Quality Assessment System Research Articles

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep learning and traditional methods like RankIQA, BRISQUE, and SSIM, various partitioning schemes were compared to explore the highest-quality imaging heterogeneous processing scheme. The UNet-specific deep-learning processing unit (DPU) based on a field programmable gate array (FPGA) provided a 14.67× acceleration ratio for the total network and for deconvolution and max pool, the calculation latency was as low as 2.46 ms and 97.10 ms, achieving an impressive speedup ratio of 46.30× and 36.49× with only 4.04 W power consumption. The HISP consisting of a DPU and the FPGA-implemented traditional image signal processor (ISP) submodules, which scored highly in the image quality assessment system, with a single processing time of 524.93 ms and power consumption of only 8.56 W, provided a low-cost and fully replicable solution for edge image processing in extremely low illumination and high noise environments.

Интеллектуальная система оценки качества сыпучего сырья на основе нейронных сетей YOLOv5 и ViT

The lack of an effective system of raw material quality assessment during acceptance in the field is the reason for writing this article. The article describes important scientific and practical tasks related to this problem, such as the development of methods of raw material quality control, creation of algorithms of intellectual support for fast and accurate assessment of raw material quality in real time. The authors have analyzed domestic and foreign scientific papers describing approaches and methods for raw material quality control and concluded that the YOLOv5 algorithm and visual transformer are the most suitable for the present study, their combination has not been previously used in the literature. The paper presents the problem statement for the development of an intelligent raw material quality system, description of object detection algorithms, their comparative characterization. The description of the working principle of the one-stage algorithm of object detection in the image YOLOv5 and its advantage over the existing analogs of deep learning methods, which are widely used for object detection, is given. Also presented are the results of neural networks, the task of which is to identify defective raw materials from the total amount stored in a particular railcar and belonging to a particular supplier. An intelligent system for analyzing the quality of bulk raw materials includes cameras located above and to the side of railcars. The quality of raw materials is determined by transferring images from the loading cameras to a neural network for further processing. YOLOv5 algorithm is used to detect the railcar number, which provides accurate detection of railcar number. To determine the percentage of rejected cargo, in the first step, YOLOv5 approach is used to select the image region with cargo and then visualization is applied to detect the percentage of rejected cargo. Implementation of the proposed intelligent system will allow more efficient production management, reduce costs and improve the quality of delivered bulk raw materials.

Eco-geological environment quality assessment in a mining city: a case study of Jiangxia District, Wuhan City

This study investigates the eco-geological environmental challenges faced by Jiangxia District, a mining region in Wuhan City. By combining field surveys, literature analysis and multi-source data such as remote sensing images and on-site monitoring data, an eco-geological environment quality (EGEQ) assessment system was developed. The system consists of six indicators representing biological abundance, vegetation cover, land erosion, water abundance, surface deformation and socio-economic characteristics of the assessed area. The grey relational analysis method was utilized to calculate the weights of each indicator, and the eco-geological environment quality index (EGQI) value was obtained through the linear weighted method. The assessment results indicated a moderate grade of overall eco-geological condition in Jiangxia District between 2017 and 2021, with an 8.56% increase in the EGQI during that period. This suggests a stable and favorable eco-geo logical environment situation in the district during the specified timeframe.

Signal quality indices evaluation for robust ECG signal quality assessment systems

As the current healthcare system faces problems of budget, staffing, and equipment, telemedicine through wearable devices gives a means of solving them. However, their adoption by physicians is hampered by the quality of electrocardiogram (ECG) signals recorded outside the hospital setting. Due to the dynamic nature of the ECG and the noise that can occur in real-world conditions, Signal Quality Assessment (SQA) systems must use robust signal quality indices (SQIs). The aim of this study is twofold: to assess the robustness of the most commonly used SQIs and to report on their complexity in terms of computational speed. A total of 39 SQIs were explored, of which 16 were statistical, 7 were non-linear, 9 were frequency-based and 7 were based on QRS detectors. With 6 databases, we manually constructed 2 datasets containing many rhythms. Each signal was labelled as ‘acceptable’ or ‘unacceptable’ (subcategories: ‘motion artefacts’, ‘electromyogram noise’, ‘additive white Gaussian noise’, or ‘power line interference’). Our results showed that the performance of an SQI in distinguishing a good signal from a bad one depends on the type of noise. Furthermore, 23 SQIs were found to be robust. The analysis of their extraction time on 10-second signals revealed that statistics-based and frequency domain-based SQIs are the least complex with an average computational time of (mean: 1.40 ms, standard deviation: 1.30 ms), and (mean: 4.31 ms, standard deviation: 4.50 ms), respectively. Then, our results provide a basis for choosing SQIs to develop more general and faster SQAs.

