Quality Control Strategies Research Articles

Azacytidine shows potential in controlling the chilling injury of banana peel during cold storage

Banana suffers from chilling injury at low temperatures, which significantly challenges industrial banana quality control worldwide without a widely-accepted solution, to date. In this study, 5-azacytidine (5-azaC), a common DNA methylation inhibitor (DNA methylation is a conserved modification that regulates physiological processes), was used to treat banana fingers with subsequent storage at 7 °C for 9 days, which can be a novel strategy for controlling banana peel chilling injury and quality deterioration. In most sampling days, 5-azaC treatment significantly increased lightness value (L*), hue angle, and firmness while significantly decreased the browning index. Surprisingly, the chilling injury index was persistently declined by 5-azaC up to 26.8 % (p < 0.05). 5-azaC also reduced electrolyte leakage and levels of malondialdehyde and reactive oxygen species, indicating the control of chilling injury development. Further results showed that 5-azaC-inhibited DNA methylation contributed to banana chilling injury control by modulating the redox system and energy homeostasis. Collectively, this study offered new insights into fruit chilling injury development and control, as well as inspired potential strategies for postharvest quality control.

Ovarian Carcinoma - A Single Centre Case-Series Descriptive Analysis of Surgical Procedures.

Background: Ovarian surface epithelial cancer (OSEC) are an entity in which, according to genomics and pathology data accumulated in the last couple of decades, several different nosological entities with distinct etiologies are aggregated. In ovarian cancer, surgery is the pivot of treatment, to which medical oncological treatment is added by recommendation in most cases. Materials and Methods: This is a single centre sample of 263 cases with OSEC operated from January 2014 until December 2021 with a 28-month period of follow-up, until 30th April 2024. OSEC surgical procedures in stages IIB to III and IV of the disease are complex interventions in order to have the R0/optimal cytoreduction achieved, so we summarised and coded them as follows: 1 = biopsy (of the tumour/peritoneum); 2 = bilateral/unilateral adnexectomy (BA/UA) +- total hysterectomy (TH) +- omentectomy +- peritoneal biopsies; 3 = (2) with total hysterectomy with bilateral adnexectomy (THBA) +- by extraperitoneal/subperitoneal route+peritonectomies (exclusively diaphragmatic) and electrocauterization of peritoneal carcinomatous lesions; 4 = (3) with visceral (multiple) resections +- stoma; 5 = (4) with diaphragmatic peritonectomies/stripping/partial resection of the diaphragm; 6 = palliative surgery. Results: Debulking surgery (DS) was carried out for n = 182 patients with no residual tissue = R0 being registered in n = 41. Results for patients with residual tissue (n = 141) after DS recorded the following findings: 1 cm (49% cases), 1.1-2 cm (29%) and 2 cm (22%). Recorded results for endometrial ovarian carcinoma (EC) n = 27 shown a tumour free survival probability estimate (%) at 60 months of 66% as both surgery and platinum based chemotherapy are efficient. For clear cell ovarian carcinoma (CCC) n = 7 recorded results shown a tumour free estimate (%) at 60 months of 14%, being known the controversy as to whether or not paclitaxel is an active drug for CCC. Major complications were recorded in 25 patients with a fatality ratio of 5/25. Conclusion: Considering OSEC is a relatively rare disease and the importance of collecting substantial numbers of samples by histotypes to further knowledge about ovarian cancer it comes crucial to establish collaborative endeavour of tertiary centers with standardised and quality control strategies.

