Model For Evaluation Of Effects Research Articles

The Teaching Effect Evaluation of Big Data Analysis in Music Education Reform

At present, the evaluation of the implementation effect of the music teaching reform project is a hot topic. This project establishes a teaching reform implementation effect evaluation model based on improving the accuracy of teaching reform plan effectiveness evaluation. This project first analyzes the current research status of the impact evaluation of the implementation of music education reform projects and builds a set of evaluation systems. The evaluation method of teaching reform plan implementation effect based on a ”big data-driven - support vector machine” was constructed. The simulation experiment verifies that this method can evaluate the implementation of the teaching reform plan well. The relative error of the evaluation results is small. It is practical in improving the effectiveness evaluation of the teaching reform plan. After verification, it is found that the research results of this project are feasible.

An adaptive operation planning and EBO-BPNN optimization method for decision support systems

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application.

How does carbon emissions trading scheme affect the investment efficiency of companies? Evidence from China

Carbon emissions trading scheme (ETS) will change the internal and external operating environment of related companies, increase new carbon reduction costs, and may affect their investment behavior and decision-making. This study constructs the policy effect evaluation model to evaluate the net effect of China's ETS pilot policy on the investment efficiency of related companies in high-carbon industries. This study indicates that: first, China's ETS pilot policy has reduced the inefficient investment of related companies by 11.40% during the sample period. Second, innovation ability plays an enhanced moderating effect in the inhibitory effect of China's ETS pilot policy on the inefficient investment of related companies in the short term, but the effect is weakened in the long-term. Third, the policy effect is relatively obvious for non-state-owned companies and companies with more institutional shareholdings. This research has important reference value for the continuous improvement of China's national carbon market, and the steady transformation from simple investment-driven to investment-efficiency-driven economic development in China.

The Regulation Mechanism and Evaluation Model of the Hydrogen Isotope Effect for Zr-Ti-Co Alloys during the Hydrogen/Deuterium Desorption Process at High Temperatures

The Regulation Mechanism and Evaluation Model of the Hydrogen Isotope Effect for Zr-Ti-Co Alloys during the Hydrogen/Deuterium Desorption Process at High Temperatures

Application of Clustering Algorithm in the Evaluation of College Students' English Reading Ability Under the Framework of Big Data

In recent years, China has accelerated the process of internationalization and made more and more achievements in transnational communication and cooperation. English learning is very important for contemporary college students. And English reading is an important means to acquire English language knowledge, understand external information and improve English language practice ability. Therefore, cultivating college students' English reading ability is a pivotal element in college English teaching. This study will focus on the application of clustering algorithm based on big data framework in the evaluation of college students' English reading ability, aiming at exploring and establishing an effective evaluation model to promote the improvement of college students' English reading ability and academic achievement.

Exploration of Issues in Online Learning for Undergraduates in Sichuan Province and Construction of an Evaluation Model

Online learning gradually becomes an important way for people to acquire knowledge. The current study aims to construct an effective online learning evaluation model to enhance online learning. In order to achieve the research aim, this study interviewed 21 respondents regarding online learning to analyze the current problems in the development of online learning among undergraduate students in Sichuan Province. Secondly, the Delphi method was used to establish a graphical model based on the feedback from 21 experts. Finally, referring to the CIPP evaluation principle, 5 experts evaluated the online learning evaluation model. Research results show that online learning suffers from low student engagement and motivation, limited teaching content and methods, inadequate personalized teaching know-how, insufficient online teaching capabilities, imperfect privacy and security, technical environment and conditions mismatched, and not available resource updates and sharing, etc. To solve the above-mentioned problems, the current study constructed an online evaluation model which comprised four core components, namely, students, teachers, technical support, and learning resources. For students, this model encourages students’ learning engagement, family support, and learning desire. At the same time, this model takes teaching method, instructional design, and teaching feedback into consideration. Moreover, this model provides support for technical support, platform availability, and technical security. Last but not least, this model generates learning resources with appropriate content, interactive tools, and managing shared resources

A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions

A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions

PDHL-MARCOS method based on cumulative prospect theory for MAGDM and its application in network security evaluation

The increasingly large and complex network brings convenience to our life and production, but it also brings potential dangers. Any minor vulnerability may lead to failure of application software configuration, software maintenance, user management and system management. Eventually, our network is interrupted, and even life and production data are leaked. Therefore, establishing a scientific and effective network security evaluation model is an effective way to supervise, manage and improve network security; It is also an effective measure to reduce the loss of life and production. Therefore, we first selected six factors closely related to network security to form a complete network security evaluation index system. Then, the utility function in the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is improved by using the value function in cumulative prospect theory (CPT), and a PDHL-MARCOS evaluation model that can reflect the decision-makers’ (DMs’) attitudes towards gains and losses is obtained in the probabilistic double hierarchy linguistic term set (PDHLTS). In addition, the relative entropy (RE) method is also used to obtain expert weights. The combination weights of attributes are obtained using the correlation coefficient and the standard deviation (CCSD) method and the weight function in CPT.

Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Construction of a Multimedia Instructional Effect Assessment System of College Foreign Language Translation Based on Artificial Intelligence

The teaching evaluation module is an important guarantee of teaching effectiveness, and as an important part of the multimedia teaching system, it needs special attention. This paper combines artificial intelligence and multimedia teaching effect evaluation, proposes a foreign language translation multimedia teaching effect evaluation model based on improved support vector machine (SVM) algorithm, and provides theoretical support for the construction of foreign language translation multimedia teaching effect evaluation system. The results show that the accuracy of this assessment algorithm is improved by 22.18% compared with the traditional assessment algorithm. Therefore, it is theoretically feasible to use the improved SVM to analyse the application effect of multimedia teaching mode in foreign language translation teaching. It can be found that the automatic acquisition of model data, the cumulative search of spatial knowledge and the adaptive control of the search phase of the optimised SVM algorithm have also been significantly improved, and conclusions drawn are more reliable.

Evaluation Model of Sports and Health Knowledge Dissemination Effect Based on Social Media Data and Sentiment Analysis

In the contemporary era, social media platforms serve as prominent channels for the dissemination of sports and health-related information. Understanding the effectiveness of such dissemination efforts is crucial for optimizing public health interventions and promoting well-being. This paper proposes an innovative evaluation model that harnesses social media data and sentiment analysis techniques to assess the dissemination effect of sports and health knowledge. By leveraging sentiment analysis, the proposed model delves deeper into the public's perceptions and attitudes towards sports and health-related content on social media platforms. Through the integration of advanced analytics, including machine learning algorithms, the model provides insights into the reach, engagement, and impact of information dissemination campaigns. The application of this evaluation model facilitates evidence-based decision-making for policymakers, health practitioners, and stakeholders involved in sports and health promotion initiatives. This paper outlines the theoretical framework, methodology, and practical applications of the proposed evaluation model, offering a comprehensive approach to evaluating the effectiveness of knowledge dissemination efforts in the digital age.

Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies.

Construction and Application of a College English Blended Teaching System Based on Multi-Source Data Fusion

By using modern data analysis techniques, this study aims to construct an innovative university English teaching effectiveness evaluation model based on particle swarm algorithm and support vector machine. The model is designed to improve assessment accuracy and personalization. The research process includes the methodology of data collection, preprocessing, model construction and evaluation. The experimental results show that the model can more accurately assess students' English learning effectiveness and provide customized suggestions for personalized education. This research is important for improving the quality of university English education, promoting personalized learning, and providing support tools for educational decision makers.

Manipulation and Wakefield Effects on Multi-Pulse Driver Beams in PWFA Injector Stages

Particle-driven plasma wakefield acceleration (PWFA) exploits the intense wakefields excited in a plasma by a high-brightness driver beam in order to accelerate a trailing, properly delayed witness electron beam. Such a configuration offers notable advantages in achieving very large accelerating gradients that are suitable for applications in particle colliders and photon production. Moreover, the amplitude of the accelerating fields can be enhanced by resonantly exciting the plasma using a multi-pulse driver beam with a proper time structure. Before the injection into the plasma stage, the pulsed electron beam, conventionally termed the comb beam, is usually produced and pre-accelerated in a radio-frequency (RF) linear accelerator (linac). In this pape, we discuss challenging aspects of the dynamics that comb beams encounter in the RF injector stage preceding the plasma. In particular, the examples we analyze focus on the use of velocity bunching to manipulate the time structure of the beam and the impact of dipole short-range wakefields on the transverse emittances. Indeed, both processes crucially affect the phase space distribution and its quality, which are determinant features for an efficient acceleration in the plasma. In addition, the analyses we present are performed with the custom tracking code MILES, which utilizes semi-analytical models for a simplified evaluation of wakefield effects in the presence of space charge forces.

Retracted: Evaluation Model of Enterprise Lean Management Effect Based on Data Mining

Retracted: Evaluation Model of Enterprise Lean Management Effect Based on Data Mining

Objective and subjective evaluation of the efficacy of trans-sutural distraction osteogenesis: A correlation analysis between computed tomography measurements and FACE-Q scores