Fuzzy assessment and improvement path of green finance quality in “Belt and Road”: Survey evidence from China

The “One Belt and One Road” is a major strategic deployment proposed by Chinese President Xi Jinping in 2013, and it is important to study the construction of green financial system under the “One Belt and One Road” initiative, especially the quality assessment of green finance to promote high-quality economic development along the route. At the same time, green finance and green “Belt and Road” have become a hot academic topic in the world. In this study, firstly, on the basis of the existing research on green finance, focusing on the evaluation of economic quality of green finance, we innovatively draw on the quality function deployment theory in marketing to logically transform our research ideas and propose a combined comprehensive evaluation method based on the hierarchical analysis (AHP) and entropy method in fuzzy mathematical theory, which makes up for the traditional single fuzzy evaluation method’s influence on the evaluation results. This method makes up for the shortcomings of the traditional single fuzzy evaluation method to evaluate the results of subjective or objective weighting results. In this study, we apply this method to the assessment of the quality of green finance development in “One Belt, One Road”, and it is important that we construct a system of “One Belt, One Road” green finance quality assessment indicators, including one primary indicator, four secondary indicators and 12 tertiary indicators. It is worth noting that our indicator system is different from the traditional quality system of financial quality assessment in that we take green factors into account in the construction of the tertiary indicators, and then use this assessment method to calculate and rank the weights (importance) of the 12 tertiary indicators, taking the actual situation in China as an example. More importantly, our study not only extends the academic research results of economic quality evaluation, but also combines quantitative research with qualitative analysis to propose three targeted countermeasures for the development of green finance in the countries along the Belt and Road. This study can also provide theoretical support for the quality assessment of green finance in countries along the Belt and Road, and promote the high-quality development of green finance in countries along the Belt and Road.

BESS patient care pathway: Tennis elbow.

This article provides a guidance summary for the management of lateral elbow tendinopathy (LET) using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system of the rating quality of the literature and grading the strength of available evidence. The process began by assembling a guideline development group of volunteers including orthopaedic surgeons, trainees, physiotherapists, rheumatologists, radiologists and patients. Virtual meetings were organised to set out explicit PICO questions, including specification of all important outcomes (including patient reported tennis elbow evaluation (PRTEE) as an important primary outcome) to determine the clinical effectiveness of common treatment options for LET compared with no treatment or placebo. Clinical librarian searched (date 31 April 2022) for available systematic reviews and randomised controlled trials reviewing the management of the LET January 2011 onwards and evidence was collected and summarized using explicit GRADE criteria for rating the quality of evidence that include study design, risk of bias, imprecision, inconsistency, indirectness, and magnitude of effect. Recommendations were characterized as strong or weak (alternative terms conditional or discretionary) according to the quality of the supporting evidence and the balance between desirable and undesirable consequences of alternative management options. This informative summary provides the quality of available evidence for the management of LET.

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

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

Application of Association Rule Mining Algorithm in Talent Training Assessment in Universities Under Social Sustainability

From the contradiction between the difficulty of employment in enterprises and the difficulty of employment in college students, we can see that the strategies of cultivating talents in colleges and universities are biased. This research combines the talent cultivation mode of colleges and universities with the current situation of social development, constructs a comprehensive quality assessment system for students, and uses hierarchical analysis and expert opinions to determine the weight coefficients of each index. In response to the shortcomings of traditional mining algorithms, an improved APRIORI algorithm based on the bit-array matrix is proposed to improve the mining efficiency by introducing the duplicate terms of identification and variable records in data compression. The experimental results show that the improved algorithm can achieve fast convergence on different data sets. The minimum running time of BM Apriori algorithm in different nodes is 102s; under different supports, the average running time of BM Apriori algorithm is 1.49s, and the efficiency is improved by 81.4%. BM Apriori algorithm is used for data mining, and the association rules between courses and students’ grades obtained show that students’ grades are prone to decline during the freshman to sophomore years. Schools should focus on cultivating students’ good learning habits, cultivating excellent talents, and promoting the sustainable development of society.

Insights into end-of-life vehicle recycling and its quality assessment systems in Malaysia reveals the need for a new stakeholder-centric approach for vehicle waste management

ABSTRACT To identify potential limitations that may impact the quality and efficiency of end-of-life vehicle (ELV) recycling and to provide solutions for effective management, this study investigates the current ELV recycling practice and the quality assessment systems around it in Malaysia by employing a mixed-methods approach. The approach encompasses qualitative and quantitative research alongside reviewing pertinent literature. The findings reveal that ELV recycling and its accompanying quality assessment systems in Malaysia are restricted and confront many critical challenges. The challenges include the lack of appropriate processes for recycling to maximize the material recovery, and the quality and reliability of the components put into circular economy practice. As a practical solution, a stakeholder-centric framework is proposed that allows better management of ELV waste from transportation and other related sectors. This framework enhances the efficiency and quality of ELV recycling practices in Malaysia and helps assess the quality of components recovered from ELVs.

Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion

Since the initiation of economic reforms and opening up, China has witnessed an unprecedented rate of development across all sectors. However, the country has also experienced severe ecological damage, surpassing that of many other nations. The rapid economic growth has come at the expense of the environment, revealing a significant lack of coordination between urbanization and eco-environmental protection in China. Consequently, there is an urgent need for a comprehensive and continuous historical dataset of China’s eco-environmental quality (EEQ) based on remote sensing, allowing for the analysis of spatial and temporal changes. Such data would provide objective, scientific, and reliable support for China’s eco-environmental protection and pollution prevention policies, while addressing potential ecological risks resulting from urbanization. To achieve this, the entropy value method is employed to integrate multi-source remote sensing data and construct an evaluation system for China’s EEQ. Historical data from 2000 to 2017 is plotted to illustrate China’s EEQ over time. The findings of this study are as follows: (1) The entropy method effectively facilitates the construction of China’s eco-environmental quality assessment system. (2) From 2000 to 2017, approximately 39.7% of China’s regions witnessed a decrease in EEQ, while 60.3% exhibited improvement, indicating an overall enhancement in EEQ over the past eighteen years. (3) The Yangtze and Yellow River basins experienced improved EEQ due to China’s ecological restoration projects. (4) The future EEQ in China demonstrates a subtle positive trend across diverse contexts. This study departs from conventional approaches to EEQ evaluation by leveraging the advantages of multivariate remote sensing big data, including objectivity, timeliness, and accessibility. It provides a novel perspective for future eco-environmental quality evaluation.

Developing a closed captioning quality assessment system using a multi-label classifier with active learning from deaf and hard of hearing viewers

Developing a closed captioning quality assessment system using a multi-label classifier with active learning from deaf and hard of hearing viewers

System for assessment and forecast of air quality in populated areas

The paper provides an account of a system for collecting data, forecasting and assessing the quality of ambient air in a given locality. The developed system allows for extremely sustainable analysis of the results and due consideration of the utilization of artificial intelligence algorithms and methods for the development of accurate forecasts. The obtained results are expected to detect the problems related to the quality of air before their actual occurrence.

LEGAL ASPECTS OF COMPLIANCE WITH THE REQUIREMENTS FOR INTERNAL QUALITY ASSESSMENT SYSTEM OF EDUCATION

The subject of the study is the principles of compliance with the mandatory requirements for the implementation of educational programs of higher education in terms of the internal quality assessment system (IQAS). The purpose of the article is to determine the impact of the current status of the requirements for IQAS in higher education programs on public legal relations in the field of education and the growth of the quality of education in the Russian Federation. The relevance of the presented topic is determined by the importance of social relations arising in the framework of control and supervisory actions. The author's special contribution is the study of short stories that have recently come into force and have not yet been fully analyzed in the research literature. The novelty of the study lies in the application of interdisciplinary approaches to the study of the subject, in combining the results of the analysis of legal acts with an assessment of the impact of law enforcement practice on the management system of educational institutions of higher education and new opportunities to improve the quality of the implementation of educational programs. It is concluded that taking into account the full range of requirements for IQAS allows not only to optimize risk management in terms of the consequences of non-compliance with mandatory requirements, but also provides support for building a quality management system as a driver for the development of educational programs.

Система оцінки якості монолітних активних мікропіксельних детекторів

A system for quality assessment of micropixel detectors is presented. The system includes a laser scanning microprobe and a setup for studying the response of micro detectors to minimum ionizing particles. The results of the validation of the developed system indicate its suitability for assessing the quality of the latest monolithic active pixel sensors (MAPS), promising elements of large-area tracking systems for future high-energy physics experiments. Comparison of MAPS with the double-sided microstrip detectors of the CBM experiment (FAIR, Darmstadt) indicates the feasibility of the upgrade of its Silicon Tracking System using MAPS.

Validation of the German classification system for quality assessment of right-sided colon cancer specimens.

The German classification system of the completeness of mesocolic excision aims to assess the quality of right-sided colonic cancer surgery by review of photographs. We aimed to validate the reliability of the classification in a clinical context. The study was based on a cohort of patients undergoing resection for right-sided colon cancer in two university hospitals served by the same group of pathologists. Prospectively collected photographs of the specimens were assessed twice by six colorectal surgeons to determine the intra-rater and inter-rater accuracy of the German classification and a modification assessing extended right-sided resections. Specimens from 613 resections for right-sided colon cancer were reviewed. Twenty-one specimens were found to be non-assessable, leaving 436 right hemicolectomies, 139 extended right hemicolectomies and 17 right-sided subtotal colectomies. Intra-rater reliability was 0.57-0.74 and weighted kappa coefficients 0.58-0.74, without differences between subgroups. The percentage of agreement between all six participants was 20.3% for all specimens, 21.1% for right hemicolectomy specimens and 18.1% for extended hemicolectomy and right-sided subtotal colectomy specimens. For the right hemicolectomy specimens, the model-based kappa coefficient for agreement was 0.27 (95% CI 0.24-0.30) and for association 0.45 (95% CI 0.41-0.49). The German classification of right hemicolectomy specimens showed low intra-rater reliability and inter-rater agreement and association. The use of this classification for scientific purposes appearednot to be reliable.

