Quality Assurance Applications Research Articles

An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Despite the growing rich and fragmented literature focusing on quality assurance (QA) and Industry 4.0, the implementation of associated individual digital technologies has not been fully evaluated and synthesised to achieve adequate QA in the construction industry; hence, it has received limited focus. This study, thus, aimed to organise, evaluate, and synthesise the current literature on individual digital technology applications in QA in the construction industry and propose future research directions. A literature review approach was adopted for this study along with Deming’s cycle framework to address four research questions: (1) What is the status of the state-of-the-art in the literature? (2) What digital technologies have been applied for QA in the construction industry? (3) Which areas in QA processes have experienced digital technology applications, and what are the applications? (4) What are the limitations of the existing studies and future research directions of digital technologies for QA in the construction industry? The findings showed an increasing trend of research on digital technology for QA in construction since 2017. This cuts across 23 countries with six different research methods published across 18 different publication sources. Four categories of digital technologies were revealed to have been adopted for QA in construction based on the functionality of the technologies: data collection technologies, decision-oriented technologies, collaborative technologies, and transparency and security-related technologies. Evaluation with Deming’s cycle framework revealed that digital technologies have a high level of application at the “do” phase, improving the quality management process during construction towards achieving pre-stated quality requirements. This includes mostly collaborative technologies, consisting of BIM technologies. Limitations of the existing studies were further identified, and this led to five research directions: interoperability of technology development, integrated digital technologies for QA of prefabricated and modular construction, integrated digital technologies for QA of cross-border construction logistics and supply chain, digital innovation for sustainable QA, and moving beyond the technical solution. The study showed a significant contribution to both academia and the industry in the built environment.

The The Influence of Auditor Competence and Integrity on Audit Quality with the Implementation of Quality Assurance as a Moderating Variable (Study at the Audit Unit of the Regional Office of DJBC East Java I)

This research aims to test and analyze the influence of auditor competence and integrity on audit quality by applying quality assurance as a moderating variable, research at the audit unit of the East Java I Regional Office of the Directorate General of Customs and Excise. This type of research is quantitative research. The population of this study are functional auditors and executive examiners who are tasked with assisting the implementation of audits. The sampling technique uses a saturated sample, namely the entire population of 47 people as the research sample. The data analysis technique for testing hypotheses uses Partial Least Square – Structural Equation Modeling with the SmartPLS program. The research results show that: 1) auditor competency has a significant and positive effect on audit quality, 2) auditor integrity has a significant and positive effect on audit quality, 3) the application of quality assurance has a significant and positive effect on audit quality, 4) the application of quality assurance does not moderate the effect auditor competency on audit quality, 5) implementation of quality assurance does not moderate the influence of auditor integrity on audit quality

Systems and Mechanisms of Applying Quality Assurance in Higher Education.

Systems and Mechanisms of Applying Quality Assurance in Higher Education.

ICH Q14-inspired novel approach to establish an SFC-based purity method for carbamazepine.

The proposed ICH Q14 guideline "Analytical procedure development" describes science and risk-based approaches for development and maintenance of analytical procedures suitable for the assessment of the quality of drug substances and drug products. As a case study, the systematic development and validation of a supercritical fluid chromatography (SFC)-based purity method for carbamazepine is presented. Systematic analytical quality by design (AQbD) principles were applied using the software package Fusion QbD to the method development approach. The relationship between chromatographic parameters and the responses of interest were examined to improve the reliability of the method by understanding, reducing, and controlling sources of variability. Method performance qualification in terms of method robustness was finally carried out with the parameters that were classified as critical after method development and a validation study met previously set acceptance criteria. The developed SFC purity method for carbamazepine demonstrated readiness as a viable alternative to the official HPLC method published in the Ph.Eur. with improved peak resolution, improved peak symmetry, and faster analysis times (3min vs. 80 min for the official method). Its inherent reliability illustrates the superiority of AQbD in method development and application for drug quality assurance.

