Three-stage Model Research Articles

Three-Stage Optimization Model to Inform Risk-Averse Investment in Power System Resilience to Winter Storms

Three-Stage Optimization Model to Inform Risk-Averse Investment in Power System Resilience to Winter Storms

The impact of digital economy on the upgrading of manufacturing structure.

The global economic situation is in a downturn, and the upgrading of manufacturing structure is a necessary transformation path for the manufacturing industry to achieve high-speed and stable development. The article analyzes the theoretical mechanism of the digital economy affecting the upgrading of manufacturing structure through the endogenous economic growth model, constructs a three-stage mediation effect model, and empirically researches the path of the digital economy affecting the upgrading of manufacturing structure in the Pearl River Delta. The study finds that the digital economy has a significant positive promoting effect on the upgrading of manufacturing structure. In terms of the influencing mechanism, the enhancement of the level of independent innovation and the advancement of the human capital structure are the important paths of the digital economy in promoting the upgrading of manufacturing structure. Among them, the mediating effect is 17.5% for the level of independent innovation and 17.4% for the level of the advancement of the human capital structure. The results of the study also found that the upgrading of manufacturing structure cannot be separated from government support, and the influence of government support on t the upgrading of manufacturing structure reaches 44.9%, and government deployment and control is conducive to accelerating the process of advanced manufacturing structure.

Evaluation of Regional Carbon Neutrality Efficiency in China

In the context of “3060 Carbon Neutrality”, there will be both theoretical and practical significance to finding out the status quo, laws and key factors about efficiency of carbon neutrality in various regions of China. This paper considers carbon neutrality system as a black-box combining “carbon absorption” with “carbon fixation”. According to these two major functions, we aim to measure efficiency of carbon neutrality among 30 provinces in China by constructing the three-stage DEA model. The results point that there exists obvious regional differentiation of efficiency distribution patterns; Key factors that determine the level of carbon neutralization efficiency mainly include spatial effects, industrial modernization, technological and innovative levels, and natural resources, etc. In summary, this paper provides suggestions and counter measures to promote carbon neutralization efficiency within various regions from two perspectives of regional low-carbon economy and regional linkage development.

Study on the Application of Multi-Communication Mode of Integrated Media in Revitalizing the Industrial Development of Rural Culture and Sports Sector

Abstract The communication of rural culture and sports not only carries the inheritance and development of local culture but is also an important manifestation of rural revitalization and cultural self-confidence. With reference to the communication characteristics of information related to rural culture and sports, this paper combines traditional and new media to design a multimedia communication model for rural culture and sports. A three-stage DEA model, including the SFA model, is used to measure the development efficiency of the rural culture and sports industry under the multimedia communication model. It is found that after the implementation of the multivariate communication model of integrated media, the audience of rural culture and sports in Z city shows an increasing trend, and the social support efficiency index of rural culture and sports industry reaches 0.9770, which achieves a considerable communication effect. At the same time, the total output and added value of the industry in the field of rural culture and sports also increased rapidly, and its output value accounted for 43.64% of the total sports industry in 2023. The multifaceted communication model of integrated media designed in this paper is conducive to revitalizing the development of industries in the field of rural culture and sports, promoting the sharing of national culture as well as building a new era of rural economy.

Exploration and Practice of High-Quality Talent Cultivation Path in the Context of Industry-Education Integration Drive

Abstract High-quality talent cultivation is an inevitable trend driven by the development of industry-education integration. This paper analyzes the differences in project goals between enterprises and universities, selects a content-based recommendation method, establishes an interest theme model for both entities and generates cooperative recommendation partners. A new type of partner recommendation scheme for industry-education integration projects is established based on geographic proximity by introducing the factor of geographic distance. Interpret the meaning of high-quality talent cultivation, establish a joint talent cultivation mechanism among enterprises, colleges, universities, and industrial organizations, and develop a three-stage segmented cultivation model. Finally, analyze the degree of participation and influence of each subject and variable on the practice of industry-industry fusion, respectively, and explore the effectiveness of industry-industry fusion collaborative cultivation by combining the teaching characteristics of teachers from schools and enterprises with the development of industry-industry fusion activities in the college. During the three years of junior, senior, and graduates, College D’s industry-education integration activities are mainly organized by lectures, visits, seminars, and enterprise recruitment meetings, of which the proportion of inviting relevant enterprises to give lectures can reach more than 80%. The activities of the college’s industry-education integration organization are one of the effective paths to building development space for the cultivation of high-quality talents.

Changes in technological innovation efficiency and influencing factors of listed textile and apparel companies research--Based on three-stage DEA with Tobit modeling.

