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Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance.

This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix.

The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications.

This paper provides an insight into the current state and the future changes in carbon emissions.

Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

This study aims to improve the passenger accessibility of passenger demands in the end-of-operation period.

A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway, the minimum headway and the latest end-of-operation time. The objective of the model is to maximize the number of reachable passengers in the end-of-operation period. A solution method based on a preset train service is proposed, which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.

The results of the case study of Wuhan Metro show that the solution method can obtain high-quality solutions in a shorter time; and the shorter the time interval of passenger flow data, the more obvious the advantage of solution speed; after optimization, the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.

Existing research results only consider the passengers who take the last train. Compared with previous research, considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination. Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network, but due to the decrease in passenger demand, postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

This paper explores the effect of perceived service quality, trust, perceived value and perceived cost on patient satisfaction and loyalty as well as exploring the moderating role of the sociodemographic factors.

The data were gathered from 462 patients via a structured questionnaire, while structural equation modeling was utilized for the analysis.

Results indicated that trust, perceived value and patient satisfaction have important roles in shaping the patient loyalty, while patient satisfaction was found to fully mediate the patient’s perceived service quality. Loyalty relationship was also found to partially mediate the trust–loyalty relationship. Nonetheless, the patient’s satisfaction–loyalty relationship was found to be only moderated by the age factor.

Implications are provided to the Egyptian private hospitals in order for them to formulate improvement plans as well as set higher standards of conduct.

This original research is the first one, up to the researcher knowledge, that explores the drivers of patient satisfaction in the private hospitals in Egypt.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The purpose of this paper is to examine the effect of adopting certified food production on chemical fertilizer and pesticide use in China.

The authors estimate fixed effect models to track the changes in agrochemical consumption at household level over time and evaluate the effect of certified food production, using an unbalanced panel data set covering 4,830 households in six provinces over the period 2005–2013.

On average, the authors do not find significant effects of certified food production on either chemical fertilizer or pesticide consumption among Chinese farmers. The effects are heterogeneous across villages, but the heterogeneous effects show no clear pattern that is consistent with different types of certification. The findings are robust to the use of alternative panel structure and certification indicators. The lack of knowledge about certification among farmers, the price premium and differences in regulation enforcement across regions may explain why the authors do not find negative effects on agrochemical use.

This study suggests that careful inspections and strong enforcement of certified food production is needed to ensure that the environmental goals of certified food production can be achieved and the reputation of certification in China can be improved. The inspection of certification producers and the enforcement of current regulations should be stricter for the further healthy development of certified food production in China.

This study is the first attempt to systematically evaluate the impact of food certification on the use of agrochemicals in Chinese agriculture.

The purpose of this research is to investigate how to introduce a financing scheme to tackle the manufacturer's capital constraint problem, discuss the effects of data-driven marketing (DDM) quality, cross-channel-return (CCR) rate and financing interest rate on the members' pricing and delivery-lead-time decisions and optimal performances, and analyzes `how to achieve the coordination within a dual-channel supply chain (DSC) by contract coordination.

This work establishes a DSC model with DDM, and the offline retailer can provide internal financing to the capital-constrained online manufacturer. The demand under the price is determined based on DDM quality, customer channel preference and delivery lead time. Then, combined with the Stackelberg game, the optimal pricing and delivery-lead-time decisions are discussed under the inconsistent and consistent pricing strategies with decentralized and centralized systems. Furthermore, it designs a manufacturer-revenue sharing contract to coordinate the members under the two pricing strategies.

(1) The increase of DDM quality will reduce the delivery-lead-time under the inconsistent or consistent pricing strategy and will push the selling prices; (2) The growth of the CCR rate will raise selling prices and extend the delivery-lead-time under the decentralized decision; (3) Under price competition, the offline selling price is higher than the online selling price when customers prefer the offline channel and vice versa; (4) The retailer and the manufacturer can achieve a win-win situation through a manufacturer-revenue sharing contract.

This paper contributes to the studies related to DSC by investigating pricing and delivery-lead-time decisions based on DDM, CCR, internal financing and supply chain contract and proposes some managerial implications.

The purpose of this paper is to incorporate Chinese traditionality (CT) and patriotism (PAT) within the theory of reasoned action (TRA). It tests the moderating effect of gender with the aim to provide a deeper understanding of why Chinese tourists choose to take domestic travels.

Data is collected from 370 Chinese tourists. Convenience sampling is used. Structural equation modelling is used to test the proposed hypotheses.

