Quality Defects Research Articles

Strategic policy options to improve quality and productivity of biomedical research

Abstract Emerging societal expectations from biomedical research and intensifying international scientific competition are becoming existential matters. Based on a review of pertinent evidence, this article analyzes challenges and formulates public policy recommendations for improving productivity and impact of life sciences. Critical risks include widespread quality defects of research, particularly non-reproducible results, and narrow access to scientifically sound information giving advantage to health misinformation. In funding life sciences, the simultaneous shift to nondemocratic societies is an added challenge. Simply spending more on research will not be enough in the global competition. Considering the pacesetter role of the federal government, five national policy recommendations are put forward: (i) funding projects with comprehensive expectations of reproducibility; (ii) public–private partnerships for contemporaneous quality support in laboratories; (iii) making research institutions accountable for quality control; (iv) supporting new quality filtering standards for scientific journals and repositories, and (v) establishing a new network of centers for scientific health communications.

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

Enhancing software quality assurance in ubiquitous learning environments through mutation testing and diverse test oracles

Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.

Effect of nozzle clogging on vortexing during continuous casting processes: results of an experimental simulation

ABSTRACT Exogenous oxide inclusions in continuously cast steel primarily arise from the entrapment of slag during various stages of steelmaking, including in furnaces, ladles, and molds. To maintain steel cleanliness, operators often pause ladle and tundish processes before ‘slag vortexing’ begins, since this phenomenon (slag vortexing) can adversely affect product quality. Previous studies indicate that sub-entry nozzle clogging can lead to productivity issues and quality defects in cast products. Clogging can cause non-uniform reductions in nozzle diameter along the molten metal flow direction. This study examines the impact of drain port taper (a representation of nozzle clogging) on air core vortex formation during water drainage (simulating molten metal) using a parameter, ‘Characteristic Volume of Air Core.’ Results show that clogged drain ports are prone to vigorous vortexing, significantly affecting efflux velocity and static pressure at drain outlets. The current study also analyzes the dynamic behavior of air core vortexing due to nozzle clogging, revealing that clogging can induce fluctuations in liquid discharge, adversely affecting the casting process. These findings have practical implications for continuous casting processes in steel and other metals.

FORMATION OF STRATEGIC DIRECTIONS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE TO ACHIEVE THE GOALS OF SUSTAINABLE DEVELOPMENT

Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.

Causes of quality defects in cassava-based food production: an ishikawa diagram analysis

Causes of quality defects in cassava-based food production: an ishikawa diagram analysis

Skeletal muscle metabolic characteristics and fresh meat quality defects associated with wooden breast

Wooden breast (WB) is a myopathy that occurs in pectoralis major (PM) muscles, predominately affecting large, fast-growing broilers. Severe myodegeneration, increased hypoxia, reduced blood flow, and increased collagen deposition are hallmark characteristics of WB that culminate in unsatisfactory fresh meat quality attributes, such as poor water-holding capacity, tenderness, and processing characteristics. Therefore, WB meat is often downgraded resulting in economic losses for the United States poultry industry. Although WB has been well characterized, its etiology remains undefined. As the scientific community continues to resolve mechanisms responsible for WB onset, understanding biochemical changes associated with WB may facilitate solutions to negate its poor meat quality attributes. Given changes in metabolism of living muscle can alter biochemical processes during the conversion of muscle to meat, this review aims to summarize and discuss the current knowledge of WB muscle and meat biochemistry. For example, it appears metabolic pathways that support combating stress are upregulated in WB muscle at the expense of glycolytic flux, which presumably contributes to the high ultimate pH of WB meat. Further, perturbed function of WB mitochondria, such as altered calcium handling, impacts aspects of postmortem metabolism and proteolysis. Collectively, metabolic dysfunction of WB muscle alters the biochemical processes that occur during the conversion of muscle to meat, and thus contributes to the poor WB meat quality.

Optimization of aloe vera‐sodium alginate composite coating using L16 orthogonal design for extension of shelf life of mushroom

AbstractWhite button mushrooms are highly nutritive, rich in various macro and micro nutrients, however, have a short shelf life with weight loss (WL), enzymatic browning, and loss in firmness as the major quality defects. The present study was carried out to investigate the effect of aloe vera‐sodium alginate (AV‐SA) composite coating on the quality attributes of mushroom. The development of coating formulations containing 2% sodium alginate along with varying concentration of aloe vera gel 20%–80% and storage time 4–16 days was planned using a 2‐factor 4‐level orthogonal array (L16) design of Taguchi technique. The influence of both the process parameters on the WL, total soluble solids (TSS), respiration rate (RR), browning index (BI), firmness, electrolyte leakage rate (EL), malondialdehyde (MDA), ascorbic acid (AA), total phenolic content (TPC), enzymes activity (polyphenol oxidase and peroxidase), and yeast and mold count (YMC) were evaluated and the optimized level of parameters was determined on the basis of the highest S/N ratios of the responses. The results indicated that 60% AV‐SA composite coating could extend the shelf life of white button mushrooms for up to 8 days with retarded loss in weight, firmness, AA, and TPC and reduced increase in TSS, BI, EL, MDA with controlled enzymatic activity, RR, and YMC.

