Multi-method Integration Research Articles

Exploration of aesthetic education path for college students based on cognitive multi-method integration of innovation and entrepreneurship cultivation

Abstract This paper builds a cultivation system of innovation and entrepreneurship ability, and puts forward the corresponding cultivation mode and implementation path. At the same time, under the implementation conditions, a multiple linear regression model is used to explore the influence of the integration of aesthetic education on innovation and entrepreneurship ability. The results show that aesthetic interest, aesthetic perception, innovation consciousness and innovation and entrepreneurship ability cultivation are significantly positively correlated, with correlation coefficients of 0.039, 0.025, 0.12 respectively. Multiple covariate diagnosis shows that the model fit is good, the value of VIF is more than 3, and that the influence of aesthetic interest, aesthetic perception, and innovation consciousness on college students’ entrepreneurial intention is significant. In addition, regression analysis showed that the standardized regression coefficient of aesthetic interest on the cultivation of innovation and entrepreneurship was −0.113 (P<0.001), and the standardized regression coefficient of innovation awareness was −0.128, which indicated that aesthetic education had a significant favorable influence on the cultivation of innovation and entrepreneurship. The study verifies the effectiveness of the innovation and entrepreneurship ability cultivation system incorporating aesthetic education. It provides data support for implementing innovation and entrepreneurship education in colleges and universities.

Exploration of college teacher education path based on cognitive multi-method integration for innovation and entrepreneurship ability cultivation

Abstract In this paper, a cognitive multi-method integration-based approach combined with a genetic algorithm for optimization is used to set the random variables as Bayesian network nodes, and the joint probability distribution is obtained by analyzing the strength of association between the nodes. An independently distributed observation data set with random variables is used to determine the log-likelihood function. After generating the random data, the roulette wheel is used to select the individuals with higher fitness values for transformation, and the excellence of the individuals in the population is improved through cyclic iteration. 20 points improved the educational practice’s 4th test score compared to the pre-training score after the innovation and entrepreneurship training. Therefore, the method of cultivating innovation and entrepreneurial abilities based on cognitive multi-method integration has a positive effect on improving the education level and talent cultivation quality of college teachers.

A Bibliometric Analysis of Sustainable Product Design Methods from 1999 to 2022: Trends, Progress, and Disparities between China and the Rest of the World

Effective product design strategies play a crucial role in promoting sustainable production, consumption, and disposal practices. In the literature, many such practices have been proposed by various researchers; however, it is challenging to understand which is more effective from the design point of view. This study employs bibliometric analysis and visualization software, CiteSpace, to comprehensively assess the literature on sustainable product design methods (SPDMs) from two major citation databases, namely, China National Knowledge Infrastructure and Web of Science, covering the period between 1999 and 2022. The objective of this review is to identify the latest research trends, progress, and disparities between China and the rest of the world in the field of SPDMs. The findings reveal that the development of SPDMs is characterized by a combination of multi-method integration and expansion, as well as qualitative and quantitative hybrids. However, research processes differ between China and other countries. Chinese studies focus on digital-driven development, rural revitalization, and system design, while research from other countries emphasizes a circular economy, distribution, additive manufacturing, and artificial intelligence. Nevertheless, both Chinese and international studies lack quantitative research methods in relation to socio-cultural sustainability. Future research should aim to deepen sustainable design methods and standards for specialized products, as well as to incorporate quantitative methods that address cultural and social sustainability dimensions. Open-source and shared SPDMs should be encouraged to promote methodological innovation that prioritizes multidimensional and systematic sustainable benefits, leveraging the strengths of new technologies.

Trends in Research on Forest Ecosystem Services in the Most Recent 20 Years: A Bibliometric Analysis

Forest resources and the flow of ecosystem services they provide play a key role in supporting national and regional economies, improving people’s lives, protecting biodiversity, and mitigating the impacts of climate change. Based on the ISI (Institute of Scientific Information) Web of Science (WoS) database, we used a bibliometric approach to analyze the research status, evolution process, and hotspots of forest ecosystem services (FES) from a compilation of 8797 documents published between 1997 and 2019. The results indicated that: (1) research on forest ecosystem services has developed rapidly over the past 23 years. Institutions in the United States and other developed countries have significantly contributed to undertake research on the topic of ecosystem services. (2) The 11 hotpot key focus areas of completed research were payments for ecosystem services, biodiversity conservation, forest governance, ecosystem approaches, climate change, nitrogen, ecosystem management, pollination, cities, ecological restoration, and policy. (3) The trade-off relationships among ecosystem services, ecosystem resilience and stability have become the research frontier in this field. (4) Future research on FES will likely focus on the formation and evolution mechanism of ecosystem services; the interaction, feedback and intrinsic connections of ecosystem services at different scales; analysis of the trade-offs and synergies; unified evaluation standards, evaluation systems, model construction and scenario analyses; in-depth studies of the internal correlation mechanism between forest ecosystem services and human wellbeing; and realization of cross-disciplinary and multi-method integration in sustainable forest management and decision-making.

Research on the process planning and multi-method integration strategy of product conceptual design with cognitive mechanisms.

