Quality Control Procedures Research Articles

Model-based dimensional NDE from few X-ray radiographs: Application to the evaluation of wall thickness in metallic turbine blades

The extraction of 3D dimensional measurements based on a limited number of 2D X-ray radiographs of a part would offer a significant speed-up of quality control procedures in industry. However, there are challenges with respect to both measurements and uncertainties. This work addresses these challenges by creating an estimated numerical model of the imaged part on which dimensional measurements can be made. The numerical model is chosen as a parametric deformable model that encodes the expected shape variability of the parts resulting from the manufacturing process. The parameters and uncertainties of the numerical model of the imaged part are estimated by the registration of the computed projections of the model and the observed radiographs without the need of any segmentation. The registration requires the model, the initial parameters, and the observed radiographs. The proposed approach is applied to the inspection of turbine blades manufactured by investment casting, and in particular to the measurement of their wall thickness, which is a critical control. The deformable model consists in partitioning the inner ceramic core into multiple subparts, which may undergo a rigid body motion with respect to the master die. Wall thickness measurements are determined from the estimation of these rigid body motions. To assess the reliability of the proposed procedure, a repeatability study is performed. In addition, wall thickness measurements were compared to corresponding measurements from the surface of the metal boundary obtained by X-ray computed tomography. This surface was determined from a reconstructed tomogram using commercial software. Both analyses show that such measurements are reliable and efficient. Furthermore, residual differences between captured and computed projections reveal localized shape deviations from the CAD model, meaning that despite localized model errors, the approach is operable.

Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions

The application of chatbots and Natural Language Processing (NLP) in cytology and cytopathology is an emerging field, which is currently characterized by a limited but growing body of research. Here, a narrative review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This narrative review explores the early developments and potential future impact of these technologies in medical diagnostics. The current literature, comprising 11 studies (after excluding comments, letters, and editorials) suggests that chatbots and NLP offer significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and improve patient engagement. By automating the extraction and classification of medical information, these technologies can reduce human error and increase precision. They also promise to make patient information more accessible and facilitate complex decision-making processes, thereby fostering greater patient involvement in healthcare. Despite these promising prospects, several challenges need to be addressed for the full potential of these technologies to be realized. These include the need for data standardization, mitigation of biases in Artificial Intelligence (AI) systems, and comprehensive clinical validation. Furthermore, ethical, privacy, and legal considerations must be navigated carefully to ensure responsible AI deployment. Compared to the more established fields of histology, histopathology, and especially radiology, the integration of digital tools in cytology and cytopathology is still in its infancy. Building on the advancements in related fields, especially radiology’s experience with digital integration, where these technologies already offer promising solutions in mentoring, second opinions, and education, we can leverage this knowledge to further develop chatbots and natural language processing in cytology and cytopathology. Overall, this review underscores the transformative potential of these technologies while outlining the critical areas for future research and development.

Probabilistic analysis of crack widths associated with quality control procedures

AbstractExisting concrete specifications limit the crack width in order to fulfill the limits of serviceability and durability. The crack width calculation results differ depending on the influences of the variability of the used materials, the geometry, the actions and the uncertainty of the used calculation model itself. In this contribution, the influence of the variability of the input parameters on the calculated crack width is investigated using probabilistic methods in a real case study of a highly loaded tunnel lining cross‐section. The primary objective of this study is a sensitivity analysis to identify the geometric and material‐related input parameters with the most significant influence, which must be particularly controlled during execution for quality control purposes. The study in this article is based on the Eurocode deterministic model for calculating crack width for a stabilized cracking stage in late concrete age.

AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

Fast spectroscopic and multisensor methods for analysis of glucosamine and hyaluronic acid in dietary supplements

There is a lack of fast and inexpensive analytical methods for quantification of key ingredients in dietary supplements. Here we explore the potential of near infrared (NIR) spectrometry, attenuated total reflection infrared (ATR-IR) spectrometry and potentiometric multisensor system (MSS) in quantitative determination of glucosamine and hyaluronic acid in commercial samples of dietary supplements. All three methods have demonstrated their applicability for this task when combined with chemometric data processing. Principal Component Analysis (PCA) revealed similarities across the three techniques, indicating the presence of distinct sample composition. Partial least squares (PLS) models were constructed for glucosamine and hyaluronic acid quantification. The root mean square error of cross validation (RMSECV) for glucosamine quantification varied between 7.7 wt% and 8.9 wt%. NIR spectrometry has demonstrated the best accuracy for hyaluronic acid (RMSECV = 9.9 wt%), while ATR-IR and MSS yielded somewhat worse performance with RMSECV values of 12.1 and 11.3 wt%, respectively. The findings of this study indicated that NIR, ATR-IR and MSS exhibit reduced accuracy in comparison to complex and high-precision analytical techniques. However, they can be employed for the rapid, semi-quantitative evaluation of glucosamine and hyaluronic acid in dietary supplements, with the possibility of integration into routine quality control procedures.

