Search Results (478)

Many hospitals still lack digital traceability in hygiene and cleaning management, leading to operational inefficiencies and inconsistent quality control. This study aims to establish cleaning and hygiene processes in healthcare services that are planned in accordance with standards, as well as to enhance the traceability and sustainability of these processes through digitalization. This study proposes a Hyperledger Fabric-based blockchain architecture to establish a reliable and transparent quality assurance system in process management. The proposed Quality Assurance Model utilizes digital technologies and IoT-based RFID devices to ensure the transparent and reliable monitoring of cleaning processes. Operational data related to cleaning processes are automatically recorded and secured using a decentralized blockchain infrastructure. The permissioned nature of Hyperledger Fabric provides a more secure solution compared to traditional data management systems in the healthcare sector while preserving data privacy. Additionally, the execute–order–validate mechanism supports effective data sharing among stakeholders, and consensus algorithms along with chaincode rules enhance the reliability of processes. A working prototype was implemented and validated using Hyperledger Caliper under resource-constrained cloud environments, confirming the system’s feasibility through over 100 TPS throughput and zero transaction failures. Through the proposed system, cleaning/hygiene processes in patient rooms are conducted securely, contributing to the improvement of quality standards in healthcare services. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Quality assurance programs are critical to ensuring the consistency and reliability of point-of-care surveillance test results. In 2022, we launched Laos’ inaugural quality control (QC) and external quality assessment (EQA) program for national HIV recent infection surveillance. Our study aims to implement the first QC and EQA program for national HIV recent infection surveillance in Laos, utilizing non-infectious dried tube specimens (DTS) for quality control testing. This initiative seeks to monitor and assure the quality of HIV infection surveillance. We employed the Asante HIV-1 Rapid Test for Recent Infection (HIV-1 RTRI) point-of-care kit, using plasma specimens from the Thai Red Cross Society to create dried tube specimens (DTS). The DTS panels, including HIV-1 negative, HIV-1 recent, and HIV-1 long-term samples, met ISO 13528:2022 standards to ensure homogeneity and stability. These panels were transported from the Thai National Institute of Health (Thai NIH) to the Laos National Center for Laboratory and Epidemiology (NCLE) and subsequently shipped to 12 remote laboratories at ambient temperature. The laboratory results were electronically transmitted to Thai NIH 15 days after receiving the panel for performance analysis. The concordance results with the sample types were scored, and laboratories that achieved 100% concordance across all sample panels were considered to have satisfactorily met the established standards. Almost all laboratories demonstrated satisfactory results with 100% concordance across all sample panels during all three rounds of QC: 11 out of 12 (92%) in June, 10 out of 12 (83%) in July, and 11 out of 12 (91%) in August. The two rounds of EQA performed in June and August 2022 were satisfied by 8 out of 11 (72%) and 5 out of 10 (50%) laboratories, respectively. QC and EQA monitoring identified errors such as testing protocol mistakes and insufficient DTS panel dissolution, leading to improvements in HIV recency testing quality. Laboratories that reported errors were corrected and implemented further preventive actions. The QC and EQA program for HIV-1 RTRI identified errors in HIV recent infection testing. Implementing a specialized QC and EQA program for DTS marks a significant advancement in improving the accuracy and consistency of HIV recent infection surveillance. Continuous assessment is vital for addressing recurring issues. Full article

Increased market demand for plant herbs has prompted growers to ensure a continuous and assured supply of superior nutritional quality over the years. Apart from the nutritional value, culinary herbs contain phytochemical benefits that can improve human health. However, a significant amount of research has focused on enhancing yield, frequently overlooking the impact of production practices on the antioxidant and phytonutritional content of the produce. Thus, the study aimed to evaluate the yield, phytonutrients, and essential mineral profiling in selected aromatic herbs and their intricate role in nutritional quality when grown under different production systems. Five selected aromatic herbs (coriander, rocket, fennel, basil, and moss-curled parsley) were evaluated at harvest when grown under three production systems: in a gravel-film technique (GFT) hydroponic system and in soil, both under the 40% white shade-net structure, as well as in a soilless medium using sawdust under a non-temperature-controlled plastic tunnel (NTC). The phytonutritional quality properties (total phenolic, flavonoids, β-carotene-linoleic acid, and condensed tannins contents) as well as 1,1-diphenyl-2-picrylhydrazyl (DPPH) were assessed using spectrophotometry, while vitamin C and β-carotene were analyzed using HPLC-PDA, and leaf mineral content was evaluated using ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometry). The results show that the health benefits vary greatly owing to the particular culinary herb. The fresh leaf mass (yield) of coriander, parsley, and rocket was not significantly affected by the production system, whereas basil was high in soil cultivation, followed by GFT. Fennel had a high yield in the GFT system compared to in-soil and in-soilless cultivation. The highest levels of vitamin C were found in basil leaves grown in GFT and in soil compared to the soilless medium. The amount of total phenolic and flavonoid compounds, β-carotene, β-carotene-linoleic acid, and DPPH, were considerably high in soil cultivation, except on condensed tannins compared to the GFT and soilless medium, which could be a result of Photosynthetic Active Radiation (PAR) values (683 μmol/m²/s) and not favoring the accumulation of tannins. Overall, the mineral content was greatly influenced by the production system. Leaf calcium and magnesium contents were highly accumulated in rockets grown in the soilless medium and the GFT hydroponic system. The results have highlighted that growing environmental conditions significantly impact the accumulation of health-promoting phytonutrients in aromatic herbs. Some have positive ramifications, while others have negative ramifications. As a result, growers should prioritize in-soil production systems over GFT (under the shade-net) and soilless cultivation (under NTC) to produce aromatic herbs to improve the functional benefits and customer health. Full article

