Quality Control Model Research Articles

Shale gas play of the silurian hot shale, Ghadames Basin, North Africa: Insights from integrated geochemistry and basin modeling

The lower Silurian hot shale in the Ghadames Basin was investigated to provide a regional assessment of the future prospects of shale gas play as an important unconventional resource system in one of the top African superglobal petroliferous sedimentary basins. The unconventional natural gas potential of the hot shale was studied based on regional improved organofacies assessment, thermal maturity, the timing of generation and expulsion, and sweet gas spot analysis, such as gas in place (GIP), total expelled gas (TEG), and total retained gas (TRG). The integration between regional geology and organic geochemistry, together with the proper regional utilization of the multi-dimensional basin modeling to better appreciate the importance of the Silurian hot shale as a key future shale gas unconventional-rich petroleum system (URPS) in the Ghadames Basin. The relationship between the richness and thermal parameters, such as the total organic carbon indicator (%TOC), hydrogen index (HI), measured and time step (calibrated models) vitrinite and equivalent vitrinite (%VRo) reflectance were used to develop quality control models for the regional natural gas spots of the Silurian hot shale were present-day mapping presentations are utilized as well for each parameter to better appreciate the lateral variability of the dry gas volumes. Regional basin control parameters from several key wells, such as heat flow levels, formation pressure system, and basin porosity were successfully utilized for the hot shale gas play resource screening (sweet spots) study utilizing multi-dimensional modeling approaches. The established regional spatial type B marine organofacies of the hot shale with excellent geochemical properties (∼18 %TOC) and regionally sufficient thermal maturation (∼2.45 %VRo, post-mature stage) indicate a significant amount of natural gas expelled and retained that has occurred in the Silurian hot shale system. The estimated GIP of the hot shale is about 120 bcf/km2 (equivalent to 320 bcf/section). The maximum expelled gas (TEG) amount is defined with values up to 88 bcf/section and the total estimated volume of the retained gas (TRG) is up to 96 bcf/km2, with the current geological architecture, geochemical properties, volumes, and distribution of the hot shale dry gas sweet spot in the Ghadames Basin. The hot shale can be considered in the near future for extensive further studies and exploration and development plans in Libya, Algeria, or Tunisia.

Quality control of manufacturing enterprises based on computer and big data technology

AbstractThe traditional manufacturing industry is facing the impact of Big Data, and all aspects of product research and development, process design, quality management, production and operation are urgently looking forward to the birth of innovative methods to cope with the challenges of big data in the industrial background. Although traditional manufacturing enterprises have initially established quality control information system, there are still many problems and limitations in the existing quality control system, which cannot meet the operation needs of manufacturing enterprises. This paper chooses the quality management of manufacturing enterprises as the research object, and uses advanced technologies such as computer, internet, big data and cloud computing to build a data system model of quality control of manufacturing enterprises. this paper optimizes the quality data processing process by establishing the quality monitoring model, and builds an intelligent quality supervision platform, and sets up the quality alarm rules for manufacturing enterprises. If the deviation between the actual value and the predicted value of the quality monitoring index is mapped between [0,1], the quality monitoring system can generate an outlier probability score. In this study, the traditional manual quality management is transformed into the management mode of the “internet + quality data” in order to realize the information, digital and intelligent quality management of manufacturing enterprises. This comprehensive research method combines modern digital technology and relevant theories of quality management to explore the optimization scheme of quality management of manufacturing enterprises, and also provides reference experience for the information construction of other similar enterprises.

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Abstract The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. Key to the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with increasing forecasting horizons for prolonged anomalous events.

Exploring optimization algorithms for establishing patient-based real-time quality control models

BackgroundPatient-based real-time quality control (PBRTQC) models must be optimized for use in different clinical laboratories, but the grid search (GS) algorithm explored in recent studies for this purpose is inefficient. Thus, finding an efficient optimization algorithm is critical for future research and implementation of the PBRTQC. MethodsWe compared the efficiency and performance of five commonly used optimization algorithms, including GS, simulated annealing (SA), genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO), to optimize conventional PBRTQC and regression-adjusted real-time quality control (RARTQC) models for serum alanine aminotransferase and sodium. ResultsThe GS, GA, DE, and PSO provided models with similar performances. However, GA and DE required significantly less computation time than GS. The results also demonstrate a general tradeoff between the optimization method's chance of discovering the optimum and the computation time required. ConclusionMore efficient optimization methods should be adopted when establishing PBRTQC or RARTQC models to save time and computing power that will enable the development of more complex models and increase the scalability of extensive PBRTQC applications.

