Quality Measurement System Research Articles

Lake Water Quality Measurement System at Situ Tekno using an ESP32-based Autonomous Surface Vehicle

Lake Water Quality Measurement System at Situ Tekno using an ESP32-based Autonomous Surface Vehicle

Design of Real-Time Measurement and Intelligent Path Selection System for Network Quality under Big Data

With the emergence and development of new businesses such as video and language, people's requirements for the internet are constantly increasing. This article mainly uses the intelligent path selection method to design a real-time measurement system for network quality. On this basis, this article uses instructions to send messages to the target host, then waits for the receiver's response message, and parses the message to obtain performance indicators such as latency. This article uses intelligent path selection to query and analyze network devices, and cooperates with network devices to actively group and send data to the network. It can measure the inflow and outflow of device interfaces. The packet loss rates of path 1 during T1 and T2 are 0.066% and 0.044%, while the packet loss rates of path 2 are 0.41% and 0.11%, respectively. From the data conclusion, it can be seen that intelligent path selection can effectively improve network quality and provide users with a better internet experience.

High-frequency eddy current measurement for the characterization of graphite anode slurries in battery cell production

The global demand for lithium-ion batteries and their production in large quantities while maintaining high product quality and minimal reject rates poses new challenges for cell manufacturers. Quality control in slurry processing and electrode coating is becoming increasingly important due to high material costs. High-frequency eddy current measurement could enable characterization of the active material and reduce the reject rate by applying an inline quality measurement system. In this paper, the applicability of high-frequency eddy current measurement for characterization of graphite suspensions is investigated and its feasibility confirmed. Process parameters such as solids contents and type of graphite have been analyzed.

Methods of Measuring Air Pollution in Cities and Correlation of Air Pollutant Concentrations

The monitoring of air quality continues to be one of the most important tasks when ensuring the safety of our environment. This paper aims to look at correlations between different types of pollutants, so that robust air quality measurement systems can be deployed in remote, inaccessible areas, at a reduced cost. The first matter at hand was to design an affordable and portable system capable of measuring different air pollutants. A custom PCB was designed that could support the acquisition of readings of, among others, particulate and CO sensors. Then, correlations between the concentrations of different pollutants were analyzed to identify if measuring the concentration of one type of pollutant can allow the extrapolation of the concentration of another. This particular study focuses on the correlations between the concentrations of particulate matter and CO. Finally, after observing a moderate correlation, it was proposed to measure the concentrations of pollutants that require less expensive sensors, and to extrapolate the concentrations of pollutants that require a more expensive sensor to measure their concentration. The link between particulate pollution and CO concentrations was identified and discussed as the result of this study.

A vendor-neutral business analytics and quality measurement system for radiation dose index monitoring

Radiation Dose Index (RDI) analytics tools are main components of the dose management systems in healthcare. Business analytics for radiation data can go from simple statistics to dynamic data analysis. It treats data visualization, analytics, and quality measurements. The complexity behind RDI analytics is not only about the technology used to create dashboards and compute quality measurements, but it is especially related to the complexity of collecting the data needed for the analytics tool. Most Dose Management Systems (DMS) are complex applications with many modules that provide multiple functionalities, like data collection, enhancement, and access. The central DMS module is the dose index tracker, which contains the data structure containing the radiation data collected from the different irradiating modalities from the various connected healthcare facilities. Most DMS modules are proprietary and connected to the dose index tracker without being independent of the vendor-specific dose database. Business analytics components in dose management systems have the same interoperability issue. Most of the radiation analytics tools and components have proprietary connectivity with a specific dose management system provider and cannot connect agnostically to different DMS providers. We design, test, and document multiple methods for radiation analytics to be agnostic and independent from the radiation dose index monitoring providers based on standardized Fast Healthcare Interoperability Resources (FHIR®) Application Programming Interfaces (API) connectivity.

A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement.

The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO2 that can represent a risk for human health. In this context, there exist many monitoring systems for lots of specific applications where gas detection is required. In this paper, the authors present a distributed sensing system based on commercial sensors aimed at monitoring the presence of toxic compounds generated by a melting furnace with the aim of reliably detecting the insurgence of dangerous conditions for workers. The system is composed of two different sensor nodes and a gas analyzer, and it exploits commercial low-cost commercially available sensors.

