Comprehensive Evaluation Algorithm Research Articles

MECOM Locus classical transcript isoforms affect tumor immune microenvironment and different targets in ovarian cancer

The MECOM locus is a gene frequently amplified in high-grade serous ovarian carcinoma (HGSOC). Nevertheless, the body of research examining the associations among MECOM transcripts, patient prognosis, and their role in modulating the tumor immune microenvironment (TIME) remains sparse, particularly in large cohorts. This study assessed the expression of MECOM transcripts in 352 HGSOC patients and 88 normal ovarian tissues from the combined GTEx/TCGA database. Using resources such as the UCSC Genome Browser, Ensembl, and NextProt, two transcripts corresponding to classical protein isoforms from MECOM were identified. Cox proportional hazards regression analysis, Kaplan-Meier survival curves, and a comprehensive TIME evaluation algorithm were employed to elucidate the connections between the expression levels of these transcripts and both patient prognosis and TIME status. Chromatin Immunoprecipitation sequencing (ChIP-seq) data for the two protein isoforms, as well as RNA sequencing data post-targeted silencing, were analyzed to identify potential regulatory targets of the different transcription factors. Elevated expression of the MECOM isoform transcripts was correlated with poorer survival in HGSOC patients, potentially through the modulation of cancer-associated fibroblasts (CAFs) and immunosuppressive cell populations. In contrast, higher levels of EVI1 isoform transcripts were linked to enhanced survival, possibly due to the regulation of CD8+ T cells, macrophages, and a reduction in the expression of JUN protein, or its DNA-binding activity on downstream genes. Diverse protein isoforms derived from MECOM were found to differentially affect the survival and tumor development in ovarian cancer patients through specific mechanisms. Investigating the molecular mechanisms underlying disease pathogenesis and identifying potential drug target proteins at the level of splice variant isoforms were deemed crucial.

Idiopathic Ventricular Fibrillation - Just How Much Idiopathic is it?

Idiopathic ventricular fibrillation is diagnosed in survivors of sudden cardiac death that has been caused by ventricular fibrillation without known structural or electrical abnormalities, even after extensive investigation. It is a common cause of sudden death in young adults. Although idiopathic ventricular fibrillation is a diagnosis of exclusion, in many cases only a partial investigation algorithm is performed. The aim of this review is to present a comprehensive diagnostic evaluation algorithm with a focus on diagnostic assessment of inherited arrhythmic syndromes and genetic background.

Research on Student Learning Attitude Evaluation Model Based on TOPSIS Comprehensive Evaluation Algorithm

In order to ensure the quality of teaching in universities, this article uses the TOPSIS comprehensive evaluation algorithm to help analyze the learning attitude of school students, as well as the class atmosphere and academic atmosphere of the college. At the same time, a list of five classes with the best academic atmosphere, three colleges, and sixty students with positive and negative learning attitudes were selected. Firstly, this article conducts a quantitative analysis of the scores and participation in the tests of 9048 students. The article identifies three factors that reflect students' learning attitudes: average test score, standard deviation, and participation rate in the tests. Then, based on student test data, an evaluation model reflecting the academic atmosphere, class atmosphere, and student enthusiasm of the college was established based on student performance information. The model results were ranked to obtain the ranking of model scores for each college, class, and student. By studying student data and quantifying the characteristics that reflect their learning attitudes, three indicators that can effectively reflect student attitudes and enthusiasm were established by analyzing the attached data. When assigning weights to factors, it is necessary to consider the importance of the factor in describing student attitudes, and make judgments and trade-offs based on specific situations. In response to this situation, although the standard deviation can reflect the stability of students' grades, it is greatly limited by the number of tests, while test completion and average score can better reflect students' learning attitude and performance. Therefore, subjective weighting method can be considered to assign weights to these three indicators, which are average score, standard deviation, and test participation rate, respectively; 0.3, 0.2, 0.5. By using the TOPSIS method and combining three indicators, establish an evaluation model for the academic atmosphere of the college, a class atmosphere evaluation model, and a student motivation evaluation model. Finally, evaluate the academic atmosphere of each college, class atmosphere, and the learning enthusiasm of each student. Based on the evaluation results of the model, this article selected a list of five classes with the best academic atmosphere, three colleges, and sixty students with positive and negative learning attitudes.