Scaling up the task-sharing of psychological therapies: A formative study of the PEERS smartphone application for supervision and quality assurance in rural India

Abstract Measurement-based peer supervision is one strategy to assure the quality of psychological treatments delivered by non-mental health specialist providers. In this formative study, we aimed to 1) describe the development and 2) examine the acceptability and feasibility of PEERS (Promoting Effective mental healthcare through peER Supervision)—a novel smartphone app that aims to facilitate registering and scheduling patients, collecting patient outcomes, rating therapy quality and assessing supervision quality—among frontline treatment providers delivering behavioral activation treatment for depression. The PEERS prototype was developed and tested in 2021, and version 1 was launched in 2022. To date, 215 treatment providers (98% female; ages 30–35) in Madhya Pradesh and Goa, India, have been trained to use PEERS and 65.58% have completed the supplemental, virtual PEERS course. Focus group discussions with 98 providers were examined according to four themes—training and education, app effectiveness, user experience and adherence and data privacy and safety. This yielded commonly endorsed facilitators (e.g., collaborative learning through group supervision, the convenience of consolidated patient data), barriers (e.g., difficulties with new technologies) and suggested changes (e.g., esthetic improvements, suicide risk assessment prompt). The PEERS app has the potential to scale measurement-based peer supervision to facilitate quality assurance of psychological treatments across contexts.

إجراءات تطبيق عمليات ضمان وضبط الجودة في الجامعات اليمنية من وجهة نظر القيادات الاكاديمية وأعضاء هيئة التدريس

This study displays the Yemen experience in the field of quality assurance and academic accreditation for post secondary institutions to build on when constructing quality assurance and academic accreditation for general education. It aims at identifying the implementation level of postsecondary institutions of evaluation activities required for quality assurance and academic accreditation through the opinions of those who are in charge of this process and others involved in it. It also aims at identifying needs that help in applying quality assurance in post-secondary institutions in Yemen Arabia، hurdles faced، and proposing suitable solutions at the individual، institutional، and system levels. The two researchers have applied a questionnaire at purposeful sample consisting of those who are in charge of evaluation and quality assurance activates and those interested in it at their universities. Interviews were also conducted to specify the practicing level of evaluation for individuals، programs، and institutions، and its barriers. Results showed that there are differences amongst the universities in regard to implementing evaluation and tools for quality assurance and accreditation purposes. The main implementation barriers were the lack of sufficient knowledge and training. The researchers stressed the necessity of adopting capacity building model to assess training needs for individuals، programs، and institutions. This model can be used to design graded and detailed training programs that fit each university، and the implementation and monitoring of it should happen at each university site.

Object-Oriented Approach: Applying ISO 21001 at Vietnamese Higher Education Institutions

To evaluate whether an educational institution has quality or not, requires standards that reflect all the requirements of the complex relationships operating within the educational institution. At the same time, this standard must be recognized by all nations for it to apply widely. It is in this context that the ISO 21001:2018 series of standards was published. This standard mentioned three areas of activities that an educational organization must pay attention to: training, scientific research, and community service. They are also the main activities that the educational accreditation standards refer to. To meet the requirement of quality guarantee and enhancement, a higher education institution has to apply quality assurance norms in general and quality criteria on management systems. Putting the ISO 21001 quality standard into practice is the choice of Vietnamese educational institutions to ensure they meet the requirements for improving management system quality. There are a set of quality system standards for educational organizations to satisfy their specific needs. Not following the traditional approach as they apply other ISO standard systems, Vietnamese educational institutions have chosen an object-base approach, where the beneficiaries are related to the management system of the university. They then approach the PDCA cycle to build and implement an ISO 21001 quality management system. We found that, according to this orientation, the implementation processes might be ensured given that the quality thresholds set by the university are met in order to satisfy the demands of the learners and related stakeholders. The paper aims to analyze the approach of Vietnamese educational institutions in implementing ISO 21001 standards, and at the same time identify advantages and disadvantages in developing policies in the quality management system at educational institutions. The paper also gives recommendations that can be adjusted by management at other higher education institutions to improve quality assurance activities towards sustainable development.
 Keywords: ISO 21001:2018, quality assurance, quality management system, PDCA (Plan-Do-Check-Act), sustainable development

Delineation of prototypical degradation mechanism, characterization of unknown degradation impurities by liquid chromatography-quadrupole-time-of-flight-tandem mass spectrometry and stability-indicating analytical method of selumetinib.

Selumetinib (SELU) was recently approved by the US Food and Drug Administration (US FDA) in 2020. However, the degradation impurities of SELU have not been characterized or identified to date. The mechanism for impurity formation and the degradation behavior have not been previously studied. This study aims to elucidate the prototypical degradation mechanism of SELU. Furthermore, the degradation impurities have been identified using LC-quadrupole-time-of-flight tandem mass spectrometry and are reported in this article for the first time. In addition, a stability-indicating analytical method (SIAM) has been developed for this drug. Forced degradation studies revealed the degradation of SELU under various stress conditions, including hydrolytic stress (acid and base), oxidative stress, and photolytic stress (ultraviolet and visible). Three degradation impurities were identified. This article presents the first validated SIAM, capable of accurately quantifying SELU in the presence of its degradation impurities. Furthermore, we have proposed the degradation pathway for SELU and its degradation impurities, a first in the field. The developed SIAM can find applications in process development and quality assurance of SELU in both research laboratories and pharmaceutical industries. Moreover, the identified degradation impurities may serve as impurity standards for quality control testing in pharmaceutical industries.

