Assurance Process Research Articles

NON-DESTRUCTİVE TESTİNG (NDT) APPLİCATİONS İN ENSURİNG THE RELİABİLİTY AND SAFETY OF MEDİCAL EQUİPMENT: A COMPREHENSİVE REVİEW

The increasing complexity and criticality of medical equipment in healthcare settings demand rigorous testing methodologies to ensure their reliability and safety. This paper explores the diverse applications of Non-Destructive Testing (NDT) in the realm of medical equipment testing. NDT techniques, traditionally employed in industries such as aerospace and manufacturing, offer unique advantages for assessing the structural integrity, functionality, and performance of medical devices without causing harm. The paper begins by providing an overview of NDT principles and methodologies, highlighting their non-invasive nature and ability to detect flaws and defects at an early stage. Subsequently, it delves into the specific NDT techniques applicable to medical equipment, including ultrasound, radiography, magnetic particle testing, and eddy current testing. Each technique's principles, advantages, and limitations are discussed in the context of medical device testing. Furthermore, the paper reviews real-world case studies and examples where NDT has been successfully applied to assess the quality and reliability of various medical equipment, ranging from imaging devices to surgical instruments. The challenges and potential limitations of implementing NDT in the medical field are also addressed, along with suggestions for overcoming these obstacles. In conclusion, this comprehensive review emphasizes the pivotal role of NDT in ensuring the integrity and safety of medical equipment. By leveraging NDT techniques, healthcare professionals can enhance the quality assurance processes, minimize the risk of equipment failure, and ultimately contribute to the improvement of patient outcomes. This paper aims to serve as a valuable resource for researchers, practitioners, and stakeholders in the healthcare and NDT communities, fostering a deeper understanding of the synergies between these two critical domains. Keywords: Non-Destructive Testing (NDT), Medical Equipment, Reliability, Safety, Quality Assurance.

Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing

The medical device sector adheres to strict regulatory frameworks, requiring precise adherence to quality assurance (QA) processes during the production process. Conventional quality assurance (QA) approaches, although successful, sometimes require substantial time and resource allocations, resulting in possible obstacles and higher expenses. The emergence of Artificial Intelligence (AI) in recent years has completely transformed quality assurance (QA) methods in different sectors, providing unparalleled prospects for improved productivity, precision, and scalability. This research examines the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices. Manufacturers may improve product quality and streamline production workflows by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures. Artificial intelligence systems can analyse large amounts of data to find abnormalities, uncover flaws, and anticipate any problems in real-time. This allows for proactive intervention and reduces the chances of non-compliance hazards. In addition, AI-powered QA systems provide adaptive learning capabilities, constantly enhancing performance through feedback and adapting to changing regulatory needs. The incorporation of artificial intelligence (AI) into current quality management systems enables smooth and efficient sharing of data and compatibility, promoting a comprehensive approach to quality control throughout the whole production process.

Current Use and Barriers to Point-of-Care Ultrasound in Rheumatology: A National Survey of VA Medical Centers.

Point-of-care ultrasound (POCUS) can assist rheumatologists in monitoring disease activity, establishing diagnoses, and guiding procedural interventions. POCUS use has been increasing, but little is known about current use and barriers among rheumatologists.The purpose of this study was to characterize current POCUS use, training needs, and barriers to use among rheumatologists in practice. A prospective observational study of all Veterans Affairs (VA) medical centers was conducted using a web-based survey sent to all chiefs of staff and rheumatology chiefs about current POCUS use, training needs, barriers, and policies. All chiefs of staff (n = 130) and rheumatology chiefs at VA medical centers (n = 95) were surveyed with 100% and 84% response rates, respectively.The most common diagnostic POCUS applications were evaluation of synovitis, joint effusion, tendinopathies, bursitis, and rotator cuff. The most common procedural applications were arthrocentesis and joint, bursa, and tendon injection. Most rheumatology chiefs (69%) expressed interest in training for their group. The most common barriers to POCUS use were lack of trained providers (68%), funding for training (54%), training opportunities (38%), funding for travel (38%), and ultrasound equipment (31%). Lack of POCUS infrastructure was common, and few facilities had POCUS policies (20%), image archiving (25%), or quality assurance processes (6%). Currently, half of rheumatology groups use diagnostic and procedural ultrasound applications. Most rheumatology groups desire training, and lack of training and equipment were the most common barriers to ultrasound use. Deliberate investment is needed in ultrasound training and infrastructure for systematic adoption of POCUS in rheumatology. Graphical Abstractavailable for this article. NCT03296280.

