Search results

The purpose of this paper is to attempt to investigate farmer’s positional concerns in rural China, and how the positional concerns correlate with household expenditures on visible goods.

The authors conduct a survey-based experiment to measure farmers’ positional concerns, and employ econometric models to examine the determinants of the degree of positional concern and how the positional concern affects household expenditures on visible goods.

The authors find that Chinese farmers have strong positional concerns for income, and high-income households are more concerned with relative position. Furthermore, there is a significant difference between males and females with respect to correlation between degree of positionality and household expenditures on visible goods. For females, there is a positive correlation between degree of positionality and household expenditures on clothes, restaurants, and mobile phones, respectively. For males, there is a positive correlation between degree of positionality and household expenditures on mobile phones.

The government policy thus should pay attention to the positional goods, and the relevant consumption tax by increasing the prices of visible goods could be considered or suggested in the future even in the rural areas.

This paper provides complementary evidence on Chinese farmers’ positional concerns, and how the degree of positional concern relates to household expenditures on visible goods.

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

The purpose of this study is to assess the impact of land rights and tenure types on farmers' investment behavior in Chinese collective forests, using household survey data from Fujian Province.

In this study, the authors conducted a household survey in Fujian province of 520 randomly selected forest farmers. The authors used a random‐effects Tobit model to estimate the impact of land rights and other components on, for example, tenure security and harvest quota, and the impact of tenure types on farmers' investment incentives.

This study produced three main findings: perceived tenure security in the context of frequent agricultural land redistribution negatively affects input intensity; farmers still perceive some tenure arrangements to be more uncertain than others, which discourages them from undertaking investments on such plots; and the harvest quota regulation, introduced to conserve forest stock, has in fact acted as a disincentive in forestry management.

Almost all previous studies are based on national or regional data, which have primarily focused on the links between tenure types and investment incentives. In this study, based on the plot‐level data, the authors are able to assess not only the impacts of tenure types but also how specific land rights and their components affect farmers' investment behavior.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Rapid advancements in nanotechnology are going to bring radical changes in the society and particularly in wireless communication where small, smart and speedy systems are everyone's first choice. This is possible as application of nanotechnology is taking place in WiMAX/WiFi and other wireless communication systems, which is ‘State-of-the-Art’ technology at the moment. Evolution of microelectronics towards miniaturization is one of the main motivations for nanotechnology. The continued improvements in miniaturization, speed and power reduction in information processing devices, sensors, displays, logic devices, storage devices, transmission devices, etc. will bring another technical revolution, which will change our life. In our research work, we would like to focus on design and development of programmable frequency synthesizer for WiMAX/WiFi wireless communication (to the scale of < 50 nanometer). The transceiver will support fixed, portable, and mobile WiMAX operation. The design strategies focus on maximum operating frequency, low power consumption, low voltage operation, minimize number of gates/transistors, CMOS Technology (< 50 nanometer), reduced fabrication cost, high speed applications in WiMAX/WiFi/Satellite communications, flexibility, programmability, and service efficiency. The proposed ‘Programmable frequency synthesizer will be a new device with its varied application for WiMAX/WiFi/Satellite and other wireless communication systems. The transceiver will support fixed, portable, and mobile WiMAX operation.

This paper employs nationwide, large-scale field survey data to provide the first empirical evidence on the impact of human educational capital on the acquisition of health entitlement among Chinese migrant workers. The findings of this study hold significant practical implications for the formulation of policies aimed at improving the health protection of migrant workers, as well as for socioeconomic policies during China's transitional period.

Using the IVProbit model, this research examines how human educational capital influences the attainment of health entitlement among migrant workers in China by analyzing the impact and mechanism of education on health entitlement. The study is based on the China Migrants Dynamic Survey (CMDS) data from 2018, which include 100,177 observations.

For migrant workers in China, higher levels of education have a significant positive effect on the acquisition of health entitlements, including medical insurance, health records and health education. The positive impact of human educational capital on health entitlements is more significant for non-provincial cities and young-generation migrant workers. The results also show that human educational capital can influence the acquisition of health entitlements through mediators such as financial status, social integration and health status.

This study represents the first empirical attempt to evaluate the influence of human educational capital on the access of migrant workers in China to health rights and interests. Additionally, the study develops a theoretical framework to examine how the impact of human educational capital varies across migrant workers with different characteristics and their access to health entitlements.

The purpose of this paper is to conceptualize the indigenous concept of suzhi at individual and organizational levels, and identify its dimensions for human resource management (HRM) research and practice in China.

Based on a comprehensive review of suzhi literature, Chinese cultural and historical literature, as well as Western mainstream HRM research, a multidimensional suzhi framework is conceptualized.

As an indigenous expression, suzhi can be and has been adopted for Chinese HRM research and practice. As a multidimensional construct, one’s cognitive suzhi is jointly determined by corresponding moral suzhi, wenhua (knowledge-based) suzhi and zhuanye (professional) suzhi. Cognitive suzhi, in turn, determines one’s behavioral suzhi that drives employees to enhance organizational performance, and this relationship is moderated by psychological suzhi.

The proposed framework provides new insight for Chinese indigenous management research, particularly in developing suzhi measurement for different dimensions. It also informs HRM practices in recruiting, selection, performance analysis and employee career development.

The complexity of suzhi dimensions from an organizational HRM perspective is analyzed. The resulting suzhi framework offers new insight for HRM research and practices in China.

This study aims to consider active vision in low-visibility environments to reveal the factors of optical properties which affect visibility and to explore a method of obtaining different depths of fields by multimode imaging.Bad weather affects the driver’s visual range tremendously and thus has a serious impact on transport safety.

A new mechanism and a core algorithm for obtaining an excellent large field-depth image which can be used to aid safe driving is designed and implemented. In this mechanism, atmospheric extinction principle and field expansion system are researched as the basis, followed by image registration and fusion algorithm for the Infrared Extended Depth of Field (IR-EDOF) sensor.

The experimental results show that the idea we propose can work well to expand the field depth in a low-visibility road environment as a new aided safety-driving sensor.

The paper presents a new kind of active optical extension, as well as enhanced driving aids, which is an effective solution to the problem of weakening of visual ability. It is a practical engineering sensor scheme for safety driving in low-visibility road environments.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.