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This paper aims to examine state adoption of climate action plans (CAPs) and investigates the factors driving the adoption of these climate policies in the states.

The framework that is formulated to explain the state climate actions involves four dimensions: climate risks, climate politics, climate economic and climate policy diffusions. These hypotheses are tested with event history analysis on a panel data set on 48 US continental states from 1994 to 2008.

This paper found empirical evidence to support climate politics, economics and policy diffusion explanations. It also found that climate risks are not taken into account in states’ climate actions. A comparison is conducted to compare the differences in state and local climate policymaking.

The paper investigates the motivations of state governments in adopting CAPs, and makes comparisons with local climate strategies. It contributes to academic understanding of the multilevel governance of climate protection in the USA.

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

This paper aims to explore the key risk factors affecting the Personnel Localization Management of international construction projects under the major public emergencies represented by the novel coronavirus pneumonia pandemic (hereinafter COVID-19) and how the public emergency affected the Personnel Localization Management from three levels: staff turnover rate, the number of different personnel, the salary and performance of workers. The paper also helps to enhance the construction enterprises' response capacity of major public emergencies and provides a comprehensive framework of optimization strategies for the Personnel Localization Management of international construction projects (hereinafter projects).

The main research method of this paper is the case study, and ten representative international construction projects are selected for case study in China construction enterprises (hereinafter CCE). And this study used the failure mode and effects analysis (FMEA) and comparative analysis to find out all potential risk factors under the COVID-19 and analyze how the epidemic affects the Personnel Localization Management of projects which based on the primary data from 10 projects obtained through in-depth interviews and the secondary data from China First Metallurgical Group and Central South Construction Group's Overseas Enterprise.

The findings show that the outbreak of the major public emergencies not only greatly increased eight risk factors but also directly led to an increase in staff turnover rate. Meanwhile, the numbers of Chinese and local managers and workers are all affected, and an increase in the number and the salary performance of local workers can be reduced, to a certain extent, to the cost-to-output ratio of the projects. The findings would help construction enterprises better cope with Personnel Localization Management and enhance the response capacity of major public emergencies.

This study will broaden researchers' horizons regarding “Personnel Localization Management under major public emergencies” and “risk factors of Personnel Localization Management in an international context.” Furthermore, construction enterprises looking for a better mechanism of Personnel Localization Management can benefit from research findings and lessons learned from the authors' case study during or before an outbreak of major public emergency. Lastly, the framework of optimization strategies for Personnel Localization Management can be used both for research purposes and practice issues in international construction projects.

The findings from the authors' case study offer the direction for international construction enterprises in China and other countries to formulate effective measures, strengthen overseas business and establish a crisis management mechanism for Personnel Localization Management under major public emergencies, and the findings provide emergency plans for projects to improve the public crisis handling capacity and respond to major public emergencies such as the COVID-19.

This study analyzes the impact of the COVID-19 on the Personnel Localization Management of international construction projects from the perspective of personnel. This study provides a theoretical reference for the international construction industry to actively respond to major public emergencies. Besides, the research is conducive to improving the emergency response mechanism in the construction industry, and further promoting the high-quality and globalized development of international construction.

This study provides other researchers with a comprehensive understanding of the risk factors affecting the Personnel Localization Management of projects under the COVID-19 and insight for further research on localization management, risk management, and project management.

The purpose of this paper is to develop a new hybridized controller based on fuzzy reasoning and neural network (NN) for hydropower generator unit (HGU).

The approach contains fuzzy neural networks controller (FNNC), RBF network identification (RBFNI) and HGU system. FNNC may give control value to control HGU via fuzzy NN reasoning and computing according to HGU rotate speed error and error varying rate. RBFNI is used to identify the character of HGU system and predict its output. FNNC may adjust parameters and member function according to the identifying and predictive outcome of RBFNI.

Sees that the hybridized control system is feasible and stable, and the controlling performance of the hybridized system is superior to conventional fuzzy controller.

The theoretical proof of stability of the proposed scheme still remains to be studied. Accessibility and availability of membership functions and control rules is also a limitation applied.

The main advantage of the proposed method is that FNNC has reasoning, learning, and optimizing capability which can control effectively HGU. This will be useful for control engineers to control complex industrial plants.

The paper proposes new combined approach to optimal control of HGU using FNNC, and it is aimed at operational researches and engineers, especially those who dealt with HUG controller.

To take relative actions to cope with the threat which network finance information security now encounters by constructing controlling tactical and synergetic model.

It is practical to use the synergetic self‐organization theory to calculate the effects that the force of synergetic system of controlling tactics to financial information security makes on network financial system, and it is also practical to construct the synergetic model of controlling tactics to network financial information security on the basis of it.

Through applying synergetic analysis to controlling tactical system of network financial information security, it can be found out that controlling tactical system is an open system which changes from disorder to order and which keeps away from a balancing state. As an opening system, controlling tactics are interacting with outside from now and then.

Network financial information security takes on dynamics, relativity, integrity and complexity. Accessibility of data is the main limitations which model will be applied.

From the view of network financial information security, constructing controlling tactical and synergetic model of information security are explained.

Network finance is orientated as a special social and economic system. The author does analysis on the network financial system, and expounds order parameters and model of network financial system.

As an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.

This study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.

First, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.

The scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.