Integration of cad/ tool path data for 5-axis step-nc machining of free form / irrregular contoured surfaces

This research paper presents the work on feature recognition, tool path data generation and integration with STEP-NC (AP-238 format) for features having Free form / Irregular Contoured Surface(s) (FICS). Initially, the FICS features are modelled / imported in UG CAD package and a closeness index is generated. This is done by comparing the FICS features with basic B-Splines / Bezier curves / surfaces. Then blending functions are caculated by adopting convolution theorem. Based on the blending functions, contour offsett tool paths are generated and simulated for 5 axis milling environment. Finally, the tool path (CL) data is integrated with STEP-NC (AP-238) format. The tool path algorithm and STEP- NC data is tested with various industrial parts through an automated UFUNC plugin.

Feature recognition & tool path generation for 5 axis step-nc machining of free form / irregular contoured surfaces

This research paper presents a five step algorithm to generate tool paths for machining Free form / Irregular Contoured Surface(s) (FICS) by adopting STEP-NC (AP-238) format. In the first step, a parametrized CAD model with FICS is created or imported in UG-NX6.0 CAD package. The second step recognizes the features and calculates a Closeness Index (CI) by comparing them with the B-Splines / Bezier surfaces. The third step utilizes the CI and extracts the necessary data to formulate the blending functions for identified features. In the fourth step Z-level 5 axis tool paths are generated by adopting flat and ball end mill cutters. Finally, in the fifth step, tool paths are integrated with STEP-NC format and validated. All these steps are discussed and explained through a validated industrial component.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Understanding next generation sequencing in oncology: A guide for oncologists

DNA sequencing is now faster and cheaper than ever before, due to the development of next generation sequencing (NGS) technologies. NGS is now widely used in the research setting and is becoming increasingly utilised in clinical practice. However, due to evolving clinical commitments, increased workload and lack of training opportunities, many oncologists may be unfamiliar with the terminology and technology involved. This can lead to oncologists feeling daunted by issues such as how to interpret the vast amounts of data generated by NGS and the differences between sequencing platforms. This review article explains common concepts and terminology, summarises the process of DNA sequencing (including data analysis) and discusses the main factors to consider when deciding on a sequencing method. This article aims to improve oncologists' understanding of the most commonly used sequencing platforms and the ongoing challenges faced in expanding the use of NGS into routine clinical practice.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

The mental health and wellbeing of first generation migrants: a systematic-narrative review of reviews

Background
First generation migrants are reportedly at higher risk of mental ill-health compared to the settled population. This paper systematically reviews and synthesizes all reviews on the mental health of first generation migrants in order to appraise the risk factors for, and explain differences in, the mental health of this population.
Methods
Scientific databases were searched for systematic reviews (inception-November 2015) which provided quantitative data on the mental ill-health of first generation migrants and associated risk factors. Two reviewers screened titles, abstracts and full text papers for their suitability against pre-specified criteria, methodological quality was assessed.
Results
One thousand eight hundred twenty articles were identified, eight met inclusion criteria, which were all moderate or low quality. Depression was mostly higher in first generation migrants in general, and in refugees/asylum seekers when analysed separately. However, for both groups there was wide variation in prevalence rates, from 5 to 44 % compared with prevalence rates of 8–12 % in the general population. Post-Traumatic Stress Disorder prevalence was higher for both first generation migrants in general and for refugees/asylum seekers compared with the settled majority. Post-Traumatic Stress Disorder prevalence in first generation migrants in general and refugees/ asylum seekers ranged from 9 to 36 % compared with reported prevalence rates of 1–2 % in the general population. Few studies presented anxiety prevalence rates in first generation migrants and there was wide variation in those that did. Prevalence ranged from 4 to 40 % compared with reported prevalence of 5 % in the general population. Two reviews assessed the psychotic disorder risk, reporting this was two to three times more likely in adult first generation migrants. However, one review on the risk of schizophrenia in refugees reported similar prevalence rates (2 %) to estimates of prevalence among the settled majority (3 %). Risk factors for mental ill-health included low Gross National Product in the host country, downward social mobility, country of origin, and host country.
Conclusion
First generation migrants may be at increased risk of mental illness and public health policy must account for this and influencing factors. High quality research in the area is urgently needed as is the use of culturally specific validated measurement tools for assessing migrant mental health.