Influence of Loading Density and Gender on the Welfare and Meat Quality of Horses During Transport for Slaughter

The aim of this study was to determine the effects of loading density and gender on blood welfare indicators, carcass bruises and horsemeat quality. Data were collected from twelve transports of 89 slaughter horses originating from the same collection centre. The transportation of slaughter horses at high loading densities (>200 kg/m2), especially stallions, resulted in increased levels of lactate (p = 0.021), glucose (p < 0.0001), ceruloplasmin (p < 0.0001) and AOPP (p < 0.0001), but lower GSH levels (p < 0.0001). Compared to stallions and mares, geldings subjected to high loading density (>200 kg/m2) during transport had lower levels of the aforementioned blood metabolites. In addition, stallions exposed to a high load density (>200 kg/m2) had the highest frequency of severe (p = 0.0002), large (p < 0.0001) and circular (p = 0.0001) carcass bruises, which were predominantly located on the abdominal (p = 0.0056) and thoracic (p = 0.0004) wall. In contrast, a higher percentage (p < 0.0001) of undamaged carcasses was found in slaughter horses exposed to a low loading density (≤200 kg/m2) during transport. Stallions exposed to high loading densities (>200 kg/m2) during transport had the highest initial pH (p < 0.0001) and ultimate pH (p = 0.005) in terms of m. longissimus lumborum and redness (p = 0.017), but the lowest drip loss (p = 0.050) and lightness (p < 0.0001), which, consequently, led to the highest (p = 0.0045) proportion of DFD-like defects in the meat quality. In conclusion, the results of this study showed that high loading densities (>200 kg/m2), regardless of gender, negatively affect horse welfare during transport. In addition, stallions were more sensitive to poor pre-slaughter conditions and produced the lowest meat quality, while geldings were the most resistant. To determine the optimal transport density, further research is needed to determine the effects of different loading densities on the behaviour, physiology, carcass and meat quality of slaughter horses.

The Potential for Hyperspectral Imaging and Machine Learning to Classify Internal Quality Defects in Macadamia Nuts

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling.

Generalized SmartScan: An Intelligent LPBF Scan Sequence Optimization Approach for Reduced Residual Stress and Distortion in 3D Part Geometries

Abstract Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer; (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic; and (4) improving SmartScan’s computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation and 50% in geometric inaccuracy were observed using SmartScan. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

Control Analysis of Building Under Construction from Indoor Mobile Mapping Systems

Abstract. During the execution phase of construction projects, quality defects arise due to various factors such as human intervention, technical aspects, logistical issues and environmental influences. Early detection of these defects is imperative to minimize their impact and prevent possible delays, increased costs and safety hazards. This paper explores the effectiveness of indoor mobile laser scanners for geometric quality control, adhering to established European technical norms and Spanish standards. Our developed approach presents a simple method for rapid assessment, capable of identifying vertical deviations in columns and walls, axis deviation in columns, flatness deviation in horizontal planes and level deviation using LIDAR data from Mobile Mapping Systems. This methodology has been validated through a real-world case study, demonstrating its practical applicability.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Control of quality defects of the glass fiber injection molded product based on LSO-RF and NSWOA

Control of quality defects of the glass fiber injection molded product based on LSO-RF and NSWOA

Recent Progress of Thin Crystal Engineering for Perovskite Solar Cells.

Metal halide perovskite single crystals hold promise for photovoltaics with high efficiency and stability due to their superior optoelectronic properties and weak bulk ion migration. The past several years have witnessed rapid development of single-crystal perovskite solar cells (PSCs) with efficiency rocketed from 6.5% to 24.3%, however, which still lags behind their polycrystalline counterparts. Moreover, the poor device stability under light illumination is contrary to the high ion migration barrier of perovskite single crystals. The key limiting factors should be the low crystalline quality and high surface defect density of solution-grown thin single crystals. Under this circumstance, a review paper summarizing the recent progress and challenges will be instructive for future development of this emerging field. In this manuscript, the crystal engineering used to enhance carrier transport and suppress carrier recombination in vertical single-crystal PSCs will be summarized initially, including crystal growth, component control, surface and interface modification. Subsequently, the application of perovskite single crystals in lateral single-crystal PSCs will be discussed and compared with the conventionally vertical structure. Finally, the challenges and proposed strategies for the development of single-crystal PSCs are provided.