The key to high-quality development in the textile and apparel industry lies in enhancing technological innovation and optimizing the efficiency of technological innovation. Based on data from 60 A-share listed companies in the textile and apparel sector in China from 2013 to 2022, this study employs a three-stage DEA model and the Malmquist index model to measure changes in technological innovation efficiency from static and dynamic perspectives. Additionally, it uses a Tobit model to analyze the impact and mechanisms of management and financial factors on technological innovation efficiency. The results indicate that: (1) Compared to the manufacturing industry and its sub-sectors, the overall technological innovation efficiency of listed textile and apparel companies was relatively low and showed a declining trend between 2013 and 2022; (2) Over the decade, the average total factor productivity of these listed companies increased by 1.7%, exhibiting a "W" shaped fluctuation, with technological progress, pure technical efficiency, and scale efficiency all showing weak improvement; (3) Management and financial factors significantly influence technological innovation efficiency. Specifically, employee quality, profitability, and operational capability are positively correlated with technological innovation efficiency and have long-term effectiveness, while firm age, management costs, equity concentration, development ability, and debt repayment capacity are negatively correlated with technological innovation efficiency; (4) Different types of enterprises show differences in the significance of management factors, while whether the same person holds both managerial positions significantly affects financial factors.

Research on optimization of flight crew scheduling considering pilot fatigue

Safety is a top concern for the civil aviation industry, and the risk of safety accidents will increase due to pilot fatigue. To ensure the safety of civil aviation, this paper proposes a method to solve the crew scheduling problem considering pilot fatigue. In order to reflect individual differences and fatigue levels of pilots, an improved three-stage alertness calculation model is first proposed based on subjective and objective perspectives to represent pilots’ alertness levels and fatigue working duration quantitatively. Then, for the crew scheduling problem considering pilot fatigue, a mixed integer programming model is constructed to simultaneously achieve the optimization objectives of reducing the overall scheduling cost and crew fatigue working duration. Next, since the actual crew scheduling problem is large-scale, a solution algorithm based on a column generation framework is developed to improve the quality and efficiency of solving the large-scale crew scheduling problem. Furthermore, in the case study, we collected actual data from an airline company to validate the effectiveness of our proposed method. Finally, through multiple experimental comparisons and analyses, to balance the two optimization objectives mentioned above, it is more reasonable to handle pilot fatigue working duration with soft constraints. Sensitivity analysis reveals the variation rules of the crew cost and fatigue, providing some valuable managerial insights for the crew scheduling problem considering pilot fatigue.

Pricing, greening, and recycling decisions of capital-constrained closed-loop supply chain with government subsidies under financing strategies

BackgroundDue to the growing awareness of environmental protection and their economic benefits, there has been rapid development in the closed-loop supply chain (CLSC) in recent years, with a general occurrence of CLSCs led by collectors. MotivationEven with government support such as subsidies, the operation of CLSC still requires immense investment, such as purchasing recycling equipment. Nevertheless, the unprecedented economic recession during the post-pandemic era has heightened the risk of insufficient funding for CLSC. This highlights the importance of understanding the complexities involved in CLSC financing strategies, as they are crucial for ensuring the sustainability and resilience of CLSC in the face of challenges. MethodsTo investigate the equilibrium decisions and financing strategy selection of CLSCs, we developed and analyzed a series of three-stage Stackelberg models, considering practical scenarios involving capital constraints, environmentally friendly green activities, government subsidies for End-of-Life (EOL) recycling, as well as introducing common trade credit financing and bank financing for generality. ConclusionThe findings demonstrate: (1) Under trade credit financing, it is always beneficial for the collector granting credit. Interest income can motivate the collector to recover more EOL products. (2) In addition to financing interest rates, government subsidies and remanufacturing cost savings can affect the financing strategy selection of the capital-constrained manufacturer. Our study provides guidance for managers in selecting optimal financing strategies under specific conditions. (3) The total profit of CLSC under trade credit financing is always higher and we propose a revenue-sharing contract to realize the win-win status with the optimal profit of CLSC. (4) Furthermore, our results underscore the importance of government recycling subsidies, remanufacturing cost savings, and the positive impact achieved by reducing recycling difficulties and enhancing customer awareness of green consumption.

A Comparative Study on Financing Efficiency of Media Companies Listed in China and America

Abstract The media industry has become a new growth point in China’s economic development since the 21st century. However, due to the imperfections of China’s media capital market and other problems, the media industry faces financing constraints. The media industry in China is facing financing constraints as a result of the domestic media capital market’s imperfections and other issues. To compare the financing efficiency difference between domestic and U.S.-listed media companies, this paper selects 34 domestic and 16 U.S.-listed media companies as research samples. The three-stage DEA model is used to empirically analyse the samples. The results show that the financing efficiency of U.S.-listed media companies is higher than that of domestic listed media companies. The paper also looks at how to improve the financing efficiency of domestic listed media companies from both the external market environment and the internal operation level. From the viewpoint of the external market environment and internal operating levels of enterprises, the study proposes relevant countermeasures to improve the financing efficiency of domestic listed media companies.

