Quality Monitoring System Research Articles

Association between Air Quality Index and the risk of rosacea: A Nationwide population-based Cohort Study.

Air pollution is associated with several inflammatory skin disorders. However, the association between air quality and rosacea remains unclear. To investigate the association between air quality index and incidence of rosacea. Overall, 21,709,479 participants without rosacea before 2008 were recruited from the Taiwan National Health Insurance Research Database. The long-term average air quality index (AQI) value for each participant was acquired from the Taiwan Air Quality Monitoring System Network and calculated from 2008/1/1 until the diagnosis of rosacea, withdrawal from the National Health Insurance, or December 31, 2018. We observed a significant association between AQI and the incidence of rosacea, with each unit elevation in AQI increasing the risk of rosacea by 5 %. Compared with the Q1 group, the Q2, Q3, and Q4 cohorts exhibited 1.82-fold, 4.48-fold and 7.22-fold increased risk of rosacea, respectively. Additionally, exposure to PM2.5, SO2 and CO increased the risk of rosacea, whereas exposure to PM10 was associated with a lower risk. This study supported a significant dose-response relationship between AQI and the incidence of rosacea.

Remote Water Quality Monitoring System In Shrimp Ponds With Photovoltaic (PV)-Based Energy Source

Introduction: Shrimp has great potential to be used as a business field in Indonesia. Especially in Parigi Regency itself, there are now many shrimp ponds that can be found because of the benefits obtained, so people are interested in making shrimp farming a livelihood. One of the main problems of shrimp ponds is pond water quality. There are several factors that affect pond water quality, namely water temperature, water pH, and water salinity, good pond water quality management can maintain quality standards and can increase shrimp yield and productivity Method: To facilitate the shrimp farmers, innovations need to be made in order to help the shrimp farmers manage their shrimp ponds, therefore using a remote water quality monitoring was system in shrimp ponds with photovoltaic (PV) based energy sources is expected to be an innovation in managing shrimp ponds, especially in monitoring water quality in shrimp ponds. The monitoring system designed using the NRF24L01 module as a remote communication module, at the research location the distance from the shrimp pond to the house is approximately 100 meters, so the tools that have been designed can facilitate shrimp farmers to monitor the shrimp pond Results and Disscussion: The results of testing the DS18B20 temperature sensor compared with a digital thermometer measuring instrument get an average error of 0.014%. The test results of the pH sensor compared to the pH meter get an average error of 0.026%. TDS sensor test results compared with the TDS meter get an average error of 0.04%. Conclusion: The temperature, pH, and salinity monitoring system uses transmitter and receiver modules, where the transmitter module reads and sends data to the receiver module wirelessly with NRF24L01. Using DS18B20 temperature, pH, and TDS sensors, as well as energy from photovoltaics, this system helps shrimp farmers monitor pond water quality remotely.

Dentists who can auscultate: Microphone-based toothbrushing quality monitoring system for electronic toothbrush

Tooth brushing is a widely recognized and effective method for daily oral care. However, the lack of real-time feedback for monitoring the brushing process can hinder individuals in maintaining their oral hygiene. Currently, only high-end electric toothbrushes offer evaluation functions based on built-in sensors, making them unaffordable for the majority of people. In this paper, we propose a novel and cost-effective wearable acoustic-based system (both hardware and software) for monitoring the quality of daily tooth brushing. Our system combines a throat-microphone and a Bluetooth earphone to capture brushing noises, which are unique acoustical signals generated from the air and the human body during tooth brushing, respectively. By leveraging the distinctive characteristics of brushing sounds in these two different conductive media, we introduce an intelligent detection algorithm based on Single-Input Multiple-Output (SIMO) modeling and frequency response functions (FRFs) to detect the corresponding brushing area. Through experimentation, we have found that the divergence of FRFs provides more stable features compared to general statistical features used in previous studies. After comparing various machine learning algorithms, we have selected XGBoost as the final detection model. Additionally, we utilize the Symmetric Nearest Neighbor (SNN) filter in the post-processing stage to further reduce the jitter noise in continuous detection. The final system achieves an average precision of 98.46% when tested on tooth brushing data collected from different volunteers. To enhance user experience, we have developed an Android App with a user-friendly interface that incorporates the detection system. With a total wearable hardware cost of less than 10 dollars, our system shows great potential for integration into future smart wearable devices.