Automated quality assurance is gaining popularity across application areas; however, automatization for monitoring and control of product quality in waste processing is still in its infancy. At the same time, research on this topic is scattered, limiting efficient implementation of already developed strategies and technologies across research and application areas. To this end, the current work describes a scoping review conducted to systematically map available sensor-based quality assurance technologies and research based on the PRISMA-ScR framework. Additionally, the current state of research and potential automatization strategies are described in the context of construction and demolition waste processing. The results show 31 different sensor types extracted from a collection of 364 works, which have varied popularity depending on the application. However, visual imaging and spectroscopy sensors in particular seem to be popular overall. Only five works describing quality control system implementation were found, of which three describe varying manufacturing applications. Most works found describe proof-of-concept quality prediction systems on a laboratory scale. Compared to other application areas, works regarding construction and demolition waste processing indicate that the area seems to be especially behind in terms of implementing visual imaging at higher technology readiness levels. Moreover, given the importance of reliable and detailed data on material quality to transform the construction sector into a sustainable one, future research on quality monitoring and control systems could therefore focus on the implementation on higher technology readiness levels and the inclusion of detailed descriptions on how these systems have been verified. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

Integrating Industry 4.0 technologies into food manufacturing processes transforms traditional quality management practices. This study aims to understand how these technologies are applied across managerial quality functions in the food industry. A systematic literature review was conducted using the Scopus and Web of Science databases, selecting 69 peer-reviewed articles. The analysis identified quality control (QC) and quality assurance (QA) as the most frequently addressed functions. Sensor technology was the most cited, followed by blockchain and artificial intelligence, mainly supporting food safety, process monitoring, and traceability. In contrast, quality design (QD), quality improvement (QI), and quality policy and strategy (QPS) were underrepresented, revealing a gap in strategic and innovation-focused applications. Based on these insights, the Food Quality Management 4.0 (FQM 4.0) framework was developed, mapping the relationship between Industry 4.0 technologies and the five managerial quality functions, with food safety positioned as a transversal dimension. The framework contributes to academia and industry by offering a structured view of technological integration in food quality management and identifying future research and implementation directions. This study highlights the need for broader adoption of advanced technologies to improve transparency, responsiveness, and overall quality performance in the food sector. Full article

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans. Full article

The present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish states without interrupting production. In order to address this, a novel classification approach was developed that maps audio waveform data into predictive indicators of surface quality. In particular, an experimental dataset was employed consisting of sound signals that were captured during milling procedures applying various machining conditions, where each signal was labeled with a corresponding roughness quality obtained via offline metrology. The formulated classification pipeline commences with audio acquisition, resampling, and normalization to ensure consistency across the dataset. These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. Finally, the results of the work demonstrate that sound signals could contain sufficient discriminatory information enabling a reliable classification of surface finish quality. This approach not only facilitates in-process monitoring but also provides a foundation for intelligent manufacturing systems capable of real-time quality assurance. Full article

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending. Full article

Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

A vital step for any hydrochemical assessment is properly carrying out quality assurance and quality control (QA/QC) techniques to evaluate data confidence before performing the assessment. Understanding the processes governing groundwater evolution in coastal aquifers is critical for managing freshwater resources under increasing anthropogenic and climatic pressures. This study reassesses the hydrochemical and isotopic data from the Deep Confined Aquifer System (DCAS) in the Jiangsu Coastal Plain, China, by firstly applying QA/QC protocols. Anomalously high Fe and Mn concentrations in several samples were identified and excluded, yielding a refined dataset that enabled a more accurate interpretation of hydrogeochemical processes. Using hierarchical cluster analysis (HCA), principal component analysis (PCA), and stable and radioactive isotope data (δ²H, δ¹⁸O, ³H, and ¹⁴C), we identify three dominant drivers of groundwater evolution: water–rock interaction, evaporation, and seawater intrusion. In contrast to earlier interpretations, we present clear evidence of active seawater intrusion into the DCAS, supported by salinity patterns, isotopic signatures, and local hydrodynamics. Furthermore, inconsistencies between tritium- and radiocarbon-derived residence times—modern recharge indicated by ³H versus Pleistocene ages from ¹⁴C—highlight the unreliability of previous paleoclimatic reconstructions based on unvalidated datasets. These findings underscore the crucial role of robust QA/QC and integrated tracer analysis in groundwater studies. Full article

In modern manufacturing, ensuring quality control for geometric features is critical, yet detecting anomalies in circular components remains underexplored. This study proposes a real-time defect detection framework for metal parts with holes, optimized for deployment on a Raspberry Pi 5 edge device. We fine-tuned and evaluated three deep learning models ResNet50, EfficientNet-B3, and MobileNetV3-Large on a grayscale image dataset (43,482 samples) containing various hole defects and imbalances. Through extensive data augmentation and class-weighting, the models achieved near-perfect binary classification of defective vs. non-defective parts. Notably, ResNet50 attained 99.98% accuracy (precision 0.9994, recall 1.0000), correctly identifying all defects with only one false alarm. MobileNetV3-Large and EfficientNet-B3 likewise exceeded 99.9% accuracy, with slightly more false positives, but offered advantages in model size or interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that each network focuses on meaningful geometric features (misaligned or irregular holes) when predicting defects, enhancing explainability. These results demonstrate that lightweight CNNs can reliably detect geometric deviations (e.g., mispositioned or missing holes) in real time. The proposed system significantly improves inline quality assurance by enabling timely, accurate, and interpretable defect detection on low-cost hardware, paving the way for smarter manufacturing inspection. Full article

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance. Full article