Improve Quality Control Research Articles

Modeling and comparison of dissolution profiles for different brands of albendazole boluses

BackgroundAddressing critical veterinary drugs, especially drugs with solubility problems like albendazole, and their implications for therapeutic efficacy, in-vitro dissolution studies can indeed provide valuable insights into how different brands of albendazole boluses perform under standardized conditions, helping to assess their dissolution profiles and potential bioavailability.MethodsSix brands of albendazole 300 mg boluses were collected from December 2020 to May 2021 G.C. The laboratory work was conducted from December 2020 to May 2021 in the National Animal Products and Veterinary Drugs and Feed Quality Assessment Centre (APVD-FQAC) laboratories. The collected brands from government veterinary clinics and private veterinary shops were subjected to model independent and dependent parameters. The dissolution test was conducted according to the USP monograph.ResultsThe study found that none of the six brands met the requirements of the dissolution test, as their API release was less than 80% within the specified 60-minute timeframe according to USP standards. Model independence indicated that only one brand (Alb002 = 3.72) achieved a difference factor of ≤ 15%. The remaining four brands (4/6) did not meet this criterion. However, the similarity factor (f2) revealed that all five brands (5/6) were comparable to the comparator products, with f2 values of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\ge\\:$$\\end{document}50%. The mean dissolution time results confirmed that three brands (3/6) had the highest dissolution rate and the fastest onset of action. The model-dependent kinetics indicated that the Weibull and Korsemeyer-Peppas models were the best fit for the release of drug substances.ConclusionThe study highlights issues with albendazole boluses’ quality, highlighting the need for national in-vitro dissolution studies. These recommendations could improve quality control, streamline regulatory frameworks, and offer practical, cost-effective methods for evaluating drug efficacy and safety, ensuring veterinary pharmaceuticals meet safety and efficacy standards.

(Invited) Empowering Clean Energy Manufacturers with Low-Temperature Plasma: A Novel Approach to Real-Time Thin Film Metrology

Thin films, or micro- and nano-scale materials with unique functional properties, enable many clean energy industries, such as lithium-ion batteries, solar cells, and carbon conversion. Minimal-waste manufacturing of high-performance thin films will be vital for achieving society’s net-zero carbon emission goals. Because existing thin film sensors cannot operate within most sections of manufacturing lines, manufacturers often complete hundreds of processing steps before they can test (a small portion of) their products and detect problems. Current thin-film manufacturing thus often yields many low-quality products, or off-spec products that must be discarded. A real-time, in-situ metrology strategy can create unprecedented opportunities for implementation of smart manufacturing practices and advanced process control solutions to realize higher thin film throughput and performance.SirenOpt has developed a real-time low-temperature plasma-based thin film metrology sensor. The sensor is compact, can be integrated within existing manufacturing lines, and can collect multiple thin film property measurements (such as thickness with a precision of 1/10th of a nanometer, density, resistivity, chemical composition, and contaminate identification) in parallel and in real-time, thus allowing for the collection of critical manufacturing data that is otherwise unobtainable. SirenOpt is currently applying the sensor to industrial thin film coating processes such as Lithium-ion battery electrodes, where this flexible sensor can be used to accelerate R&D and process optimization, improve quality control, and enable real-time process control.

Neural algorithm for optimization of multidimensional object controller parameters

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.

Transforming Tofu Quality Control: Integrating Statistical Process Control, Ishikawa, and Interpretive Structural Modeling for Superior Outcomes

