Quantitative Evaluation System Research Articles

Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback.

Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients’ motor function clinically.

Closer to Cayuga’s Waters

In recent years, an invasive plant species, Hydrilla verticillate, has been identified to pose significant threats to the local ecology of Cayuga Lake. This paper aims to provide a practical, quantitative, and real time evaluation system that helps monitor and predict the population dynamics of the plant. The system includes two main parts: sampling and simulation. In Section 3, we discuss in detail our stochastic Monte Carlo simulation based on the enhanced Invasive Plant Population Dynamic (IPPD) model. Human activities, such as boating, are identified to greatly accelerate the spread of Hydrilla. In Section 4, we employ statistical methods to promote confidence in the sample results, which in turn guarantees more accurate simulation results. We provide an adaptive algorithm to calculate the minimum sample size in real time. Other than the practical utility of our study, there is also the theoretical importance in that various approaches discussed in this paper, such as the IPPD model and the adaptive algorithm, can serve as the basis for future work on this subject.

A Community Resilience Evaluation and Optimization Strategy based on Stormwater Management

In the context of disaster normalization, the concept of “resilience” has been gradually introduced into the field of disaster prevention and mitigation in urban communities. In order to resist the increasingly frequent disasters caused by extreme weather, it is necessary to shift the focus of building resilient urban communities to the level of stormwater management. Community resilience is a disaster prevention and mitigation capability based on community resources. In order to solve the deficiency of storm and flood management in the current construction of resilient communities in China, it is necessary to establish a quantitative evaluation system to evaluate it. This paper uses the analytic hierarchy process and Delphi method to establish a community resilience evaluation system from the perspectives of community material space level, community management level and individual level. Then three communities in Hefei City, Anhui Province are selected for practical application of the system, and corresponding optimization and transformation strategies are proposed. The results show that: (1) The resilience of community stormwater management is closely related to the integrated environment of the community, the allocation of flood control facilities and the daily disaster prevention and mitigation management; (2) The ability of disaster prevention and mitigation and the awareness of public participation of the residents in all communities are relatively weak, and the communities invest less in the popularization of stormwater management wisdom; and (3) Resilient communities should not only pay attention to the construction of non-engineering disaster prevention measures, but also to the application of small-scale green infrastructure oriented toward stormwater management.

Research and Implementation of Intelligent Evaluation System of Teaching Quality in Universities Based on Artificial Intelligence Neural Network Model

Scientific and objective education quality assessment is an important demand in the current education industry. Artificial intelligence empowering various industries has become an inevitable trend for future social development. The education quality assessment system combines big data and artificial intelligence technology. The system uses various human intelligence algorithms to analyze the collected image. Text data are related to teachers’ teaching quality and can give objective results for education quality evaluation. This paper introduces the research and implementation methods of constructing the quantitative evaluation index system of university teachers’ teaching quality and its quantitative processing method as well as the intelligent quantitative evaluation system of teaching quality and explores the technical ways to discover the effectiveness of measurable factors on the evaluation of teaching quality, and the obtained results have important practical significance for the scientific evaluation of teaching quality.

A Cell Double-Barcoding System for Quantitative Evaluation of Primary Tumors and Metastasis in Animals That Uncovers Clonal-Specific Anti-Cancer Drug Effects.

Simple SummaryThe main problem in treating advanced cancers is a metastatic spread when individual cancer cells leave the primary tumor and colonize to distant organs. In drug development, it is important to quantitatively assess effects of novel drug candidates on both primary tumors and metastasis. Unfortunately, current methods of monitoring metastasis in mouse models have low sensitivity and are not quantitative. Here, we developed a methodology to monitor drug effects on metastasis that is quantitative and has a very high sensitivity and resolution. In fact, it allows monitoring effects of drugs on individual cancer cells in animals.Imaging in monitoring metastasis in mouse models has low sensitivity and is not quantitative. Cell DNA barcoding, demonstrating high sensitivity and resolution, allows monitoring effects of drugs on the number of tumor and metastatic clones. However, this technology is not suitable for comparison of sizes of metastatic clones in different animals, for example, drug treated and untreated, due to high biological and technical variability upon tumor and metastatic growth and isolation of barcodes from tissue DNA. However, both numbers of clones and their sizes are critical parameters for analysis of drug effects. Here we developed a modification of the barcoding approach for monitoring drug effects on tumors and metastasis that is quantitative, highly sensitive and highly reproducible. This novel cell double-barcoding system allows simultaneously following the fate of two or more cell variants or cell lines in xenograft models in vivo, and also following the fates of individual clones within each of these populations. This system allows comparing effects of drugs on different cell populations and thus normalizing drug effects by drug-resistant lines, which corrects for both biological and technical variabilities and significantly increases the reproducibility of results. Using this barcoding system, we uncovered that effects of a novel DYRK1B kinase inhibitor FX9847 on primary tumors and metastasis is clone-dependent, while a distinct drug osimertinib demonstrated clone-independent effects on cancer cell populations. Overall, a cell double-barcoding approach can significantly enrich our understanding of drug effects in basic research and preclinical studies.