Smart and cost-effective water quality monitoring system: University of Baghdad canal case study

Polluted water causes diseases, affecting the ecosystem. Water quality is crucial due to high demand. Regular monitoring is essential for a consistent supply of fresh water. Internet of Things (IoT) advancements enable real-time monitoring and timely intervention for water quality issues. The current investigation evaluated a proposed smart, cost effective, high-efficacy IoT application of a water quality detection device that real-time monitors six measurement points along the University of Baghdad canal for five water quality parameters. With the Arduino platform and specific sensor properties, a low-cost monitoring system has been built. The data is shown on an LCD monitoring screen and presented on a Wi-Fi module before being transferred to the cloud and presented on a smartphone and internet platform. The results from the monitoring system for both winter and spring water quality are within the permissible limit according to standard limitations were average pH 7.687, total dissolved solid 406.75 mg/l, turbidity 25.883 NTU, and electrical conductivity 630.8 µS/cm. This framework may successfully offer an environment where there is enough water for agricultural purposes while also keeping a careful check on the contamination of water resources. On the other hand, this permits a well-managed water quality standard.

Proposal of an Automated Monitoring System of Aquamarine Water Quality in Coral Reef Regions

Proposal of an Automated Monitoring System of Aquamarine Water Quality in Coral Reef Regions

Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation.

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems.

Towards the Development of an In-Process Quality Monitoring System for Polyethylene Recyclates by Pyrolysis Gas Chromatography Ion Mobility Spectrometry

Over the past few decades, the topic of recycling has become increasingly prominent in the field of sustainable materials and circular economy. One significant challenge is the physical separation of different types of plastics to obtain recyclates of one plastic type as pure as possible with comparable quality and properties to those of virgin material. Given the substantial effort involved in such separation, small amounts of contamination from other plastics may be tolerated. However, these contaminations must be monitored to ensure high-level recyclate quality. In recent years, compact, low-cost ion mobility spectrometers (IMS) with high analytical performance have been developed, and have thus become widely used in a variety of sensing applications. Due to their high sensitivity, IMS are particularly suited for detecting lowest concentration levels of various compounds, as required for the detection of impurities in recyclate quality monitoring. When coupled to a miniature gas chromatograph (GC), GC-IMS reach even higher separation power while being still compact. To bring recyclate samples to the gas phase, pyrolysis (Py) is used in this work. A first feasibility study was conducted to assess the potential of such a pyrolysis–gas chromatography-ion mobility spectrometer (Py-GC-IMS) with the objective of detecting contaminations of polyethylene terephthalate (PET) in polyethylene (PE) recyclates. The study clearly demonstrates the ability to identify PET-related fingerprints while suppressing the PE background matrix by design so that Py-GC-IMS seems a promising approach for in-process monitoring PET contaminations in PE recyclates.

Air Quality Monitoring System Design Based on Wireless Sensor Network Integrated with the Internet of Things

Government and officials set rules to keep the air clean and healthy. To accommodate this, an efficient air quality monitoring system is required. Real-time monitoring is crucial for observing air quality. This allows for immediate action if air quality declines. However, current systems often rely on just one measurement point, risking inaccurate results due to rapid pollutant dispersion. To overcome this problem, researchers propose designing an air quality monitoring system based on a wireless sensor network. Sensor nodes will be installed at various points within the area to be monitored, forming a connected sensor network using the ESP-Now protocol. The data obtained from each node will be sent to the base station, then the data will be transmitted via the Message Queuing Telemetry Transport (MQTT) protocol using the internet network. Thus, this design produces a wireless sensor network that is integrated with the internet of things (IoT). The advantages of the IoT system include ease of data storage and accessibility that can be accessed from anywhere as long as it is connected to the internet and has appropriate authorization.

Design and experiment of an intelligent testing bench for air-suction seed metering devices for small vegetable seeds

In order to realise the real-time monitoring of seeding quality of small vegetable seeds, and solve the problem of inconvenience caused by the small particle sizes of vegetable seeds, an intelligent testing bench for air-suction seed metering device for small vegetable seeds with a particle diameter of 0.5–3 mm was designed, and a complementary quality monitoring and controlling system was developed. The photoelectric sensing technology and image recognition technology were used together in the system. Photoelectric sensing technology was used for real-time monitoring of seed counting, while the image recognition technology can dynamically monitor the missed seeding, repeated seeding and the falling trajectory of seed deposition in real time. The system was installed on the air-suction intelligent testing bench to test the monitoring accuracy of the monitoring system. The experimental results demonstrated that the monitoring accuracy of drilling quality monitoring system was over 97%, the monitoring accuracy of miss seeding was over 95%, and the monitoring accuracy of repeated seeding was over 93%. Furthermore, the monitoring system was less affected by the seed particle size, shape, and plant spacing, etc., and the system was stable, which effectively realised the real-time monitoring of the operation of air-suction metering device.

