Quality Control Model Research Articles

Capabilities of auxiliary techniques for low strain impact testing of piles foundations

Introduction. An increase in the complexity of tasks arising from the quality control of geotechnical structures ultimately increases the role of indirect methods as part of the survey set. Low strain impact testing of piles length and integrity is currently one of the most common non-destructive geophysical methods used in the state diagnostics of foundations. The capabilities and limitations of the method are associated with the peculiarities of its basic theoretical model describing the propagation of elastic waves in a thin rod. In this regard, specialists often have recourse to the auxiliary techniques of field research.Aim. To inform test engineers, designers and employees of supervisory organizations about the potential of auxiliary methods for the low strain impact quality control of pile foundations.Methods and materials. The features of the theoretical model of the low strain impact pile quality control, as well as the associated capabilities and limitations of the method are described. The auxiliary methods of data collection and analysis were used for the survey of pile foundations, including those in the composition of existing structures.Results. The results of the work are represented by additional information obtained about the foundations surveyed using the auxiliary methods of data collection and analysis. An alternative arrangement of sensors relative to the surveyed structure was used for determining the depth of piles in the composition of grade beam footings and existing structures. The common shot point profiling (ultra-seismic method) was used to assess the velocity of rod wave propagation in the body of the structure. The options of data presentation in the parallel seismic method were used to demonstrate its potential for the quality control of foundation concrete.Conclusion. The auxiliary methods of data collection and analysis used in the low strain impact survey of pile foundations can significantly increase the reliability of conclusions about the depth and integrity of a structural material.

Impaired Integrated Stress Response and Mitochondrial Integrity Modulate Genotoxic Stress Impact and Lower the Threshold for Immune Signalling.

Mitochondria-nucleus communication during stress dictates cellular fate with consequences on the etiopathology of multiple age-related diseases. Impaired mitochondrial quality control through loss of function of the mitochondrial protease HtrA2 associates with accumulation of damaged mitochondria and triggers the integrated stress response, implicating the transcription factor CHOP. Here we have employed a combined model of impaired mitochondria quality control, namely HtrA2 loss of function, and/or integrated stress response, namely CHOP loss of function, and genotoxicity to address the distinctive roles of these cellular components in modulating intracellular and intercellular responses. The genotoxic agents employed were cancer therapeutic agents such as irradiation with X-ray and protons or treatment with the radiomimetic bleomycin. The irradiation had an enhanced effect in inducing DNA damage in cells with CHOP loss of function, while the bleomycin treatment induced more DNA damage in all the transgenic cells as compared to the control. The genetic modifications impaired the transmission of DNA damage signalling intercellularly. Furthermore, we have dissected the signalling pathways modulated by irradiation in selected genotypes with RNA sequencing analysis. We identified that loss of HtrA2 and CHOP function, respectively, lowers the threshold where irradiation may induce the activation of innate immune responses via cGAS-STING; this may have a significant impact on decisions for combined therapeutic approaches for various diseases.

Using machine learning prediction models for quality control: a case study from the automotive industry

This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.

Risk Analysis for Quality Control Part 3: Practical Application of the Precision Quality Control Model.

We developed a theoretical framework (Precision Quality Control [PQC]) to minimize the cost of quality, but it is not known whether the method can be applied in practice. We used data for 2 analytes, cadmium and carbohydrate-deficient transferrin (CDT), and applied the PQC framework to find the optimal control limits. These analytes were selected because they differed with respect to sigma values that are major determinants of control limits. We explored different ways to visualize the results: (a) risk trade-off (false-positive risk vs false-negative risk), (b) cost-risk trade-off (false-positive cost vs false-negative risk), and (c) cost minimization. We were able to use the PQC limit to produce 3 different visualizations to suggest control limits. The risk-based analysis was the simplest to apply, but the most difficult to interpret. The cost vs risk method was easy to apply but was still difficult to interpret. The cost minimization method was easy to interpret but required users to declare a willingness to pay that may be difficult to estimate. The PQC method can be used to find control limits that minimize the cost of quality.

