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The purpose of this paper is to study the pipe-type electromagnetic induction heating device under power frequency condition.

To reduce eddy current loss and improve heating efficiency, the structure of a pipe-type power-frequency electromagnetic heating device was optimized. Based on the maximum load flow formula, a parallel excitation winding structure is designed, and the distribution of electromagnetic field under four different powers is analyzed by simulation. Four heating modes were proposed according to the structure of diversion ring, inner wall and outer wall. Two heating modes with better heating effect were obtained by comprehensively considering the factors such as magnetic field distribution, thermal power and energy consumption.

The double-wall structure of the pipe-type electromagnetic heating device can make the heat source distribution more uniform, and the use of power-frequency power supply can increase security, the installation of diversion ring can make the heating more sufficient and the heating efficiency of the two heating methods selected according to the structural performance is more than 90%.

In view of the medium or high frequency of pipe-type electromagnetic heating device, it is necessary to configure high power electronic frequency conversion drive system, and eddy current can only be produced on the tube wall, resulting in uneven distribution of heat sources. A pipe-type power-frequency electromagnetic heating device with double-wall structure was proposed.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Despite growing interest in smart sustainable city models, empirical evidence on progress, public perceptions and major adoption obstacles remains sparse. This study aims to assess the readiness of the world’s largest urban agglomerations, including megacities and metacities, to transition towards smarter and more sustainable models, while also exploring the primary challenges and obstacles to this transformation.

Drawing from pragmatism as a theoretical standpoint, a mixed methodology utilizing an online survey as an instrument of exploration was employed to investigate the perspectives of 400 residents of large urban centers globally regarding their city’s readiness to transition towards a smart sustainable city model.

Participants prioritize sustainability over intelligence, focusing on quality of life, environmental benefits and resource efficiency in smart sustainable city initiatives. Key considerations include sustainable urban planning, resource management and smart buildings. Challenges persist in energy management, waste reduction, economic development and education. While policies support sustainability, gaps remain in education, political will, funding, collaboration and technology infrastructure. The study highlights a global trend towards smarter, more sustainable cities and a strong correlation between participants' prioritization of sustainability and their commitment to sustainable practices.

By exploring residents’ viewpoints, priorities and aspirations, this research provides empirical evidence for decision-makers, urbanists and others involved in shaping future cities.

This research contributes to urban studies by exploring the emerging trend towards smart sustainable cities, aligning with perspectives from futurists and urban theorists. Future research should focus on predictive analysis and enhancing collaboration across government, private sector and civil society to accelerate this transformative process.

In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.