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Under the prior information that upper and lower bounds of the random quantity are symmetric with respect to the best estimate, this paper analyses the Bayesian prior distribution assignment using the principle of maximum entropy. With the exact lower and upper bounds, it approves uniform for the probability density function of the quantity and it has a curvilinear trapezoidal form for the inexact lower and upper bounds.

The purpose of this paper is to develop a suitable framework of sustainable urbanization performance evaluation from the view of efficiency for mitigating the unbalance between the theoretical research of sustainable urbanization and the practical need of sustainable urbanization.

A slack-restricted slack-based measure data envelopment analysis (DEA) model and two sustainable urbanization benchmarking methods with individual and centralized view, respectively, are developed based upon the classical DEA theory.

The empirical results show that China is now suffering a relatively low level of sustainable urbanization. Too much waste water emission, too small an urban population proportion, and too much resource wastage are the major factors that drive China’s urbanization away from sustainability.

Having the overall aim of practical urbanization, the proposed framework can help stakeholders recognize different sustainable urbanization performance levels by offering them accurate sustainable urbanization performance scores. The framework can also direct governments to improve the sustainable urbanization performance. The empirical analysis of China’s provincial region sustainable urbanization performance with the proposed framework further demonstrates the theoretical and practical value of this research.

This paper aims to study the oscillation phenomenon before chaos as well as its mechanism of occurrence in the energy-saving and emission-reduction system.

The system dynamics analysis, phase portrait analyses, equilibrium point analysis and bifurcation curve were applied to this paper.

First, the authors find an oscillation phenomenon previous to chaos. Second, on the one hand, the existence of two unstable saddles is the reason for the occurrence of oscillation phenomenon. On the other hand, the increasing of carbon emissions can arouse oscillation phenomenon.

This paper finds an oscillation phenomenon previous to chaos in the energy-saving and emission-reduction system. The mechanism of occurrence of oscillation phenomenon is studied. The existence of two unstable saddles is the reason for the occurrence of such oscillation phenomenon. The oscillation is related with fold bifurcation. The study also provides a theoretical basis for the further study of chaos control.

Reductions in emissions intensity have been expressed in commitments of many countries’ intended nationally determined contribution. Energy structure adjustment is one of the main approaches to reduce carbon emissions. This paper aims to study the causal relationship between carbon emission intensity and energy consumption structure in China based on path analysis.

After data collection, this paper performs correlation analysis, regression and path analysis.

Correlation results display clear collinearity among energy structure variables. Regression finds that coal, oil, natural gas and technology can be used as indicators for carbon intensity while primary electricity has been excluded. Path analysis shows that coal had the largest direct and positive impact on emission intensity. Natural gas had a positive direct and negative indirect effect through its negative relationship with coal on emission intensity. Technology has the largest negative elasticity while all fossil energies are positive. Results indicate a negative effect of energy structure adjustment on China’s national carbon intensity.

Given the major role of China in global climate change mitigation, significant future reductions in China’s CO₂ emissions will require transformation toward low-carbon energy systems. Considering the important role in mitigating global climate change, China needs to transition toward a low-carbon energy system to significantly reduce its carbon intensity in the future.

With the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.

Research in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.

The proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.

E-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

RF transceiver module is considered a vital part of any wireless communication system. This module consists of two important parts the RF transceiver and analog-to-digital converter (ADC). Usually, both these parts – RF transceiver and ADC – are used to enhance the perspective of size and power. The data processing in 4G communication makes hurdles and need research attention to make it faster and smaller in size. Accuracy and fast processing are the critical challenges in the modern communication system.

After theoretical and practical investigations, this research work proposes key new techniques for the RF transceiver module. These techniques will make RF transceiver small, power-efficient and on the other hand, make dual SAR-ADC more effective as well. The proposed design has no intermediate frequency where the RF transceiver is reduced its major blocks from five to four, which includes crystal oscillator, phase lock loop, power amplifier and low noise amplifier. Moreover, the shared circuitry is introduced in the architecture of the SAR-ADC for the production of dual outputs, specifically in bootstrapped switch and comparator.

The miniaturized RF transceiver and SAR-ADC are well tested separately before the plantation on the printed circuit board (PCB). The operating voltage and frequency of the RF transceiver module are 1.2 V and 5.8 GHz, where the sampling rate, bandwidth and output power are 25 MHz, 200 MHz and 5 dBm, respectively. The core area of the PCB is 58.13 mm². The bandwidth efficiency is 93% using surface acoustic wave less transmitter. The circuit is based on the library of 90 nm CMOS technology.

The entire circuit is highly synchronized with the input and reference clocks to avoid self-interference.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

The spinel NiFe₂O₄ has been identified as being a potentially successful anti‐corrosion ceramic that can be used in aluminium electrolysis under molten salt corrosive conditions (970°C). The evaluation and fabrication processing of NiFe₂O₄ has been studied in this paper. According to the analysis and comparison of material properties, it has been concluded that fabrication technology for making anti‐corrosion electrode material would not be exceptionally difficult and therefore that the material has a strong potential for use in this service.