Machine learning-assisted high-throughput screening for Anti-MRSA compounds.

Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening. A machine learning (ML) model was developed for the in silico screening of low molecular weight molecules. We used the results of a high-throughput Caenorhabditis elegans methicillin-resistant Staphylococcus aureus (MRSA) liquid infection assay to develop ML models for compound prioritization and quality control. The compound prioritization model achieved an AUC of 0.795 with a sensitivity of 81% and a specificity of 70%. When applied to a validation set of 22,768 compounds, the model identified 81% of the active compounds identified by high-throughput screening (HTS) among only 30.6% of the total 22,768 compounds, resulting in a 2.67-fold increase in hit rate. When we retrained the model on all the compounds of the HTS dataset, it further identified 45 discordant molecules classified as non-hits by the HTS, with 42/45 (93%) having known antimicrobial activity. Our ML approach can be used to increase HTS efficiency by reducing the number of compounds that need to be physically screened and identifying potential missed hits, making HTS more accessible and reducing barriers to entry.

A simulation-based optimization model for quality control in solid waste collection process

A simulation-based optimization model for quality control in solid waste collection process

A spiculated mass target model for clinical image quality control in digital mammography.

Quality assurance of breast imaging has a long history of using test objects to optimize and follow up imaging devices. In particular, the evaluation of new techniques benefits from suitable test objects. The applicability of a phantom consisting of spiculated masses to assess image quality and its dependence on dose in flat field digital mammography (FFDM) and digital breast tomosynthesis systems (DBT) is investigated. Two spiculated masses in five different sizes each were created from a database of clinical tumour models. The masses were produced using 3D printing and embedded into a cuboid phantom. Image quality is determined by the number of spicules identified by human observers. The results suggest that the effect of dose on spicule detection is limited especially in cases with smaller objects and probably hidden by the inter-reader variability. Here, an average relative inter-reader variation of the counted number of 31% was found (maximum 83%). The mean relative intra-reader variability was found to be 17%. In DBT, sufficiently good results were obtained only for the largest masses. It is possible to integrate spiculated masses into a cuboid phantom. It is easy to print and should allow a direct and prompt evaluation of the quality status of the device by counting visible spicules. Human readout presented the major uncertainty in this study, indicating that automated readout may improve the reproducibility and consistency of the results considerably. A cuboid phantom including clinical objects as spiculated lesion models for visual assessing the image quality in FFDM and DBT was developed and is introduced in this work. The evaluation of image quality works best with the two larger masses with 21 spicules.

The ER membrane protein complex restricts mitophagy by controlling BNIP3 turnover.

Lysosomal degradation of autophagy receptors is a common proxy for selective autophagy. However, we find that two established mitophagy receptors, BNIP3 and BNIP3L/NIX, are constitutively delivered to lysosomes in an autophagy-independent manner. This alternative lysosomal delivery of BNIP3 accounts for nearly all its lysosome-mediated degradation, even upon mitophagy induction. To identify how BNIP3, a tail-anchored protein in the outer mitochondrial membrane, is delivered to lysosomes, we performed a genome-wide CRISPR screen for factors influencing BNIP3 flux. This screen revealed both known modifiers of BNIP3 stability as well as a pronounced reliance on endolysosomal components, including the ER membrane protein complex (EMC). Importantly, the endolysosomal system and the ubiquitin-proteosome system regulated BNIP3 independently. Perturbation of either mechanism is sufficient to modulate BNIP3-associated mitophagy and affect underlying cellular physiology. More broadly, these findings extend recent models for tail-anchored protein quality control and install endosomal trafficking and lysosomal degradation in the canon of pathways that tightly regulate endogenous tail-anchored protein localization.

Novel multidisciplinary cooperation model for obstetric medical quality control: A quasi-experimental study.