Green hydrogen standard in China: Standard and evaluation of low-carbon hydrogen, clean hydrogen, and renewable hydrogen

With the proposal of carbon neutral goals in various countries, the deepening of global action on climate change and the acceleration of green economy recovery in the post epidemic era, building a low-carbon and clean hydrogen supply system has gradually become a global consensus. In order to promote the development of clean hydrogen market, the standards of green hydrogen have been discussed at global level. The quantitative definition of different hydrogen production methods based on the greenhouse gases (GHG) emission of life cycle assessment (LCA) methods is gradually recognised by the industry. China issued the “Standard and evaluation of low-carbon hydrogen, clean hydrogen and renewable hydrogen” in December 2020. This is the first formal green hydrogen standard worldwide, which provides calculation methods for GHG of different hydrogen production paths. This chapter discusses the major green hydrogen standards initiative in the world, analyses the key factors of the global green hydrogen standard, and introduces how to establish the quantitative standards and evaluation system of low-carbon hydrogen, clean hydrogen, and renewable hydrogen by using the method in China.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot.

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients’ upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models′ scores and the doctors′ scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.

An Optimal Evader-Pursuer Strategy based on Machine Learning and Differential Game

Based on the special differential game model of evader and pursuers, in this paper, we propose an optimal escape and containment strategies of evader and pursuers, and gives the escape boundary conditions of evader. Through the training of DDPG and A3C algorithms, the evader have the ability to escape autonomously, and the evaluation system is established with the reward value, the number of rounds, the success rate of escape and the ratio of escape speed as the main indicators. Taking different pursuer-evader speed ratios under DDPG and A3C as key indicators, a quantitative evaluation system was established, and the evaluation results verify the effectiveness of the proposed approach.

Research on Comprehensive Quantitative Evaluation System of Occupational Stress of Civil Aviation Pilots

Through theoretical analysis, expert survey and issuance of the questionnaire to all age civil aviation pilots this paper summarized several influential factors for occupational stress of civil aviation pilots and established an occupational stress risk comprehensive valuating index system. According to the comprehensive analysis of occupational stress influential factors we took 7 factors, i. e. working load factor, physiological factor, organization factor, psychological factor, environmental factor, equipment factor and incident factor as the criterion. Then an occupational stress risk comprehensive valuating index system was established. Order relation analysis method (G1 method) was used to calculate the weights of indexes. Thus occupational stress risk three grade comprehensive evaluation index system was established. To quantify the level occupational stress index by four levels each occupational stress index was respectively corresponded to different degrees of occupational stress. Civil aviation pilots occupational stress questionnaires are compiled according to the comprehensive evaluation index system. Through the questionnaire statistical analysis results of occupational stress index distribution value interval of occupational stress index corresponding to the degree of occupational stress for civil aviation pilots was divided reasonably which reflected the comprehensive quantitative evaluation of occupational stress. The results showed that the quantitative evaluation system provided a practical way for the evaluation of occupational stress level and risk for civil aviation pilots.

Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP). Objective—In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis. Method—The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features. Results - The ErrP data recorded from 11 subjects’ have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is and for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods. Significance - The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

ObjectivesAn accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)–based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB.MethodsFrom December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning–based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A “TB score” was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model.ResultsCT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08–91.05%. A moderate to strong correlation was demonstrated between the AI model–quantified TB score and the radiologist-estimated CT score.ConclusionsThe proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB.Key Points• Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis.• Artificial intelligence helps clinicians to assess patients with tuberculosis.• Pulmonary tuberculosis disease activity and treatment management can be improved.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-08365-z.