Previous studies on the efficacy of trans-sutural distraction osteogenesis (TSDO) to treat midface hypoplasia caused by cleft lip and palate (CLP) have mainly focused on objective measurements while ignoring the subjective feelings of patients. This study aimed to analyse the changes in and correlation between computed tomography (CT) measurements and FACE-Q scores in patients who underwent TSDO by performing a comprehensive evaluation from both objective and subjective perspectives. This retrospective study included 25 patients with an average age of 10.7 years who had midface hypoplasia caused by CLP and underwent TSDO between August 2018 and December 2022. The average follow-up time was 18.8 ± 7.7 months. Facial morphology and CT measurements, including A-CR, N-A⊥HR, the SNA angle and the L-ZA, indicated significant improvements in midface concavity (all p < 0.0001). All FACE-Q scores (except for facial function) exhibited a significant increase. The ΔA-CR, ΔN-A⊥HR, and ΔSNA angle were strongly correlated with specific aspects of the FACE-Q-Appearance items, including the ΔFACE-Q-Appearance of the cheeks (all p < 0.0001), the ΔFACE-Q-Appearance of the face (all p < 0.0001), the ΔFACE-Q-Appearance of the jaws (all p < 0.01), the ΔSatisfaction with decision (all p < 0.0001) and the ΔSatisfaction with outcome (all p < 0.001). However, the ΔA-CR, ΔN-A⊥HR, and ΔSNA were weakly correlated with other FACE-Q-Health-related quality of life and function items. These findings suggest that both CT findings and FACE-Q scores have their own emphases and advantages. It is necessary to establish an integrated curative effect evaluation model that combines FACE-Q scores with CT measurements to evaluate both the physical health and psychological status of patients.

Construction of College English teaching effect evaluation model based on artificial intelligence and output-oriented approach

Construction of College English teaching effect evaluation model based on artificial intelligence and output-oriented approach

Evaluation Model of the Mental Health Education Effectiveness Based on Deep Neural Networks

This research develops a deep neural network model called DNN-MHE to evaluate mental health education effects. A questionnaire survey collected data on 916 students' mental health knowledge, attitudes, and behaviors. DNN-MHE uses five fully connected layers to predict mental health metrics. Experiments demonstrate that DNN-MHE achieves 99.46% accuracy, outperforming RNN, CNN, and shallow MLP models. Ablation studies validate the impact of training iterations, number of neurons, and number of data samples on performance. Overall, DNN-MHE enables accurate and efficient analysis of mental health education with practical implications for improving university programs.

A mixed methods evaluation of the effectiveness of an oral health training program for disability care workers in Burkina Faso

BackgroundWhile efforts to improve the oral health of vulnerable populations have received little attention in general, the situation of children with disabilities in low- income countries (LICs) remains especially challenging. The present study evaluated the effectiveness of an oral health training provided to disability care workers in Ouagadougou, Burkina Faso thereby contributing to closing the knowledge gap in disability research in relation to oral health in LICs.MethodsThis was a single-arm pre-post study following an embedded mixed methods design using the New World Kirkpatrick training effectiveness evaluation model. For the purposes of this study, three levels of the Kirkpatrick (KP) evaluation were considered: reaction, learning and behaviour.ResultsA total of 44 care workers from 6 disability centres participated in the study. Care worker post-training scores (Md = 17) were significantly higher compared to pre-training scores (Md = 13) [Wilcoxon signed-ranks test: Z= -5.53, p < .001, r = .59.] The median value for care worker confidence in applying training material in their everyday job was 7 out of 10 points (IQR = 3). At the 1-month training follow-up, 3 centres had implemented daily toothbrushing for people with disabilities.ConclusionThese findings suggest that tailored training led to an increase in care worker confidence and motivation to implement oral health activities, in knowledge about oral health and a partial implementation uptake of daily toothbrushing in disability centres. Further long-term evaluations with dental care provision in rural and urban settings are needed to lower the high oral disease burden of people with disabilities in Burkina Faso.

Creation of Deep Learning Scenarios in the Network Teaching of Physical Education Technical Courses

The network teaching evaluation of sports professional technical courses has positive significance for the sustainable development of education. And how to establish an effective evaluation model is the key part. The research introduces the creation of in-depth learning scenarios (LS) into the network teaching of sports professional technical courses, and then constructs a new network teaching mode of sports professional technical courses. The Particle Swarm Optimization algorithm - Attention- Long Short-Term Memory network (PSO-Attention-LSTM) Chinese Emotion Classification Model (ECM) is constructed to classify the online evaluation text to realize the evaluation of online teaching. This model combines the improved PSO, Attention and LSTM classification models. The optimal number of hidden layer nodes for LSTM model is about 100, and the optimal data size for batch processing is 25. The overall error rate of online teaching teachers of male and female sports professional skill courses is 10.1%, and the overall error rate of online teaching teachers of general and advanced sports professional skill courses is 12.8%. The application effect of the creation of in-depth learning scene in the network teaching of physical education technical courses is shown. When the classification threshold is 0.6 and 0.8 respectively, the AUC of PSO-Attention-LSTM Chinese ECM is 0.821 and 0.809 respectively. The research institute has put forward that the online teaching platform of sports professional technical courses has extremely high practical application value and has been unanimously praised by network users.