Bias‐corrected high‐resolution vertical nitrate profiles from the CTD rosette‐mounted submersible ultraviolet nitrate analyzer

AbstractMeasurements by the submersible ultraviolet nitrate analyzer (SUNA) can be used to derive high‐resolution in situ nitrate concentration with reliable accuracy and precision. Here we report our operational practices for SUNA deployment (including pre‐cruise instrument preparation and in‐cruise instrument maintenance) and detailed post‐cruise nitrate quality control procedures for SUNA integrated onto the CTD rosette. This work is based on experiences and findings from over 500 individual SUNA casts collected from 24 cruises (of which 14 cruises have been quality controlled so far) over the past 5 yr. After applying previously published spectral corrections for temperature, salinity, and pressure effects, we found residual biases in SUNA nitrate estimates compared to independently measured discrete samples. We further develop and assess a new two‐step procedure to remove remaining biases: (1) a general temperature‐dependent adjustment at low‐nitrate concentrations; and (2) a cruise‐specific full‐range bias correction. Our final quality‐controlled SUNA nitrate data achieve an accuracy of 0.34–0.78 μM, with a precision of 0.08–0.21 μM, at a vertical resolution of 1 m. Additional comparisons between the nitrate and density data confirm the high quality of the quality‐controlled SUNA data. Although applying spectral correction algorithms increases the accuracy and precision of the instrument‐output nitrate concentration, we emphasize that additional constraints of SUNA measurements against other independent sources (e.g., bottle data, temperature, and density) are irreplaceable to ensure the accuracy of final nitrate data.

Delineation of the reservoir petrophysical parameters from well logs validated by the core samples case study Sitra field, Western Desert, Egypt.

In the northern section of the Western Desert, there are many extremely profitable petroleum and natural gas deposits in the Abu EL-Gharadig Basin. This study aims to highlight the hydrocarbon potential of Abu Roash F Formation, which stands for high organic content unconventional tight reservoirs, and Abu Roash G Formation which stands for conventional sand reservoirs, in Sitra field located in the central-western part of the Abu EL-Gharadig Basin. The research employed well-log data from four wells to ascertain petrophysical properties combined with core samples of two wells for a comprehensive examination and description of lithology. Initially, we commenced the execution of petrophysical analysis, encompassing log quality control procedures. Subsequently, we identified and revealed zones of interest and hydrocarbon indicators in both formations. Additionally, we ascertained the three most influential parameters, shale Volume, effective Porosity, and water saturation, which serve as defining factors for reservoir quality. Subsequently, an examination of the core samples, which encompassed lithologic description, lithofacies analysis, paleoenvironmental interpretation, petrographic analysis, and porosity assessment is conducted. For the sake of a more accurate interpretation, we conclude our research with cartographic maps created to evaluate the geographical distribution of hydrocarbon potential based on petrophysical characteristics, Distribution of the net-to-gross ratio among wells by correlating the litho-saturation models (rock models) for the four wells. The foregoing results declare that The Abu Roash F carbonate-rich rocks are a contender for unconventional tight oil reservoir potential with thin secondary porosity and high organic content, which normally requires a kind of hydraulic fracturing for prospective oil extraction, Furthermore, the upper section of Abu Roash G formation, particularly in well sitra8-03, has highly favorable conventional reservoir characteristics.

Quality control of eddy covariance fluxes of two ecosystem types with local flux-variance similarity functions in West Africa

Ecosystem capacity studies for carbon sequestration and water extraction are largely conditioned by Eddy Covariance (EC) measurement availability and quality. Our study aims at evaluating several procedures of EC data qualification. Indeed, quality control of EC data is generally ensured by stationarity and well-developed turbulence tests. The latter is based on integral turbulence characteristic (ITC) models. In this study, we revisited the quality control procedure using locally established ITC models for wind speed components and atmospheric scalars (temperature, humidity and carbon dioxide molar densities) in unstable and stable conditions. We also considered the interdependence of flux qualities because of the corrections applied to fluxes during their preprocessing. The analyses were carried out using one year of data collected above a woodland forest and mixed crop sites, both situated in Northern Benin, West Africa. From these analyzes we note that the quality flags vary depending on the use of local or existing ITC models. Also, the test of turbulence development on atmospheric scalars, previously neglected, is of great importance in the quality control of data. Thus, taking into account the ITC models associated with the atmospheric scalars established locally in the well-developed turbulence test as well as taking into account the interdependence of the flux quality have considerably improved the standard quality control procedure used in effectively filtering unrealistic flux data while keeping a considerable percentage of good and medium quality data.