EQGP concept: Principles and models of expert quality control of the government policy

The article studies the problems of quality and efficiency of public administration. The article describes foundations of a new concept of public administration – the concept of expert quality control of the government policy (EQGP), which is an effective way to improve the quality and efficiency of government in post-industrial (information) society. In order to improve the efficiency of government administration, it is necessary to improve the mechanisms for forming public bodies and to implement monitoring mechanisms into the system of public bodies formation. To achieve this goal, it is necessary to activate expert communities (“expert factories”), to enhance expert and analytical activities both at the stage of formation of public bodies (examination of election programs) and monitoring of their activities (examination of reports). Depending on the positions occupied by experts in the decision-making system, four models of expert control have been identified. The features of each model are presented. The article also describes the basic principles of EQGP.

An Evolutionary Game Model for Quality Control of Agricultural Supermarket Matched Supply Chain with Government Participation

Based on the basic problems such as the motivation of the quality and safety management and control of the agricultural supermarket docking supply chain, this paper studies the game behavior of the three parties involved in the agricultural professional cooperatives, large chain supermarkets and government regulatory departments in the agricultural supermarket docking supply chain under the influence of government participation. To build a three-party evolutionary game model for the quality and safety management and control of agricultural supermarket docking supply chain with the participation of the government, the goal is to find a stable strategy for the game parties to maintain cooperation, so as to ensure the safety of agricultural supermarket docking supply chains.

Information Retrieval Method of Natural Resources Data based on Hash Algorithm

In order to improve the ability of searching and identifying information in natural resources data, this paper puts forward a method of searching information in natural resources data based on hash algorithm. Through the data center technology, the problems of information source positioning, data directory organization, data semantic definition and expression, and data entity relationship construction in natural resource data center are solved. Combined with the distribution of resource data stream, the information structure reorganization and data encryption in natural resource data center are realized by using hash algorithm, and the parameters of information quality control model in natural resource data center are established. Through natural resource data governance and semantic reconstruction, the characteristics detection and redundancy arrangement of information data in natural resource data center are realized by standardizing data collection rules, and they are stored in the intermediate database. Through data governance rules, the information in the natural resources data is structured and managed, and stored in the publishing library. Through all kinds of data processing tools, all kinds of data are processed, cleaned and reconstructed, and through Hash algorithm and data aggregation processing, information detection in natural resources data is realized. The simulation results show that the precision rate of natural resource data retrieval by this method is high.

Impact of assay stability on the false negative and false positive rates in quality control

BackgroundThe dynamic Precision QC (PQC) model can be used to evaluate the performance of quality control (QC) monitoring systems. The model depends on inputs that describe the intrinsic shift behavior (i.e., stability) of an assay. The output of the model is a trade-off curve that shows the relationship between false negative (FN) and false positive (FP) risk events. The relationship between the inputs and outputs of this model has not yet been explored. MethodsWe used Monte Carlo simulation to generate trade-off curves using the PQC. We varied the input parameters that determine assay stability (shift probability and shift size distribution) and studied the impact of these inputs on the output (i.e., the trade-off curve relating FN risk to FP risk). ResultsFN risk is sensitive to the shift probability and the width of the control limits. FN risk is sensitive to the shape of the shift size distribution when the standard deviation (SD) of the shift size distribution is relatively narrow (i.e., SD < 2) but is less sensitive to the width of the shift size distribution when the SD is relatively large (i.e., SD > 2). ConclusionsPractical use of the PQC model may require the estimation of the shift probability and shift size distribution.

Quality Islamic Boarding School Model: Linking the Principles of Teacher Professionalism and Organizational Management

Contradictory professional and managerial principles such as quality monitoring and evaluation are integrated to shape contemporary professional action. This study aims to determine the directions of teacher professionalism and organizational management as a quality control model in Islamic boarding schools/dayah in Southeast Aceh district. This type of qualitative research with descriptive survey techniques. Primary data sources were obtained from informants and survey results, while preliminary data was obtained from various expert studies in the form of journals, books and other encyclopedias as support. Data analysis using an interactive model. The research data was then transcribed, and a data reduction process was carried out, which consisted of coding, choosing keywords, and categorization. The research results provide a model of professionalism as an aspect of change and 'organizing professionalism' as a new model. The Islamic boarding school teacher professionalism model leads to three parts: The formation of (1) Satisfaction Value of Quality Commitment; (2) Moral Commitment Trust Value, and (3) Performance Value for Continuous Change. The social implications expected through this research are a model of building organizational values in shaping teacher professionalism as an evaluative means of sustainable quality.&#x0D;

Implementation of Quality Control of the Educational Process in Higher Education Institutions Based on Models