In a highly competitive market, maintaining high product quality is essential for maintaining customer satisfaction and loyalty. Producers are famous in East Java and face the challenge of ensuring consistent quality products through strict production processes. Know that as easy products broken and soft, needy control of careful quality to meet hope consumers about freshness, texture, and quality in a way whole. Although traditional control methods often fail to overcome the production complex that causes a disabled product. This research proposes integrating Statistical Process Control (SPC), Ishikawa diagrams, and Interpretive Structural Modeling (ISM) to improve control quality. SPC makes it possible to monitor and control production processes in real-time, identify deviations, and repair deviations. However, the SPC limitations include a focus on quantitative data and post-incident detection problems. To overcome root problems, the Ishikawa diagram categorizes the reasons for potency as material, machine, method, power work, and environment. ISM prioritizes action repair based on impact and relationship. Approach This integrated approach provides comprehensive solutions to improve the quality of knowledge. In this study, the control process quality was evaluated using SPC, the cause of defects was identified using Ishikawa diagrams, and priority action repair was performed via ISM. Findings show that SPC directly effectively monitors process control, the Ishikawa diagram identifies main defects, and ISM prioritizes impactful action improvements, emphasizes excessive additions to subtraction materials, and improves health workers’ and mixing process material standards. Approach integrated possible identification of additional problems and improved the quality strategic makes a significant contribution to enhancing product quality knowledge.

Detection of Piston Ring Deficiency in The Assembly of Automotive Ball Joint and Tie Rod End Parts

In today's intensely competitive environment, businesses strive to optimize production efficiency to reduce costs, increase profitability, and ensure customer satisfaction. This focus on efficiency and quality enables businesses to operate more effectively, gain a competitive advantage in the market, and move towards sustainable growth. This study uses image processing techniques to detect missing segments in the assembly of ball joints automatically. In the automotive industry, performing quality control of critical components before assembly and detecting and classifying the defective ones is essential. Many quality control methods can be applied with existing technologies. This paper proposes an automatic real-time control based on image processing techniques to detect ball joint missing segments, a common defect in the automotive industry. In the company, operators perform defect detection by visual inspection. In this system, production continues in cases where the operator cannot detect the defect. This system aims to detect the errors made by the operator during the assembly operations and provide instant feedback. The developed system uses OpenCV library algorithms that are highly accurate in detecting defects in manual assembly processes so that missing components are removed from the production chain, and production quality is significantly improved. Accuracy is over 94% when identifying missing segments, about 30% better than traditional methods. In tests, 1200 ball joints were run through the system, resulting in 1150 defects being correctly identified and removed from the production line. Accuracy is high thanks to the application of various image processing techniques such as grayscale conversion, edge detection, and shape recognition. This also provides real-time feedback to the operator so the system can reduce detection and response time from 15 seconds to 5 seconds. This increases production speed and reduces the error rate in manual assembly processes by 20%. This paper also highlights the potential of image processing technology in manufacturing. It will contribute to improved quality control mechanisms to increase the reliability and efficiency of production lines in the automotive industry.

Improving quality control of whole slide images by explicit artifact augmentation

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research.

Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

The detection of organic polymers as contaminants in foodstuffs by means of the fluorescence decay of their auto fluorescence

Products such as food can become contaminated during their manufacture or afterwards. Depending on the type of substance causing the contamination, these contaminants can be harmful to health and difficult to detect by visible inspection. The suitability of fluorescence decay and FD-FLIM for the detection of plastics contamination in foodstuffs is demonstrated. Therefore, a procedure for the detection of contaminating organic polymers (plastics) in processed meat such as salami by means of the fluorescence decay time of auto fluorescence is described. The auto fluorescence of processed meat was found to decay according to first order with a typical time constant of about 2 ns, whereas the time constant of significant polymers for the processing of meat is generally appreciably higher (2.5 ns – 5.5 ns depending on the polymer). As a consequence, contaminating organic polymers can not only be globally detected by means of the fluorescence decay but also localised in two-dimensional imaging. The present study reports a high potential of FD-FLIM for rapidly identifying and differentiating different plastics on and in different foodstuffs. The method allows an improved quality control of foodstuffs.

Barriers to retention in inpatient and residential drug treatment among persons who use opioids and/or injection drugs living in the rural U.S.