Development and optimization of an in vitro release assay for evaluation of liposomal irinotecan formulation

Onivyde®, a pegylated irinotecan liposomal formulation, is approved by the USFDA for treating metastatic pancreatic adenocarcinoma. Despite the substantial interest in developing its generic versions, the unique structural and manufacturing complexities of liposomal formulations pose challenges. In this study, we address this gap by developing a robust in vitro release test (IVRT) using dialysis membrane techniques. The release of Onivyde® is influenced by several key factors, including the composition of the release medium, temperature, initial formulation concentration, the materials and molecular weight cut-offs of dialysis bags, and the pH of the release medium. Our optimized IVRT for Onivyde® incorporates a release medium containing 5 mM ammonium bicarbonate in a HEPES solution with a pH of 7.4. Additionally, the method includes an initial formulation concentration of 4.6 µg/mL and 50 kDa dialysis bags, while maintaining a temperature of 37 °C with continuous agitation at 80 rpm. This optimized IVR assay effectively differentiates between varying qualities of irinotecan liposomal formulations. Our findings contribute to optimizing IVRT for liposomal irinotecan formulations, enabling better quality control procedures. This assay serves as a reliable tool for evaluating generic irinotecan liposomal formulations, aiding in their development and ensuring in vitro comparability.

Implementation of a community-based LC-UV drug checking service: promising preliminary findings on feasibility and validity

BackgroundThe increasing diversity of psychoactive substances on the unregulated drug market poses significant health, psychological, and social risks to people who use drugs (PWUD). To address these risks, various harm reduction (HR) policies have been implemented, including drug checking services (DCS). Many analytical methods are used for DCS. While qualitative methods (e.g., thin layer chromatography, spectroscopy) are easier to implement, they are not as accurate as quantitative methods (e.g., LC-UV, LC-MS). Some HR programmes have implemented high-performance liquid chromatography coupled with UV detection (LC-UV). This article presents the cross-validation of this quantitative method with a reference liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) method.MethodsDrug samples were provided by PWUD to a DCS called DrugLab in Marseille, France. The samples were weighed and prepared through dissolution in methanol, followed by ultrasonic bathing. Samples were analysed onsite using LC-UV analysis. They were then subsequently analysed with the reference LC-HRMS method. The LC-UV instrument in DrugLab was calibrated after being purchased; analysis of standard solutions was routinely performed once a month and after maintenance operations. For the LC-HRMS instrument, calibration and quality control procedures followed European Medicines Agency (EMA) guidelines. Statistical analyses were conducted including Spearman correlation tests using IBM® SPSS® Statistics version 20.ResultsA total of 102 samples representing different product classes and cutting agents were cross-validated. Differences between both analyses methods for each molecule analysed were ≤ 20%, with significant correlations between both methods’ results for most substances. Notably, LC-HRMS provided lower concentration values for cocaine and acetaminophen, whereas it provided higher values for other substances. Correlations were significant for cocaine, ketamine, MDMA, heroin, amphetamine, caffeine, acetaminophen, and levamisole.ConclusionsThis study demonstrates that the results provided by DrugLab were accurate and reliable, making LC-UV an adaptable, stable, and suitable analytical method for simple matrices like drugs in a DCS context. However, this cross validation does not guarantee accuracy over time. A proficiency test project in HR laboratories across France is currently under development in order to address potential drifts in LC-UV accuracy.