Modern Ukrainian society is in a complex process of transformation in various spheres at the present stage of its historical development, including, in the field of education. The humanitarian development of the country and the world sets new tasks for the national education system, the solution of which is inefficient within the framework of the existing methodology. Currently, the issue of analysing the efficiency of the higher education system and its adaptability in the field of training specialists of various profiles to constant and changing processes in various spheres of the society is of particular relevance. The inconsistency of the scientific-theoretical and scientific-practical level of the quality control model of higher education has determined the problem of the necessity to analyse the existing models in order to improve their individual aspects. The purpose of the academic paper is to study the existing models of quality control of the educational process in higher education and to clarify the practical features of the development of such models from the perspective of participants in the learning process in higher educational institutions. Methodology. In the course of the research, the analytical-bibliographic method was used to study the scientific literature on the issues of quality control of the educational process in higher educational institutions, as well as a questionnaire survey in order to clarify in practice the certain aspects of developing the models for the quality management of education in higher educational institutions. Results. Based on the results of the research, the features of implementing the individual models of quality control of the educational process in higher educational institutions were studied, and the main principles and characteristics that should be taken into account when developing such a model for each individual institution of higher education were clarified.

Quality Control Model of Ore Processing Based on Elman Neural Network

In this paper, by establishing Elman neural network and decision tree model, the quality control of ore processing is studied and reasonably predicted.Predict the product quality and the temperature corresponding to the target quality. Elman neural network model is established, training set and output set are input for training, and test set is input into neural network to obtain the relative error of real value and training value; After verification, the relative error between our real value and training value is within 100% ± 5%, and the model is accurate; Finally, the input value is substituted to obtain the forecast data.Establish the mathematical model of product qualification rate. The decision tree model is established by using SPSS, and the decision tree system is constructed by using the four factors of system setting temperature, raw ore parameters, process data and final ore quality. Through inspection, our R 2 is 0.999991, close to 1, and the model fitting is good. Compared with other parameters, it can be found that the model has good sensitivity and high accuracy. Finally, the input data is sent into the model to get the final prediction results.

Quality evaluation of Spacecraft control system under the implementation of product assurance elements

For the satellite Control system, the product assurance work is evaluated from design process, production development process, product testing and validation process, subsystem acceptance process, subsystem test and validation process, test and validation process of the subsystem under the spacecraft. This paper mainly describes the product assurance quality control model of the satellite control system development process, quantifying the results of product assurance quality management, obtaining the accurate quality assessment data of the satellite control system, and obtaining the final quality reliability results of the satellite control system.

An optimized stochastic model for smartphone GNSS positioning

With the increasing popularity of high-precision applications of smartphone, more and more scholars carry out studies in the field of smartphone GNSS positioning. In the previous studies, more attention has been paid to data quality control, data preprocessing and observation models. However, the research on stochastic models is rare. The stochastic model is significant for the subsequent optimal positioning parameter estimation, meanwhile, the stochastic models of smartphones and professional geodetic receivers are very different for the completely different characteristics of smartphone GNSS observations. It is necessary to develop a stochastic model suitable for smartphone observations. Based on the characteristics of smartphone observations, this paper proposes an optimized stochastic model. The specific process of this method is as follows: firstly, the code-minus-phase (C-L) combi-nation and double-differenced measurement were used to quantify the noise of smartphone code and phase observations. Then, an optimized carrier-to-noise density ratio (C/N0) dependent stochastic model was proposed on the basis of the characteristic of smartphone observations. To validate the superiority of the proposed model, single point positioning (SPP) and real time kinematic (RTK) experiments were carried out by Xiaomi 8 in 2 days. The 3-dimensional root mean squares (RMS) of SPP were 6.18 and 5.38 m, with improvements varying within 0.00%-10.02% compared with the customary models. Likewise, the RMS of RTK were 0.14 and 0.24 m, with improvements fitting in range of 10.06%-39.92%. This research plays an important role in improving the positioning accuracy of smartphone and promoting the popularization of high-precision applications.

Application of Big Data Technology in Environmental Pollution Control in Energy Ecological Economic Zone

In order to solve the problem of environmental pollution control in the energy ecological economic zone, this paper proposes a technology to realize environmental pollution control by using big data. The main content of this technology is based on the characteristics and application of big data technology; through the collection of online monitoring data of ecological environment pollution, the construction of the online monitoring data quality model of ecological environment pollution, and finally through the experiments in the simulation environment, the feasibility of big data technology is obtained. The experimental results show that the average quality factor data of the quality control model in the environmental scientific data supervision is 2.16, the average quality factor data of the Internet of things in the marine pollution monitoring is 5.88, and the average quality factor data of the model in the ecological environment pollution online monitoring based on the big data technology is 8.64. Big data technology has good applicability. It is proven that the research of big data technology can meet the needs of environmental pollution control in the energy ecological economic zone.