High-Quality Regional Economic Development Paths in China—QCA-Based Linkage Effect

High-quality economic development is an organic whole, involving macro, meso, and micro factors, therefore it is necessary to explore the mechanism for quality development in the construction of a modern economic system. Following the new development concept, this paper constructs a measurement system for the economic development quality of 30 provinces and cities in China by using the entropy weight and TOPSIS method. In addition, the linkage effects of factors on high-quality economic development were analyzed by using the fuzzy qualitative comparative analysis (fsQCA) method. The results show that among factors contributing to diversified forms of the high-quality regional economy, the following three stand out: scientific and technological innovation, marketization, and infrastructure construction. On this basis, three paths were proposed for quality economic development, and they are: the innovation- and market-driven mode, the synergy mode of market and innovation, and the market-led conversion mode.

Ozone and Nitrogen Dioxide Pollutants Detection System Based on IoT

Air pollution is one of the main causes of health problem in urban areas. Vehicles are the major sources of the air pollution in urban cities. To prevent the air pollution situation from increasing, we have developed an advanced outdoor air quality measurement system using a ESP8266 microcontroller and senores. It can be placed at crowded automobile locations to monitor pollutants emitted from vehicles. Ozone gas (O3) and nitrogen dioxide (NO2) was measured in this study. The system provides continuous monitoring of vehicle air pollution with a high time accuracy.

Implementing ISO 9126 in Quality Testing for Information Systems for Menu Ordering Using QR Codes in Panongkrongan Shop

Panongkrongan Shop is a business that emphasizes the quality of its service. The limited number of employees serving customers has an impact on services that are not optimal, such as ordering errors, delays in ordering menus, price discrepancies because prices are not updated on the menu, transaction errors, and are also prone to loss of order data because the recording is still done manually. A menu ordering system can be built to reduce this problem. However, in its use, the system must be of good quality to support the store's operational activities. The poor quality of the system can cause errors so that those who use it can experience financial losses. For this reason, it is necessary to develop a quality measurement system to run correctly and meet ISO 9126 quality standards. Based on the test results, the system meets ISO 9126 standards with a total score of 91.9% with a functionality aspect of 95.3%, reliability of 83.7%, usability of 93.5%, efficiency of 89.0%, and portability of 98%. Improvements are needed in the reliability aspect, namely in maturity and fault tolerance, so that the system can be even better in terms of quality for error handling and maturity.

Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living

PurposeIndoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.Design/methodology/approachThis study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.FindingsThe IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.Originality/valueThis study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

PROFESSIONAL APPROACHES TO QUALITY ASSESSMENT AND EVALUATION: IMPLICATIONS FOR TRANSLATION TEACHING AND LEARNING

This paper provides a critical overview of the approaches to the assessment and evaluation translation quality across professional practice. The analysis is aimed at singling out the professional knowledge, skills and communicative abilities and attitudes necessary for a translator in the field of education to control and evaluate the quality of translation. The identified features of the top-down approaches, which are based on centralized quality management, allowed us to determine the components of the professional competence of full-time and freelance translators of translation companies and translation departments of organizations. They relate to ensuring the quality of human and technical resources, quality control of the process and evaluation of the translation product, feedback. The characteristic features of the bottom-up approaches made it possible to outline general and specific knowledge, skills and communication abilities and attitudes of independent professionals determined by the increase in demand for fast, high-quality translation services at a low price. The components of a freelancer’s professional competence include knowledge of crowdsourcing technologies, the ability to demonstrate compliance with translation industry standards, critically evaluate information and one’s own strengths and weaknesses. Consideration of approaches to determining quality levels and requirements for them is conditioned by the importance of relevant knowledge and skills to self-control and selfassess translation quality. Results of the comparative analysis of professional translation quality measurement systems LISA QA Model 3.1, SAE J2450. QT21 D3.1: Harmonized Metric became the basis for developing a typology of translation errors and building a model for evaluating the quality of translation of educational texts.