Development of an IoT-based multi-level system for real-time water quality monitoring in industrial wastewater

Industrialization and urbanization contribute significantly to environmental pollution, particularly in water bodies. The consequences of this pollution are dire, impacting both the environment and public health. A serious hazard to life on Earth, contaminated water can cause food poisoning, diarrhea, respiratory diseases, and skin ailments, among other health problems. In countries like Bangladesh, where industrial activities, particularly in the garment sector, are prevalent, waste dumping into rivers and canals has reached alarming levels. This has resulted in a drastic decline in water quality, endangering aquatic life and ecosystems. To solve this important issue, we built an innovative real-time water quality monitoring system using the Internet of Things (IoT). Our system operates at three levels, collecting data from different layers of wastewater. Key parameters, including temperature, turbidity, total dissolved solids (TDS), and pH, are continuously monitored to assess water quality. The analysis revealed that wastewater temperature varied between 25 and 37 °C across different sites, with higher temperatures observed in industrial zones, indicating potential thermal pollution. Total dissolved solids (TDS) levels exhibited substantial variations, ranging from 170 to 360 ppm, surpassing standard thresholds and posing a threat to aquatic ecosystems. Turbidity readings ranged from 1.30 to 12.89 NTU, with higher turbidity observed in areas receiving industrial discharge, highlighting the impact of suspended solids on water clarity. pH values exceeded 7.0 across all sites, ranging from 9.5 to 10.32, indicating alkaline wastewater, particularly in regions with dyeing and textile industries. By integrating these specific metrics into our monitoring system, we provide valuable insights into industrial wastewater quality and its environmental implications. These findings underscore the urgent need for real-time monitoring and targeted interventions to address water pollution. Implementing sustainable water management strategies in industrial contexts is critical to protecting public health and preserving ecosystems for future generations.

Strategic use of resources to enhance colorectal cancer screening for patients with diabetes (SURE: CRC4D) in federally qualified health centers: a protocol for hybrid type ii effectiveness-implementation trial

BackgroundPersons with diabetes have 27% elevated risk of developing colorectal cancer (CRC) and are disproportionately from priority health disparities populations. Federally qualified health centers (FQHCs) struggle to implement CRC screening programs for average risk patients. Strategies to effectively prioritize and optimize CRC screening for patients with diabetes in the primary care safety-net are needed.MethodsGuided by the Exploration, Preparation, Implementation and Sustainment Framework, we conducted a stakeholder-engaged process to identify multi-level change objectives for implementing optimized CRC screening for patients with diabetes in FQHCs. To identify change objectives, an implementation planning group of stakeholders from FQHCs, safety-net screening programs, and policy implementers were assembled and met over a 7-month period. Depth interviews (n = 18–20) with key implementation actors were conducted to identify and refine the materials, methods and strategies needed to support an implementation plan across different FQHC contexts. The planning group endorsed the following multi-component implementation strategies: identifying clinic champions, development/distribution of patient educational materials, developing and implementing quality monitoring systems, and convening clinical meetings. To support clinic champions during the initial implementation phase, two learning collaboratives and bi-weekly virtual facilitation will be provided. In single group, hybrid type 2 effectiveness-implementation trial, we will implement and evaluate these strategies in a in six safety net clinics (n = 30 patients with diabetes per site). The primary clinical outcomes are: (1) clinic-level colonoscopy uptake and (2) overall CRC screening rates for patients with diabetes assessed at baseline and 12-months post-implementation. Implementation outcomes include provider and staff fidelity to the implementation plan, patient acceptability, and feasibility will be assessed at baseline and 12-months post-implementation.DiscussionStudy findings are poised to inform development of evidence-based implementation strategies to be tested for scalability and sustainability in a future hybrid 2 effectiveness-implementation clinical trial. The research protocol can be adapted as a model to investigate the development of targeted cancer prevention strategies in additional chronically ill priority populations.Trial registrationThis study was registered in ClinicalTrials.gov (NCT05785780) on March 27, 2023 (last updated October 21, 2023).