AimBarriers to retention in inpatient and residential care for persons who use drugs are understudied in the rural context. We sought to better understand barriers to retention in inpatient and residential drug treatment in a large, multi-site, geographically diverse sample of persons who use opioids and/or injection drugs in the rural U.S. MethodsWe conducted semi-structured individual interviews with persons currently using opioids and/or injection drugs in 9 U.S. states, including Illinois, Kentucky, Massachusetts, North Carolina, New Hampshire, Ohio, Oregon, Vermont, and Wisconsin. Content areas included substance use history and experiences with all modalities of drug treatment. We performed initial structural coding followed by an iterative “open-coding” process of itemizing and categorizing content within each code, and a multi-coder memoing process to summarize themes. We identified themes using three levels of the Social-Ecological Model (SEM): individual, interpersonal, and facility-level (organizational) barriers. ResultsAmong 304 interviewed, over half (n = 166, 54 %) reported having experienced inpatient and residential treatment. Lack of treatment retention was driven by interrelated factors at all levels of the SEM. Person-level factors inhibiting retention included lack of readiness to stop using, which was particularly true for court-ordered treatment, and dislike of “freedom limitations”. The sole interpersonal-level factor was the influence of other patients on re-initiation of drug use. Facility-level barriers included unaddressed withdrawal symptoms and lack of access to MOUD, staff relatability, inadequate staff training, and, particularly in residential treatment, lack of structure and supervision. Lack of preparation for coping with real-world triggers was seen as a barrier to engagement in ongoing treatment. ConclusionBarriers to retention in inpatient and residential substance use treatment were present at three levels of the SEM. Interviews suggest much room for improvement in inpatient and residential drug treatment programs with respect to improving access to MOUD, tailoring content to better address social challenges in the rural context, and improving quality control measures with respect to staff and resident supervision.

Ultrasonic full guided wavefield for damage detection in curved CFRP parts

Guided waves can propagate from flat plates to curved structures and carry structural health information. In this paper, an ultrasonic guided wave inspection method is proposed for detecting damages in curved parts made of carbon fibre reinforced plastics (CFRPs), such as hat-stringer-stiffened panel. By collecting full wavefield information, we obtain the standing wave information formed by the interaction between guided waves and damage. The interaction mechanism between guided waves and damage is simulated through finite element simulation, and the wavefield curvature imaging algorithm is used to generate a damage image in the curved area. Furthermore, a specimen with delamination damages is fabricated, and full wavefield information is collected through a scanning laser Doppler vibrometer (SLDV). We then use the proposed imaging algorithm to detect delamination damages of different sizes. The experimental results were consistent with the simulation results, confirming the effectiveness of the proposed method. Notably, the technology combines the advantages of guided waves and SLDV, which can obtain full wavefield information without contact, including damage-related characteristics. This work provides a convenient and comprehensive method for collecting wavefield information on complex curved CFRP parts for structural health monitoring, which would significantly improve quality control for CFRP components in various fields.

Optimal harvest period and quality control markers of cultivated Flos Chrysanthemi Indici using untargeted/targeted metabolomics, chemometric analysis and in vivo study

Ethnopharmacological relevanceFlos Chrysanthemi Indici (FCI), the flower of Chrysanthemum Indicum L., is a popular traditional Chinese medicine (TCM) for treatment of inflammatory diseases in China. FCI is also a functional food, and is widely used as herbal tea for clearing heat and detoxicating. Aim of the studyTo explore quality control markers of FCI based on the optimal harvest period. Materials and methodsFirst, UPLC-Q-TOF/MS based untargeted metabolomics was applied to explore the chemical profiles of FCIs collected at bud stages (BS), initial stages (IS), full bloom stages (FS) and eventual stages (ES) from eight cultivated regions in China. Subsequently, lipopolysaccharide (LPS)-induced RAW264.7 cell inflammatory model and carrageenan-induced rat paw edema model were used to confirm the anti-inflammatory effect of FCIs collected at IS/FS. Then, UPLC-PDA targeted metabolomics was used to quantitatively analyze 9 constituents with anti-inflammatory activity (7 flavonoids and 2 phenolic acids) changed significantly (VIP > 4) during flowering stages. Finally, ROC curves combined with PCA analysis based on the variation of 9 active constituents in FCIs from different flowering stages were applied to screen the quality markers of FCI. ResultsFCIs at IS/FS had almost same chemical characteristics, but quite different from those at BS and ES. A total of 32 constituents in FCIs including flavonoids and phenolic acids were changed during flowering development. Most of the varied constituents had the highest or higher contents at IS/FS compared with those at ES, indicating that the optimal harvest period of FCI should be at IS/FS. FCI extract could effectively suppress nitric oxide (NO) production in LPS-induced RAW264.7 cells and regulate the abnormal levels of cytokines and PGE2 in carrageenan-induced paw edema model rat. The results of quantitatively analysis revealed that the variation trends of phenolic acids and flavonoids in FCIs were different during flowering development, but most of them had higher contents at IS/FS than those at ES in all FCIs collected from eight cultivated regions, except one sample from Anhui. Finally, linarin, luteolin, apigenin and 3,5-dicaffeoylquinic acid were selected as the Q-markers based on the contribution of their AUC values in ROC and clustering of PCA analysis. ConclusionsOur study demonstrates the optimal harvest period of FCI and specifies the multi-constituents Q-markers of FCI based on the influence of growth progression on the active constituents using untargeted/targeted metabolomics. The findings not only greatly increase the utilization rate of FCI resources and improve quality control of FCI products, but also offer new strategy to identify the Q-markers of FCI.