Research on quality control and continuous improvement strategies of clinical application of medical technology

Research on quality control and continuous improvement strategies of clinical application of medical technology

Audit Quality Control and Financial Risk Prevention Strategies for CPAs under Multi-Objective Optimization Calculation Considering Sustainable Development Strategy

Abstract Audit functions as the “guardian” of the capital market by overseeing economic activities and providing forensic analysis on the major projects and financial transactions of the entities it examines. The sustainability of auditing as a profession hinges critically on audit quality. This study aims to investigate the characteristics and elements that influence audit quality control, employing modern risk-oriented audit theory to formulate a financial audit risk assessment process. The research uses a multiple linear regression model to analyze factors affecting audit quality control among certified public accountants (CPAs). The model analyzes data from 513 A+H share listed companies, with parameters estimated using the Singular Value Decomposition (SVD) method within an overall least squares framework. Furthermore, the Particle Swarm Optimization (PSO) algorithm, a technique within the multi-objective optimization algorithm suite, is utilized to refine parameters in the manipulability Jones model. This adjustment is intended to better quantify the level of earnings management of the audited entities, thus enhancing the reflection of financial audit risks. Findings from the study reveal that the coefficient reflecting the impact of a CPA’s practice experience on audit quality control is 0.249. Additionally, it was observed that a 1% deterioration in the financial condition of an audited entity correlates with a 0.505% decrease in audit quality. For high-risk clients classified under ‘Special Treatment’ (ST), the range of practice years for CPAs is predominantly between 6 to 14 years, which may not fully meet the clients’ auditing needs. The study suggests that tailored audit surveys should be conducted for various client types prior to audit engagements. This strategic approach would enable better allocation of CPA resources, thereby enhancing audit quality control and the capability to mitigate financial audit risks.

Research on Quality Control and Safety Management Strategies in Construction Engineering Construction

Research on Quality Control and Safety Management Strategies in Construction Engineering Construction

Case Study on Advanced Hoisting, Positioning, and Control Techniques for Non-Standard Pier Construction in the Quanli-Dongfeng Viaduct Project

This study, reliant on practical projects, conducts an in-depth analysis of key technologies amid the construction of prefabricated viaducts. The primary focus is on the integrated hoisting, positioning, and control techniques for the bridge abutments and pier columns, culminating in a proposal of an innovative integrated and prefabricated construction approach. This approach optimizes the construction process and methods by factoring in the structural characteristics of different types of pier columns and addressing construction challenges. Simultaneously, it underscores the significance of material selection, prefabricated component design, and innovative construction techniques. The study also explores on-site management and quality control strategies to ensure the efficiency and safety of the construction process. The research indicates that this approach significantly improves construction efficiency, reduces safety risks, and solidly guarantees structural quality. These findings offer crucial theoretical and practical guidance for advancing the industrialization, modularization, and innovative development of construction techniques in bridge construction. They contribute to the continual optimization of technical innovation and engineering practices in the field of bridge construction.

Operator-integrated cluster analysis for production quality control

The paper introduces the Quality Clusters for Operators (QC4O) approach, aimed at improving quality control strategies in modern manufacturing. It identifies a dual challenge: the lack of communication between data analysts and operators, along with the insufficient integration of their respective methodologies, hindering the acceptance of novel control strategies. To address these challenges, the paper proposes integrating modern machine learning-based methods with methods used by operators, enhancing usability and acceptance. This integration aims to facilitate better communication and expedite quality decision-making. The approach is demonstrated through its application in hairpin stator production, representing complex manufacturing systems.

A hybrid digital twin approach for proactive quality control in manufacturing

Quality control is a critical aspect in today's fast-paced and competitive business landscape. The increasing digital transformation of manufacturing companies allows for the implementation of even proactive quality control strategies. This, however, requires the proper integration and analysis of shop floor data, regarding monitoring, diagnosis, and prognostics. This is to support defect recognition and recuperation, along with potential system reconfiguration based on knowledge extraction and human experience integration. Digital twins, being virtual replicas of physical assets, support real-time monitoring, analysis, and optimization. However, quality-related aspects may not be related to monitored parameters, thus solely data-driven models may not be accurate enough for proactive quality control. In this work, a hybrid digital twin is proposed, where data-driven models are used to finetune the behaviour of the digital twin based on its physics model. A use case concerning an industrial asset and the heat transfer to a steel bar is investigated with the results presented and commented on.