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas

With the rise of healthy lifestyles, sports arenas have become important exercise venues for people. However, indoor air quality issues are becoming increasingly prominent, posing a potential threat to the health of athletes. Traditional indoor air quality monitoring systems often suffer from problems such as unreasonable layout of monitoring points and inaccurate data processing, making it difficult to effectively cope with complex indoor environments. This article proposes an intelligent monitoring system for indoor air quality in sports venues based on an improved cohesive hierarchical clustering algorithm to address these issues. The system optimizes the layout of monitoring points, combines real-time data collection on the Android platform, and accurately processes non smooth data using an improved cohesive hierarchical clustering algorithm based on BiLSTM. The experimental results show that the algorithm not only improves the accuracy of monitoring point selection, but also optimizes the number of monitoring points and effectively supplements missing data. In addition, the algorithm significantly reduces computational complexity and improves computational efficiency while ensuring clustering quality. Application testing shows that the indoor air environment monitoring system constructed in this article can obtain and analyze data in real time, effectively control the concentration of key pollutants such as PM2.5 and carbon dioxide, and create a healthier sports environment for sports venues. This study provides new ideas and methods for indoor air quality monitoring, which has important practical application value.

Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review.

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed.

Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River

Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehensive monitoring system along the Vaigai River. The system was designed to collect real-time data on several crucial water quality parameters. The collected characteristics encompassed factors like water pollution levels, turbidity, pH readings, temperature, and total dissolved solids, offering a comprehensive view of river water quality. The monitoring system was methodically set up, with sensors strategically positioned at various locations along the river. This ensured that data collection would take place at regular intervals. The CNGRU-WQM model achieved a validation accuracy of 97.86%, surpassing the performance of other state-of-the-art approaches. Particularly noteworthy is the fact that the actual use of this system incorporates real-time warnings, which enable stakeholders to be instantly informed if water quality measurements surpass pre-set criteria. The study's contributions include its efficient river water quality monitoring system, which encompasses a variety of indicators, and its ability to significantly affect environmental conservation efforts by offering a helpful tool for informed decision-making and timely interventions.

Reliable Air Quality Assessment in Dynamic Environments using Reward-learning Routing with Flying MANET Technology

<p>Air pollution poses significant threats to ecosystems, atmosphere, and the health of both humans and wildlife. It is primarily caused by industrial and chemical pollutants, leading to deterioration in air, water, and soil quality. As a result, effective air quality monitoring is crucial for environmental management. Flying Mobile Ad hoc Networks (FMANETs) offer a promising solution for intelligent air quality monitoring and assessment by employing unmanned aerial vehicles (UAVs) to measure air pollutants. However, FMANET-based systems face unique challenges, including UAVs' three-dimensional movement, high dynamism, rapid topological changes, resource constraints, and low UAV density. Therefore, routing optimization becomes a fundamental issue in these systems. In this paper, we propose a novel routing method named RFMAN (Reward-learning-based FMANET) for intelligent air quality monitoring systems. RFMAN consists of two main components: route discovery and route maintenance. In the route discovery phase, we introduce a Reward-learning-based mechanism to identify optimal routes, while also implementing a filtering parameter to refine the UAV selection process and reduce the search space. The route maintenance phase of RFMAN focuses on detecting and correcting paths nearing breakdowns, as well as promptly replacing failed paths to ensure continuous monitoring efficiency. The result section pictures that RFMAN outperforms other methods. Overall, RFMAN offers a promising solution for enhancing the effectiveness and reliability of air quality monitoring in FMANET-based systems.</p>

Monitoring and Analyzing Air Quality in Several Locations around Medan City Using IoT-Based Integrated Sensors