From Protons to Picasso: Recreating Famous Paintings Using Proton Beams as a “Paintbrush”

To many, medicine is just as much an artform as it is a science. From the colorful stains used in diagnostic pathology, to the tactile sculpting of surgery, to the dose painting in radiation oncology, there is beauty and art to be found across all disciplines. In pencil-beam-scanning proton therapy, the dose is painted layer-by-layer, spot-by-spot, allowing for significantly more control compared to conventional radiation. This work intends to showcase the immense capability of intensity-modulated proton therapy (IMPT) through the recreation of some of the most renowned artworks throughout history, and has potential applications in patient education, medical/physics education, and quality assurance. Five (5) well-recognized paintings spanning from the Golden Age to the modern era were recreated as treatment plans in the Eclipse (version 16.1) External Beam Planning environment. For each "painting" (treatment plan), clinical protons within the range of 70-250 MeV were used to deposit "paint" (radiation dose) onto a "canvas" (water phantom) to a prescription of 100 Gy. The individual elements of each artwork were first delineated as separate structures. Colors were then assigned to appropriate isodose lines to mimic the corresponding palettes found in the original artworks. For each treatment plan, a single field was used along with inverse optimization to deposit the different doses at a depth of 10 centimeters. The resulting dose distribution was calculated with the proton convolution-superposition algorithm used in clinical practice. The dose distributions of the "paintings" demonstrated a marked semblance to the original artworks with sufficient resolution to elucidate fine details. The "paintings" varied in dimension from 20-30 cm at isocenter and on average took between 5-6 hours to complete. The number of structures used for planning ranged from 6-65 depending on the style and complexity of the original artwork. For a fractional dose of 200 cGy, the number of monitor units ranged from 73,145-269,970. With the advent of IMPT and modern planning techniques, we can now realize increasingly complex treatment plans, allowing for the creation of dose distributions that are highly targeted and specific. This is demonstrated by the capability to create recognizable works of art using proton beams as a "paintbrush".

A QbD Based RP-HPLC Method for Stability Indicating Impurity Profiling of Pyridoxine: Method Development, Validation, and Application

Pyridoxine impurity profiling in bulk and formulations was devised and validated using a reversed-phase high-performance liquid chromatography (RP-HPLC) strategy. The technique was fine-tuned using an Analytical quality by design (QbD) approach, ensuring its dependability and sturdiness. Linearity, accuracy, and precision were carefully examined as key performance indicators. With a relative standard deviation (RSD) < 2%, the approach showed exceptional precision, excellent recovery rates (100 and, 101.2%), and a strong correlation value (R2 = 0.9990). The technique has been used successfully for impurity profiling, and because it indicates stability, it can be used for long-term stability studies. The work contributes to the body of knowledge and has applications for pharmaceutical quality assurance.

Evaluation of a Scintillating Plastic Optical Fiber Device for Measuring kV-Cone Beam Computed Tomography Dose.

Justification of imaging procedures such as cone beam computed tomography (CBCT) in radiotherapy makes no doubt. However, the CBCT composite dose is rarely reported or optimized, even though the repeated CBCT cumulative dose can be up to 3% of the prescription dose. This study aimed to evaluate the performance and utility of a new plastic scintillating optical fiber dosimeter for CBCT dosimetric quality assurance (QA) applications before a potential application in patient composite CBCT dosimetry. The dosimeter, made of 1 mm diameter plastic fiber, was installed under a linear accelerator treatment table and linked to photodetectors. The fiber impact on the fluence and dose delivered was respectively assessed with an electronic portal imaging device (EPID) and EBT3 Gafchromic® film. The presence of artifacts was visually evaluated on kV images. The dosimeter performances were determined for various acquisition parameters by comparison with ionization chamber values. The maximum impact of the fiber on the fluence measured by the EPID was -1.2% for the 6 MV flattening filter-free beam. However, the fiber did not alter the film dose profile when measured for all the beams tested. The fiber was not visible at energies ≥ 80 kV and was merely visible on the CBCT images. When the rate of images per second or mA was changed, the maximum relative difference between the device and the ionization chamber CTDIs was <5%. Changing collimation led to a -7.2% maximum relative difference with an absolute dose difference that was insignificant (-0.3 mGy). Changing kV was associated with a -8.7% maximum relative difference, as published in the literature. The dosimeter may be a promising device for CBCT recurrent dosimetry quality control or dose optimization. According to these results, further developments are in progress in order to adapt the solution to the measurement of patient composite CBCT doses.