An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach

AbstractThis paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems.

Analysis of the application of BIM combined with GIS in engineering digital management

Abstract This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object, and evaluates and predicts the grade of the danger of slope disaster and mudslide disaster in this area. The study begins with an in-depth analysis of the integrated fusion of BIM and GIS technologies. The integrated assessment and prediction model of geologic hazards was constructed using fuzzy comprehensive evaluation and support vector machine algorithm. The study unfolded the analysis of the hazard class and risk prediction of geohazards. The results showed that most of the hazard classes in slope hazards were assessed as low danger, accounting for 33.33%. Meanwhile, about 70% of the mudslide hazards were assessed to have a low to medium hazard rating. Regarding risk prediction, the prediction accuracy of slope and mudslide hazards reached 89% and 90%, respectively. In addition, the detection rate of all geohazards in the ROC (receiver operating characteristic) curve was above 85%, while the false alarm rate remained between 14% and 22%. This result verifies the accuracy of the model in geohazard risk prediction. The comprehensive assessment and prediction model proposed in this paper will help the risk assessment and prediction of geohazards, provide adequate technical support for managing disaster information, and enhance the science and rationality of disaster prevention and mitigation.

The Establishment and Quality Improvement Effect of Physical Education Teaching Evaluation System in higher education institution

Abstract The assessment findings are given too much weight in the current system of evaluation for physical education instruction at higher education institutions. In contrast, the method of teaching and students’ basic health literacy are given insufficient weight. To address this phenomenon, this paper utilizes educational evaluation theory, fuzzy comprehensive evaluation theory, and multiple evaluation theory as the basis for setting up the higher education institution’s physical education teaching assessment system. Three first-level indexes comprise the entire index system, namely, “condition quality,” “process quality,” and “result quality.” Through fuzzy comprehensive evaluation, hierarchical analysis, and genetic algorithm, it is determined that the “process quality” is the most important factor. The “process quality” has the highest weight coefficient of 0.4419, which enhances the importance of the physical education teaching process in higher education institutions. After determining the evaluation index system, the average physical education scores of X physical education classes in University H were calculated by conventional teaching evaluation means and the evaluation means established in this paper, respectively, and compared and analyzed. The difference between the two final average scores was 2.9, which was attributed to the fact that the multivariate evaluation means paying more attention to the core literacy of students’ health. The standard deviation of the statistical results is 2.3, and the skewness, kurtosis, and S-W test values meet the statistical requirements and satisfy the normal distribution, which is thorough and scientific and can encourage academic institutions to pay more focus on how they teach and students’ fundamental physical literacy. These institutions can enhance their instruction by implementing the evaluation system established in this paper, which will improve the quality of instruction and learning in higher education sports.

Research on the reform and effect evaluation of three-dimensional motion animation blended teaching based on fuzzy evaluation model

Abstract This paper combines the teaching of a 3D animation course with the blended teaching mode and constructs a framework for the design of 3D animation blended teaching reform. Based on the fuzzy comprehensive evaluation algorithm, we adopt qualitative and quantitative methods to determine the evaluation index system of teaching reform, divide the course teaching objectives, teaching content, learning resources, online learning and teaching design and classroom organization into five first-level indexes, establish the weights of the evaluation index system, and complete the construction of the fuzzy comprehensive evaluation system of 3D animation blended teaching. Examine the design and teaching effects of 3D animation that blends learning from different dimensions, and examine the correlation between learners’ multidimensional recognition of teaching reform and its learning impact. By combining the weights of each index, the fuzzy comprehensive evaluation results are obtained by determining the affiliation degree of each index using the fuzzy statistical method. The operation results show that the fuzzy comprehensive evaluation scores of the five first-level indicators of teaching reform are 0.754546, 0.69943, 0.638926, 0.66888, and 0.5966. It can be seen that the comprehensive evaluation results of the five aspects have achieved good results. The evaluation of teaching reform’s effects can be targeted and feasible using a fuzzy comprehensive evaluation model.

