Taming outliers in pulsar-timing datasets with hierarchical likelihoods and Hamiltonian sampling

Pulsar-timing datasets have been analyzed with great success using probabilistic treatments based on Gaussian distributions, with applications ranging from studies of neutron-star structure to tests of general relativity and searches for nanosecond gravitational waves. As for other applications of Gaussian distributions, outliers in timing measurements pose a significant challenge to statistical inference, since they can bias the estimation of timing and noise parameters, and affect reported parameter uncertainties. We describe and demonstrate a practical end-to-end approach to perform Bayesian inference of timing and noise parameters robustly in the presence of outliers, and to identify these probabilistically. The method is fully consistent (i.e., outlier-ness probabilities vary in tune with the posterior distributions of the timing and noise parameters), and it relies on the efficient sampling of the hierarchical form of the pulsar-timing likelihood. Such sampling has recently become possible with a "no-U-turn" Hamiltonian sampler coupled to a highly customized reparametrization of the likelihood; this code is described elsewhere, but it is already available online. We recommend our method as a standard step in the preparation of pulsar-timing-array datasets: even if statistical inference is not affected, follow-up studies of outlier candidates can reveal unseen problems in radio observations and timing measurements; furthermore, confidence in the results of gravitational-wave searches will only benefit from stringent statistical evidence that datasets are clean and outlier-free.Comment: 6 pages, 2 figures, RevTeX 4.

Influence of reaction conditions on the properties of solution-processed Cu2ZnSnS4 nanocrystals

Cu2ZnSnS4 nanocrystals were fabricated by hot injection of sulphur into a solution of metallic precursors. By careful control of the reaction conditions it was possible to control the elemental composition of the nanocrystals such that they are suitable for earth abundant photovoltaic absorbers. When the reaction temperature increased from 195 oC to 240 oC the energy band gap of the nanocrystals decreased from 1.65 eV to 1.39 eV. This variation is explained by the identification of a mixed wurtzite-kesterite phase at lower reaction temperatures and secondary phase Cu2SnS3 at higher temperatures. Moreover, the existence of wurtzite structure depends critically on the reaction cooling rate. The reaction time was also found to have a strong effect on the nanocrystals which became increasingly copper poor and zinc rich as the reaction evolved. As the reaction time increase from 15 minutes to 60 minutes, the energy band gap increased from 1.42 eV to 1.84 eV. This variation is discussed in terms of the sample doping. The results demonstrate the importance of optimising the reaction conditions to produce high quality Cu2ZnSnS4 nanocrystals

Analysis of approximate nearest neighbor searching with clustered point sets

We present an empirical analysis of data structures for approximate nearest neighbor searching. We compare the well-known optimized kd-tree splitting method against two alternative splitting methods. The first, called the sliding-midpoint method, which attempts to balance the goals of producing subdivision cells of bounded aspect ratio, while not producing any empty cells. The second, called the minimum-ambiguity method is a query-based approach. In addition to the data points, it is also given a training set of query points for preprocessing. It employs a simple greedy algorithm to select the splitting plane that minimizes the average amount of ambiguity in the choice of the nearest neighbor for the training points. We provide an empirical analysis comparing these two methods against the optimized kd-tree construction for a number of synthetically generated data and query sets. We demonstrate that for clustered data and query sets, these algorithms can provide significant improvements over the standard kd-tree construction for approximate nearest neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan 15-16, 199

Sustainability management accounting system (SMAS): towards a conceptual design for the manufacturing industry

[Abstract]: The study reported in this paper aims to identify an effective management accounting system using sustainability accounting concept for environmental and social cost measurement to add value to organizations. The motivation for undertaking this research is driven by the current practice of activity based costing (ABC), which has not identified and allocated costs of environment and social impacts to a single production activity. This has resulted in inaccuracies in cost accounting information when preparing environmental and social performance disclosures for internal management decisions, as well as external disclosures. This study therefore develops a conceptual model for a Sustainability Management Accounting System (SMAS) to improve the identification and measurement of environmental and social impact costs. A SMAS also provides sustainable organizations with a way to enhance cost allocation and analysis efficiently, thus creating more accurate cost accounting information for management decisions and reporting disclosure purposes. This paper describes preliminary work undertaken to date. Currently, it would appear that most Australian firms fail to report on their environmental performance, however, social indicators make it increasingly important for organisations to embrace corporate social reponsibility in their financial reporting and disclosure. Further, the results of quantitative data anlaysis will be used to identify an effective management accounting of sustainable organizations while supporting the development of a SMAS conceptual model

End-of-life vehicle (ELV) recycling management: improving performance using an ISM approach

With booming of the automobile industry, China has become the country with increasing car ownership all over the world. However, the end-of-life vehicle (ELV) recycling industry is at infancy, and there is little systematic review on ELV recycling management, as well as low adoption amongst domestic automobile industry. This study presents a literature review and an interpretive structural modeling (ISM) approach is employed to identify the drivers towards Chinese ELV recycling business from government, recycling organizations and consumer’s perspectives, so as to improve the sustainability of automobile supply chain by providing some strategic insights. The results derived from the ISM analysis manifest that regulations on auto-factory, disassembly technique, and value mining of recycling business are the essential ingredients. It is most effective and efficient to promote ELV recycling business by improving these attributes, also the driving and dependence power analysis are deemed to provide guidance on performance improvement of ELV recycling in the Chinese market

Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors

Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competently and similarly identify the machines’ performance. Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.” This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive calibration, on-machine checking and lost production due to inaccuracy. This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making. Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration

Shainin methodology: An alternative or an effective complement to Six Sigma?

Purpose: The purpose of this paper is to provide a brief overview of Six Sigma and Shainin RedX (R) methodology and to propose the modification of Six Sigma methodology in order to achieve the improved efficiency of DMAIC in the diagnostic journey using some of the approaches of Shainin RedX (R) methodology. Methodology/Approach: The diagnostic journey of Six Sigma has been revised by bringing key elements of Shainin RedX (R) methodology into DMAIC: task domain character of the method, focus on the dominant root-cause, use of the progressive elimination method and the application of a problem-solving strategy. Findings: This paper presents a proposal of DMAIC framework modification using selected tools and procedures of Shainin RedX (R) methodology in the diagnostic phase. Research Limitation/implication: Although the improved methodology is used in the environment of the automotive supplier, in this paper, practical examples are not included in order not to violate the licensing rules applied by Shainin LLC. Originality/Value of paper: The contribution of this article is the proposal of modified methodology, which should improve the effectiveness of problem-solving.Web of Science192311