Applying Fuzzy Fault Tree Method to Evaluate the Reliability of College Classroom Teaching.

The evaluation of classroom teaching quality is closely related to the development of higher education as a scientific and effective evaluation system that can provide a solid foundation for formulating educational policies. Therefore, an evaluation model of classroom teaching quality in colleges and universities is established based on the fuzzy fault tree theory with “classroom teaching failure” as the top event to effectively evaluate the reliability of college classroom teaching and optimize the teaching strategies. In consideration of the lack of availability and dynamics of classroom teaching data, fuzzy numbers are used to describe the probability of underlying events. In addition, the top event probability of the fuzzy fault tree is calculated by the double-layer Monte Carlo method (MCM), which analyzes the classroom teaching effect based on the fuzzy fault tree reasonable. In summary, the quantitative evaluation system of classroom teaching quality based on fuzzy fault trees can evaluate classroom teaching more comprehensively and dynamically and help to improve the teaching quality of higher education.

The Background Significance and Results Combing of the Research on the Quality Evaluation System of the Party Building Work in Colleges and Universities in the New Era

The party building work of colleges and universities occupies a key position in all the work of colleges and universities. It is an important guarantee for promoting colleges and universities to fulfill the mission of cultivating people and achieve long-term and steady development. Entering the new era, under the new environment and situation, the party building work in colleges and universities is facing a series of opportunities and challenges. The current international political situation is becoming more and more severe. The domestic comprehensive and strict governance of the party continues to advance, and improving the quality of party building is an inevitable requirement of the new era and an important task of the new era. The work of party building in colleges and universities, like other grassroots party organizations, must have a scientific and reasonable quantitative evaluation system, otherwise its quality and level cannot be judged objectively. Based on the background and significance of the research on the quality evaluation system of party building work in universities, this article combs the research results of domestic scholars on the system, hoping to provide some reference for perfecting the system, improving the quality of party building work in universities and realizing the path and method of the overall development of universities.

A new quantitative evaluation system for distal radioulnar joint instability using a three-dimensional electromagnetic sensor

BackgroundThe accurate assessment of distal radioulnar joint (DRUJ) instability is still challenging as there is no established objective evaluation method. This study aimed to develop a noninvasive measurement method using a three-dimensional electromagnetic sensor system (EMS) to quantitatively assess and characterize the normal DRUJ movement in healthy volunteers.MethodsThe DRUJ movement was mimicked using both a block model and saw bone. Movement of the models was measured by EMS, and the accuracy and reproducibility of the measurements were assessed. In vivo measurement was performed in a sitting position with the elbow flexed and the forearm pronated. One sensor each was attached to the distal radial shaft and the ulnar head. The examiners fixed the distal radius and the carpal bones, moved the ulnar head from the dorsal to the volar side and measured the dorsovolar translation. The volar translation was measured by EMS and ultrasonography, and the correlation coefficient was calculated. The dorsovolar translation was evaluated in 14 healthy volunteers (7 men and 7 women) by three hand surgeons. The intraclass and inter-rater correlation coefficients (ICCs), the differences between the dominant and non-dominant sides and between men and women were assessed.ResultsThe accuracy and reproducibility assessment results of the EMS showed high accuracy and reproducibility. In the comparison between EMS and ultrasonography, the correlation coefficient was 0.920 (p = 0.16 × 10-3). The ICC (1,5) for the intra-rater reliability was 0.856, and the ICC (2,5) for inter-rater reliability was 0.868. The mean ulnar head translation and difference between dominant and non-dominant sides were 6.00 ± 1.16 mm (mean ± SD) and − 0.12 ± 0.40 mm, respectively. There were no significant differences between any of the parameters.ConclusionsA new measurement method using EMS could evaluate DRUJ movement with high accuracy, reproducibility, and intra- and inter-rater reliability. In healthy volunteers, the dorsovolar ulnar head translation was 6.00 mm. The difference between the dominant and non-dominant sides was < 1.0 mm with no significant difference. EMS provided an objective, non-invasive, real-time assessment of dynamic changes in the DRUJ. These findings could be useful in the treatment of patients with DRUJ instability.