Assessing thermal resistance as a degradation metric for solder bump arrays in discrete SiC MOSFET packages

The development of innovative power electronics solutions for automotive applications introduces new reliability requirements for electronic assemblies and interconnection technologies. Many of the reliability methods used in power electronics require extensive experimental data, resulting in long product design cycles. This work focuses on developing a simulation-driven approach to assess the solder joint reliability of a discrete silicon carbide MOSFET by monitoring its degradation under power cycling in the thermal and thermo-mechanical domains using the thermal resistance of the stack as a failure metric. Active power cycling tests are performed to determine the loading condition at which end-of-life is reached due to a 20% increase in thermal resistance. Numerical analysis using finite element simulations is conducted to gain a physical understanding of the failure criterion, allowing to monitor solder degradation based on the thermal resistance and to pinpoint failure at individual interconnects within a solder bump array. The proposed methodology aims to accelerate the quality assurance and product qualification processes of discrete power electronic devices.

Role of Strategic Planning in Quality Enhancement of Higher Education Institutions: Case Study of Bamyan University

Afghanistan is a conflict-impacted country with a higher education sector that is undergoing rapid growth in the last two decades. The Ministry of Higher Education established the Quality Assurance & Accreditation Commission in 2009; and established the Quality Assurance & Accreditation Directorate (QAAD) in 2011. This body is to administer and oversee the accreditation process in all public and private HEIs of Afghanistan with a strategic outlook. By providing a comprehensive QA Framework and aiming to meet international standards; QAAD is functioning for Internal and External Quality Assurance of HEIs. This Framework emphasizes the development of a Strategic Plan for Higher Education Institutions in its first main criteria. Since QAAD and its relevant processes of Quality Assurance are newly implemented phenomena for the Higher Education Institutions; the processes of Developing Strategic Plans seem to be ineffective, challenging, and not used as a fundamental tool for quality enhancement. Furthermore, there are considerable challenges facing the implementation level of developed plans. Considering the important role of strategic planning in quality assurance; this study aims to evaluate the effectiveness and impacts of strategic planning on the processes of quality enhancement of Bamyan University as one of the National Higher Education Institutions. Nine major aspects of quality issues focusing on three main levels of strategic planning at the institution, Faculty, and Department are among the core subjects of this study. This study is carried out from the implementers’ point of view and includes the responses of all academic and non-academic members of Departments, Faculties, and Leadership of Bamyan University. Furthermore, this provides lessons learned and recommendations for institutions, policy makers, and other Higher Education Institutions. This study is conducted from February to August 2021 in Bamyan University of Afghanistan. A questionnaire was adopted and developed as the instrument of this study and nine major aspects of the qualification issues were included in the instrument. SPSS tool was employed to descriptively analyze the findings of the study. This study has shown that the exercise and experiences of strategic planning have not meaningfully and satisfactorily contributed to the enhancement of the quality of education of this University. The mean value of all aspects is somehow below average or average level. From the respondents’ point of view; the impact of strategic planning on quality assurance activities is greater than other aspects and this impact is lower in the sustainability of financial resources.

Induced seismicity data prep: Automate data processing and data set production in Texas and New Mexico using Python and ArcGIS Pro tools

Causal factors of induced seismicity in the Permian Basin are investigated by collecting and processing data on reported earthquakes, hydraulic fracture operations, and salt water disposal (SWD). Data are collected from five online sources: (1) the TexNet Earthquake Catalog, which provides earthquake data for Texas; (2) the TexNet Injection Volume Reporting Tool, which provides daily SWD data for select Texas wells; (3) the FracFocus Chemical Disclosure Registry, which provides hydraulic fracture data to the public; and (4) B3 Insight and (5) IHS Enerdeq Browser, which are proprietary database services that provide current and historical well data through paid subscriptions. TexNet makes their data available to the public at dynamic map websites. We automate data processing and data management using Python and ArcGIS Pro tools. The workflow produces quick, reliable, consistent, and reproducible output. We developed a Python script for each collected data table to filter, select fields, and write a new table. We created ArcGIS Pro Model Builder models for each new table to control format properties at import to the geodatabase. Further models contain customized ArcToolbox tools arranged to run geospatial, quality assurance, and quality control processing steps. In addition to discussing the source data and general workflow, we also review the results of the automated data processing. To illustrate our method, we create areas of investigation around the 5.4 magnitude Coalson earthquake to collect and process available data to create maps, charts, and data products for use in subsequent analysis. We make our Python scripts available on GitHub.

