Analisis Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Pada Perusahaan Daerah Air Minum (PDAM) Kabupaten Flores Timur

PDAM is one of the agencies that often gets complaints from customers regarding the quality of service. These include complaints about the handling of complaints that the response was slow, slow handling of the pipe leak. This study aims to analyze (1) the extent to which the influence of service quality (reliability, responsiveness, assurance, empathy, and tangible) to customer satisfaction in the PDAM Kabupaten Flores Timur together, and (2) the extent to which the influence of service quality (reliability, responsiveness, assurance, empathy, and tangible) to customer satisfaction in the PDAM Kabupaten Flores Timur partially. The method used in this research is descriptive quantitative research methods. The independent variable in this study is the quality of service, which consists of reliability (X1), responsiveness (X2), assurance (X3), empathy (X4), and tangible (X5). Meanwhile, the dependent variable in this study is customer satisfaction (Y). As for the population in this study is the PDAM Kabupaten Flores Timur customers. Specified number of respondents of 100 respondents. The samples using nonprobability sampling, is by purposive sampling.. The data was collected through questionnaires and observations. Analysis of data using multiple regression analysis. The results showed that (1) Taken together or simultaneously all the variables, namely the reliability factor (reliability) (X1), factor responsiveness (responsiveness) (X2), the belief factor (assurance) (X3), factor empathy (empathy) ( X4), and intangible factors (tangible) (X5) and a significant positive effect on customer satisfaction PDAM Kabupaten Flores Timur, and (2) Partially reliability factor (reliability) (X1), security (assurance) (X3), and intangible factors (tangible) (X5) and a significant positive effect on customer satisfaction PDAM Kabupaten Flores Timur. In contrast, factor responsiveness (responsiveness) (X2) and factor empathy (empathy) (X4) in this model and no significant positive effect on customer satisfaction PDAM Kabupaten Flores Timur

Stochastic EM methods with variance reduction for penalised PET reconstructions

Expectation-maximisation (EM) is a popular and well-established method for image reconstruction in positron emission tomography (PET) but it often suffers from slow convergence. Ordered subset EM (OSEM) is an effective reconstruction algorithm that provides significant acceleration during initial iterations, but it has been observed to enter a limit cycle. In this work, we investigate two classes of algorithms for accelerating OSEM based on variance reduction for penalised PET reconstructions. The first is a stochastic variance reduced EM algorithm, termed as SVREM, an extension of the classical EM to the stochastic context that combines classical OSEM with variance reduction techniques for gradient descent. The second views OSEM as a preconditioned stochastic gradient ascent, and applies variance reduction techniques, i.e., SAGA and SVRG, to estimate the update direction. We present several numerical experiments to illustrate the efficiency and accuracy of the approaches. The numerical results show that these approaches significantly outperform existing OSEM type methods for penalised PET reconstructions, and hold great potential

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

Youth-Friendly Health Services in Ethiopia: What Has Been Achieved in 15 Years and What Remains to be Done

AbstractSince 2005, Pathfinder International has partnered with the Ethiopian Ministry of Health to implement integrated youth-friendly services (YFS), an evidence-based approach that reduces barriers to sexual and reproductive health (SRH) services for young people. Program experience shares the 15-years journey, including milestones, achievements, scale-up efforts, learnings, and remaining YFS-provision gaps that must be filled to improve adolescent health in Ethiopia.The first phase of YFS initiation was an interactive process that involved consensus-building among public-sector stakeholders, the establishment of selection criteria for participating sites, readiness-assessment of facilities selected to initiate YFS, and collaborative development of action plans with the facilities. In the second phase, Pathfinder International and partners designed and implemented YFS using the World Health Organization (WHO) ExpandNet systematic scale-up framework to achieve institutionalized and sustainable impact at scale. As a result, from 2005 to 2020, more than 39,000 peer educators and 12,000 YFS providers received training on YFS, sexually transmitted infections (STIs), and contraception including long-acting reversible contraceptives (LARCs). More than 25-million-person contacts with young people were done on quality health information, and more than 8 million have received YFS, such as modern contraceptives, including LARCs, STI and HIV testing and treatment, and post-abortion care.The lessons from Pathfinder International’s more than a decade of YFS implementation in Ethiopia, showed YFS that responding to the needs of young people beyond providing accessible, unbiased, and confidential (with privacy) services are critical to improving health service uptake among young people. This required continuous effort to create an enabling environment, use evidence for decision making, engagement of the public sector and young people from inception to scaling-up YFS. Improvement in SRH service uptake was possible by tailoring implementation to local context and institutionalizing YFS within existing public health systems. Furthermore, rigorous age-disaggregated data and evidence were needed to inform adolescent health and development programs in Ethiopia. [Ethiop. J. Health Dev. 2021: 35(SI-5):70-77]Keywords: Health service, Youth friendly service, scale-up, Ethiopi

Unsupervised knowledge-transfer for learned image reconstruction

Abstract Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not available in medical imaging. To circumvent this issue we develop a novel unsupervised knowledge-transfer paradigm for learned reconstruction within a Bayesian framework. The proposed approach learns a reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images of ellipses and the corresponding simulated measurement data. The second phase fine-tunes the pretrained network to more realistic measurement data without supervision. By construction, the framework is capable of delivering predictive uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography showing that the approach is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques. Moreover, for test data distributed differently from the training data, the proposed framework can significantly improve reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, when compared with learned methods trained on the synthetic dataset only

Quantifying sources of uncertainty in deep learning-based image reconstruction

Abstract Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction. In this work we propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We build on a Bayesian deep gradient descent method for quantifying epistemic uncertainty, and incorporate the heteroscedastic variance of the noise to account for the aleatoric uncertainty. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data. The estimated uncertainty captures the variability in the reconstructions, caused by the restricted measurement model, and by missing information, due to the limited angle geometry