Quality Frameworks for Virtual Care: Expert Panel Recommendations

Given the significant advance of virtual care in the past year and half, it seems timely to focus on quality frameworks and how they have evolved collaboratively across health care organizations. Massachusetts General Hospital’s (MGH) Center for TeleHealth and Mass General Brigham's (MGB) Virtual Care Program are committed to hosting annual symposia on key topics related to virtual care. Subject matter experts across the country, health care organizations, and academic medical centers are invited to participate. The inaugural MGH/MGB Virtual Care Symposium, which focused on rethinking curriculum, competency, and culture in the virtual care era, was held on September 2, 2020. The second MGH/MGB Virtual Care Symposium was held on November 2, 2021, and focused on virtual care quality frameworks. Resultant topics were (1) guiding principles necessary for the future of virtual care measurement; (2) best practices deployed to measure quality of virtual care and how they compare and align with in-person frameworks; (3) evolution of quality frameworks over time; (4) how increased adoption of virtual care has impacted patient access and experience and how it has been measured; (5) the pitfalls and barriers which have been encountered by organizations in developing virtual care quality frameworks; and (6) examples of how quality frameworks have been applied in various use cases. Common elements of a quality framework for virtual care programs among symposium participants included improving the patient and provider experience, a focus on achieving health equity, monitoring success rates and uptime of the technical elements of virtual care, financial stewardship, and clinical outcomes. Virtual care represents an evolution in the access to care paradigm that helps keep health care aligned with other modern industries in digital technology and systems adoption. With advances in health care delivery models, it is vitally important that the quality measurement systems be adapted to include virtual care encounters. New methods may be necessary for asynchronous transactions, but synchronous virtual visits and consults can likely be accommodated in traditional quality frameworks with minimal adjustments. Ultimately, quality frameworks for health care will adapt to hybrid in-person and virtual care practices.

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

Research on chemical oxygen demand based on laser Fluorescence-Raman spectroscopy

In this invited paper, a novel water chemical oxygen demand (COD) detection method based on laser spectroscopy is proposed, and the COD value is obtained according to the intensity ratio of laser Fluorescence-Raman signal produced by laser incident into water. Based on the laser Fluorescence-Raman ratio (LFRR) method, we design a portable water quality measurement system. In which, a 405 nm semiconductor laser is used as the light source. The laser is coupled into the delivering port of Y-type fiber and then transmitted into water. The generated Mie scattered light, water Raman light and corresponding fluorescence are received and transmitted into spectrometer through the detecting port of Y-type fiber, and the COD value can be obtained by analyzing the LFRR of water spectra. The mixed solution of sodium humate and glucose is used as the COD standard solution to calibrate the designed system. The experiment results show that the optimal volume ratio of the two solutions is 1:29. When the COD of standard solutions is in the 1–12 mg/L concentration range, a good linear relationship can be found between the COD value and LFRR value with a correlation coefficient of 0.974. In addition, the COD of natural water samples are measured with LFRR method, the results of which are consistent with COD values obtained by rapid digestion spectrophotometry. Meanwhile, experimental results prove that the COD detection method proposed in this paper has the advantages of high sensitivity, high precision, high detection speed and simplicity, which can be widely used in various water areas for real-time COD monitoring.

Findings From Implementing a Patient Experience Survey in a Quality Measurement System for Substance Abuse Disorder Treatment Facilities in 6 States.

Patient experience surveys (PESs) are an important component of determining the quality of health care. There is an absence of PES data available to people seeking to identify higher quality substance use disorder treatment providers. Our project aimed to correct this by implementing a PES for substance use disorder treatment providers and publicly disseminating PES information. We created a population frame of all addiction providers in 6 states. Providers were asked to disseminate a survey invitation letter directing patients to a survey Web site. No personally identifiable information was exchanged. We developed a 10-question survey, reflecting characteristics National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), Substance Abuse and Mental Health Services Administration (SAMHSA) have identified as reflecting higher-quality addiction treatment. Nineteen percent of facilities participated; among participating facilities, 9627 patients completed the survey. Patient experience varied significantly by facility with the percentage of a facility's patients who chose the most positive answer varying widely. We calculated that between-facility reliability will meet or exceed 0.80 for facilities with 20 or more responding patients. We searched for but did not find evidence of data falsification. This cost-efficient survey protocol is low burden for providers and patients. Results suggest significant differences in quality of care among facilities, and facility-level results are important to provide to consumers when they evaluate the relative patient-reported quality of facilities. The data are not designed to provide population-based statistics. As more facilities and patients per facility participate, public-facing PES data will be increasingly useful to consumers seeking to compare and choose facilities.

What informs the multi-actor indicator on school coexistence in Chile? A mixed design analysis

The “Safe Classroom” bill has created in Chile the need to discuss and review the way in which school coexistence is understood and measured. Although the discussion among citizens, parliamentarians and researchers has oscillated between a security approach and a democratizing approach to school coexistence, it is still pending to identify how school coexistence is conceptualized and measured in the questionnaires that accompany the national standardized test of the Educational Quality Measurement System (SIMCE). Given the above, we sought to understand this conceptualization and explore the indicators of students and parents in relation to the School Coexistence Indicator reported to each establishment. A mixed sequential qualitative (documentary analysis) and quantitative (secondary analysis of the SIMCE 2014, 2015 and 2017 databases) sequential design study was conducted. The findings show the existence of a narrow conceptualization of school coexistence linked to order, safety and discipline. For their part, the indicators for students and proxies are significantly different from each other. Recommendations are presented for public policy aimed at broadening the construct, revising the weightings, disaggregating the indicator by educational actor, and reporting the results of the evaluation by dimensions.