Quality Control for Offsite Construction: Review and Future Directions

The quality of building products is vital for the safety of construction and building occupants. The application of off-site construction (OSC) has been increasing in the industry, one of the reasons being its potential for improving project quality. Yet, various quality problems have been reported, such as cracks and air pockets of prefabricated components, sleeve grouting noncompactness in key nodes, which hinders the development of the OSC sector. It is urgent to carry out quality control (QC) for OSC projects. Substantial research efforts have thus been devoted to these topics in recent decades, resulting in a certain amount of literature regarding QC for OSC. The objective of this study is to conduct a state-of-the-art review of QC for OSC projects to deeply understand its knowledge gaps and future research directions. An integrated review method was used to carry out bibliometric analysis and in-depth qualitative content analysis based on selecting targeted articles. Results show that the identified knowledge gaps include: (1) OSC lacks suitable QC rules, (2) QC means for OSC is out of date, (3) QC information technology for OSC is immature, and (4) existing limitation connotation of QC for OSC. Finally, based on the identified knowledge gaps, corresponding future research directions were suggested, including quality risk transmission control for OSC, supply chain quality management for OSC projects, BIM-based quality data management for OSC, blockchain technology and quality traceability for OSC, and performance and assessment model of QC for OSC. This research contributes to an in-depth and comprehensive understanding of QC for OSC, which offers valuable implications for the quality improvement of the OSC industry.

An optical water classification and quality control model (OC_QC model) for spectral diffuse attenuation coefficient

Spectral diffuse attenuation coefficient of down-welling irradiance (Kd(λ)) is a fundamental optical parameter for water color and of significance for heat budget calculation, photosynthesis modeling and carbon sequestration. Uncertainties exist in both in situ measured and satellite derived Kd(λ), therefore, evaluation of its data quality is essential. In addition, optical water classification of Kd(λ) is still limited. To address these issues, we proposed an optical water classification and quality control model (OC_QC model) based on a globally collected high-quality in situ Kd(λ) data. The model consists of the reference Kd(λ) spectra for 7 optical water types (OWT, ranging from clear open ocean water to highly turbid water) and a scoring system. An individual Kd(λ) spectrum can be assigned an OWT and quality control score (QC score) with this model. The performance of the OC_QC model is tested with independent in situ Kd(λ) spectra from the turbid coastal water (Bohai Sea) and clear oceanic water (Indian Ocean). Questionable or erroneous Kd(λ) spectra can be identified, suggesting the model’s robustness. The model was also evaluated with the Medium Resolution Imaging Spectrometer (MERIS) Level-2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 Kd(λ) data in the Bohai Sea, Yellow Sea, East China Sea and South China Sea. Satellite derived maps clearly showed the spatial and temporal variations of OWTs and QC scores, and the regions of low-quality Kd(λ) were reasonably extracted. Global maps of Kd(λ)-based optical water types and corresponding QC scores were also obtained with OC_QC model. With the quality controlled Kd(λ) data, the retrieval accuracy of Secchi disk depth was improved. The retrieval accuracies of Chlorophyll-a concentration and total seawater absorption coefficient were also improved with the Kd(λ) optical water classification based algorithms. The OC_QC model would be valuable for evaluating Kd(λ) data (especially the operational global Kd(λ) products), investigation of water optical properties, and development and merging of bio-optical algorithms.

Internal quality control - past, present and future trends.

This paper offers an historical view, through a summary of the internal quality control (IQC) models used from second half of twentyth century to those performed today and wants to give a projection on how the future should be addressed. The material used in this work study are all papers collected referring IQC procedures. The method used is the critical analysis of the different IQC models with a discussion on the weak and the strong points of each model. First models were based on testing control materials and using multiples of the analytical procedure standard deviation as control limits. Later, these limits were substituted by values related with the intended use of test, mainly derived from biological variation. For measurands with no available control material methods based on replicate analysis of patient' samples were developed and have been improved recently; also, the sigma metrics that relates the quality desired with the laboratory performance has resulted in a highly efficient quality control model. Present tendency is to modulate IQC considering the workload and the impact of analytical failure in the patent harm. This paper remarks the strong points of IQC models, indicates the weak points that should be eliminated from practice and gives a future projection on how to promote patient safety through laboratory examinations.