Neural Network-Based Filter Design for Compressive Raman Classification of Cells.

Cell-based therapies are bound to revolutionize medicine, but significant technical hurdles must be overcome before wider adoption. In particular, nondestructive, label-free methods to characterize cells in real time are needed to optimize the production process and improve quality control. Raman spectroscopy, which provides a fingerprint of a cell's chemical composition, would be an ideal modality but is too slow for high-throughput applications. Compressive Raman techniques, which measure only linear combinations of Raman intensities, can be fast but require careful optimization to deliver high performance. Here, we develop a neural network model to identify optimal parameters for a compressive sensing scheme that reduces measurement time by 2 orders of magnitude. In a data set containing Raman spectra of three different cell types, it achieves up to 90% classification accuracy using only five linear combinations of Raman intensities. Our method thus unlocks the power of Raman spectroscopy for the characterization of cell products.

Batch-to-Batch Variation in Laser-Inscribed Graphene (LIG) Electrodes for Electrochemical Sensing.

Laser-inscribed graphene (LIG) is an emerging material for micro-electronic applications and is being used to develop supercapacitors, soft actuators, triboelectric generators, and sensors. The fabrication technique is simple, yet the batch-to-batch variation of LIG quality is not well documented in the literature. In this study, we conduct experiments to characterize batch-to-batch variation in the manufacturing of LIG electrodes for applications in electrochemical sensing. Numerous batches of 36 LIG electrodes were synthesized using a CO2 laser system on polyimide film. The LIG material was characterized using goniometry, stereomicroscopy, open circuit potentiometry, and cyclic voltammetry. Hydrophobicity and electrochemical screening (cyclic voltammetry) indicate that LIG electrode batch-to-batch variation is less than 5% when using a commercial reference and counter electrode. Metallization of LIG led to a significant increase in peak current and specific capacitance (area between anodic/cathodic curve). However, batch-to-batch variation increased to approximately 30%. Two different platinum electrodeposition techniques were studied, including galvanostatic and frequency-modulated electrodeposition. The study shows that formation of metallized LIG electrodes with high specific capacitance and peak current may come at the expense of high batch variability. This design tradeoff has not been discussed in the literature and is an important consideration if scaling sensor designs for mass use is desired. This study provides important insight into the variation of LIG material properties for scalable development of LIG sensors. Additional studies are needed to understand the underlying mechanism(s) of this variability so that strategies to improve the repeatability may be developed for improving quality control. The dataset from this study is available via an open access repository.

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.

