Top Research Papers on Quality Assurance

The QA/QC programme allowed the identification of major difficulties and needs in HBM analysis as well of gaining insight in the analytical capacities of European laboratories, and is the first step towards the establishment of a sustainable European network of HBM laboratories.

This review was conducted to understand current contamination control practices, and seven measures to control for contamination are suggested in order to improve the quality of results in microplastic sampling in future assessments.

The prevalence of microplastics has heightened awareness of cross-contamination concerns in recent years, making quality control and assurance critical for the quantitative assessment of microplastics. Environmental scientists dealing with microplastics employ both field and laboratory blanks to determine the extent of cross-contamination in unknown samples (samples of interest) in their analyses, but this source of uncertainty has yet to be assessed and is being overlooked. This paper intends to present: (1) an overview of the approaches for monitoring external contamination; (2) issues relat...

Abstract This study examines the extent of and determinants for sustainability assurance quality. Data comprise sustainability assurance statements published by the top 100 listed companies in Australia and New Zealand from 2017 to 2019. The findings indicate that Australian companies lead their New Zealand counterparts in sustainability assurance. Although sustainability reporting has risen, assurance rates remain significantly low. Accountants dominate the market, and companies prefer to use their own auditors for sustainability assurance work. Sustainability assurance quality is poor and do...

Conclusive evidence showed that the pharmacological activities reported in crude extracts and chemical markers are supporting and provides confidence in the traditional use of A. paniculata as a herbal medicine.

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning applicat...

Generally applicable recommendations on implementation and quality assurance of AI models are presented for commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography.

The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA and was shown to be effective on externally available datasets.

JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China, is presented and baselines are released to help improve the reasoning ability of machine comprehension models.

This work presents an overview of QA/QC techniques for NTA workflows published to date, specifically focusing on the analysis of environmental samples using liquid chromatography coupled to HRMS, and discusses methods used for each step of analysis: sample preparation, chromatography, mass spectrometry, and data processing.

Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added.

The continuous improvement in good practices and implementation of hazard analysis and critical control points (HACCP) remains very crucial for food hygiene quality safety to steadily thrive in the agro-food product industry sector. To improve the agro-food product quality, the dependency of quality management (QM) on such key facets as quality assurance (QA), control, improvement, and planning appears to be on the rise. Herein, how food hygiene quality safety standards and their associated processes have been assured is described. To understand the relevance of QM in the (above-mentioned) pro...

BACKGROUND: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or act...

This work develops and evaluates machine learning-based predictive models using height map-derived quality metrics for single tracks and the accompanying pyrometer and high-speed video camera data collected under a wide range of laser power and laser velocity settings and finds the SeDANN model outperforms other purely data-driven black box models in both accuracy and speed.

This work constructs a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for and introduces a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingually document retrieval from multilingual and English resources.

Recommendations for commercially available MR simulators are provided, including initial equipment selection, siting, acceptance testing, quality assurance, optimization of dedicated radiation therapy specific MR-SIM workflows, patient-specific considerations, safety, and staffing.

This article provides a comprehensive survey of the state of the art in the assurance of ML, i.e., in the generation of evidence that ML is sufficiently safe for its intended use, at different stages of the machine learning lifecycle.

This work proposes a new model, QA-GNN, which addresses the problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) through two key innovations: relevance scoring and joint reasoning.

A framework in which two separated BERT-based models were fine-tuned to identify and classify events in Portuguese documents, producing a new corpus for Portuguese event extraction and developing an automatic translation pipeline.

This study presents a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data and introduces the evidence information containing a reasoning path forMulti-hop questions, and demonstrates that the dataset is challenging formulti-hop models and it ensures that multi-Hop reasoning is required.