Online and offline hybrid teaching evaluation and monitoring system based on hierarchical analysis method and fuzzy comprehensive evaluation algorithm

Online and offline hybrid teaching evaluation and monitoring system based on hierarchical analysis method and fuzzy comprehensive evaluation algorithm

Influencing Factors and Realization Strategies of Teachers’ Professional Development in Higher Vocational Colleges and Universities Based on the Background of Deep Learning

Abstract The fuzzy comprehensive evaluation algorithm is employed in this paper to evaluate teachers’ professional competence and improve the traditional evaluation system to construct a comprehensive evaluation system. According to the Zagan theory, twenty-three influencing factors of teachers’ professional development were screened, and five main influencing factors were derived after exploratory factor analysis and structural equation test. Countermeasures and suggestions were proposed after analyzing the factors that affect the professional development of teachers in higher vocational colleges. The results show that the model KMO value is 0.923, Bartlett’s spherical test chi-square value is 2712.752, and the significance probability value P=0.000<0.005 is statistically significant. The correlation coefficients between the five main factors are in line with the condition of correlation attribute defined among the factor analysis variables, with a range of 0.37 to 1.00. The optimization countermeasures proposed in this paper have important theoretical significance and practical significance for the professional development of teachers in higher vocational colleges.

Innovation of tax incentives in the context of integration of traditional industries and digital economy

Abstract With the development of the digital economy, the innovation of the tax preference method is necessary. This paper integrates traditional industries with the digital economy through digital technology, standardizes the tax preference data of digital industries by using a fuzzy comprehensive evaluation algorithm, and builds an innovative mechanism of tax preference by establishing two weighting indicators of tax relief ratio and Q value (tax relief locational entropy) through data feedback. To ensure the accuracy of the feedback indicators, the full-factor algorithm is invoked to detect the intensity of the mechanism’s preferences in the digital industry. To verify the effectiveness of the mechanism, the results of the practical analysis show that after the innovation mechanism constructed in this paper is put into practice, the proportion of tax breaks in two digital industries, manufacturing and industry, increases by 29% and 34%, respectively, and the Q values of the five industries increase by about 4.2% on average, and the yields all increase by about 5.12 percentage points. This illustrates that the tax preference innovation mechanism constructed in this paper can accelerate the economic development of industries and is conducive to giving full play to the positive incentive effect of tax preference on their material benefits.

Research and Application of Multi-model-based Comprehensive Evaluation and Scale Prediction Algorithm for Eco-Regional Economy

This paper establishes a mathematical model to analyze the data of Saihanba area, and establishes a prediction and evaluation model to evaluate the ecological zone planning and carbon neutrality. This paper reviewed a large number of literature and screened out 15 factors, then established a factor screening model based on Pearson correlation coefficient, drew a correlation heat map, and then screened out 8 factors to establish a model for evaluating the impact of Sehampa on the ecological environment based on EWM-TOPSIS. Thirty-four major observation points in China are selected for data collection, then the environmental scores of the 34 observation points are calculated based on the 2020 data, and then the scores are visualized as heat maps. Finally, this paper establishes a time-series prediction model to compare and predict China's current carbon stock, with a view to providing some reference for other fields.