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Evaluation of a Structured Review Process for Emergency Department Return Visits with Admission

Review of emergency department (ED) revisits with admission allows the identification of improvement opportunities. Applying a health equity lens to revisits may highlight potential disparities in care transitions. Universal definitions or practicable frameworks for these assessments are lacking. The authors aimed to develop a structured methodology for this quality assurance (QA) process, with a layered equity analysis. The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher's exact tests. The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education. The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.

Systematic Implementation of Effective Quality Assurance Processes for the Assessment of Radiation Target Volumes in Head and Neck Cancer

Systematic Implementation of Effective Quality Assurance Processes for the Assessment of Radiation Target Volumes in Head and Neck Cancer

Evaluation of End Users’ Participation in the Design and Delivery of Public Building Projects in Ekiti State, Nigeria

Building projects are expensive, complicated, and frequently involve several parties with various demands. Since users are the ones who will ultimately use, maintain, and benefit from the projects, successful construction projects are planned, developed, and equipped to meet their needs. The purpose of this study was to assess end users’ involvement in the planning and execution of public building projects in Ekiti State, Nigeria, in order to improve project performance. Information on the extent of end users’ participation in the design and implementation of public projects was gathered through the use of questionnaires with professionals in the ministry of works, directors in the ministry of education, state universal basic education board, contractors and direct users of public building projects. A total of 138 of 143 copies of questionnaires that were distributed were recovered to achieve a rate of 96.5%. Findings revealed that end users were not involved in the planning, execution, or quality control of public building projects when it came to examining the ongoing work or the process of quality assurance. The study concluded that end users are typically satisfied with the outcomes when they are closely involved in the project’s management and participate often. The study therefore recommended that, prior to the project being closed and turned over, end users should be involved in the quality control process to ensure that the project deliverables and outcomes satisfy needs and requirements.

COVID-19 Information Platform for Primary Care: Emergence and Development of a Web-based Knowledge Platform in the COVID-19 Pandemic

The SARS-Cov-2 outbreak in the spring of 2020 challenged the health care system, and thus primary care, on an unprecedented scale. Knowledge about the new disease was low, whereas the dynamics of knowledge generation were high and inhomogeneous. A number of new primary care tasks related to the pandemic situation emerged. Guidance in the management of COVID-19 was therefore needed, although robust evidence was not yet available. The information required concerned not only the virus and the new disease COVID-19, but also regulatory requirements and organizational issues. In this situation, a flexible, web-based information tool, easy to update and usable at the point of care, was developed at Karl Landsteiner Private University Krems and put online under the name of "COVID-19: prevention and management in primary care practices" on March 25, 2020. In a retrospective process description, we describe the needs-triggered process of developing and disseminating a practice-based tool to support practicing primary care physicians in a period of high uncertainty with an urgent need for information. Afterwards, we reflect on the learning process from a purely pragmatic to an increasingly structured approach and try to draw conclusions regarding optimization possibilities in terms of creation and dissemination. In situations of high uncertainty combined with an acute need for action and decision-making, there is a significant need for information that is as reliable as possible. Science transfer must be done in such a way that information can be implemented quickly. Dissemination, as always, plays an essential role. Gaps must be accepted. A structured process of quality assurance must be established in parallel. Funds and resources for knowledge transfer should be included in future pandemic plans.

