Quality Assurance Process Research Articles

Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity

In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.

Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies

Abstract Radiation therapy is a localized cancer treatment that relies on precise delineation of the target to be treated and healthy tissues to guarantee optimal treatment effect. This step, known as contouring or segmentation, involves identifying both target volumes and organs at risk on imaging modalities like CT, PET, and MRI to guide radiation delivery. Manual segmentation, however, is time-consuming and highly subjective, despite the presence of contouring guidelines. In recent years, automated segmentation methods, particularly deep learning models, have shown promise in addressing this task. However, challenges persist in their clinical use, including the need for robust quality assurance (QA) processes and addressing clinical risks associated with the use of the models. This review examines the challenges and considerations of the clinical adoption of deep learning target auto-segmentation in radiotherapy, focused on the target volume. We discuss potential clinical risks (e.g. over- and under-segmentation, automation bias, and appropriate trust), mitigation strategies (e.g. human oversight, uncertainty quantification, and education of clinical professionals), and we highlight the importance of expanding QA to include geometric, dose-volume, and outcome-based performance monitoring. While deep learning target auto-segmentation offers significant potential benefits, careful attention to clinical risks and rigorous QA measures are essential for its successful integration in clinical practice.

Photodiode-based focus monitoring in ultrashort-pulsed laser structuring of graphite anodes for lithium-ion batteries

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.

Clinical assessment strategies for competency-based education in prosthetic dentistry.

Reflective practice is viewed as a theoretical and pedagogical concept in higher education having several diverse approaches and interpretations. The most important aspect of reflective practice is that it is a necessary quality assurance aspect of higher education which should occur recurrently and at different stages of the program. It usually entails an evaluation of advanced instructions which has become the norm in an educational setting, in order to improve the learning outcomes. Reflective practice must therefore be seen as a tool which allows continuous improvement, modifications, and changes to educational approaches, which include theoretical and clinical assessment strategies. Academics in prosthetic dentistry at a research-led university reflected on their current assessment strategies used in the senior undergraduate dental program as part of a quality assurance process and its global comparability. This paper aims to share and explain the importance of reviewing assessment strategies in higher education, especially in such a clinical program using reflective practice as a framework. Different assessment strategies used over a 5-year period are explored and their different structures, expectations, and appropriateness for a clinical program are reported from the literature. The concerns were addressed in a cyclical manner within this framework, and Blooms and blueprinting implemented where appropriate. We conclude that without a validated definition and framework for regular reflective practices, and guidelines to modify the included assessment strategies, the quality assurance within a competency-based dental program may be compromised.

Innovation in Teacher Training: Curricular and Operational Keys in Pedagogy Programs

This study addresses the curricular components and operational conditions required for the success of curricular innovations in teacher education programs. This quantitative study analyzed components related to epistemic meanings, organization, design, and curricular policies, as well as operational conditions for the development of curricular innovations in teacher training. A Likert-scale survey was administered to academics from public universities in Chile that train teachers. The method of analysis used SPSSv28 software, establishing a descriptive and inferential analysis through descriptive statistics of sex, age, gender, and position in the organization. The results show that operational conditions, academic teams, organizational aspects, updating of regulatory frameworks, infrastructure, and curricular policies are essential for the success of curricular innovations. We conclude that curricular innovations at the institutional level require the generation of curricular policies that contemplate the design, monitoring, and evaluation of the curriculum to favor the processes of curriculum review and quality assurance.

Customer perceptions about traceable meat: research from Northern China

Food traceability and food safety, including quality assurance processes, are essential for ensuring the health of members of society and can prevent food-supply-related problems. Meat-related food safety issues are significant, especially in developing countries like China. The main aim of this paper was to explore customer perceptions and purchase behavior, including willingness to pay, related to traceable meat. A qualitative exploratory approach was applied. Five focus groups were implemented with interviewees from Northern China, and grounded theory methodology was used in their analysis. The results showed that participants were influenced by product characteristics, sales-related factors, and subjective norms when purchasing meat. Their level of trust in traceability information was variable; respondents preferred independent quality assurance associations and information obtainable by scanning QR codes. The findings of this study may contribute to the introduction and development of food traceability systems in the meat industry, reinvigorating consumer trust.

Empowering decentralized production: A distributed manufacturing system for additive manufacturing processes

This work presents a pilot distributed manufacturing system for decentralized additive manufacturing, focusing on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) capabilities at Auburn University. The paper outlines the first phase of a multi-phase implementation plan and demonstrates the developed capabilities. The vision is to coordinate geographically separated manufacturing facilities as a cohesive unit. In our work, we prioritize instantaneous communication for file and information sharing. The Manufacturing Hub and Remote Cell, two facilities within driving distance, are coordinated in terms of part design, printing, and inspection procedures for a distributed Additive Manufacturing process. To enable coordination between the two facilities, ICAMS addresses three technological needs. These needs consider information sharing, process standardization, and data collection and monitoring. A network attached storage facilitates information sharing, a database-connected graphical user interface streamlines and standardizes the manufacturing process, and a data collection system facilitates process monitoring. The graphical user interface allows for order placement, progress monitoring, and communication updates. The developed phased plan, collaborative workflow, technological solutions, and production protocols demonstrate the potential for decentralized additive manufacturing. Successful integration of communication, design, printing, and quality assurance processes sets the foundation for future advancements in decentralized manufacturing systems.