Evaluation of Dhofar University Performance according to Quality Assurance and Academic Accreditation Criteria of Islamic Universities Federation

Objectives: The aim of this research is to evaluate the performance of Dhofar University in Sultanate of Oman based on two criteria: quality assurance and academic accreditation of Islamic Universities Federation. Methods: This research is categorized under quantitative approach by using descriptive statistics analysis. 236 participants of academics and Employee were selected randomly from population of university which included 423 lecturers and employees. Data was analyzed by using the Statistical Package for Social Sciences (SPSS-25). Results: The results indicated that criteria used in Dhofar University was congruent with Islamic Universities Federation criteria with an average (3.81 of 5) and a standard deviation (0.63) which is relatively high. The first criteria were the "Education Resources and Facilities Standard" with an average of 4.06, then the "Teaching and Learning Standard" with an average of 4.01, and finally the "International Standard" with an average of 3.56 respectively. The results also revealed that there are no significant differences between the responses of participants in the extent of quality assurance application standards and academic accreditation at Dhofar University, based on gender and years of experience. Conclusions: Finally, researcher presented a set of recommendations including adoption of the model (quality assurance standards and academic accreditation of Islamic Universities Federation) as long as there is an appropriate ground for the application at Dhofar University, with the initiation of forming a team to undertake this work, in addition to possibility of applying the module in other universities.

Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation

Spatially explicit information on crop distribution is essential for market information, food security, and agricultural sustainability. However, high-resolution crop maps are unavailable for most countries of the world. In this study, we developed an operational workflow and produced the first openly-available 10-m resolution maize and soybean map over China. We also derived area estimates for maize and soybean extent for 2019 using a stratified, two-stage, cluster sampling design and ground data collected for the entire country. We developed a multi-scale, multi-temporal procedure for mapping, in which field data were used as training to map maize and soybean over the first-stage sample of 10 km × 10 km equal-area blocks with PlanetScope and Sentinel-2 data. Then, the classified blocks were used as training to map maize and soybean for the country with wall-to-wall Sentinel-2 data using a random forests approach. We used all available Sentinel-2 surface reflectance data acquired between April and October 2019, applied quality assurance, including cloud and shadow masking, and created monthly image composites as inputs for the random forests analysis. We derived maize and soybean area estimates using the field sample data and a regression estimator. Utilizing the probability output layer of the random forests models, we found and applied empirical probability thresholds that matched map-based crop area estimates with sample-based area estimates. Maize area in China in 2019 was estimated to be 330,609 ± 34,109 km2 (± value is the standard error), and soybean area was estimated to be 78,107 ± 12,969 km2. Validated using the field sample data as reference, our crop map had an overall accuracy of 91.8 ± 1.2%. The user's and producer's accuracies for the maize class were 93.9 ± 2.5% and 79.2 ± 3.6%, and for the soybean class were 63.6 ± 12.1% and 61.9 ± 11.8%. Our map-based maize and soybean area estimates had close agreement with government reports at the provincial and prefectural levels, with r2 of 0.90 and 0.92 for maize, and 0.93 and 0.94 for soybean, respectively. Our workflow can generate internally consistent results for crop area estimation and crop mapping simultaneously. As Sentinel-2 data are being acquired consistently and very-high-resolution commercial satellite data are increasingly available, our established workflow may be applied in an operational setting for annual crop mapping in China and other countries.