THREE OBSTACLES OF INTERNAL QUALITY ASSURANCE SYSTEM OF PRIVATE HIGHER EDUCATION IN INDONESIA

Objective: The implementation of internal quality assurance in higher education faces many obstacles. This research is focused on the obstacles of implementing the internal quality assurance system in Private higher education. Internal quality assurance system is a system carried out by universities independently. Method: The approach used in this research is qualitative with case study method. The case studied is the obstacle to the implementation of internal quality assurance system. Data collection uses combining an internal document review with semi-structured interviews. The qualitative interview data were subject to thematic analysis, a method for systematically identifying, organizing, and offering insight into patterns of meaning across a dataset. Result: In terms of administrative obstacles, it was found that the internal quality assurance agency at Puangrimaggalatung University only had 62.5% of standard documents. The organizational constraints found include 1) Limited understanding of internal quality assurance system only for structural officials, not for all but not for all stake holders in tertiary institutions. 2) Difficulties in determining measurable indicators of successful implementation of the internal quality assurance system, and 3) Unclear responsibilities among various structural positions resulting in overlapping of authority, which complicates internal quality assurance system implementation. In terms of psychological constraints, data is obtained in the form of neglect, disdain or cynical attitude towards quality assurance systems and processes. Conclusion: Based on an in-depth study of the data that has been collected, it is concluded that the implementation of the internal quality assurance system at private universities is going well however. there are obstacles in 3 areas, namely 1) Administrative obstacles 2) organizational obstacles and 3) psychological obstacles.

Managing quality assurance at community colleges in Ontario, Canada: experiences and perspectives of front-line quality managers

PurposeThis paper aims to examine the roles of quality managers at community colleges, their experiences balancing accountability and improvement and their insights into the future of quality assurance.Design/methodology/approachThis phenomenological, qualitative study used semi-structured interviews with eight community college quality managers to investigate their roles, experiences and perspectives. A reflexive thematic approach was used to analyze the interview data.FindingsFour themes were identified from participant responses: quality managers frame and enable program quality, quality managers drive program change, quality managers cultivate a culture of quality and quality managers seek system change. The findings illustrate the roles played by quality managers as they work to improve college education at program, institution and system-wide levels.Research limitations/implicationsThe decision of participants to accept the recruitment invitation might reflect particular attitudes, perspectives or experiences.Practical implicationsQuality assurance has emerged as a key mechanism for ensuring postsecondary programs are current, relevant and meeting the evolving needs of students and employers. This study advances the understanding of how quality assurance processes play out at the operational level and explores the experiences of quality managers as they navigate various quality tensions.Originality/valueQuality managers play key roles in leading, evaluating and influencing quality assurance processes in postsecondary education yet they are underrepresented in the literature. The findings of this study shed new light on the aspirational and influential roles they play in advancing quality assurance.

A comprehensive dataset of environmentally contaminated sites in the state of São Paulo in Brazil

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil.

Improving PCB defect detection using selective feature attention and pixel shuffle pyramid

– Due to the ongoing miniaturization of electronic products and the use of miniature printed circuit boards (PCBs), existing AI-based defect detection methods have exhibited poor performance in detecting tiny PCB defects. This issue can potentially compromise safety, degrade manufacturing quality, and increase production costs. To tackle this problem, we propose two novel techniques for PCB defect detection, namely Selective Feature Attention (SF attention) and Pixel Shuffle Pyramid (PSPyramid). SF attention identifies important features from a pyramid feature map to fuse the semantic and spatial information, while PSPyramid effectively fuses semantic features to detect various types of defects on PCBs, especially tiny defects. Moreover, a customized training strategy, specifically for PCB defect detection, is devised. To evaluate the performance of our proposed algorithms, extensive experiments have been conducted on two well-known PCB datasets containing tiny defects: the DeepPCB and TDD datasets. Our proposed non-referential method achieves performance comparable to existing referential methods on the DeepPCB dataset, making it more feasible for industrial applications. Compared to state-of-the-art methods, our method reduces the error by 16%, in terms of AP50, on the TDD dataset. The experimental results demonstrate the effectiveness of our proposed method in improving the quality assurance process for PCBs in the electronics industry.

Gradual error detection technique for non-destructive assessment of density and tensile strength in fused filament fabrication processes

Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products.