Development of Risk-Based Methodologies for Highway Pavement Construction Inspection

Construction inspection plays a critical role in Portland cement concrete pavement (PCCP) and hot mix asphalt (HMA) pavement projects. To ensure the quality of pavement projects, state departments of transportation (DOTs) in the U.S. typically conduct construction material tests and inspect workmanship processes using quality assurance (QA) programs. Given today’s reality of reduced QA inspection resources such as funding, staff ability, and availability, several state DOTs have used a risk-based inspection (RBI) approach to optimize their QA processes. A thorough review of existing literature revealed limited studies examining RBI methodologies in highway pavement construction projects. The aim of this study is to investigate the use of RBI for pavement construction projects. It employed a triangulation research methodology, including an extensive literature review, a survey questionnaire of 50 state DOTs, focus groups, and a vetting workshop. A list of 16 core items in field inspection for both PCCP and HMA pavement projects was developed and verified. A generic risk rating of these critical inspection activities was determined. Additionally, inspection frequency, documentation effort, and inspector experience associated with these critical inspection activities identified through focus groups and the vetting workshop served as a reference point when conducting RBI for PCCP and HMA pavement projects. This study contributes to the body of knowledge and the highway construction industry by identifying and prioritizing 16 core inspection items for PCCP and HMA pavement construction. Construction project managers and inspectors may benefit from this study by analyzing the risks of core inspection items and implementing appropriate risk mitigation plans.

Cross-border higher education in Europe and the role of quality assurance agencies

This article discusses the multiple challenges cross-border higher education (CBHE) in Europe faces nowadays, namely regarding the assurance of its quality. The views of a set of European quality assurance agencies are analysed to understand their role in assuring the quality of this type of educational provision. Empirically, the article is based on the material collected through interviews conducted with the responsible for quality assurance agencies from 10 European countries: Estonia, Finland, Germany, Ireland, Italy, Lithuania, Netherlands, Portugal, Spain and the United Kingdom. The results reveal that almost all quality assurance agencies have at least partial experience with CBHE provision, mainly related to the evaluation of joint-degree programmes offered by European higher education institutions. The cooperation between countries and agencies emerges as one of the main challenges to the development of a common tool to enhance CBHE, which could facilitate the quality assurance process for all stakeholders.

Synthesis of junction box quality assurance for KM3NeT-ARCA neutrino detector

The KM3NeT-ARCA neutrino detector is the largest underwater neutrino telescope operating in the Mediterranean Sea at a depth of 3500 m, 100 km off the coast of Portopalo di Capo Passero (Italy). It is composed of strings anchored in the deep sea and kept vertical using a buoy. Each string hosts 18 Digital Optical Modules (DOMs), containing 31 optical sensors. The entire telescope, once completed, will be composed of 230 identical strings named Detection Units (DUs) that will send data to shore in real time. A Junction Box (JB) and an electro-optical cable permit the connection to the shore and the powering of the entire system. Each JB connects to the shore, handling up to 12 strings. It is a critical element that requires strict quality assurance rules. This article will focus on the quality assurance process that has allowed the JB to be a robust and reliable element of the entire KM3NeT-ARCA background network.

‘It’s paradoxical, but it works' – Towards ambidexterity in external quality assurance: the case of Roman Catholic ecclesiastical higher education

ABSTRACT This article focuses on organizational responses to seemingly paradoxical mandates, in the context of external quality assurance in higher education. With the promulgation of the Apostolic Constitution Veritatis Gaudium in 2018, Roman Catholic ecclesiastical higher education institutions have been required to be evaluated by a designated external quality assurance agency, the Holy See’s Agency for the Evaluation and Promotion of Quality in Ecclesiastical Universities and Faculties (AVEPRO). Operating internationally, the Agency is tasked to both evaluate quality with an accountability mindset and promote quality with a continuous enhancement outlook. These two approaches to quality processes can be considered conflicting notions. Using the lens of paradox theory and organizational ambidexterity to observe the nature of and responses to tensions, this study considers how AVEPRO managed the challenge of reconciling both QA and QE imperatives. Although ambidexterity studies have mainly focused on the private sector, this analysis shows that this theoretical framework can provide a relevant lens in other contexts where paradoxes exist, such as external quality assurance agencies in higher education. Findings show that an innovative organizational structure and a creative mindset can reconcile quality assurance and quality enhancement processes through different channels.