Research on the Efficiency of China’s Higher Education System Based on the Three-Stage DEA Model

Research on the Efficiency of China’s Higher Education System Based on the Three-Stage DEA Model

Network Security Threat Intelligence Prediction in Network Traffic Analysis Based on Machine Learning

Abstract With the increasingly complex network architecture under the development of information technology, it is more and more difficult to recognize various network traffic. Based on the direction of machine learning for network traffic anomaly detection, this paper proposes a three-stage CNN-LSTM attention mechanism model for feature extraction of original traffic and a PCA-based improvement for spatial feature learning of CNN to construct its anomaly traffic detection model. Meanwhile, based on the abnormal traffic detection, with discriminative network and generative network as the main parts, we propose the cyber security threat intelligence prediction model based on the use of a domain adaptive model to realize the shared representation of source and target domains. In the experiment of the abnormal traffic detection model, the accuracy rate of this paper’s detection model is 93.56%, the check-all rate is 99.2%, the F1 value is 84.9%, and the Kappa value is 80.66%. The detection level in the face of DOS attacks is the highest, with 98.28% and 99.63% checking full rate and checking accuracy, respectively. In the experiments of the network security threat intelligence prediction model, the prediction model of this paper reduces the time consumed by about 72% on average compared to rule file matching in dealing with large-scale network traffic. In high-risk cybersecurity threat attack types, it has an average prediction accuracy of 88.83% and a recall rate of over 90%.

Estimating the Innovation Efficiency of the Artificial Intelligence Industry in China Based on the Three-Stage DEA Model

Estimating the Innovation Efficiency of the Artificial Intelligence Industry in China Based on the Three-Stage DEA Model

A three-stage model of quantifying and analyzing power network resilience based on network theory

A three-stage model of quantifying and analyzing power network resilience based on network theory

Multistage Graph Convolutional Network With Spatial Attention for Multivariate Time Series Imputation.

In multivariate time series (MTS) analysis, data loss is a critical issue that degrades analytical model performance and impairs downstream tasks such as structural health monitoring (SHM) and traffic flow monitoring. In real-world applications, MTS is usually collected by multiple types of sensors, making MTS and correlations between variates heterogeneous. However, existing MTS imputation methods overlook the heterogeneous correlations by manipulating heterogeneous MTS as a homogeneous entity, leading to inaccurate imputation results. Besides, correlations between different data types vary due to ever-changing environmental conditions, forming dynamic correlations in MTS. How to properly learn the hidden correlation from heterogeneous MTS for accurate data imputation remains unresolved. To solve the problem, we propose a multistage graph convolutional network with spatial attention (MSA-GCN). In the first stage, we decompose heterogeneous MTS into several clusters with homogeneous data collected from identical sensor types and learn intracluster correlations. Then, we devise a GCN with spatial attention to explore dynamic intercluster correlations, which is the second stage of MSA-GCN. In the last stage, we decode the learned features from previous stages via stacked convolutional neural networks. We jointly train these three-stage models to predict the missing data in MTS. Leveraging this multistage architecture and spatial attention mechanism makes MSA-GCN effectively learn heterogeneous and dynamic correlations among MTS, resulting in superior imputation performance. We tested MSA-GCN with the monitoring data from a large-span bridge and Wetterstation weather dataset. The results affirm its superiority over baseline models, demonstrating its enhanced accuracy in reducing imputation errors across diverse datasets.

THE DEVELOPMENT OF CRITICAL THINKING: WRITING A TERM PAPER VIA FRAME ANALYSIS

The paper examines the experience of using the technology of critical thinking through reading and writing, supplemented by a frame analysis when organizing students’ independent work – writing a term paper. By frame we mean one of the ways of representing a concept, which, in turn, appears as a mental representation of the picture of the world recorded in the individual’s psyche. Frame analysis is considered as one of the ways to develop students’ critical thinking. The paper focuses on clarifying the correlation between four key concepts: critical thinking, cognitive theory, constructivism as a learning theory, and frame analysis. The connection between critical thinking and students’ independent work is examined. A detailed description of the three-stage model is presented as a technological basis for critical thinking, which is essential and fundamental while writing a term paper. This model reflects the stages of challenge, comprehension and reflection. At the stage of challenge, students need to familiarize themselves with a certain number of information sources to get a general idea of the topic of the term paper. At this step, which consists in updating knowledge on a certain topic and determining directions for its further study, the use of such a technique as “clusters” is offered. At the stage of comprehension, students process new information, comparing it with the existing one and, thus, master a new structure of knowledge. At this point, the “Logbook” method is proposed. Moreover, frame analysis involves working on the key concept/concepts of a specific topic of the term paper. At the stage of reflection, the received data are interpreted and described, that is, a term paper is created directly. Students in the form of an essay briefly outline the essence of the topic, formulate conclusions and express their points of view on the subject of their researches.