The Assessment on the Process of Carrying out Quality Assurance of Educational Materials in Tanzania

Teaching and learning materials enhance teaching and learning by providing information, understanding concepts, developing problem-solving skills, encouraging reflective thinking, and being scripted to ensure children&amp;apos;s success. Careful planning, monitoring, evaluation, and quality assurance processes like internal and external critical review, ethics, and copyright approval are necessary to ensure the effectiveness of learning materials. The study aimed to assess the quality assurance process of educational materials in Tanzania. The study employed a qualitative research approach, and data were gathered through the literature review. The United Republic of Tanzania&amp;apos;s education and training policies, curriculum framework for basic and teacher education, basic, secondary, and teacher education quality assurance framework, and other related materials were consulted as the primary data sources. Content analysis of data was used to come up with a clear interpretation of information and a conclusion of the results. The finding shows that the Tanzania Education Policy talks about quality education and needs to give a straightforward guide on developing quality assurance tools for implementing the policy. The study found that the Tanzania Institute of Education has an excellent and well-organised guideline for the educational materials quality assurance process that can be adopted and included in the policy. The study recommends the inclusion of statements directing the quality assurance of educational materials in the education policy. Also, the study suggests requiring teachers to be included and involve student teachers in quality assurance since they are the critical implementers and users of educational materials.

Evaluation of monte carlo to support commissioning of the treatment planning system of new pencil beam scanning proton therapy facilities

ABSTRACT Objective. To demonstrate the potential of Monte Carlo (MC) to support the resource-intensive measurements that comprise the commissioning of the treatment planning system (TPS) of new proton therapy facilities. Approach. Beam models of a pencil beam scanning system (Varian ProBeam) were developed in GATE (v8.2), Eclipse proton convolution superposition algorithm (v16.1, Varian Medical Systems) and RayStation MC (v12.0.100.0, RaySearch Laboratories), using the beam commissioning data. All models were first benchmarked against the same commissioning data and validated on seven spread-out Bragg peak (SOBP) plans. Then, we explored the use of MC to optimise dose calculation parameters, fully understand the performance and limitations of TPS in homogeneous fields and support the development of patient-specific quality assurance (PSQA) processes. We compared the dose calculations of the TPSs against measurements (DDTPSvs.Meas.) or GATE (DDTPSvs.GATE) for an extensive set of plans of varying complexity. This included homogeneous plans with varying field-size, range, width, and range-shifters (RSs) (n = 46) and PSQA plans for different anatomical sites (n = 11). Main results. The three beam models showed good agreement against the commissioning data, and dose differences of 3.5% and 5% were found for SOBP plans without and with RSs, respectively. DDTPSvs.Meas. and DDTPSvs.GATE were correlated in most scenarios. In homogeneous fields the Pearson’s correlation coefficient was 0.92 and 0.68 for Eclipse and RayStation, respectively. The standard deviation of the differences between GATE and measurements (±0.5% for homogeneous and ±0.8% for PSQA plans) was applied as tolerance when comparing TPSs with GATE. 72% and 60% of the plans were within the GATE predicted dose difference for both TPSs, for homogeneous and PSQA cases, respectively. Significance. Developing and validating a MC beam model early on into the commissioning of new proton therapy facilities can support the validation of the TPS and facilitate comprehensive investigation of its capabilities and limitations.

Does Quality Matter? Quality Assurance in Research for the Chilean Higher Education System

This study analyzes the research quality assurance processes in Chilean universities. Data from 29 universities accredited by the National Accreditation Commission were collected. The relationship between institutional accreditation and research performance was analyzed using length in years of institutional accreditation and eight research metrics used as the indicators of quantity, quality, and impact of a university’s outputs at an international level. The results showed that quality assurance in research of Chilean universities is mainly associated with quantity and not with the quality and impact of academic publications. There was also no relationship between the number of publications and their quality, even finding cases with negative correlations. In addition to the above, the relationship between international metrics to evaluate research performance (i.e., international collaboration, field-weighted citation impact, and output in the top 10% citation percentiles) showed the existence of three clusters of heterogeneous composition regarding the distribution of universities with different years of institutional accreditation. These findings call for a new focus on improving regulatory processes to evaluate research performance and adequately promote institutions’ development and the effectiveness of their mission.

Analysing Television Captioning: Production, Readings and Effects

The study is grounded in the encoding and decoding model by Stuart Hall, to map a connection between the creators of captions and the audiences who receive them. The study employs a mixed methods approach that takes on a descriptive design to gather data through questionnaire, interviews, and the observation. The study concluded that there are habitual caption problems in television programmes that emanate from various problems in the human resources and the available technologies. As such, the study recommended strengthening and establishment of programmes editorial units, establishment of quality assurance processes as essential in spearheading the quality of programmes production.