The “Notch” in Unstable Layers and the Stability Minimum in the Tropics

Abstract In a previous paper, we identified a “notch” in unstable layers at Koror (7.3 0N, 134.5 0E), where there was a relative deficiency in thin unstable layers and a corresponding relative excess in thicker layers, at altitudes centered at 12 km. We hypothesized that this feature was associated with the previously identified stability minimum in the tropics at that same altitude. In this paper, we extend our studies of this “notch” and its association with the tropical stability minimum by examining other stations in the deep tropics and also some stations at higher latitudes within the tropics. We find that this “notch” feature is found at all the other radiosonde stations in the deep tropics that we examined. We also find that the annual variations in unstable layer occurrences at stations at higher latitudes within the tropics show variations consistent with our hypothesis that this “notch” is associated with the region of minimum stability in the tropics at altitudes centered around 12 km, in that the annual variation in this “notch” feature is consistent with the annual variation of minimum stability in this region. Two factors contribute to the “notch” feature. One is that the data quality control procedure of the analysis rejects many thin layers due to the small trend-to-noise ratio in the region of minimum stability. The other is that the cloud-top outflow, which was previously identified with the stability minimum, advects thicker unstable layers throughout the deep tropics at the altitudes of the “notch.”

Research Progress on the Mechanism of the Impact of Myofibrillar Protein Oxidation on the Flavor of Meat Products.

Myofibrillar proteins primarily consist of myosin, actin, myogenin, and actomyosin. These proteins form complex networks within muscle fibers and are crucial to the physical and chemical properties of meat. Additionally, myofibrillar proteins serve as significant substrates for the adsorption of volatile flavor compounds, including aldehydes, alcohols, ketones, and sulfur and nitrogen compounds, which contribute to the overall flavor profile of meat products. A series of chemical reactions occur during the processing, storage, and transportation of meat products. Oxidation is one of the most significant reactions. Oxidative modification can alter the physical and chemical properties of proteins, ultimately impacting the sensory quality of meat products, including flavor, taste, and color. In recent years, considerable attention has been focused on the effects of protein oxidation on meat quality and its regulation. This study investigates the impact of myofibrillar protein oxidation on the sensory attributes of meat products by analyzing the oxidation processes and the factors that initiate myofibrillar protein oxidation. Additionally, it explores the control of myofibrillar protein oxidation and its implications on the sensory properties of meat products, providing theoretical insights relevant to meat processing methods and quality control procedures.

Improving quality control procedures for the measurement of total daily energy expenditure using the two-point doubly labeled water method.

The precision of the doubly labeled water (DLW) method is determined by the precision and accuracy of the isotopic measurements. Quality control (QC) procedures to mitigate sample variability require additional measurements if sample duplicates differ more than a factor of instrument precision. We explored the effect of widening QC ranges on total daily energy expenditure (TDEE) determined using the two-point sampling method. We screened DLW data from 121 individuals for instances where samples were analyzed more than twice using our existing QC criteria (±2.0 per mil [δ] for 2H and ±0.5 δ for 18O). We then applied wider QC ranges for accepting duplicate measures and recalculated TDEE. Widening the 2H QC range to ±10.0 δ in samples collected on the first day (most enriched) and to ±5.0 δ in samples collected on the final day (less enriched) produced almost identical mean TDEE compared to the originally calculated TDEE (2684 ± 508 vs. 2687 ± 512 kcal/day, p= 0.40). There was a strong correlation with the originally calculated TDEE (r2=0.97, p< 0.001). Expanding the 2H QC range to ±10.0 δ for samples collected on the first day and ±5.0 δ for samples collected on the final day provides similar mean TDEE results. These findings may help DLW labs optimize QC criteria and reduce analytical costs.

Amputated life-testing based on extended Dagum percentiles for type of group inspection plans: optimal sample sizes, termination time ratios analysis

This paper introduces a novel approach to life-testing using Extended Dagum (EXD) percentiles within the framework of group inspection plans. The methodology focuses on optimizing sample sizes and analyzing termination time ratios to enhance the reliability of quality control procedures. By leveraging the flexibility of the EXD distribution, the proposed approach accurately models complex survival data, accommodating heavy-tailed characteristics often encountered in practice. The study presents a detailed analysis of optimal sample sizes and the critical termination time ratios that balance producer and consumer risks. Through comprehensive simulations and real applications, the paper demonstrates the effectiveness of the proposed methodology in ensuring robust and efficient life-testing strategies, offering valuable insights for practitioners in industries such as manufacturing, engineering, and reliability analysis.

