Quality Control Methodology Research Articles

An Environmental Data Collection for COVID-19 Pandemic Research

The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.

A cell-level quality control workflow for high-throughput image analysis

BackgroundImage-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected.ResultsWe present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics.ConclusionsOur cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.

Análisis del proceso de tallado de lentes en un laboratorio óptico

This report aims to demonstrate the importance of the application of quality techniques such as the cause-effect diagram and the Pareto diagram to detect the causes of non-conformities that a simple view cannot solve and are You need quality control techniques and methodologies to solve these problems. In this case we apply the techniques or tools with a company in the optical sector, which is in charge of the elaboration process of carving the lenses. A series of surveys were conducted with managers, operators and a group of customers, and a checklist was implemented in the production process of the glasses, the problem with the most incidents in the process of preparing the glasses was determined. lenses is: the size without formula; The production of lenses depends on the prescription of the optometrist, when the formula prescribed by the optometrist is very high and is not available on the market as a finished lens, the base of the lens must sometime be a carving process. That said, it was determined to apply the Ishikawa method to identify the possible causes of the problem, identify and detect the causes, we will proceed to design the Pareto diagram, where we can observe those little vitals, in fact, they will be seen in many vitals. This we have analyzed through the Pareto diagram that we must implement better techniques and methodologies to contribute to the improvement of quality in each of the laboratory processes.

Real-time space occupancy sensing and human motion analysis using deep learning for indoor air quality control

This study proposed a novel indoor air quality control methodology that uses occupancy sensing and motion recognition techniques in combination with human motion analysis. The automated occupancy sensing systems that are most prevalent today analyze either environmental conditions (i.e., room temperature or data from entities in the room, such as states of electronic devices) to make human occupancy predictions. Since little emphasis is placed on observing humans directly, the estimations of these sensing systems are often inaccurate. Erroneous occupancy estimation leads to poor control of building resources such as HVAC (Heating, Ventilation, and Air-conditioning) and lighting systems. To address this issue, the paper puts forth a lean, vision-based system that estimates the number of occupants and recognizes their activities using the stacked history of unconstrained non-deterministic human movements over transient intervals. The system implements a multi-stream deep neural network to identify human activities and uses the YOLO V3 deep neural network for object detection to estimate occupancy count in a room. The study uses a publicly available action recognition dataset – NADA – to train the neural networks and experiment with a variety of video classification techniques to achieve higher accuracies.

Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires

The present paper discusses a comparative application of image processing techniques, i.e., Discrete Fourier Transform, K-Means clustering and Artificial Neural Network, for the detection of defects in the industrial context of assembled tires. The used Artificial Neural Network technique is based on Long Short-Term Memory and Fully Connected neural networks. The investigations focus on the monitoring and quality control of defects, which may appear on the external surface of tires after being assembled. Those defects are caused from tires which are not properly assembled to their respective metallic wheel rim, generating deformations and scrapes which are not desired. The proposed image processing techniques are applied on raw high-resolution images, which are acquired by in-line imaging and optical instruments. All the described techniques, i.e., Discrete Fourier Transform, K-Means clustering and Long Short-Term Memory, were able to determine defected and acceptable external tire surfaces. The proposed research is taken in the context of an industrial project which focuses on the development of automated quality control and monitoring methodologies, within the field of Industry 4.0 facilities. The image processing techniques are thus meant to be adopted into production processes, giving a strong support to the in-line quality control phase.

Integrated metabolomics and network pharmacology strategy for ascertaining the quality marker of flavonoids for Sophora flavescens

Traditional Chinese medicines (TCMs) have been widely used in Asian countries for thousands of years due to their supreme quality and good clinical efficacy. However, the increasing demand for TCMs in recent decades warrants effective quality control methodology to avoid clinical problems. Therefore, comprehensive quality evaluation systems should be established for ensuring TCM's quality, in terms of chemical components, as well as bioactivity for identifying quality markers in TCM and developing suitable analytical methods for quality control. In this study, we selected Sophora flavescens (S. flavescens) as the research object and developed a novel integrated strategy combining metabolomics and network pharmacology to explore the quality markers. Firstly, we determined the targeted metabolomic profiles of seventy-four batches of S. flavescens (aged from 1 to 6 years) by UHPLC/QE-MS. Six potential markers were successfully screened, quantified and reverse-verified as the most influential effective compounds by UHPLC/QE-MS and multivariate statistical analysis. Secondly, the network of "components-targets-pathways" was constructed, and the pharmacological activities of six potential markers were predicted. Finally, we determined the anti-tumor activity of six flavonoids (kurarinone, norkurarinone, kuraridin, kushenol N, trifolirhizin, and genistein) as the quality markers for Sophora flavescens, evaluated their pharmacokinetic profiles and reviewed their existing pharmacological researches. Thus, integrated metabolomics and network pharmacology technology were applied for the effective discovery of quality markers of Chinese material medica.