Construction Management Characteristics and Quality Control Strategies for Higher Education Infrastructure Projects

Construction Management Characteristics and Quality Control Strategies for Higher Education Infrastructure Projects

Sigma Metrics in Thyroid Testing: Striving for Perfection- An Observational, Cross-sectional Study

Introduction: Six Sigma is a potent tool for evaluating the quality of the analytical phase by combining bias, imprecision, and Total Allowable Error (TEa). Considering the variation in TEa values from different sources, analysis on the sigma scale needs to be carefully monitored. Aim: To assess the performance of Thyroid Stimulating Hormone (TSH) and free Thyroxine (fT4) on the Sigma scale. Materials and Methods: The present observational, cross- sectional study Immunoassay laboratory at the Department of Biochemistry, IPGMER and SSKM Hospital, Kolkata, India, from February 2021 to January 2022. The study involves 12 months of tri-level (L1, L2, L3) Internal Quality (IQ) control data and External Quality Assessment (EQAS) data. The bias percentage was obtained from EQAS, and the Coefficient of Variation (CV%) was obtained from IQ Control (IQC) data run on Advia Centaur CP (CLIA) each month. Sigma (σ) was calculated applying TEa from the desirable biological variation database. Sigma values of L1, L2, L3 of TSH and fT4 have been calculated using Microsoft spreadsheet software version 2010, applying the formula σ= (TEa-bias)/CV. Results: The CV% and bias% were found to be within an acceptable range, always less than the cut-off percentage of imprecision (I%) and inaccuracy (B%) for TSH and fT4 in the desirable specifications for imprecision and inaccuracy (updated 2014). However, sigma levels are near the satisfactory mark, found to be &lt;5σ over 17 months for TSH and 23 months for fT4, considering all sigma values of L1, L2, and L3 IQ levels. Better sigma values (&gt;5σ) in tri-level IQ for TSH with a higher numeric TEa% value (23.7%) were obtained in more months, whereas for fT4 with a lower TEa% value (8%), better sigma values (&gt;5σ) were obtained in a lesser number of months. Conclusion: The present study establishes that Sigma values are affected by the numeric values of TEa% of a particular parameter taken from the source. Sigma values showed average performance despite satisfactory CV% and bias% for fT4 and TSH, creating chaos in the laboratory’s operational routine. The Sigma matrix is a good indicator, but it is difficult to maintain a good sigma value for parameters that have low TEa%. It becomes crucial to choose appropriate TEa to plan a quality control strategy for thyroid hormones.

European research project on the state of pollution in the Black Sea «Black Sea SIERRA»: mission and partic ipation of Ukrainian oceanographers

The destruction of civil and industrial objects in Ukraine caused by a decade-long armed conflict, especially since February 2022, has led to extensive environmental pollution within the country, particularly in the waterways of the Black Sea basin.&#x0D; The consequences of these actions have also affected the state of the Black Sea, causing concern for the European Union and the global scientific community. Therefore, within the framework of the European program «Horizon Europe», funds were allocated for scientific research into the consequences of pollution in coastal water bodies and the Black Sea as a whole. In the 2023 «Horizon Europe» project competition, a proposal by an intergovernmental consortium of 7 scientific organizations from 5 countries was awarded the grant, including the State Institution «Scientific Hydrophysical Center of the National Academy of Sciences of Ukraine».&#x0D; The project aims to create effective monitoring and quality control strategies for water, as well as the implementation of innovative technologies to reduce the impact of pollutants on the ecosystems of the Black Sea. The application of research results is planned for the development of environmentally sound recommendations and strategies for the conservation of the marine environment. The project involves collaboration between countries to address pollution issues and study the Black Sea, as well as the development of scientific approaches to water resource management in the region. Special attention is given to the development of educational programs regarding the impact of military pollution on the water ecosystems of the Black Sea region.&#x0D; This article describes the localization, factors, and types of pollution in waterways and the Black Sea basin, the project’s structure, types of activities, and the investigated water bodies in Ukraine, as well as the participation of SI «Scientific Hydrophysical Center of NAS of Ukraine» as a project participant.