Machine Learning and Artificial Intelligence Improve Data Validation

Artificial intelligence (AI) can assist with time‐series data corrections, relieving a bottleneck in a water utility's quality assurance processes. AI's speed and accuracy can help reduce water scarcity and ultimately improve the global water crisis.

Analyse de risques appliquée au suivi des concentrations des immunosuppresseurs chez les patients transplantés pulmonaire

Mastering and monitoring immunosuppressant concentrations is central to the care of lung transplant patients and involves multiple stakeholders. The objective is to conduct a risk analysis to evaluate the impact of various actions taken. The lung transplantation team was convened to carry out a failure mode effect analysis. The process was divided into stages where different risks were identified. The risk priority number (RPN) (severity, frequency, detectability) and risk level of criticality (frequency, severity) were established before implementation of actions (before 2009) and then after (in 2022) to classify risks according to four levels of criticality. The implemented actions included the establishment of a quality assurance process, computerization of monitoring, and double analysis by a physician/pharmacist pair. Thirty-two risks were identified during the four stages of the process: biological sampling (n=13), reception (n=3), analysis and treatment of levels (n=5), transmission of information/prescriptions to the patient (n=11). The total raw RPN (before 2009) was 839, with 12 major risks. The current total RPN (in 2022) was 452 (a decrease of 46.1%), with 7 major risks identified. The analysis enabled the objective evaluation of the effectiveness of the actions taken. The most secure stage of the process is the reception of residual level results. Efforts should focus on empowering and involving patients, as well as engaging local stakeholders in collaboration with the specialized transplantation team.

Impact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry

Global health and well-being largely depend on the pharmaceutical and medical device industries. Manufacturing and quality assurance (QA) processes are crucial to maintaining product efficacy, safety, and regulatory compliance in these sectors. Artificial intelligence (AI) integration presents ground-breaking opportunities to enhance these processes. This study aims to systematically assess the impact of AI on manufacturing and QA in these pharmaceutical and medical device industries. It examines the benefits, challenges, and ethical and legal implications of integrating AI. It offers a thorough understanding of how AI technology can and has been successfully integrated to enhance business operations. An extensive literature analysis was carried out to investigate AI's application, role, benefits, and challenges in manufacturing and quality assurance processes in both industries. Research was also conducted on emerging trends, future developments, and regulatory issues. Increased productivity, early detection of defects, safer and higher-quality goods, improved regulatory compliance, reduced costs, and more flexibility and scalability are some advantages of AI technologies. However, significant obstacles are also to overcome, such as high capital costs, data quality and availability issues, legacy system integration, ethical concerns about bias and data privacy, difficulties with regulatory compliance, and a lack of AI-skilled workers. Case studies show how AI has been utilized to guarantee regulatory compliance and optimize processes. AI integration has much to offer the pharmaceutical and medical device industries in terms of improved manufacturing and quality assurance procedures. By addressing restrictions and seizing novel opportunities, these industries can use AI's transformative potential to support innovation, enhance product quality and safety, ensure regulatory compliance, and improve global health outcomes.

Ensuring excellence: A deep dive into the quality monitoring of Ayushman Bharat health and wellness Centre

This review evaluates the quality monitoring mechanisms of Ayushman Bharat Health and Wellness Centres, integral to India's pursuit of Universal Health Coverage. AB-HWCs are designed to deliver comprehensive primary healthcare, particularly targeting undeserved communities. Despite the significant expansion and progress in healthcare access, the review identifies persistent service quality and infrastructure disparities. The study underscores the necessity of a robust quality monitoring framework, incorporating standardized checklists to ensure consistent and high-quality service delivery. These checklists play a crucial role in systematically assessing the availability of medical supplies, infrastructure adequacy and others, facilitating timely interventions and modification. Additionally, the integration of cultural competence into healthcare delivery is emphasized to enhance the acceptance and effectiveness of services among diverse populations. The review also highlights the critical roles of Community Health Officers and mid-level healthcare providers in the successful operation of HWCs. While acknowledging the commendable progress made by the AB-HWCs initiative, the study concludes that achieving sustainable and equitable health outcomes necessitates ongoing quality monitoring, strategic resource allocation, and adaptive policy measures. The consistent application of checklists in quality assurance processes is essential for maintaining high care standards and advancement of India towards its UHC objectives.