Checking the sensitivity and accuracy of humidity indicators

Discusses the problems of protecting the goods during transport from exposure to high humidity and ways of its solutions; the presence of some consumers ‘ requirements for adherence to the maximum values of humidity and confirmation of its actual value in the packed products; the use of paper humidity indicators to control the environment during transit; the principle of operation of the indicator based on the change in color of cobalt salts depending on the composition of the crystalline.There is a lack of methodology for quality control of paper humidity indicators. The method developed by the laboratory of OJSC «BSW» – Management Company of the Holding «BMC», to determine their sensitivity and accuracy is given. The essence of the test is to assemble an installation that provides a certain value of air humidity. To do this, we use solutions of sulfuric acid of the appropriate concentrations. Air is passed first through water, then through solutions and fed into a flask with an indicator. Depending on the humidity of the supplied air, the corresponding indicator points change colour. Sensitivity and accuracy are evaluated on a colour scale.

A comprehensive data quality evaluation method for the currents of marine controlled-source electromagnetic transmitters based on the analytic hierarchy process

Abstract. We present a quality control methodology for the currents of marine controlled-source electromagnetic transmitters . The quality level of the transmitting current directly affects the signal-to-noise ratio (SNR) of the electromagnetic-field data, as received by a multicomponent electromagnetic receiver from the seabed. Although the transmitting-current stability is sufficient under normal circumstances, the SNR of the received signal can change owing to factors such as outside noise. In some emergency cases such as instrument failure or a sudden increase in electromagnetic interference that we are not aware of, the frequency and properties of the transmitting current, such as its size and waveform, may change. The traditional current monitoring and data playback tools fail to detect and evaluate the anomalies well and in a timely manner, which introduces considerable errors in the later data-processing procedure. Pertaining to these issues, this paper proposes a comprehensive quality evaluation method for the transmitting current. The proposed algorithm, based on the analytic hierarchy process, is first used to analyze five current stability parameters – current frequency, positive amplitudes, negative amplitudes, discrepancy of ideal waveform, and waveform repetition – and then to define the harmonic energy and calculate the quality of transmitting current (QTC) index of the final data to assess the quality of the transmitting current comprehensively. The results of a marine experiment performed in 2016 show that the algorithm can identify abnormal current data and quantitatively evaluate the current conditions. Under normal circumstances, the QTC index is less than 2 %. The key findings are that the QTC index changes to more than 4 % and some curvilinear features are observed if the transmitting-current quality is poor. These results will provide a positive, significant guide for the evaluation and monitoring of transmitting-current data in marine experiments.

Применение методологии акустико-эмиссионного контроля качества сварных швов в процессе изготовления и испытаний металлоконструкций

Применение методологии акустико-эмиссионного контроля качества сварных швов в процессе изготовления и испытаний металлоконструкций

A CUSTOMER-EMPLOYEE ENCOUNTER: A REVIEW OF CUSTOMER QUALITY CONTROL ON RESTAURANT FOOD SERVICE

Purpose of the Study: The purpose of this study review was to fill the literature gap into the customer quality control on restaurant food and beverage service, with the objective of identifying customer quality control methodologies within the hospitality’s food and beverage operations.
 Methodology: For purposes of carrying out the study review, the concept of customer employees encounter in the process of not only creating and offering goods and services but also the quality control aspect, and the various methodologies in doing so were considered and reviewed. The study employed a meta-analysis in gathering, analyzing, presentation and discussion of the study results.
 Main Findings: The study review findings reveal that hospitality organizations are facing a drift from the conventional restaurant standard operating procedures in reference to foodservice quality control with the customer taking a central position in the production and presentation of food services.
 Limitations: This is a study review and therefore the study findings were arrived at in consideration of mainly secondary sources. Some studies are traditionally region and/ or country-specific and therefore much caution is needed when generalizing the study findings.
 Social implications: There is a myriad of ways through which restaurant food service quality control can be integrated into the customer employee service encounter. They reviewed three main methodologies in this study review may provide the best tools not only for quality control function but also build confidence among the customer base, thus yielding customer satisfaction and retention on the one hand, while creating business sustainability on the other hand.
 Originality: This study will, therefore, help the hospitality restaurant business to appreciate the role of customers in the process of quality services provision, thus enable organizations to achieve a strategic business competitive position.