To explore the impact of a novel multidisciplinary cooperation model in obstetric medical quality control. This quasi-experimental study analyzed the quality indicators of full-term pregnant women who underwent vaginal trial labor in Zibo Maternal and Child Health Hospital between July 2021 and June 2022. The pregnant women were divided into two groups based on implementation of novel multidisciplinary cooperation: multidisciplinary and non-multidisciplinary. We compared the rate of labor analgesia, postpartum hemorrhage in vaginal delivery, transfer to cesarean section, and the 5-min Apgar score ≤7 in full-term neonates. A total of 3751 pregnant women were enrolled into the study, of whom 2004 were included in the non-multidisciplinary group and 1747 in the multidisciplinary group. The analgesic rate of delivery of the multidisciplinary group was higher than that of the non-multidisciplinary group (P = 0.000). We established that the rate of postpartum bleeding (P = 0.040), transfer cesarean section (P = 0.003) and the incidence of Apgar score ≤7 in 5 min of full-term neonates (P = 0.038) of the multidisciplinary group was lower than that of the non-multidisciplinary group. There was no significant difference in the mean ages (29.40 ± 3.99 vs. 29.90 ± 4.27 years; P = 0.126), mean delivery gestational ages (39.65 ± 0.87 vs. 39.64 ± 1.06; P = 0.221), mean gravidity values (1.93 ± 1.09 vs. 2.00 ± 1.18; P = 0.586) and mean parity (1.40 ± 0.56 vs. 1.42 ± 0.59; P = 0.635) of the women in the two groups. Multidisciplinary cooperation in delivery management can significantly improve some quality indicators. We established the analgesic rate of delivery can be increased and the rate of postpartum bleeding, transfer cesarean section and the incidence of Apgar score ≤7 in 5 min of full-term neonates can be decreased with the implementation of novel multidisciplinary cooperation.

Process Quality Control Method for Three-Cylinder Engine Balance Shaft System Oriented to Manufacturing Supply Chain

Automobile manufacturers often outsource key components and parts to suppliers, hence ensuring that process quality has been a hot topic in current research. The quality control demands at different stages of the manufacturing supply chain cycle have been ignored in traditional process quality control methods. To solve this issue, this paper takes the three-cylinder engine balance shaft system as the research object, and builds a process quality control model for the three-cylinder engine balance shaft system oriented to the manufacturing supply chain. The metagraph theory is introduced, and parts suppliers in the manufacturing supply chain, product process quality attributes, supply chain revenue function, and other key elements are defined. By constructing the revenue function of the three-cylinder engine balance shaft system process quality control model, the feasibility of the proposed method is verified by taking the process quality control of one certain three-cylinder engine balance shaft system as a verification example. The results show that, with other conditions unchanged, the application of this method can increase the overall profit of the manufacturing supply chain by 100% at the stage of preparation before product machining, and more than 8% at the stage of product performance evaluation. The proposed method of process quality control based on the metagraph theory provides a theoretical basis for building an information sharing platform of product process quality, and effectively reduces the machining cost for product improvement and upgrading in the manufacturing supply chain.

Elevating Pedagogical Outcomes: A Study on the Efficacy of State Islamic Religious Universities Management in Aceh, Indonesia

Objective: This research aims to examine the efficacy of higher education administration in quality control of Islamic university lecturers in Aceh. Method: The study employs a qualitative paradigm, with data gathered through observation, interview findings, and documentation. Informants include campus policymakers (rectors, deans, and department heads) and numerous students who are users. Results: Quality planning outcomes are provided through research under the institution's vision, purpose, and objectives. Planning, revision, and verification are used to carry out the program. Its substance encourages attempts to strengthen educational quality control but does not specify the desired results. Quality control management for lecturers is controlled by each component (faculties and departments) using formal and written references. The projected consequences of senior policy managers being able to design an integrity-based university governance model based on enhancing organizational values, norms, laws, management, and organizational structures. Novelty: Quality control model novelty consists of five stages, namely (1) control principles, (2) quality implementation, (3) professionalism development, (4) evaluation monitoring, and (5) collaboration strategy.

Evaluation of deep learning models for quality control of MR spectra.

While 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets. A total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels. All models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling. ML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions.