Retracted: Quality Control Strategy and Evaluation Algorithm for Noncontact Instrument Testing

Retracted: Quality Control Strategy and Evaluation Algorithm for Noncontact Instrument Testing

Evolutionary game analysis of information service quality control of e-commerce platforms under information ecology

Due to the information asymmetry and imperfect supervision system, the problem of information service quality of e-commerce platforms is becoming increasingly prominent. Based on the perspective of information ecology, this paper constructs a three-party evolutionary game model including merchants, e-commerce platforms and governments, and analyzes the dynamic process of the three-party game under bounded rationality, thereby characterizing the behavior and optimal strategies of information service quality control. We carry out numerical simulation using data of the Pinduoduo platform. The results show that the cost of each party, control strength of e-commerce platforms, proportion of margin deducted and other factors are important factors affecting the quality of information service; Only when the sum of the costs of passive management of e-commerce platforms, penalties for merchants, and the additional revenue generated from the active management exceeds the cost of the active management of the e-commerce platforms, it ensures effective control over the quality of information service in a stable market environment. Finally, some suggestions are put forward to optimize the e-commerce information service quality control.

Integrated quality control strategy for red yeast rice combining DSC and HPLC profiles

Red yeast rice (RYR) is a popular food product with significant quality control challenges. This study first established a gradient elution tower plate theory suitable for gradient elution and proposed a dual-label system calibration method to monitor and correct chromatographic systems for changes. Subsequently, high-performance liquid chromatography as the analytical method, and a five-wavelength maximization profile was employed to obtain comprehensive chromatographic information on RYR. A novel multi-marker assay by monolinear method (MAML) was employed for the accurate quantitative determination of quality markers. A differential scanning calorimetry (DSC) quantized fingerprint was established to study complete thermal changes. The comprehensive linear quantitative fingerprint method (CLQFM) was also developed to evaluate fingerprints. Results showed that the CLQFM divides the samples into four quality grades, MAML yielded better results than quantitative analysis of multi-components by a single marker method. Thermal analysis revealed that treatment temperatures higher than 193.7 °C for extended periods should be avoided to maintain sample quality. This study promotes the dual control of the quality and thermal stability of RYR, providing a new direction for food control.

Research on Control Strategy of Power Engineering Quality Management Based on Safety Management

Especially after the 18th National Congress of the Communist Party of China, with the rapid development of social economy, the development of electric power engineering in China has made significant improvements. In the quality control process of electric power engineering, we must pay attention to the importance of safety management. Only the quality control strategy of electric power engineering based on safety management can ensure the sustainable development of China's electric power engineering construction industry.

Novel quality control strategies for the determination of 25-hydroxyvitamin D by LC-MS/MS

Mass spectrometry analysis has been applied in many important diagnostic fields of laboratory medicine. However, there is little literature to guide quality management systems for LC-MS/MS methods. In this study, LC-MS/MS 25-hydroxyvitamin D (25(OH)D) was used as an example to establish internal quality control strategies to ensure the accuracy of clinical vitamin D results. A total of 141 batches of samples were analyzed. Sample internal standard peak area variability, ion pair ratio, and physical examination population data were monitored as quality control strategies for 25(OH)D results. The analytical performance was evaluated by calculated Sigma metrics. Applying our quality control strategies, several abnormal data were monitored in the routine analysis. The daily peak area CV of 25(OH)D fluctuated within a certain range. By selecting P99 CV as the control target, two abnormal batches were found. The ratio of 25(OH)VD3 ion pairs was relatively stable. Among them, batch20230120 had a high CV value, which may be due to the bias caused by the limited number. According to the physical examination data, batch20220913 and batch20220919 exceeded the alarm limit. Sigma level of 25(OH)VD3 in the laboratory was 6.52, which achieved “excellent” performance. In conclusion, we established comprehensive quality control strategies for the determination of 25(OH)D by LC-MS/MS, which has high analytical performance and can provide more accurate reports for the clinic.