Innovative product design is essentially an activity involving creative cognitive thinking. Therefore, research on the innovative design process and of methods, computer-aided innovation tools should be conducted based on systematic exploration based on the principles of innovative cognitive thinking. We aim to uncover some general principles that can serve as a systematic thinking framework for product designers, provide a feasible framework and method of innovative thinking for designers, and provide theoretical and methodological support for further development of computer-aided innovation platforms. This paper summarizes the sources and content of studies on the idea generation and design processes behind product innovation. Specifically, we break down the general innovative design process of products, outlining the cognitive mechanisms and propose an integrated application strategy that incorporates multiple methods. And taking "the solution program to the coking of oily sludge pyrolysis equipment" as an example, this paper demonstrated and verified the effectiveness and practicability of the method and strategy proposed. Based on the results on improvements in the equipment, the designers put forward some creative and implementable design schemes. The entire thinking process was relatively smooth and efficient. The example presented in this paper shows that the proposed method can effectively guide/assist/motivate designers to think creatively.

Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method’s high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method’s good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form.

Structural elements of the southern Thomson Orogen (Australian Tasmanides): a tale of megafolds

Multi-scale, multi-method integration of geological constraints, with new interpretations of potential field data and seismic reflection data, has resulted in a comprehensive structural interpretation of the southern Thomson Orogen, eastern Australia. The interpretation reveals ∼50 major faults and shear zones, many of which can be traced for several hundred kilometres. The interpretation suggests that the southern Thomson Orogen can be subdivided into several structural domains that can be distinguished by differences in: (i) spatial orientation, (ii) geographic distribution, and (iii) partly the timing of major faults, but also to varying degrees by (iv) the evolution and spatial orientation of other structural elements, such as folds, minor faults and fractures, (v) broader lithological trends, (vi) stratigraphy, and (vii) structural style. The two largest domains are the Western Structural Domain that contains numerous faults and shear zones, and the fold-dominated Eastern Structural Domain, which is more strongly affected by late- to post-Devonian thrusting than the Western Structural Domain. Notwithstanding their differences, the domains can be integrated into a coherent structural model for the southern Thomson Orogen, which suggests that the area represents a set of megafolds or oroclines, which may have formed during the Bindian Orogeny.

Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration

On the basis of analysing the characteristics of hand movement surface electrocardiogram electromyogram (sEMG) signals, we propose a feature extraction and classification method for hand movement sEMG signals based on a multi-method integration combining the wavelet, fractal and statistics methods. To start, the hand movement sEMG signals are de-noised by using the wavelet transform, the de-noised and reconstructed signals are decomposed, and the average high frequency coefficients in each scale space are calculated to constitute the feature vectors as the first part of the hand movement sEMG signals classification features. Next, according to the characteristics of hand movement sEMG signals and the classification needs, we analyse the multi-fractal spectrum of the de-noised and reconstructed signals at multiple scales and extract the relevant parameters of multi-fractal spectrum as the second part of the hand movement sEMG signals classification features. Then, according to the characteristics of hand movement sEMG signals, we extract the relevant statistical characteristics of sEMG signals as the third part of hand movement sEMG signals classification features. According to the extracted features, we use the Least Square Support Vector Machine and the Backpropagation neural network as classifiers to individually classify and combine the characteristics of hand movement sEMG signals and the experimental results. The final classification features are identified to accomplish the classification of hand movement sEMG signals. Finally, the advantages of the proposed method are illustrated by comparative analysis from multiple perspectives.

Study on the Generation of Fuzzy Frond End Innovative Ideas in Multi-method Integration

Study on the Generation of Fuzzy Frond End Innovative Ideas in Multi-method Integration

Feature extraction of time series classification based on multi-method integration

On the basis of analyzing the characteristics of time series data, we propose a feature extraction method of time series classification combining wavelet, fractal and statistic methods. First of all, the original time series is de-noised by using wavelet transform, the de-noised and reconstructed signal is decomposed and the average high frequency coefficients in each scale space are calculated to constitute the feature vectors as the first part of time series classification features; Secondly, we analyze the multi-fractal spectrum of the de-noised and reconstructed signal at multiple scales, and extract the relevant parameters of multi-fractal spectrum as the second part of time series classification features according to the characteristics of specific time series data and classification need; And then according to different characteristics of time series data, extract the relevant statistical characteristics of time series as the third part of time series classification features; Finally, combining the characteristics of time series and experimental results, the extracted features by using wavelet, fractal and statistical methods are analyzed, and the final time series classification features are identified. By comparison with other feature extraction methods, we show the feasibility and superiority of the proposed method using Japanese Vowels data from UCI dataset.

Partitioned and Implicit–Explicit General Linear Methods for Ordinary Differential Equations

Implicit–explicit (IMEX) time stepping methods can efficiently solve differential equations with both stiff and nonstiff components. IMEX Runge–Kutta methods and IMEX linear multistep methods have been studied in the literature. In this paper we study new implicit–explicit methods of general linear type. We develop an order conditions theory for high stage order partitioned general linear methods (GLMs) that share the same abscissae, and show that no additional coupling order conditions are needed. Consequently, GLMs offer an excellent framework for the construction of multi-method integration algorithms. Next, we propose a family of IMEX schemes based on diagonally-implicit multi-stage integration methods and construct practical schemes of order up to three. Numerical results confirm the theoretical findings.

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12.

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.