Improving Quality Control Measures in the Dairy Industry: Utilizing Dynamics of Dirt Resistance Factor (Rd) Value and Time of Effective Bacterial Contamination Prevention

The number of customer complaints related to decreased quality of dairy products was the background of this study. Objective: (i) examined the effect of the highest dirt resistance factor (Rd) on the number of bacterial contaminants in finished goods; (ii) examine the effect of time on the growth of bacterial contaminants in the fouling layer of UHT pipes when not in operation. The research method was based on the mathematical derivation of the formation process of Rd in the form of an equation. Bacterial species were analyzed using the Operation Taxonomic Unit (OTU) with 16S rRNA. The ANOVA test was used to determine the mean value, standard deviation and the Tukey LSD post hoc test to see the difference in heat balance and bacterial growth. Findings: (i) During the UHT sterilization process, the Rd value (0.0205 h.Ft2.F.Btu-1) had an impact on total bacterial contamination &gt;20 Cfu.mL-1; (ii) In the event of engine damage or malfunction of the heater on the UHT. Time had a significant effect (p&lt;0.05) on the number of lactic acid bacteria in the fouling layer in UHT pipes. Conclusion: The study recommends a sanitation schedule every 110 minutes (± 2 hours) or Rd value around 0.0205 h.Ft2.F.Btu-1.

A single-step microdistillation procedure for the determination of total vicinal diketones in beers

Vicinal diketones (VDK) strongly affect the quality of beers, as they are responsible for off-flavors and deterioration markers, which are recognized as safe for humans when ingested but are toxic when inhaled. Thus, their determination is required in both the production process and the final beverage. A novel approach for determining the total VDK content in beers using microdistillation as a simple approach for sample pretreatment is proposed. The lab-made microdistillation apparatus consists of a capped Falcon® tube with the sample at the bottom and the acceptor solution inside a suspended Eppendorf® tube. The sample is heated (90 °C, 20 min) to volatilize the analytes, which are collected from the headspace by an acceptor-derivatizing solution (o-phenylenediamine in acid medium), and the product is determined by ultraviolet spectrophotometry. Under conditions optimized by experimental design, a linear response was achieved from 0.05 to 1.00 mg L-1 diacetyl, with a coefficient of variation (n = 11) and limit of detection (95 % confidence level) estimated at 4.3 % and 0.01 mg L-1 diacetyl, respectively. The procedure requires as low as 40 μg of o-phenylenediamine, 1.7 μL of 37 % (v/v) hydrochloric acid, and 5000 μL of sample, generating only 5400 μL of waste per determination. It allows for the simultaneous analysis of 10 samples, a capability not previously reported in the literature. Results obtained for Ale and Lager beers using the proposed and reference procedures agreed at a 95 % confidence level. Therefore, the current proposal is a reliable and environmentally friendly alternative for the rapid determination of VDKs in beer, contributing to improved quality control in both the process and final products of the brewing industry.

Advanced analysis and construction techniques for long-span spatial structures in steel engineering

This paper explores advanced analysis methods and construction techniques for long-span spatial structures in steel engineering. It delves into the principles of the Direct Analysis Method (DAM), emphasizing equilibrium, compatibility, plasticity, and stability, as well as load path and redundancy considerations. The DAM provides a robust framework for analyzing complex steel structures, ensuring stability, resilience, and efficiency. Furthermore, the paper discusses modeling techniques, including Finite Element Analysis (FEA), dynamic analysis, and nonlinear analysis, highlighting their significance in optimizing design solutions and predicting structural behavior under diverse loading conditions. Practical considerations in material selection, connection design, and construction are addressed, focusing on enhancing performance, durability, and constructability. Prefabrication and modular construction techniques are explored as effective strategies for accelerating project schedules, improving quality control, and enhancing site safety. Through a comprehensive review of literature and case studies, this paper provides valuable insights for engineers and researchers involved in the design and construction of long-span spatial structures in steel engineering.

Applying Blockchain, Causal Loop Diagrams, and the Analytical Hierarchy Process to Enhance Fifth-Generation Ceramic Antenna Manufacturing: A Technology–Organization–Environment Framework Approach