A Three-Dimensional Scanning System for Digital Archiving and Quantitative Evaluation of Arabidopsis Plant Architectures.

A plant’s architecture contributes to its ability to acquire resources and reduce mechanical load. Arabidopsis thaliana is the most common model plant in molecular biology, and there are several mutants and transgenic lines with modified plant architecture regulation, such as lazy1 mutants, which have reversed angles of lateral branches. Although some phenotyping methods have been used in larger agricultural plants, limited suitable methods are available for three-dimensional reconstruction of Arabidopsis, which is smaller and has more uniform surface textures and structures. An inexpensive, easily adopted three-dimensional reconstruction system that can be used for Arabidopsis is needed so that researchers can view and quantify morphological changes over time. We developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method, which uses a fixed camera to capture plant images from multiple directions while the plant slowly rotates. We then developed a script to autogenerate stack images from the obtained input movie and visualized the plant architecture by rendering the output stack image using the general bioimage analysis software. We successfully three-dimensionally and time-sequentially scanned wild-type and lazy1 mutant A. thaliana plants and measured the angles of the lateral branches. This non-contact, non-destructive method requires no specialized equipment and is space efficient, inexpensive and easily adopted by Arabidopsis researchers. Consequently, this system will promote three- and four-dimensional phenotyping of this model plant, and it can be used in combination with molecular genetics to further elucidate the molecular mechanisms that regulate Arabidopsis architecture.

Analysis of Bank Credit Strategy Based on Ahp Comprehensive Evaluation

This paper builds a multi-level quantitative evaluation system for credit risk, average the weights obtained by three different methods, and use this indicator system to establish a credit risk grading model based on comprehensive evaluation to achieve credit Quantification of risk. Secondly, by establishing a comprehensive evaluation index threshold, the enterprises that exceed the threshold are removed twice; by establishing a credit amount double-mapping hierarchical ladder model, the credit risk level of the enterprise is mapped to the bank credit amount; finally, by considering the credit rationing theory Considering the relationship between the churn rate and interest rates of companies with different credit ratings, the minimum corresponding interest rate of the slope of the fitting curve is defined as the bank’s optimal interest rate, and then the interest rates of companies with different credit ratings are graded; finally, comprehensive consideration is given to whether credit, Three aspects of credit line and preferential interest rate give the specific content of bank credit strategy.

The professor support system in reality shock transition from traditional to virtual instruction in Covid-19 pandemic

Dear Editor The Covid-19 pandemic around the world made professors shocked all over the country who needed pedagogical, content and technological knowledge to overcome it. With the onset of the pandemic, professors who had to take distance education faced a severe dilemma that could lead to chaos for the university's educational leaders. In line with the efforts made throughout the country to solve this problem, the need to design a professor support system at Birjand University of Medical Sciences (BUMS) was also felt. Some efforts which were done in BUMS in this regard include: knowledge support, technical support, forming a support group, designing e-learning courses, development of virtual education in laboratory courses, providing preparation for taking student exams in a completely virtual way and development of a system for quantitative and qualitative evaluation of professors' educational performance during the course. As a conclusion, existing a professor support system is a crucial element for academic leaders, especially in a crisis. Besides, the evaluation of professors� performance and distinguishing good and poor performance between them is very effective in motivating them and it will lead to more accountability. Obviously, students in this situation also need a student support system so that they could overcome their problems.

System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)

This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.

Research on visual‐tactile cross‐modality based on generative adversarial network

Aiming at the research of assisted blind technology, a generative adversarial network model was proposed to complete the transformation of the mode from vision to touch. Firstly, two key representations of visual to tactile sense are identified: the texture image of the object and the audio frequency that generates vibrotactile. It is essentially a matter of generating audio from images. The authors propose a cross-modal network framework that generates corresponding vibrotactile signals based on texture images. More importantly, the network structure is an end-to-end, which eliminates the traditional intermediate form of converting texture image to spectrum image, and can directly carry out the transformation from visual to tactile. A quantitative evaluation system is proposed in this study, which can evaluate the performance of the network model. The experimental results show that the network can complete the conversion of visual information to tactile signals. The proposed method is proved to be superior to the existing method of indirectly generating vibrotactile signals, and the applicability of the model is verified.