Approach to systematically examine the usefulness of quality measures in practice: Minnesota’s nursing home quality indicators and scoring approach

BackgroundHealthcare quality measurement systems, which use aggregated patient-level quality measures to assess organisational performance, have been introduced widely. Yet, their usefulness in practice has received scant attention. Using Minnesota nursing...

Development of a Generalized Experimental Methodology for Investigating the Role of Steam Quality on Steamflooding Performance

Summary For steamflooding processes, steam quality plays a crucial role because it affects enhanced oil recovery mechanisms and production performance. Many numerical simulations have been performed on the role of steam quality. However, few studies have evaluated the role of steam quality on steamflooding performance by experimental measurements because of the lack of a generalized experimental methodology to accurately generate and measure steam with different qualities under reservoir conditions. The objective of this study is to propose a generalized experimental methodology for investigating the role of steam quality on steamflooding performance. A steam quality controlling box was newly designed and fabricated to generate steam with different qualities, and its reliability was verified by a novel steam quality measurement system together with a developed theoretical method. Then, a series of experiments were conducted by our designed 1D and 2D sandpack models to evaluate the steamflooding performance under different steam qualities. The results showed that the developed methodology could accurately generate and measure steam with different steam qualities. The maximum errors between desired, measured, and calculated steam qualities were 4.39% under the experimental conditions in this study. The steam quality substantially affected the steamflooding performance. A higher steam quality led to a lower water cut, a lower maximum pressure difference between the inlet and outlet of the sandpack model, a lower water/oil ratio (WOR), a lower steam/oil ratio (SOR), a higher oil recovery, and a higher oil production rate. However, there is an optimal value of steam quality from the view of heat efficiency in this study. The oil recoveries of 2D steamflooding experiments increased from 36.30 to 45.02% when the steam quality increased from 0 to 0.8. However, the optimal steam quality of 0.6 had the maximum heat efficiency at 3.16×10−5 kJ−1. This research contributes to a better understanding of steam quality on steamflooding performance and also provides a generalized methodology for other steam injection processes.

Design of Information Visualizations in the Internet of Nano-Things Air Quality Systems

Today's age is characterized by large amount of information that surrounds us in which visual information plays a significant role. Nanosensor air quality measurement systems that use Internet of Nano-Things technology enable the collection of big data. Ease of display and storage of information that the user can easily interpret are imperative in designing a visual interface. Only a good combination of visual elements complemented by data and map display will contribute to the clarity of the processed data. This paper will give an overview of the factors that affect the excellence of the transmission of visual information. Ways of presenting visualizations of air quality data measured by IoNT systems will be discussed through descriptive and empirical analysis of visualizations. A special emphasis is on the review of existing practices and principles, and the possibilities of visual presentation of information in this area will be explained through the discussion.

A STUDY ON INTERNATIONAL COMMUNICATION STANDARDS AND PERFORMANCE STANDARDS FOR SMART WATER QUALITY SENSORS

The world is now building an artificial intelligence (AI) water quality management system to detect water quality accidents at an early stage and predict water quality vulnerable areas in advance beyond measuring the water quality. In other words, by applying big data analysis and artificial intelligence technology to the conventional water quality measurement system that monitors water quality in real time, an artificial intelligence-based water quality measurement and water quality prediction system is built at the same time, and drones and unmanned ships are also introduced. It means implementing water quality management. Water quality accident prediction based on artificial intelligence measures water quality throughout the site of use through intelligent spatial analysis based on the integrated water quality database. On the other hand, the location of the site detected through artificial intelligence is displayed on the comprehensive monitoring screen, and special management such as on-site monitoring, replacement of consumables, and maintenance is carried out to prevent water quality accidents. In particular, the prediction accuracy of artificial intelligence is to create an algorithm for predicting future values by learning the characteristics and patterns of accumulated data. It depends on the function of the smart sensor itself. However, until now, there has been no international standard for smart sensors, so it was difficult to establish system interlocking or modularization for each manufacturer and site. Therefore, we present the most important standard communication standards for smart water quality sensors in the field and international standard performance specifications for smart sensors, and this research data is provided to all organizations engaged in water quality around the world for water quality measurement and prediction system data system for AI application.