A study on the application of using big data information technology to implement physics laboratory courses in universities

Abstract In today’s fast development of big data information technology, the traditional physics experiment method is difficult to adapt to the new curriculum reform physics experiment course objectives. The key topic of concern today is how to use big data information technology and physics experiment course integration for teaching. This study mainly uses the fuzzy comprehensive evaluation algorithm in big data information technology to study and analyze the physics laboratory courses in colleges and universities. Firstly, according to the principles and methods of the fuzzy comprehensive evaluation method, the model of a fuzzy comprehensive evaluation of university physics laboratory teaching is established, and the index system and evaluation level criteria of university physics laboratory teaching are established. Then the weights of each factor of the index system were determined using fuzzy comprehensive evaluation, and the evaluation results combining qualitative and quantitative were obtained by processing the data. Finally, the results were obtained regarding teaching attitude, teaching content, and teaching effect: the weights of good 55%, 64% and 38% performed better than other evaluation grades. Regarding teaching implementation: 54% weight of good evaluation is greater than other evaluation grades. From the evaluation grade, the average weight of a good grade is 48.25% greater than the other three categories, and the weight performance is better. This study is conducive to improving the experimental application ability of college students and thus has important historical significance for the development of physics in China.

Light pollution risk level assessment based on AHP-fuzzy comprehensive evaluation algorithm

Light, which helps us to see better in the dark, exists in many forms. However, on the one hand it does have benefits for us in our daily life and on the other hand the use of unrestricted artificial light can cause some damage to our body and wildlife life. In this paper, we try to develop a model that can move from qualitative to quantitative assessment. 13 typical and common causes of light pollution are selected for further study by AHP-Fuzzy Integrated Evaluation. We divided the 13 causes into 3 types, including human activities, natural environment and ecological factors, and used hierarchical analysis to give the weight of each indicator and construct a judgment matrix M-C to finally obtain the final weight of each factor. After this, we built an evaluation set containing five levels. Using the weight scores for fuzzy evaluation, according to the maximum subordination principle, we obtained an overall evaluation result: M=C*η×100.

Air data computer simulation and design credibility assessment considering ADC calculation model

Abstract Air data computer simulation system is a platform for studying aircraft air data system. It can also replace real air data computer and can be used in the experiment of ground integrated avionics system, which greatly reduces the cost and risk of the experiment. However, when the simulation system replaces the real system, whether its reliability, integrity and other indicators can meet the requirements becomes the key to the problem. Based on computer simulation theory and simulation credibility evaluation theory, this paper designs and implements two types of atmospheric data computer simulation systems and evaluates the credibility of the simulation systems. The basic theory of computer simulation and the general process of simulation are expounded, the evaluation theory of simulation credibility is introduced, and the evaluation calculation method of simulation credibility is given. The results show that the highest reliability of the reliability evaluation using the method in this paper reaches 90%, the highest error rate is 0.06%, and the average accuracy, efficiency and complexity of the hundred experiments are 97.31% and 97.00% and 0.154%. The highest reliability of the subjective comprehensive evaluation algorithm for reliability evaluation is 65%, the highest error rate is 0.27%, and the average accuracy, efficiency and complexity of the hundred experiments are 70.59% and 69.74%, and 0.502%. Therefore, the method in this paper not only has high reliability, low error rate, but also has strong comprehensive effectiveness.

Analysis of response strategies for triple La Niña events based on fuzzy evaluation algorithm