Methodological bases for assessing the quality of transport and logistics customer service of a marine terminal operator

The purpose of this article is to develop a methodology for assessing the quality of transport and logistics services for operators of sea terminals. The paper identifies the factors influencing the formation of the quality of transport and logistics services in the activities of marine operators of sea terminal in modern conditions. As a result of the conducted research, a system of comprehensive quality assessment has been formed, which allows assessing the degree of customer satisfaction. A new approach to quality assessment is proposed, involving the involvement of customers in the formation of quality criteria, as well as a method for calculating and evaluating the normative level of the generalizing indicator of the quality of transport and logistics customer service of the operator of the sea terminal. The practical significance of the developed methodology is that on its basis it becomes possible to assess the consequences of implementing various management decisions to improve the business processes.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Chinese expert consensus on the diagnosis and treatment of small bowel obstruction (2023 edition)

Small bowel obstruction is a common surgical acute abdomen, with high rates of missed diagnosis, misdiagnosis, mortality and disability. The majority of patients with small bowel obstruction can be relieved by early non-operative treatment and intestinal obstruction catheter. However, there are still many controversies about the window of observation, the time of emergency operation and the method of operation. In recent years, the basic and clinical research on small bowel obstruction has made further progress, but there is no authoritative reference in clinical practice, and there is no relevant consensus and guidelines to standardize the diagnosis and treatment of small bowel obstruction in China. Accordingly, on the initiative of the Chinese Society for Parenteral and Enteral Nutrition and Enhanced Recovery after Surgery Branch of China International Health Care Promotion Exchange Association. The experts in this field of our country constitute the editorial committee, and refer to the main results of the current domestic and foreign research. According to the GRADE system of evidence quality assessment and recommendation intensity grading, the Chinese expert consensus on the diagnosis and treatment of small bowel obstruction was formulated for the study and reference of related specialties. It is expected to improve the overall level of diagnosis and treatment of small bowel obstruction in our country.

Coupling and Coordination Relationship between Urbanization Quality and Ecosystem Services in the Upper Yellow River: A Case Study of the Lanzhou–Xining Urban Agglomeration, China

The study of the man–land relationship in the urbanization process is the current frontier and focus of international research. How to balance urban development and ecosystem conservation in the Upper Yellow River is a key issue for sustainable development in China. In this study, we evaluated the Lanzhou–Xining urban agglomeration (LXUA) by constructing a multi-dimensional assessment system for urbanization quality and ecosystem services. The efficacy function model, entropy weight method, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to quantitatively assess the subsystems’ state of development. Then, the coupling model (CD) and the coordination degree (CCD) model were used to explore the coupling coordination relationship and spatial-temporal change characteristics of the composite system. The findings indicate that: (1) In 2020, the quality of urbanization in the LXUA showed the pattern of “double core”. The development of urban centers in each city is insufficient, and the proportion of counties with a low level is too high. (2) Integrated ecosystem services showed an increasing distribution pattern from the northeast to the southwest. Water provision services, soil conservation services and carbon fixation services all showed growth trends. (3) Each county’s composite system was in the run-in stage or highly coupled stage. The subsystems were closely related to each other. (4) The CCD was decreased by 6% between two decades. The number of counties on the verge of disorder was the highest. About 80% of the counties were relatively lagging behind in ecosystem services.

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

ObjectivesThe aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features.Materials and methodsIn this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features. The validity of models was assessed by the calculation of the mean squared error in a test dataset and was compared to the reading by experienced radiologists.ResultsAccuracies of the dCNN models ranged between 93.0% for the nipple visualization and 98.5% for the depiction of the pectoralis muscle in the CC view. Calculations based on regression models allow for precise measurements of distances and angles of breast positioning on mammograms and synthetic 2D reconstructions from tomosynthesis. All models showed almost perfect agreement compared to human reading with Cohen’s kappa scores above 0.9.ConclusionsAn AI-based quality assessment system using a dCNN allows for precise, consistent and observer-independent rating of digital mammography and synthetic 2D reconstructions from tomosynthesis. Automation and standardization of quality assessment enable real-time feedback to technicians and radiologists that shall reduce a number of inadequate examinations according to PGMI (Perfect, Good, Moderate, Inadequate) criteria, reduce a number of recalls and provide a dependable training platform for inexperienced technicians.