Concentración de nitrógeno, fosforo, potasio y calcio en el compost producido a partir de residuos sólidos orgánicos de mercados populares y residencias de Huaraz-Ancash

La investigación quiere proponer metodologías de control de calidad del tratamiento de residuos sólidos orgánicos. Los componentes ricos en carbono y nitrógeno que son importantes para la generación de un compost de muy buena calidad, se lograron con una mezcla homogénea de estos, de tal manera lograr una relación de C/N óptima en un rango de 30 a 40 (materia húmeda). Para el experimento se planteó variantes de 30(CB), 35(CR) y 40(CA), combinación baja (CB), combinación regular (CR) y combinación Alta (CA) para conocer las proporciones del tratamiento. Se monitoreo los parámetros de pH que alcanza valores por encima de 8 produciendo precipitación de nutrientes como Potasio y Calcio, Temperatura promedio máximo a 30°C en la semana 10 del tratamiento; el contenido de humedad llegó hasta 40 %, cantidad de materia orgánica en promedio 20 %, componentes en materia seca de Nitrógeno fue 1,5 % y Fosforo un valor mayor a 0,5 % al final del proceso de compostaje, el que tuvo mejor resultado fue el tratamiento CA. Se hizo una prueba de germinación y se determinó un crecimiento de la planta comparado con el tratamiento CA, se concluye que la distribución C/N en base seca en promedio 7, es adecuado en calidad

A controlled process of corn sowing as a function of of seed dispensation and working speeds

An appropriate sowing process is one of the steps that need immediate attention in crop sciences, because it may compromise the profitability of crops. An effective sowing process requires correctly spaced seedlings with the right depth in order to obtain an appropriate and uniform stand. In this context, statistical control is an option that allows evaluation of the agricultural process. Our objective in this study was to evaluate the quality of maize sowing as a function of planting density and working speeds. The statistical design used was based on the methodology of statistical quality control, corresponding to 4 seed dispensers from different manufacturers denoted DA, DB, DC, and DD with two speeds for each (V1 = 7.0 km h-1 and V2 = 11.5 km h-1). Data were collected from 10 replicates for each of the 4 seed dispensers, totaling 40 samples, at each working speed. The following parameters were evaluated: Sowing depth, longitudinal distribution of seedlings, initial and final plant population, and corn grain yield for the hybrid 30F35YH variety. The statistical process control offers an interesting and reliable mean of evaluating the quality of the sowing process. The seed dispenser, DC, exhibited a better quality of sowing as compared to others. The highest percentage of normal spacing was observed at the lower displacement speed for the seed dispensers, DB, DC, and DD. These seed dispensers also showed higher productivity as compared to other seed dispensers.

Quality control of solar radiation data within the South African Weather Service solar radiometric network

This study reports on the performance results of the Baseline Surface Radiation Network (BSRN) quality control procedures applied to the solar radiation data, from September 2013 to December 2017, within the South African Weather Service radiometric network. The overall percentage performance of the SAWS solar radiation network based on BSRN quality control methodology was 97.79%, 93.64%, 91.60% and 92.23% for long wave downward irradiance (LWD), global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI), respectively, with operational problems largely dominating the percentage of bad data. The overall average performance of the surface solar radiation dataset – Heliosat data records for the GHI estimation for all stations showed a mean bias deviation of 8.28 Wm-2, a mean absolute deviation of 9.06 Wm-2 and the root mean square deviation of 11.02 Wm-2. The correlation, quantified by the square of correlation coefficient (R2), between ground-based and Heliosat-derived GHI time series was ~0.98. The established network has the potential to provide high quality minute solar radiation data sets (GHI, DHI, DNI and LWD) and auxiliary hourly meteorological parameters vital for scientific and practical applications in renewable energy technologies.

Studies on the methodology for quality control in Chinese medicine manufacturing process based on knowledge graph

According to the requirements for developing the quality control technology in Chinese medicine( CM) manufacturing process and the practical scenarios in applying a new generation of artificial intelligence to CM industry,we present a method of constructing the knowledge graph( KG) for CM manufacture to solve key problems about quality control in CM manufacturing process.Based on the above,a " pharmaceutical industry brain" model for CM manufacture has been established. Further,we propose founding the KG-based methodology for quality control in CM manufacturing process,and briefly describe the design method,system architecture and main functions of the KG system. In this work,the KG for manufacturing Shuxuening Injection( SXNI) was developed as a demonstration study. The KG version 1. 0 platform for intelligent manufacturing SXNI has been built,which could realize technology leap of the quality control system in CM manufacturing process from perceptual intelligence to cognitive intelligence.