Enhancing Software Testing Quality Assurance Using Agile Development Environment and Artificial Intelligence With Research Travelogue

This paper presents the analysis of software testing quality assurance using agile development environment and artificial intelligence with research travelogue. It explores the integration of Agile development methodologies with Artificial Intelligence (AI) to elevate the quality assurance (QA) processes in software testing. By leveraging Agile practices, such as iterative development and continuous feedback, alongside AI-driven tools for automation, predictive analytics, and anomaly detection, the approach aims to significantly enhance the accuracy and efficiency of QA processes. The research travelogue documents the journey through various Agile environments, detailing how AI technologies are applied to streamline testing workflows, identify potential issues earlier in the development cycle, and adapt to changing requirements. The findings reveal that this hybrid approach not only accelerates the testing process but also improves defect detection rates and overall software reliability, offering valuable insights for future advancements in software QA practices

Leveraging artificial intelligence and machine learning analytics for development effectiveness: a comprehensive approach to extract insights from project data

ABSTRACT In the normal course of business, international development institutions investing in development projects generate thousands of project-related documents summarising and analysing each aspect of their policymaking processes and policy lifecycles from design and financing through implementation performance and ultimately project/policy outputs, outcomes and effectiveness-related data. Moreover, institutions’ internal processes for quality assurance, monitoring and results measurement systems generate quantitative and qualitative primary and secondary data that can be employed to extract insights about lessons learned, internal decision-making processes, business model changes and project and portfolio performance. Resource and time constraints, however, result in the underutilisation of these valuable data sources for knowledge management, planning, evaluation and analysis. Specifically, qualitative insights housed in project documents remain largely inaccessible and cannot be employed systematically in combination with other quantitative data, in a unified framework. This article explores how machine learning can accelerate knowledge generation and management and aid results-based decision-making in line with development institutions’ existing development effectiveness frameworks through two main channels. The first leverages project documentation for historical portfolio systematisation to extract key strategic topics as well as lessons learned through the application of techniques such as text mining and topic modelling. The second proposes an agency-based system for predictive analytics, which analyses current and historical data to make predictions about future outcomes or trends to strengthen data-driven decision-making.

The effects of single education session on physiotherapy students' knowledge levels and thoughts on accreditation.

and Purpose: In the global landscape, quality assurance is paramount for educational institutions to adapt and thrive. The accreditation process involves evaluating an institution's quality according to standards established by experts and officially documenting its level of quality. This study aimed to assess the impact of a single educational session on physiotherapy and rehabilitation students' awareness and understanding of accreditation processes, recognizing their vital role in quality assurance. A pretest-posttest design was employed with 211 students from a physiotherapy and rehabilitation department. Data were collected using a questionnaire assessing demographic information, knowledge about accreditation, and thoughts regarding accreditation. The educational session focused on accreditation criteria and processes, involving presentations and interactive discussions. McNemar's analysis was used to compare the response rates given by the students pre-and post-session. Analysis after the education session revealed a significant increase in students' knowledge of accreditation concepts (p<0.05). Positive attitudes towards accreditation were reinforced, with students recognizing its importance in education quality. Despite pre-existing positive attitudes, the educational intervention enhanced students' understanding and engagement in accreditation processes with a significant increase in three of the eight questions on thoughts about accreditation (p<0.05). This study underscores the efficacy of educational interventions in fostering student engagement and awareness of accreditation. Findings suggest the need for integrating accreditation education into curricula and advocating its significance through seminars and literature support, ultimately enhancing student participation in quality assurance processes.

Methods of Extracting and Analyzing Metadata for Evidentiary Purposes

This paper examines methods for extracting and analyzing metadata for evidentiary purposes in civil proceedings. Through a comprehensive review of current literature, legal cases, and forensic techniques, it explores the diverse approaches to metadata analysis across various digital domains, including file systems, emails, documents, web browsers, mobile devices, cloud storage, social media, and emerging technologies. The study highlights the critical role of metadata in establishing the authenticity, reliability, and chronology of digital evidence. It also addresses the challenges posed by encrypted data, large-scale analysis, and the need for robust quality assurance processes. The findings underscore the importance of adapting forensic methodologies to evolving digital landscapes while maintaining legal and ethical standards. This research contributes to the ongoing development of best practices in digital forensics and their application in civil litigation.

Efektivitas Manajemen Penjaminan Mutu Pendidikan di SMK Bakti Karya

Assuring the quality of education is a crucial aspect in improving the quality of education and the success of educational institutions. This research aims to evaluate the effectiveness of education quality assurance management at Bakti Karya Vocational School. The research method used is a qualitative approach with descriptive analysis, involving direct observation, interviews with related parties, and documentation studies. The research results show that Bakti Karya Vocational School has implemented various quality assurance procedures which include planning, implementing and evaluating educational activities. However, there are several challenges in its implementation, such as limited resources and lack of training for teaching staff. This research recommends increasing training for teaching staff, preparing more detailed operational standard guidelines, and utilizing information technology to support the quality assurance process. In conclusion, although quality assurance management at Bakti Karya Vocational School shows significant progress, it needs continuous improvement and refinement in order to achieve optimal educational results.