Effect of Radiation Therapy Quality Assurance on Nasopharyngeal Carcinoma: Usage of a Novel, Web-Based Quality Assurance Application

We used a new web application for rapid review of radiotherapy (RT) target volumes to evaluate the relationship between target delineation compliance with the international guidelines and outcomes of definitive RT for nasopharyngeal carcinoma (NPC). The dataset consists of CT simulation scans, RT structures, and clinical data of 354 pathology-confirmed NPC patients treated with intensity-modulated RT between 2005 and 2017. Target volumes were peer-reviewed in RT QA rounds, and target contours were revised, if recommended, before treatment. We imported the contours of intermediate-risk clinical target volumes of the primary tumor (IR-CTVp) of 332 patients into the application. Inclusion of anatomic sites within IR-CTVp was determined in accordance with 2018 International guideline for CTV delineation for NPC and correlated with time to local failure (TTLF) using Cox-regression. In the peer-review QA analysis, local and distant control and overall survival (OS) rates were similar between peer-reviewed and non-reviewed cases and between cases with and without target contour changes. In the CTV compliance analysis, with a median follow-up of 5.6 years, 5-year TTLF and OS rates were 93.1% and 85.9% respectively. The most frequently non-guideline compliant anatomic sites were sphenoid sinus (n=69, 20.8%), followed by cavernous sinus (n=38, 19.3%), left and right petrous apices (n=37 and 32, 11.1% and 9.6%), and clivus (n=14, 4.2%). Among 23 patients with a local failure (6.9%), the number of non-compliant cases were 8 for sphenoid sinus, 7 cavernous sinus, 4 left and 3 right petrous apices, and 2 clivus. Cavernous sinus-conforming cases showed higher TTLF in comparison with non-conforming cases (93.6% vs 89.1%, p=0.013). Multivariable analysis confirmed that cavernous sinus non-compliance was prognostic for TTLF. Our application allowed rapid quantitative review of CTVp in a large NPC cohort. While compliance with the international guidelines was high, under-coverage of the cavernous sinus was correlated with TTLF.

Applying Quality Assurance Standards in Newly Established Universities: Issues and challenges

Applying Quality Assurance Standards in Newly Established Universities: Issues and challenges

Artificial Intelligence in Continuous Kidney Replacement Therapy.

Artificial Intelligence in Continuous Kidney Replacement Therapy.

Modeling linear accelerator (Linac) beam data by implicit neural representation learning for commissioning and quality assurance applications.

Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after. We propose to model Linac beam data through implicit neural representation (NeRP) learning. The potential of the beam model in predicting beam data from sparse measurements and detecting data collection errors was evaluated, with the goal of using the beam model to verify beam data collection accuracy and simplify the commissioning and QA process. NeRP models with continuous and differentiable functions parameterized by multilayer perceptrons (MLPs) were used to represent various beam data including percentage depth dose (PDD) and profiles of 6 MV beams with and without flattening filter. Prior knowledge of the beam data was embedded into the MLP network by learning the NeRP of a vendor-provided "golden" beam dataset. The prior-embedded network was then trained to fit clinical beam data collected at one field size and used to predict beam data at other field sizes. We evaluated the prediction accuracy by comparing network-predicted beam data to water tank measurements collected from 14 clinical Linacs. Beam datasets with intentionally introduced errors were used to investigate the potential use of the NeRP model for beam data verification, by evaluating the model performance when trained with erroneous beam data samples. Linac beam data predicted by the model agreed well with water tank measurements, with averaged Gamma passing rates (1%/1mm passing criteria) higher than 95% and averaged mean absolute errors less than 0.6%. Beam data samples with measurement errors were revealed by inconsistent beam predictions between networks trained with correct versus erroneous data samples, characterized by a Gamma passing rate lower than 90%. A NeRP beam data modeling technique has been established for predicting beam characteristics from sparse measurements. The model provides a valuable tool to verify beam data collection accuracy and promises to simplify commissioning/QA processes by reducing the number of measurements without compromising the quality of medical physics service.

New Possibility Soft Sets with Quality Assurance Application in Distance Education

The probability fuzzy soft set is an approach that is obtained by adding a probability degree to each approximate element of the fuzzy soft set and is formed by combining Fuzzy Set Theory and Soft Set Theory. This idea was later studied in different sets, such as intuitionistic fuzzy sets, neutrosophic fuzzy sets, Pythagorean fuzzy sets, etc. In this paper, a new Possibility Set is defined in the Fermatean Fuzzy environment and the essential features of new sets have been examined. The set-theoretic operations of new possibility sets, such as subset, soft equal, complement, union, intersection, AND, and OR, have been characterized with the help of elaborated examples. Their fundamental laws and properties are also discussed. A practical example concerning quality assurance in distance education is studied to demonstrate the applicability of new similarity measures in decision-making situations. Thus, it has been shown that the newly given similarity measure can be successfully applied to real-world decision problems. Finally, a comparison of the similarity of the proposed model is made with some existing models.

Towards a Deep Learning-based Online Quality Prediction System for Welding Processes

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding (GMAW). The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in GMAW. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.