.com/Abe404/localise_to_segment}{this https URL}

Small Medium Enterprises Brand Gestalt: A Key Driver of Customer Satisfaction and Repurchase Intention

Purpose:  There has been a lack of empirical research examining the relationship between brand gestalt, customer satisfaction, and repurchase intention. The present study aims to fill this theoretical gap by analyzing the influence of brand gestalt on customer satisfaction and repurchase intention in the context of Small and Medium Enterprises (SMEs).   Theoretical framework:   Brand gestalt is a crucial construct that explains the comprehensive perception of a brand held by customers, and its importance in constructing brand meaning is paramount.   Design/methodology/approach:  The study employed a quantitative survey approach, using purposive sampling to collect data from 344 SME customers in Manado, Indonesia. The hypotheses were tested using partial least squares structural equation model (PLS-SEM).   Findings:  The empirical results demonstrated that the four dimensions of brand gestalt (namely, story, sensescape, servicescape, and stakeholder) are significant predictors of customer satisfaction. Additionally, both brand sensescape and servicescape exert a significant impact on customer intention to repurchase, both directly and through the mediating effect of customer satisfaction. While the direct relationship between the story and repurchase intention was not found to be significant, this result provides support for the complete mediating role of customer satisfaction.   Research, Practical & Social implications: These findings provide valuable insights for SME practitioners in formulating brand strategies, highlighting that an effective story, sensescape, and servicescape can lead to customer satisfaction and repurchase intention.   Originality/value:  This study contributes to the existing literature on SME branding by providing the first empirical evidence on the link between brand gestalt, customer satisfaction, and repurchase intention. Moreover, it can aid in the development of effective branding strategies and improve the competitiveness and performance of small businesses, which can have a positive impact on local economies and communities

Modeling and characterization for microstrip filters in the manufacturing process through the Unscented Transformand use of electromagnetic simulators

This paper presents the unscented transform (UT) applied to uncertainty modeling of manufacturing tolerances at the design stage of microwave passive devices. The process combines the UT with electromagnetic simulations and assumes that the numerical sources of error are negligible in comparison to the imperfections due to the manufacturing process. The technique was validated with the simulation, construction, and test of several sets of identical microstrip filters with very good results. Although the combination of UT and electromagnetic simulators was presented for microstrip filters, it can also be used for different types of microwave devices

A prophylactic subcutaneous dose of the anticoagulant tinzaparin does not influence qPCR-based assessment of circulating levels of miRNA in humans

Circulating microRNAs (miRNAs) have become increasingly popular biomarker candidates in various diseases. However, heparin-based anticoagulants might affect the detection of target miRNAs in blood samples during quantitative polymerase chain reaction (qPCR)- based analysis of miRNAs involving RNA extraction, cDNA synthesis and the polymerase catalyzed reaction. Because low-molecular-weight heparins (LMWH) are widely used in routine healthcare, we aimed to investigate whether a prophylactic dose of the LMWH tinzaparin influences qPCR-based quantification of circulating miRNAs. A total of 30 subjects were included: 16 fracture patients with tinzaparin treatment and 14 non-fracture controls without anticoagulation therapy. To control for the effect of tinzaparin on miRNA analysis an identical concentration of synthetic miRNAs was added to plasma, isolated RNA and prepared complementary DNA (cDNA) from all samples in both groups. No significant difference was observed for cDNA synthesis or qPCR when comparing tinzaparin-treated patients with untreated controls. Among the tinzaparin-treated patients, plasma levels of six endogenous miRNAs (hsa-let-7i-5p, hsa-miR-30e-5p, hsa-miR-222-3p, hsa-miR-1-3p, hsamiR- 133a-3p, hsa-miR-133b) were measured before and one to six hours after a subcutaneous injection of tinzaparin 4500IU. No significant effect was observed for any of the investigated miRNAs. A prophylactic dose of 4500IU tinzaparin does not seem to affect cDNA synthesis or qRT-PCR-based quantification of circulating miRNAs

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}

Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation

Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes based on pretreatment imaging is key to biologically targeted radiation therapy. We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs. For 37 patients who had undergone primary radiotherapy for oropharyngeal squamous cell carcinoma, five oncologists contoured the relapse volumes on recurrence CT scans. Datasets of pre-treatment FDG-PET/CT, gross tumour volume (GTV) and contoured relapse for each of the patients were randomly divided into training (n=23), validation (n=7) and test (n=7) datasets. We compared a CNN trained from scratch, a pre-trained CNN, a SUVmax threshold approach, and using the GTV directly. The SUVmax threshold method included 5 out of the 7 relapse origin points within a volume of median 4.6 cubic centimetres (cc). Both the GTV contour and best CNN segmentations included the relapse origin 6 out of 7 times with median volumes of 28 and 18 cc respectively. The CNN included the same or greater number of relapse volume POs, with significantly smaller relapse volumes. Our novel findings indicate that CNNs may predict LRR, yet further work on dataset development is required to attain clinically useful prediction accuracy