Methodology of Studying Mechanical Properties of Reinforced Concrete

In this paper, we study the mechanical properties of a complex composite material formed by steel and hardened concrete. A methodology of operative quality control of new credible concrete and reinforcement under both laboratory and field conditions, including determination of the strength and strain characteristics of materials, as well as cohesion forces determining their joint operation under load, is developed. A design of a mobile unit in which by means of reversal it is possible to change the load direction and to test the reinforced element of the established form under both tension and compression is presented. This reduces the nomenclature of test equipment and the number of forms for the manufacture of concrete samples. The values ​​of intrinsic stresses and the magnitude of deformations due to concrete shrinkage are determined by the forces of the crack formation in concrete when the joint operation of concrete and reinforcement is broken. An analytical dependence of the forces and deformations of a reinforced concrete sample with central reinforcement upon axial tension and compression taking into account deformations and stresses in the reinforcement and concrete on the concrete shrinkage is established. The results of experimental studies including concrete tensile diagrams and diagrams of the development of axial deformations with increasing load under central loading of reinforced elements are presented. A methodology of accounting for account stresses and strains due to concrete shrinkage in calculations of reinforced concrete structures is developed. The effectiveness of analytical expressions that establish a relationship between stresses and strains in material diagrams for calculations of reinforced concrete structures using a deformation model is estimated.

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.

Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

AbstractAutomatic personal weather stations owned and maintained by weather enthusiasts provide spatially dense in situ measurements that are often collected and visualized in real time on online weather platforms. While the spatial and temporal resolution of this data source is high, its rainfall observations are prone to typical errors, currently preventing its large‐scale, real‐time application. This study proposes a quality control methodology consisting of four modules targeting these errors, applicable in real time without requiring auxiliary measurements. The quality control improves the overall accuracy of a year of hourly rainfall depths in Amsterdam to a bias of −11.3% (0.2% when a proxy for overall rainfall underestimation by personal weather stations is used), a Pearson correlation coefficient of 0.82, and a coefficient of variation of 2.70, while maintaining 88% of the original data set. Application on a national scale (average 1 station per ∼10 km2) yields high‐resolution nationwide rainfall maps, hence showing the great potential of personal weather stations for complementing existing often sparse traditional rain gauge networks.

Advanced design methodologies for steel fibre reinforced concrete and new methodology of real time quality control

Steel fibre reinforced concrete has developed over years for the use in structural applications. The material itself has undergone development into higher performing steel fibre types and thus into higher performing steel fibre concretes. Whilst in regards to standards the development went from recommendations into building codes the way of designing – linear elastic design methods or yield line theory – and the way of quality control seemed to remain unchanged from the past. This paper shall emphasize the recent development in a design tool that enables a non-linear design methodology and a new advanced way of real time quality control.

Use of Cucurbit[6]uril as Ionophore in Ion Selective Electrodes for Etilefrine Determination in Pharmaceuticals

AbstractCucurbiturils are macrocyclic molecules and have been used with promising results in the development of electrochemical sensors and biosensors due to their main properties such as shape, selectivity, binding interactions and solubility. However, its application for the development of ion selective electrodes has been scarce. In this work, cucurbit[6]uril was used as molecular‐recognition compound for etilefrine sensing membrane with potentiometric transduction. The etilefrine selective electrode was evaluated and proposed as an alternative methodology for pharmaceutical quality control. Different membranes composition were evaluated, considering the use of different solvent mediators and the presence of anionic additives. The best membrane comprises 9.24 mmol.Kg−1 of cucurbit[6]uril, 68.90 % (w/w) of dibutylsebacate and 30.18 % (w/w) of carboxylated polyvinyl chloride. The electrode exhibited selective cationic response toward the etilefrine in a concentration interval of more than four decades, with an slope of 57.4±0.5 mV dec−1, a practical detection limit of (8±1)×10−7 mol L−1 and a low limit of linear range of (1.25±0.28)×10−6 mol L−1. The selectivity of the electrode towards different interfering was high. A comparative evaluation of the accuracy and precision of both procedures was done using the t‐student test (t4,12=1.135) and the Fischer test (F12,4=2.42) and compared with the tabulated ones (t4,12=1.135, F11,3=5.91) for the level of confidence of 95 %.

Green methodology for quality control of elemental content of infant milk powder

A green methodology for the direct determination of elemental content in infant milk powder samples from 0 to 3 years was developed. A portable energy dispersive X-ray fluorescence (ED-XRF) tool has been used as a direct method for the quantification of five essential elements (Ca, K, Fe, Cu and Zn). For analysis pellets from 0.8 g of each sample weight were made and analyzed. Due to the high dependence of the ED-XRF signal with the sample matrix, external calibrations using cocoa powder diluted with glucose were performed. Infant milk samples were diluted with lactose and the signals were interpolated in the external calibrations. Concentrations obtained by ED-XRF were compared with the concentration found by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) determination and those indicated in the package label of the corresponding milk samples. Direct determination by ED-XRF provides accurate results, and shows a good correlation with ICP-OES with r2 values ranging between 0.80 till 0.95. Limits of detection (LOD) were 114, 178, 14, 0.9 and 1.7 μg/g for Ca, K, Fe, Cu and Zn, respectively, being relative standard deviation (RSD) for real milk powder sample analysis lower than 10% for most of the elements.