Challenges and solutions pertinent to machine learning-based audio recognition

The problem of transcribing data from an acoustic waveform has yielded multiple approaches over the last decades. The current preferred approach involves a three-stage model that breaks the problem into its constituent stages, each with an equivalent model. The first stage divides pure acoustics and language study using a Bayesian model. The second stage focuses on the acoustics model; this model makes the most sufficient and efficient division. The third stage focuses on the acoustics model; this model provides complete instruction on how to compute the probability required by the first stage, given the division specification declared within the second stage.

Effect of three-stage cascade of opinion dynamics models in coupled networks

Information technology has contributed to the spread of public opinion across multiple coupled networks, and the scope of information spread has expanded. Owing to the impact of personal subjective cognition, information attenuation occurs during the process of opinion transmission in social networks, particularly in multi-layer social networks. In this paper, we construct a three-stage cascade opinion dynamics model, based on the Hegselmann–Krause (HK) model, to analyze the effect of information dissemination attenuation on group decision behavior in coupled social networks. This model considers three-stage cascade information transmission and two competitive opinion leader subgroups in two different social network environments, and can describe a public opinion dissemination environment more closely. The results show that the combination of the network structure and the level of trust of the public can effectively guide the spread of public opinion, which provides valuable insights into the role of social media in shaping public opinion and its impact on various fields. These findings can be useful for advertisers and policymakers to control public opinion and reach a consensus in group decision-making.

Green GDP Accounting and Green Economic Efficiency Analysis in Yunnan Province under the Double Carbon Background: Based on the Three-Stage DEA-Malmquist Model

In the efforts to practice the dual carbon national strategic objectives under the premise, this paper takes Yunnan Province as the research object, firstly constructed the green GDP evaluation index system, on this basis, the use of entropy method to calculate the green GDP value and ECI index of Yunnan Province, the development of Yunnan Province on the economy and resources and environment between each other was briefly studied; then through the above data to further select the green economic efficiency input-output index application of three-stage DEA-Malmquist model of green total factor productivity in Yunnan Province to calculate and analyze, obtain the five-year green economic efficiency level of Yunnan Province; finally through a large number of data analysis of technical progress index is the most important factor affecting the green economic efficiency of Yunnan Province, and give appropriate countermeasures and suggestions.

A Theoretical Approach to the Spaces of the New Future: Planning under the Uncertainty Principle

Over the past few decades, the world has become an increasingly dangerous and complex place, and thus, expectations from spatial planning have changed. The study defines the concept of uncertainty as an important problem area of spatial planning. Based on lack of native studies on this subject, it is aimed to reveal how the uncertainties in spatial planning process are handled in international literature. It consists of two basic steps. In the first step, a three-stage model, "Uncertainty Components of Spatial Planning" is proposed. These stages involve (i) the conceptualization, (ii) the classification and (iii) the evaluation of uncertainty. In the second step, a triangular framework was formed for the conceptualization stage of this model having components of (1) identification and modelling, (2) theories and processes, (3) legal regulations. The theoretical handling suggested that the concept of uncertainty is synonymously used with the concepts of vagueness and ambiguity in everyday life despite their differences. It is also found that uncertainty is the subject of many international studies having a common point of presenting either a model or a method to evaluate uncertainty. These studies were categorized in three groups in handling uncertainty; (1) in multidisciplinary context within a general framework, (2) in the field of planning under two subcategories (2a and 2b), and (3) in the field of environment. The studies carried out in the second category allowed for regular conceptual patterns in themselves, and they were shallower and more inward-oriented than those studies in the 1st and 3rd groups, and there is an apparent interaction between the 1st and the 3rd groups. In the model proposed, the focus was only on (i) the conceptualization. However, as the origin, definition and basis of the concept of uncertainty were revealed, it might provide an important initiation for future studies. The study is original in introducing the concept of uncertainty to native literature by elaborating on how it is handled in international studies. Proposals were offered on how to place this concept on a theoretical basis before establishing an evaluation framework for uncertainties within the spatial planning process in Türkiye.

A neural network model for detection and classification of lumbar spinal stenosis on MRI

ObjectivesTo develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool.MethodsThe three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists’ diagnoses were then compared to the diagnoses of the model.ResultsThe model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics.ConclusionsThe CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.