Применение технологий искусственного интеллекта как способ обеспечения качества выполнения рентгенографии органов грудной клетки.

When performing radiographic studies, errors may occur that reduce the diagnostic value of the radiographs and complicate their interpretation by radiologists and diagnostic software based on artificial intelligence technology. The creation of automated quality assessment systems will optimize this process, especially in conditions of increased workload of medical personnel. Purpose: development of an automated quality control tool for chest radiographs, which allows for quality control of the patient’s positioning and the correctness of filling in meta-information about the study. Material and methods.To train and test automated quality control models, were used 61505 chest radiographs, obtained from open datasets and the Unified Radiological Information Service of the Unified Medical Information Analysis System of the City of Moscow. To create models we used transfer training of deep neural network architectures VGG19 and ResNet152V2. Results. 7 models were created: a model for determining the anatomical area of study, a model for determining projection, a model for determining photometric interpretation, models for determining incomplete visualization of the anatomical area on the frontal and lateral projections of the chest radiographs, a model for determining rotation on the lateral projection of the chest radiographs. All created models have diagnostic accuracy metrics above 95%, which allows them to be used in clinical practice. Based on the developed models, a web-based quality control tool of the chest radiographs was created, which allows analyzing the quality of X-ray datasets. Conclusion. The active use of this quality control tool will optimize the process of assessing the quality of diagnostic studies and facilitate the processes of classification of studies and the formation of datasets. Also, this tool can be used to support the decision-making of an X-ray technician and assess the quality of the study before sending the study for processing to artificial intelligence-based services.

Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance.

All‐sky infrared radiance assimilation of a geostationary satellite in the Japan Meteorological Agency's global system

AbstractAll‐sky assimilation of infrared (IR) radiances has been developed for water vapor bands of the geostationary satellite Himawari‐8 in the operational global data assimilation system. Cloud‐dependent quality control, bias correction, and observation error modeling are essential developments to effectively utilize the all‐sky radiances (ASRs). ASR assimilation increases the assimilated number of observations by 2.8 times and improves the coverage relative to the traditional clear‐sky radiance (CSR) assimilation. The additional observations better alleviate model dry biases in the middle and upper tropospheric humidity. ASR assimilation brings statistically significant improvements in the background (first guess) in humidity, temperature, and wind over the CSR assimilation. It also better improves short‐range forecasts of the middle and upper tropospheric temperature and humidity up to day 3 in the Tropics. A mixed impact in the stratospheric temperature is under investigation. The impacts of various aspects of the ASR assimilation configuration are evaluated with sensitivity assimilation experiments. The interband correlation and cloud‐dependent standard deviation of the observation error are crucial, whereas the cloud dependency of the correlation is not so important. Although ASRs at a single band were assimilated in many previous studies targeting severe weather using research‐based regional assimilation systems due to decreasing independent information in the presence of clouds, they are distinctly inferior to not only ASRs at multiple bands but also CSRs at multiple bands in a global data assimilation system that contains fewer cloud‐affected scenes. The cloud‐dependent bias correction predictors are essential in the presence of observation‐minus‐background bias that increases with cloud effects.

Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.

In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets. In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results. We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.

An AI-Based Image Quality Control Framework for Knee Radiographs

Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs.