Continuous Flat Pressing of MDF Quality Control Model Framework and Collaborative Programming Approach Based on Wood Fiber Hot Pressing Mechanism.

The increasing demand for forestry resources is driving the need for smarter systems capable of saving and protecting forests that can optimize agile forestry production. This study uses the continuous hot-pressing process of wooden medium-density fiberboard (MDF) to investigate the possibility of automatic quality control of the continuous flat pressing process. For this purpose, conceptual digital twin modeling for mechanism and sequence parameter control was conducted based on the cellular automata (CA) theory. A distributed coordination mode framework was constructed, and a craft control programming method was proposed for the quality control of MDF continuous flat pressing. Based on the MDF continuous flat press craft mechanism and control standards, a framework of five distributed flat press cooperative control mode elements for the cylinder array of the continuous panel system (CPS) was defined. To satisfy the distributed distance servo and pressure servo demands of the multi-stage hot pressing craft design, five kinds of synergy collaborative control modes of multiple rack groups were constructed using mode elements: For the four types of typical deviations in slab production, i.e., thickness, slope, depression, and bulge, a multi-zone mutual cooperative mode craft control sequence was programmed. According to the type and intensity of real-time deviation, the corresponding regulation sequence was applied. This effectively counteracts the deviation caused by the uncertainty interference due to the multi-field coupling effect in actual production. The application tests demonstrate that the adjustment and response time of the continuous flat press were greatly improved, and the quality superiority rate is controlled above 95%, thereby confirming the effectiveness of the control strategy.

Deep learning-based quality-controlled spleen assessment from ultrasound images

ObjectiveSplenomegaly (abnormal splenic enlargement) is a potentially life-threatening condition that occurs in a range of clinical scenarios, including in patients suffering from Sickle cell disease (SCD). Therefore, spleen size assessments from ultrasound imaging are commonly performed in SCD clinics, and typically involve measuring the length of the spleen. However, the current workflow is prone to intra- and inter-observer variability and is dependent on the experience of the sonographer. Our objective was to automate the spleen length measurement process. MethodsTwo deep learning-based approaches were investigated to achieve automated spleen length measurement from ultrasound images. One is a segmentation-based approach, where we trained a modified U-Net to obtain a spleen segmentation and then applied post-processing to measure the spleen length from the segmentation. The second approach is based on direct regression of spleen length. We also incorporated a quality control (QC) model to help less experienced sonographers ensure the quality of ultrasound images before measurement. ResultsOur best model (segmentation-based approach) reached a mean percentage length error (MPLE) of 4.58% on good quality images, which is within the range of human expert inter-observer variability (5.78%). After including bad quality images, the incorporation of the QC step resulted in a significant reduction in MPLE (from 5.76% to 4.88%). ConclusionAutomated, quality-controlled spleen length measurement from ultrasound has been achieved with human-level accuracy. SignificanceThis proposed framework has the potential to assist in making robust and accurate assessments of the spleen, especially in settings where there is a lack of experienced sonographers.

From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance. Despite achieving inter-observer variability in terms of different accuracy performance measures, visual inspections reveal errors in most segmentation results, indicating a lack of reliability and robustness of DL segmentation models, which can be critical if a model was to be deployed into clinical practice. In this work, we aim to bring attention to reliability and robustness, two unmet needs of cardiac image segmentation methods, which are hampering their translation into practice. To this end, we first study the performance accuracy evolution of CMR segmentation, illustrate the improvements brought by DL algorithms and highlight the symptoms of performance stagnation. Afterwards, we provide formal definitions of reliability and robustness. Based on the two definitions, we identify the factors that limit the reliability and robustness of state-of-the-art deep learning CMR segmentation techniques. Finally, we give an overview of the current set of works that focus on improving the reliability and robustness of CMR segmentation, and we categorize them into two families of methods: quality control methods and model improvement techniques. The first category corresponds to simpler strategies that only aim to flag situations where a model may be incurring poor reliability or robustness. The second one, instead, directly tackles the problem by bringing improvements into different aspects of the CMR segmentation model development process. We aim to bring the attention of more researchers towards these emerging trends regarding the development of reliable and robust CMR segmentation frameworks, which can guarantee the safe use of DL in clinical routines and studies.