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The purpose of this paper is to calculate the bank efficiency of removing potential risks that are hidden from the extreme portfolio of bank’s assets and further compare the differences and causes of bank’s efficiency and potential risk level between China’s representative banks and OECD representative banks in 2011-2015.

Based on the weight-limited DEA model, this paper calculates the bank’s efficiency and further compares the differences between China’s representative banks and OECD representative banks by using commercial banks’ transnational data.

By analyzing US representative banks’ data, the authors find that the excessive expansion of the scale of banks’ investment for the non-real economy shrinks after the bubble burst and would not improve the efficiency of banks immediately. The OECD representative banks rather prefer to extreme asset portfolio so that the potential risks gradually increase, while there is a diminishing effect on investments in non-real economies to improve bank efficiency. On the other hand, China’s representative banks have the signs of reducing investment in the real estate market, but the existence of the bubble in the market led to a lagged effect on the impact of adjustment of bank asset portfolio on efficiency.

This paper has practical significance for commercial banks to improve efficiency and reduce credit risks. This is conducive to the implementation of targeted supervision by the banking supervision department.

Based on the lesson that the financial crisis created by the real estate bubble burst in the USA in 2008 and the financial market active guidance of the developed economies, faced with the reality of Chinese real estate market bubble rising and the continuous improvement of Chinese financial market, this paper compares the differences between representative banks in China and OECD, and explores the causes by using the cross-country data of commercial banks.

By adjusting the weight of the input variables in the efficiency measurement, quantifying the risk is often overlooked by the changes in bank efficiency. This potential risk is caused by the bank’s investment preferences in the non-real economy represented by real estate and tradable financial assets.

This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.

The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.

The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.

This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.

This paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).

The structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.

A case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.

With the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.

The purpose of this paper is to study the effects of Cl⁻ and direct stray current on the soil corrosion of three grounding grid materials.

The electrochemical corrosion properties of three grounding grid materials, which include the Q235 steel, Q235 galvanized flat steel and copper, were measured by means of the weak polarization curve method and electrochemical impedance spectroscopy; the corrosion rate of specimens was calculated using the weight loss method; and the specimen surfaces were characterized using the scanning electron microscopy, energy-dispersive spectroscopy and X-ray diffraction analysis.

Results showed that both factors, Cl⁻ and direct stray current, can accelerate the corrosion rate of grounding grid materials. The magnitude of DC stray current density affected the mass transfer type and response frequency of the anode and cathode reaction of grounding materials, while the Cl⁻ contents of the soil only affect the mass transfer rate of the electrode material from the electrochemical impedance spectroscopy diagrams. The electric field generated by the DC stray current caused Cl⁻ directed migration. The larger the DC stray current density, the greater the diffusion process and the greater the weight loss rate of the grounding grid materials that would have a logarithmic relationship with the Cl⁻ content at the same DC stray current density. The corrosion resistance of the three materials is copper > Q235 galvanized flat steel > Q235 flat steel.

The paper provides information regarding the relationship among Cl⁻, direct stray current and corrosion of three grounding grid materials by means of electrochemical impedance spectroscopy. Meanwhile the weight loss rate is the logarithmic relationship with the Cl⁻ content, which is useful for understanding the corrosion mechanism of Q235 steel, Q235 galvanized flat steel and copper under the condition of Cl⁻ and direct stray current in soil.

Presentation of the different industrial carbon linkages of India. The purpose of this paper is to understand the direct and indirect impact of these industrial linkages.

This study uses a hypothetical extraction method with its various extensions. Under this method, different carbon linkages of a block are removed from the economy, and the effects of carbon linkages are determined by the difference between the original and the post-removal values. Energy and non-energy carbon linkages are also estimated.

“Electricity, gas and water supply (EGW)” at 655.61 Mt and 648.74 Mt had the highest total and forward linkages. “manufacturing and recycling” at 231.48 Mt had the highest backward linkage. High carbon-intensive blocks of “EGW” plus “mining and quarrying” were net emitters, while others were net absorbers. “Fuel and chemicals” at 0.08 Mt had almost neutral status. Hard coal was the main source of direct and indirect emissions.

Net emitting and key net forward blocks should reduce direct emission intensities. India should use its huge geographical potential for industrial accessibility to cheaper alternative energy. This alongside with technology/process improvements catalyzed by policy tools can help in mitigation efforts. Next, key net-backward blocks such as construction through intermediate purchases significantly stimulate emissions from other blocks. Tailored mitigation policies are needed in this regard.

By developing an understanding of India’s industrial carbon links, this study can guide policymakers. In addition, the paper lays out the framework for estimating energy and non-energy-based industrial carbon links.

This paper analyzes the advance selling and pricing strategies of fresh products supply chain where the e-retailer provides wholesale contract or agency contract to the fresh products supplier.

This paper constructed a two-period sequential-move game of fresh products supply chain members.

This analysis showed that the supply chain members had different preferences for contracts under different market conditions. The advance selling of fresh products was not a decision of the seller, but also required the support of other supply chain members. And the advance selling strategy was not always beneficial to all supply chain parties. Under the two contracts, there were market conditions in which the profits of supply chain members were Pareto-improved through the implementation of advance selling.

The model presented in this study focuses solely on the context of monopoly, overlooking the competition from alternative suppliers or retailers. Consequently, exploring the competitive landscape within the fresh products supply chain, particularly in relation to pre-sale pricing, emerges as a crucial avenue for further investigation. By employing empirical research methods, valuable insights are gleaned, thereby significantly augmenting the existing body of relevant theories.

The decision to pre-sell fresh products should be based on market conditions. Supply chain members can control production costs and fresh products circulation losses to maximize profits.

From the perspective of game theory, this study analyzed the optimal advance selling and pricing strategies of fresh products supply chain members under two kinds of contracts. These results can provide practical implications for fresh products suppliers and e-retailers.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.