Identification of the metabolic protein ATP5MF as a potential therapeutic target of TNBC

BackgroundTriple-negative breast cancer (TNBC), a distinct subtype of breast cancer, is characterized by its high invasiveness, high metastatic potential, proneness to relapse, and poor prognosis. Effective treatment regimens for non-BRCA1/2 mutation TNBC are still lacking. As a result, there is a pressing clinical necessity to develop novel treatment approaches for non-BRCA1/2 mutation TNBC.MethodsFor this research, the scRNA data was obtained from the GEO database, while the transcriptome data was obtained from the TCGA and METABRIC databases. Quality control procedures were conducted on single-cell sequencing data. and then annotation and the Copycat algorithm were applied for anlysis. Employing the high dimensional weighted gene coexpression network analysis (hdWGCNA) method, we analyzed the tumor epithelial cells from non-BRCA1/2 mutation TNBC to identify the functional module genes. PPI analysis and survival analysis were further emplyed to identify the key gene. siRNA-NC and siRNA-ATP5MF were transfected into two MDA-MB-231 and BT-549 TNBC cell lines. Cell growth was determined by CCK8 assay, colony formation and migration assay. Electron microscopy was used to examine the structure of mitochondria in cells. JC-1 staining was used to measure the potential of the mitochondrial membrane. A tumor xenograft animal model was established by injecting TNBC cells into nude mice. The animal model was usded to evaluated in vivo tumor response aftering ATP5MF silencing.ResultsUsing hdWGCNA, we have identified 136 genes in module 3. After PPI and survival analysis, we have identified ATP5MF as a potential therapeutic gene. High ATP5MF expression was associated with poor prognosis of non-BRCA1/2 mutation TNBC. The high expression of ATP5MF in TNBC tissues was evaluated using the TCGA database and IHC staining of clinical TNBC specimens. Silencing ATP5MF in two TNBC cell lines reduced the growth and colony formation of TNBC cells in vitro, and hindered the growth of TNBC xenografts in vivo. Additionally, ATP5MF knockdown impaired mitochondrial functions in TNBC cells.ConclusionIn summary, the metabolic protein ATP5MF plays a crucial role in the non-BRCA1/2 mutation TNBC cells, making it a potential novel diagnostic and therapeutic oncotarget for non-BRCA1/2 mutation TNBC.

Personal history of irradiation and risk of breast cancer: A Mendelian randomisation study.

Studies on the relationship between personal history of irradiation and breast cancer have been reported for a long time. Still, epidemiological studies have not been conclusive, and the causal relationship is unclear. To address this issue, we employed Mendelian randomisation (MR) analysis to examine the association between individual radiation exposure history and breast cancer. We used a series of quality control methods to select single nucleotide polymorphism (SNP) closely related to exposure. Meanwhile, several analysis methods were used to analyse the sample data to make the conclusion more reliable. To evaluate the horizontal pleiotropy, heterogeneity and stability of SNPs for breast cancer, the MR-Egger intercept test, Cochran's Q test and 'leave one' sensitivity analysis were used. Finally, the outlier variation determined by the Mendelian Randomisation Pleiotropy RESidual Sum and Outlier test is gradually eliminated to reduce the influence of heterogeneity and horizontal pleiotropy. After implementing rigorous quality control procedures, we carefully chose 102 qualified instrumental variables closely associated with the selected exposure for sensitivity analysis. This was conducted to evaluate the heterogeneity, level multiplicity, and stability of SNPs in the context of personal radiation history and its correlation with breast cancer. The results of the inverse variance weighted method analysis revealed a positive correlation between personal radiation and a heightened risk of breast cancer (odds ratio (OR) = 1.52; 95% confidence interval (CI) = 1.30-1.77). We also validated on another data set; the results were similar (OR = 1.51; 95% CI = 1.27-1.81). Furthermore, the findings from the sensitivity analysis were consistent. At the genetic level, our research demonstrated that personal radiation exposure is associated with an elevated risk of breast cancer. Using genetic data provides evidence and strengthens the causal link that personal radiation causes breast cancer.

A Review of Recent Advances in Chromatographic Quantification Methods for Cyanogenic Glycosides.

Cyanogenic glycosides are naturally occurring compounds found in numerous plant species, which can release toxic hydrogen cyanide upon hydrolysis. The quantification of cyanogenic glycosides is essential for assessing their potential toxicity and health risks associated with their consumption. Liquid chromatographic techniques coupled with various detectors have been widely used for the quantification of cyanogenic glycosides. In this review, we discuss recent advances in chromatographic quantification methods for cyanogenic glycosides, including the development of new stationary phases, innovative sample preparation methods, and the use of mass spectrometry. We also highlight the combination of chromatographic separation with mass spectrometric detection for the identification and quantification of specific cyanogenic glycosides and their metabolites in complex sample matrices. Lastly, we discuss the current challenges and future perspectives in the development of reliable reference standards, optimization of sample preparation methods, and establishment of robust quality control procedures. This review aims to provide an overview of recent advances in chromatographic quantification methods for cyanogenic glycosides and their applications in various matrices, including food products, biological fluids, and environmental samples.