A machine learning‐based quality control tool for functional MRI images

AbstractBackgroundChanges in functional MRI (fMRI) have been associated with Alzheimer’s disease and other neurological disorders. Quality control (QC) of images prior to their computational analysis is a key step in a clinical trial analysis workflow, and it is traditionally implemented visually by image analysts. However, the number of images acquired from large clinical studies makes neuroimaging QC a difficult task that diverts skilled resources and introduces inter‐rater variability. This problem is accentuated in fMRI, where image analysts need to visually inspect 4D sequences, and although image processing reports can be used as aid, the problem of having to inspect each report remains.MethodWe implemented an automated fMRI QC process using a stacking classifier (STC), random forest + support vector machine, with imaging quality metrics (IQM) as inputs. The IQMs were estimated with MRIQC. We also added a designed IQM that measures how centred the participant’s brain is within the image. As train/test dataset we used the ABIDE multisite database. For our experiments, only adult brains from the ABIDE database were used, leaving 14 sites available for a leave‐one‐site‐out (LOSO) cross‐validation experiment. All images included in the experiment were visually QC’d using validated protocols at IXICO.ResultThe LOSO validation procedure resulted in a mean pass‐vs‐fail accuracy of 92% (with 5.7% standard deviation, SD). Pass/fail sensitivity and specificity resulted in 97% (6.7% SD) and 69% (29% SD) respectively. We tested a fully trained STC model on an independent dataset of healthy ageing participants from OpenNeuro (ds002872), which resulted in 87% accuracy, 94% sensitivity and 50% specificity, which validates our modelling procedure.ConclusionIt is possible to design and deploy an automatic QC model for fMRI. Although sensitivity was high in all models from the LOSO experiments, specificity was on average low, highlighting the difficulty of detecting fails in a QC pipeline. However, the STC model can be deployed to detect clear passes that will not need visual QC, reliably reducing the number of cases that require visual inspection. Our future work will focus on testing our developed model on additional datasets (e.g. ADNI) to validate its performance before roll‐out to clinical trial applications.aging after age 90.

Management Program Implementation of Performance Monitoring and Control Systems Benefits as Quality Control and Teacher Competence in Banyuwangi District

Teachers who get certification are entitled to a professional allowance. The government hopes that with this allowance, teachers can improve their welfare and performance. But unexpectedly, after receiving the professional allowance, teachers are not compensated with good quality and competence. Related to this, the Banyuwangi Regency government made improvements through the Education Office Branch. A Management System for Monitoring and Control of Performance and Allowances (SRITANJUNG) management program was launched to control teacher quality and competency. This study aims to build an implementation model for the Sritanjung Program as a quality control and appropriate teacher competency. Factors that support or inhibit will be sought and try to find a solution. This study uses a descriptive qualitative method by collecting data in the field through interviews and questionnaires to all parties involved. The results showed that 1) the implementation of the program as a quality control and teacher competence in the process there are problems with legality, attitude and character of supervisors and principals, target targets/teachers, and tools. While the supporting factors for program implementation as control are: (a) the existence of implementer intentions and agreements, (b) the existence of a legal umbrella, (c) application support, and (d) the availability of implementor resources that are ready. While the inhibiting factors are (a) policy; assignment legality for school supervisors and principals, (b) tools; teacher's computer equipment, frequent errors, incomplete features, and internet signal, (c) systems and processes; lack of clear information about the program, and (d) from human resources; not all teachers are familiar with applications, honesty of information from teachers, weak mastery of information technology and computers, allocation of allowances and busyness of teachers. Meanwhile, the program implementation model for quality control and teacher competence that has been built and is appropriate is the Co-Han Model, which is presumably the initial solution. The Co-Han model is a combination of the old program implementation model and some elements of the COCO control model. This new model involves elements of attitude and characteristics of implementers and targets. Involving the elements of attitude and character in the form of goals, commitment, and abilities is considered very important. With a good attitude and character, it is hoped that the program can be implemented properly too, so that the goals will be easily achieved. The combination of management as a system, as well as commitment and ability as a character will greatly influence program objectives. This is considered a very effective, necessary and new combination in a program implementation as control.

Optimal Power Quality Control Model of Magnetic Latching Relay for Charging Station Based on Adaline

In order to eliminate the internal harmonics of the magnetic latching relay caused by the load input, an ADALINE-based magnetic latching relay power quality optimal control model for charging stations was constructed. Calculate the sampling point of the input signal through the ADALINE neural network, obtain the error signal between the input signal and the measured signal, use the VSLMS algorithm to continuously adjust the weight value to update the spectrum in real time, detect the harmonics existing in the magnetic latching relay, and use the harmonics. The droop control strategy calculates the amount of harmonic voltage that needs to be compensated, and participates in the on-off control of the magnetic latching relay by calculating the modulation wave difference formed by the fundamental wave and the harmonic voltage closed loop, eliminating the harmonics generated by the nonlinear load, and realizing electric energy optimal quality control. The experimental results show that the model can accurately detect and eliminate the internal harmonics of the magnetic latching relay caused by the load input, and has a better control effect on the power quality of the magnetic latching relay used in the charging station.