The climate anomalies caused by La Niña events have caused some economic losses and human casualties in several regions around the world. In this paper, we first visualize and analyze the climate data of four representative countries to obtain the impact of La Niña on different countries. Then a BP neural network model is built to predict the probability of La Niña events in different regions. Finally, taking six different provinces in southern China as examples, an indicator system is established, a fuzzy comprehensive evaluation algorithm is used to summarize the impacts of different regions and indicators under a La Niña event, and practical response strategies are proposed.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) have been applied in many civilian and military scenarios, but it is vulnerable to various security threats due to the broadcast transmission characteristics and the environment in which they are located. Trust management mechanism has been proven to be an effect way to improve network security. However, in the trust assessment process, some nodes may provide false suggestions, which will lead to trust conflicts and affect the normal operation of the trust mechanism. To screen out unreliable recommendations and dishonest nodes in the network and avoid potential dangers, a recommendation management trust mechanism based on collaborative filtering and variable weight fuzzy algorithm (CFFTM) is proposed in this paper. First, three kinds of evidence of trust: communication-based evidence, data-based evidence, and energy-based evidence are select as indicators. Then the variable weight fuzzy comprehensive evaluation algorithm is applied to calculate the direct trust value of the node. Secondly, in order to quantify the honesty ability of nodes, honesty degree is defined. Then the overall recommendation trust value of the node is obtained using the proposed collaborative filtering algorithm. The simulation results show that the mechanism can well filter out unreliable recommendations and improve the recognition rate and stability of the trust model under typical attack scenarios.

Comprehensive evaluation of corroded grounding electrodes considering the impulse characteristics

The grounding electrodes are buried in a harsh soil environment, which will cause corrosion. Corroded grounding electrodes directly affect the safe and stable operation of the power system. Therefore, it is particularly important to detect the corrosion degree of the grounding electrodes accurately. The existing methods have the following problems: 1) Power-frequency grounding resistance is used as a single criterion, and lack of considering the impulse characteristics, which is difficult to accurately judge the corrosion degree and could cause a potential safety hazard. 2) Lacking considering the environmental PH value, water content, salt content, and other factors of the buried soil, which is easy to lead to misjudgment of corrosion degree. To solve the problems, this paper studies a comprehensive evaluation of corroded grounding electrodes considering impulse characteristics. Based on thermal stability, impulse characteristics, and power frequency characteristics, the minimum safety diameter of grounding electrodes and the factors that affect the corrosion of the soil environment are studied. According to the relationship between each evaluation factors and corrosivity, the membership is calculated and combined with the comprehensive weight determined by the sum of the squares of deviation. The fuzzy evaluation method is adopted to obtain the corrosive evaluation matrix Y, and the corrosive index C is proposed to quantify the corrosive strength. Combined with the minimum safety diameter, the residual life range of the grounding electrodes is predicted and the corrosion degree is divided. Finally, a comprehensive corrosion evaluation algorithm considering impulse characteristics is proposed. The results show that this method not only considers the soil environmental factors but also considers the grounding characteristics of the grounding electrodes, especially the impulse characteristics. This method is more comprehensive than other evaluation methods which only consider the soil environment and can realize trenchless corrosion detection of grounding electrodes. Therefore, the contribution of this work is of great significance to the operation and maintenance of tower grounding electrodes.

A hunger-based scheduling strategy for distributed crawler

Distributed crawling is one of the mainstream text data collection technologies, which is essential for mining boundless data available on the Internet for users. Internet information is clustered by the correlation of keywords, and users employ search engines to retrieve relevant keywords to get the information they care about. Distributed crawlers are used to mine the Internet information by simulating users’ behavior, the more important keywords that users care about, the higher the correlation of data to keywords. In order to preferentially collect information that users care about with minimal resource consumption, in this paper, we design a scheduling framework and propose a novel scheduling strategy based on hunger for distributed crawler. We first define the load capacity of distributed crawler as hunger which reflects the ability to complete tasks and divide keywords queues into sub-queues based on the hunger of distributed crawlers. Then, we use a vector space model and cosine similarity algorithm to learn the correlation of keywords to text data and apply the optimized logistic algorithm to measure the importance of keywords. Meanwhile, we design a comprehensive evaluation algorithm to quantify the contribution of keywords, so that updating sub-queues order. Finally, new sub-queues are used in the deeper scheduling to preferentially get data that users desire and sacrifice the least number of resources. Experimental results demonstrate that our method optimizes the scheduling procedures and makes crawling more efficient with less run time.