Development of a virtual teaching module for advanced semiconductor fabrication and its learning effectiveness analysis

AbstractSemiconductor fabrication is the process of manufacturing semiconductor devices, typically integrated circuits (ICs) such as microprocessors and memory. This involves transferring circuit diagrams onto a silicon wafer using photomasks and photoresists. After a series of fabrication processes, ICs are created on the wafer surface and then diced into individual chips, which are packaged and tested with quality control procedures to become the final products. Virtual reality (VR) simulates imaginary experiences or environments difficult to achieve in the real world through human senses and immersive equipment, allowing users to interact in a virtual 3D space in real time, making it well suited for applications in science education and industrial training. This study transforms the essential knowledge of advanced semiconductor manufacturing processes into an easily understandable virtual teaching module, thereby creating educational resources for high school and college students. The objective is to enhance their scientific and technological literacy, yielding substantial benefits for the general public. This study utilized VR technology to simplify and clarify the knowledge about the semiconductor manufacturing process, making it more engaging for learners. The virtual teaching module's learning content includes an overview of wafer preparation, semiconductor fabrication, chip packaging, and IC testing. Users can interact with the virtual teaching module and conduct virtual experiments to enhance their understanding by trial and error. Experimental results show that it can improve students' learning achievement and learning motivation. Therefore, the virtual teaching module is suitable for high‐school students and the general public to understand semiconductor technology and its applications. The effectiveness of the virtual teaching module is heavily dependent on the availability and quality of VR hardware and software. Limited access to advanced VR equipment or technical issues could have affected the learning experience, thereby influencing the learning outcomes.

ISETECH-Analysis Detection of Real-Time Metallic Surface Defect using MobileNetv2 and YOLOv3 on Raspberry Pi

This work presents an innovative solution utilizing a Raspberry Pi detection system to identify any defects on metallic surfaces in real-time. Manual inspection has several limitations, including time-consuming, subjective assessments, and a higher probability of human error could compromise product quality, lead to potential failures, and result in substantial costs for manufacturers. The primary focus of this endeavour is to enhance manufacturing efficiency and reduce labour expenses by automating the defect identification process. This objective is realized by employing the YOLOv3-tiny and MobileNetv2 algorithms which are subsequently deployed on a Raspberry Pi to enable precise and swift defect detection on metallic surfaces. The implementation process involves training and testing the models on a computer, followed by their deployment onto the Raspberry Pi. Upon proper setup, the trained models are employed for real-time inferences, effectively identifying defects. Notably, while the MobileNetv2 exhibits impressive accuracy in classifying defect types above 0.9, it is found to be less efficient for real-time detection on the Raspberry Pi. In contrast, the YOLO model proves to be well-suited for real-time detection on this platform with above probability of 0.8 for selected types of defects. The successful integration of this model significantly transforms quality control and inspection procedures across various industries.

An extreme thermal cycling reliability test of ATLAS ITk Strips barrel modules

At the end of Run 3 of the Large Hadron Collider (LHC), the accelerator complex will be upgraded to the High-Luminosity LHC (HL-LHC) in order to increase the total amount of data provided to its experiments. To cope with the increased rates of data, radiation, and pileup, the ATLAS detector will undergo a substantial upgrade, including a replacement of the Inner Detector with a future Inner Tracker, called the ITk. The ITk will be composed of pixel and strip sub-detectors, where the strips portion will be composed of 17,888 silicon strip detector modules. During the HL-LHC running period, the ITk will be cooled and warmed a number of times from about -35°C to room temperature as part of the operational cycle, including warm-ups during yearly shutdowns. To ensure ITk Strips modules are functional after these expected temperature changes, and to ensure modules are mechanically robust, each module must undergo ten thermal cycles and pass a set of electrical and mechanical criteria before it is placed on a local support structure. This paper describes the thermal cycling Quality Control (QC) procedure, and results from the barrel pre-production phase (about 5% of the production volume). Additionally, in order to assess the headroom of the nominal QC procedure of 10 cycles and to ensure modules don't begin failing soon after, four representative ITk Strips barrel modules were thermally cycled 100 times — this study is also described.