A quality control framework for digital fabrication with concrete

The quality control of digital fabrication with concrete has more stringent requirements than traditional casting. Firstly, since formwork is typically absent, or removed at an early stage in production, the material is exposed to external influences that can result in deformations, collapse, or deterioration. Therefore, the evolution of properties during the process has to be controlled. Secondly, the fabrication systems are typically more sensitive to dosing fluctuations, and the produced, optimized objects are more sensitive to defects, which requires the process variations to be controlled at a higher resolution. A framework is presented that categorizes quality control experiments into destructive and non-destructive, according to their systematic error, and according to the location of testing with respect to the process. This framework is applied to the fresh state mechanical performance of concrete and quality control strategies are derived from it. Lastly, research gaps are identified that are critical for the further development and adoption of these quality control strategies in digitally fabricated concrete.

Generative AI in the Manufacturing Process: Theoretical Considerations

Abstract The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

Machine Learning in Precision Manufacturing: A Collaborative Computer and Mechanical Engineering Perspective

Precision manufacturing, characterized by the production of intricate components with stringent tolerances, has witnessed a paradigm shift with the integration of machine learning (ML) techniques. This research paper delves into the collaborative endeavors between computer and mechanical engineering disciplines, exploring how the fusion of expertise from both domains contributes to the optimization and advancement of precision manufacturing processes. [1] The motivation behind this research stems from the recognition of ML's transformative potential in revolutionizing precision manufacturing. This paper reviews the application of ML in key areas such as predictive maintenance, process optimization, quality control, and adaptive manufacturing strategies. It emphasizes the synergy between computer and mechanical engineers in designing and implementing intelligent algorithms that harness data from various sensors, enabling real-time adjustments to manufacturing parameters. Predictive maintenance emerges as a critical aspect of the collaborative efforts, where ML algorithms analyze sensor data to predict equipment failures, thereby reducing downtime and maintenance costs. Process optimization is explored through the integration of ML in Computer Numerical Control (CNC) machining, highlighting how algorithms optimize tool paths and enhance machining precision. Quality control in precision manufacturing is addressed by intelligent systems that utilize computer vision and sensor technologies, providing real-time defect detection. [2] The collaborative approach presents challenges, including data security and privacy concerns and the need for skillset integration between computer and mechanical engineering disciplines. The paper concludes by exploring future directions, including the integration of emerging technologies such as edge computing and the Internet of Things (IoT), underscoring the importance of interdisciplinary collaboration for the successful implementation of ML techniques in achieving heightened precision, efficiency, and quality in precision manufacturing.

Analysis of collaborative control of dairy product supply chain quality based on evolutionary games: Perspectives from government intervention and market failure

Dairy quality affects the health and quality of life of consumers. Implementing supply chain management and collaborative quality control is an effective way to solve dairy quality problems. Based on the perspective of the combination of market failure and government intervention, this paper analyzes the conditions for the realization of collaborative quality control between dairy farmers and dairy processors. At the same time, this paper uses relevant data to verify the applicability of the model and the accuracy of the conclusions. The findings show that both low yields and high spillovers lead to market failures. When farmers adopt advanced prevention strategies and dairy processors adopt advanced inspection strategies with low yields, basic prevention and basic inspection will be the best combination of quality control strategies for both parties. In this case, the government should subsidize the advanced control strategy to provide adequate subsidies. This will provide incentives for both parties to work together to ensure the quality of dairy products. Secondly, when farmers adopt advanced prevention strategies or dairy processors adopt advanced inspection strategies yields increase but spillover rates are higher. Advanced prevention, basic inspection or basic prevention, advanced inspection would be the optimal combination of strategies for both parties. At this point, the government should increase the penalties. The simulation results further indicate that the government subsidy is more effective for dairy farmers. Government penalties have both the positive and negative reinforcing effects.