This study used a technology–organization–environment (TOE) framework as the primary analytical tool to explore the burgeoning capabilities of blockchain technology in the area of 5G ceramic antenna development. A causal loop diagram (CLD) analysis is used to further clarify the complex dynamics and feedback mechanisms, and the impact of blockchain on the design, production, and deployment phases of ceramic antennas, which play a pivotal role in the development of 5G communications, is studied. We found that blockchain’s unique features, including its immutable ledger and decentralized architecture, have the potential to significantly improve the transparency, security, and efficiency of the ceramic antenna manufacturing process. Technology (T), organization (O), and environment (E) were used as the top factors, and the subfactors of TOE were selected and analyzed using the Analytic Hierarchy Process (AHP) by CLD. The AHP analysis was used to evaluate the relative importance of various internal and external factors affecting the adoption of blockchain technology. The integration of the TOE framework with AHP and CLD provides a comprehensive analytical tool that enhances the understanding of the complex dynamics in the 5G ceramic antenna manufacturing process. This methodological approach not only clarifies the interactions between technological, organizational, and environmental factors but also facilitates strategic decision-making through a structured evaluation of these factors. The AHP analysis showed that technical factors are the most important in the TOE analysis of 5G ceramic antenna manufacturing, with a weight of 0.427, which indicates the important role of technical factors in the development of ceramic antenna production. In addition, environmental and organizational factors were given weights of 0.302 and 0.271, respectively, confirming the importance of technological innovation and internal process optimization. In the subfactor of Technology (T), ‘Blockchain Technology’ has the highest ranking among the subfactors, with a global weight value of 0.129, emphasizing the importance of blockchain technology. This study explored the technical and organizational complexities of introducing blockchain technology into the 5G ceramic antenna manufacturing industry and, through an in-depth investigation of the potential benefits of such integration, it aims to propose new approaches to improve quality control and manufacturing efficiency. The research findings aim to contribute to the sustainable growth of the telecommunications industry by providing strategic recommendations for the application of blockchain technology in the production of 5G ceramic antennas.

Rapid chemical characterization and pharmacological mechanism of Fining Granules in the treatment of chronic bronchitis based on UHPLC–Q-exactive orbitrap mass spectrometer and network pharmacology

BackgroundSenecio cannabifolius Less. is a perennial herb belonging to the Compositae family that has been used in traditional medicine as an antitussive and expectorant for treating chronic bronchitis and acute respiratory infections. Traditionally, Feining Granules are prepared from water extracts of the raw plant material. However, the chemical composition and pharmacological mechanisms of Feining Granules have not been thoroughly investigated. MethodsA systematic strategy for the rapid detection and identification of the constituents of Feining Granules was developed using ultrahigh-performance liquid chromatography–quadrupole-exactive orbitrap mass spectrometry (MS) with parallel reaction monitoring. ResultsOverall, 162 compounds, including flavonoids, alkaloids, organic acids, and others, were identified unambiguously and tentatively by comparing the retention times and MS fragmentation with reference standards and literature data. Ninety-nine of these were reported for the first time to the best of our knowledge. Network pharmacology suggests that Feining Granules can be used to treat chronic bronchitis as they contain active components associated with the ALB, VEGFA, and SRC target genes influenced by HIF-1, VEGF, and other signaling pathways. ConclusionThese results provide information that can help understand the effective substances of S. cannabifolius Less. and improve quality control.

Physics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition

Abstract This work presents a physics-informed fusion methodology for deformation detection using multi-sensor thermal data. A challenge with additive manufacturing (AM) is that abnormalities commonly occur due to rapid changes in the thermal gradient. Different non-destructive in-situ thermal sensors capture parts of the thermal history but are limited by the visible temperature spectrum and sensor field of view of the fabrication process. Various sensors mitigate problems with the loss of thermal history information; however, it brings forth challenges with integrating different data streams and the need to interpolate the internal thermal history. This study develops a thermal data-informed heat flux methodology that fills the gap in fusing numerical temperature approximation with data-driven knowledge of the surface of additive manufactured components. First, this study fuses infrared (IR) thermal data complexities during the AM process with the Goldak double ellipsoidal heat flux to model the energy input into the component. Second, a thermal physics-informed model input (PIMI) is created with thermal data-informed heat flux to capture internal thermal history. Lastly, a regression convolutional neural network (CNN) captures the relationship between the three-dimensional thermal gradient and the resulting surface deformation. The rapid thermal gradient formation and identification of deformation is a key step toward using thermal history data and machine learning to improve quality control in AM. The proposed surface deformation detection model achieved an mean squared error of 1.14 mm and an R2 of 0.89 in the case study when fabricating thin-walled structures.