This research is motivated by the importance of monitoring air quality in industrial and non-industrial areas, especially in the city of Medan, Indonesia. Increasing growth of the industrial sector and use of transportation can increase air pollution. Air pollution caused by emissions of harmful gases such as NO2 and CO, as well as microscopic particles such as PM2.5 has a significant impact on human health, especially causing respiratory and heart disease and premature death. The rapid development of Internet of Things (IoT) technology can detect air quality and transmit sensor readings wirelessly via the internet to an Android or PC. This research aims to design an air quality monitoring tool related to the presence of dangerous gases NO2, CO and levels of microscopic particles PM2.5 with an integrated mics-6814 sensor and PMS5003 sensor based on IoT accurately and in real-time and analyze the results of air quality monitoring in research area. The research methodology involves measuring air quality parameters such as PM2.5, CO, NO2, temperature, and humidity using IoT-based integrated design tools. The research location consists of 3 points, namely around the industrial and residential areas of the Kawasan Industri Medan (KIM) Mabar, the Industrial Area in Tanjung Morawa, and the University of North Sumatra. Numerical data will be tabulated from the results of research carried out for 7 hours in 3 days based on the specified time. The results of this research show that the air quality monitoring system designed can measure temperature, humidity, PM2.5, NO2 and CO levels in real time and accurately. Air quality data collected at several research locations shows different trends. Overall, the air quality in the research area is in the healthy category, having no impact on the health of humans, animals and plants which is linked to Peraturan Menteri Lingkungan Hidup Dan Kehutanan Republik Indonesia. Suggestions for further research include selecting components that are more sensitive in detecting air quality and paying attention to environmental conditions which can reduce deviation values ​​from the sensor.

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

Enhancing efficiency of hospital quality monitoring and evaluation system using linked spreadsheets on Microsoft SharePoint

Background Establishment of a systematic way of measurement and utilization of indicators for improvement is one of the most challenging issues in monitoring and evaluation of indicators in healthcare settings. In realizing these fundamental challenges, we designed a monitoring and evaluation system incorporating a hospital-wide quality variance report (QVR) system using linked Microsoft Excel® spreadsheets on Microsoft SharePoint®. Methods Indicators were determined at the departmental/unit level in line with the institutional goals, departmental functions, quality expectations, inputs/outputs, clinical priorities, compliance to policies/procedures/protocols/guidelines/pathways as well as in response to gaps in service delivery picked during root cause analyses. The sample design was determined in accordance with the characteristics of the population. Drawing of sample units was done using a simple random sampling technique without replacement or systematic random sampling. The indicator’s monitoring was enhanced visually by allocating colour codes based on performance across the months and quarters. The action plan tab consisted of a platform that aids in documenting corrective actions arising from the performance reviews. Results and discussion The QVR reporting system ensured a standardized format of monitoring throughout the institution with a reduced turnaround time from data collection to analysis. Further, continuity of the monitoring and evaluation (M&E) system was guaranteed even if an individual left the institution. The analysis of the QVR allowed hospital-wide trending on cross-cutting indicators with consequent ease of communication to multiple stakeholders. The automation has saved time and increased accuracy which has enhanced credible engagements during quality meetings. Conclusions Use of this system greatly enhanced quality performance monitoring in the hospital, identification of major bottlenecks that warranted hospital-wide projects or departmental-level projects. The QVR system enhanced the efficiency and accuracy of quality monitoring from data collection through to performance reviews. The QVR structure allows for customized development of an M&E database application software.

Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images

ABSTRACTOur research addresses the imperative need for an efficient air quality monitoring and forecasting system to mitigate the significant health risks of air pollution. Departing from conventional binary data collection methods, we employ image‐based techniques to overcome inherent limitations. A pioneering aspect of our work involves the development of a novel model capable of predicting six air pollutants (PM2.5, PM10, O3, CO, SO2, NO2) along with Air Quality Index for Bengaluru, Delhi, and Tamil Nadu, achieving a commendable mean absolute error of 0.1432 on the test set. The efficacy of our approach is validated through a meticulously curated dataset comprising approximately 5455 images. We emphasize the significance of normalization by presenting outputs before and after, shedding light on the impact of parameters with varying ranges and strategies employed to mitigate such discrepancies. A detailed analysis of our model's best and worst outputs provides valuable insights into its strengths and limitations. To enhance user accessibility, we introduce an innovative image‐based, real‐time, user‐friendly dashboard that allows users to conveniently assess a location's air pollution levels by uploading an image. This holistic approach offers a promising avenue for accurate air quality prediction and real‐time monitoring.

Evaluation Activity of Teachers in the Educational Process at a Higher Educational Institution

The presented article is devoted to the organization of the control and analytical activities of teachers in a university. Based on the works by Polyakova & Lushkina, (2011), the authors clarified the concept of "control and analytical activity of university teachers", analyzed its components, systematized domestic and foreign experience in organizing the control and analytical activity of teachers in a higher educational institution. The article also presents the author complex of organizational and pedagogical conditions for the effective organization of the control and analytical activity of teachers, which helps to improve the quality of education among university students: the implementation of the basic functions of the educational process on the basis of control and analytical activity, targeted training of teachers for the quality of control and analytical activities; implementation of a system for student education quality monitoring.