Navigating Challenges and Promises for Next‐Generation CsPbIBr2 Perovskite Solar Cells: A Review

AbstractGiven the increasing demand for electricity due to modernization and population growth, there is an urgent need to develop renewable energy conversion technologies such as photovoltaics. Among these technologies, CsPbIBr2, an all‐inorganic lead halide‐based perovskite, has shown promise due to its thermal stability, phase stability, and ease of fabrication. However, challenges remain, particularly in addressing device hysteresis and stability. Novel materials and optimized device designs could help overcome these challenges. This comprehensive review discusses strategies such as interface engineering, film quality improvement, compositional engineering, defect passivation, band alignments, and metal ion doping to enhance the performance of CsPbIBr2‐based perovskite films and, in turn, their potential for photovoltaic applications.

Solar photovoltaic module defect detection based on deep learning

Abstract Defect detection for photovoltaic (PV) modules is crucial in their production process, but the dataset quality and complex defects limit the accuracy and speed of the detection. In this paper, a solar PV module defect detection method was investigated using electroluminescence (EL) images. To reduce useless information in the EL images, a PV module segmentation method was proposed to segment PV cells from PV modules. Next, aiming at the insufficient sample size and the imbalance between classes in the dataset, a hybrid data augmentation method was proposed. Then, we proposed an improved YOLOv8n model for PV cell defects with different shapes and small sizes. Experiments showed that the proposed model has good comprehensive performance compared with other SOTA models, with mAP50 reaching 0.943 at only 7.6 G Flops. In addition, the proposed method can complete the defect detection of a PV module EL image containing 144 PV cells within 3 s. Overall, the proposed method meets the requirements of accuracy and real-time detection, providing a feasible solution for defect detection in PV modules.

Classification of Quality Defects using Multivariate Control Chart with Ensemble Machine Learning Model

Multivariate control charts enable to monitor processes affected by more than one variable. But, when the process is out of control, it cannot detect which variable is causing it. It is an important requirement to know which variables in the process need corrective actions. In this study, a machine learning-based model is proposed to predict the variable/s that make the process out of control. For this purpose, ensemble algorithms, which are known to have higher prediction performance than single algorithms, were preferred. Because it is aimed to determine the variable(s) that cause the process to be out of control in the most accurate way. It is thought that a classification model in which ensemble algorithms are used together can increase the prediction accuracy. The model, which has not been encountered before in a quality control problem, was applied to a real problem and 98.06% classification accuracy was achieved. Another benefit is that it can predict the variable/variables that make the process uncontrolled without the need for multivariate control charts.

Suspected poor-quality medicines in Kenya: a retrospective descriptive study of medicine quality-related complaints reports in Kenya’s pharmacovigilance database

Poor-quality, substandard and falsified, medicines pose a significant public health threat, particularly in low-middle-income countries. A retrospective study was performed on Kenya's Pharmacovigilance Electronic Reporting System (2014–2021) to characterize medicine quality-related complaints and identify associations using disproportionality analysis. A total of 2767 individual case safety reports were identified, categorized into medicines with quality defects (52.1%), suspected therapeutic failure (41.6%), and suspected adverse drug reactions (6.3%). Predominantly reported were antineoplastic agents (28.6%), antivirals (11.7%), and antibacterial agents (10.8%) potentially linked to non-adherence to good manufacturing practices, inappropriate usage and supply chain degradation. Notably, analgesics (8.2%), and medical devices (3.5%) notified had quality defects, predominantly from government health facilities (60.0%). Antineoplastic agents (20.2%) and antivirals (3.7%) were frequently reported from suspected therapeutic failures and suspected adverse drug reactions, respectively, across both private for-profit facilities (26.5%) and not-for-profit facilities (5.4%). Underreporting occurred in unlicensed health facilities (8.1%), due to unawareness and reporting challenges. Pharmacists (46.1%), and pharmaceutical technicians (11.7%) predominantly reported quality defects, while medical doctors (28.0%) reported suspected therapeutic failures. Orally administered generic medicines (76.9%) were commonly reported, with tablets (5.8%) identified as potential sources of suspected adverse drug reactions, while quality defects were notified from oral solutions, suspensions, and syrups (7.0%) and medical devices (3.9%). The COVID-19 pandemic correlated with reduced reporting possibly due to prioritization of health surveillance. This study provides valuable evidence to supporting the use of medicine quality-related complaints for proactive, targeted regulatory control of high-risk medicines on the market. This approach can be strengthened by employing standardized terminology to prioritize monitoring of commonly reported suspected poor-quality medicines for risk-based sampling and testing within the supply chain.