The results of this paper show that PAT is positively related to tourists’ attitudes (ATs) and Chinese domestic travel intention (CTI). Moreover, CT is found to influence PAT, ATs and subjective norms (SNs) directly, as well as CTI indirectly. However, the positive relationship between PAT and SNs is not supported. Additionally, the influence of PAT on CTI for females is significantly higher than that for males.

The current paper adopts convenience sampling; data is collected using an online questionnaire which may cause sample bias and even reduce the reliability of the data. Future studies may adopt quota sampling based on the population of each province to gain more reliable data. Further research can consider including more constructs to better understand why Chinese people choose to travel domestically.

This paper is one of the first attempts to include PAT and CT within the TRA and contributes to the pool of literature on the TRA. It provides a comprehensive understanding of CTI. Second, PAT and CT are linked to the TRA, which expands the application of PAT and CT to the context of hospitality and tourism. Finally, the moderating role of gender contributes to the knowledge regarding the moderating effect between their respective relationships.

性别在中国传统与爱国主义对中国国内旅游意愿影响中的调节作用

摘要

本研究将中国传统性与爱国主义引入理性行为理论, 同时将性别作为调节变量, 目的在于探究中国游客选择国内游的原因。

通过分析便利抽样所收集的370份中国游客样本, 运用结构方程模型验证本研究的假设。

中国游客爱国主义能够正向显著影响其国内游的态度和旅游意向; 此外, 中国传统性也直接影响游客的爱国主义、态度和主观规范, 并且分别通过爱国主义、态度和主观规范间接影响旅游意向; 然而, 爱国主义和主观规范之间的关系却并没有得到验证; 最后, 女性游客的爱国主义对于旅游意向的影响作用显著高于男性游客。

首先, 本研究运用网络调研的方式进行便利抽样, 这一点有可能产生样本偏差, 甚至降低了样本的可信度, 未来研究可以根据各省人口数量采用配额抽样的方式进行抽样, 以期进一步提高数据的可信度; 其次, 更多的变量应当被引入研究, 以进一步探究中国游客选择国内游的原因。

首先, 本研究是为数不多的将爱国主义和中国传统性引入理性行为理论, 这一点进一步拓展了关于理性行为理论的研究范围, 也进一步加深了对于国内游原因的认识; 其次, 将爱国主义和中国传统性这一概念引入旅游领域, 进一步拓展了关于两者研究的边界; 最后, 在中国国内游的研究领域中, 将性别作为调节变量引入本研究一定程度上填补了在该领域研究中的不足。

La influencia de la moderación de género en la tradición China y el patriotismo en la intención de viajar dentro de China

Este research incorpora los conceptos a de la tradicionalidad China (TC) y el patriotismo (PAT) a la teoría de La Acción razonada (TAR), teniendo en cuenta el efecto moderador de género, con el objetivo de profundizar en la comprensión de porqué los turistas chinos eligen realizar viajes domésticos/nacionales.

A partir de los datos recolectados de 370 turistas chinos utilizando las muestras de cuestionarios realizados de manera on-line, éste estudio usa un modelado de ecuaciones estructurales para probar las hipótesis propuestas.

Los resultados demuestran que el PAT está positivamente relacionado con las actitudes del viajero y la intención de viaje nacional chino (IVNC).Es más, la tradicionalidad China (TC) influye en el Patriotismo (PAT), las actitudes del viajero chino (AVC) y también en las normas subjetivas (NS) directamente.Adicionalmente, la influencia del PAT en la intención de viaje del viajero doméstico chino, especialmente en mujeres, es significativamente mayor que en hombres.

Primero, el estudio adopta un muestreo de conveniencia y recopila cuestionarios on-line, lo que puede causar un sesgo en la muestra e incluso reducir la confiabilidad de los datos obtenidos.Los futuros estudios pueden adoptar un muestreo por cuotas basado en el número de población de cada una de las provincias de China, para obtener datos más relevantes.

Este estudio es uno de los primeros en incluir el PAT y el TC dentro de la TAR, que amplía la literatura sobre la TAR y proporciona una comprensión más completa de las actitudes del viajero chino.Segundo, el PAT y la TC están vinculados al TAR, el cuál expande la aplicación del PAT y la TC en el contexto del turismo en todas sus ramas.Por último, el rol moderador de género contribuye al conocimiento, sobre el efecto moderador entre sus respectivas relaciones.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.