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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.

This chapter elaborates on entrepreneurship in developed and developing countries and focuses on the optimization of entrepreneurial activities. Various scenarios are considered: independent functioning of the market, integration in the form of reorganization (mergers and acquisitions), integration in the form of clustering, and integration in the form of innovational networks and technological parks. The optimal structure of the integration processes and best-case scenarios for its implementation to accelerate the rate and increase the quality of economic growth are substantiated. The potential for uptake of integration processes in stimulating economic growth through entrepreneurship is determined by the level of institutionalization in an economy. In developed countries, all forms of company integration are characterized by the high level of institutionalization, which allows for their effective use for economic growth. Independent companies, mergers, and acquisitions restrain economic growth and reduce its quality, while clusters, technological parks, and innovational networks accelerate the rate of economic growth and increase its quality. In developing countries, integration processes in entrepreneurship have a different influence on economic growth and require further institutionalization.

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.

This chapter draws from a three-year ethnographic study focused on the educational and community interactions among working- and middle-class ethnic Chinese immigrants in a mid-western town in the United States. Aihwa Ong (1999) argues that “Chineseness” is a fluid, cultural practice manifested within the Chinese diaspora in particular ways that relate to globalization in late modernity, immigrants’ cultural background, their place in the social structure in their home society, and their new social class status in the context they enter. The study extends research focused on the complexities of social reproduction within larger global flows of Chinese immigrants. First, we describe how Chinese immigrants’ social status in their countries of origin in part shapes middle and working-class group’s access to cultural capital and positions in the social structure of their post-migration context. Second, we trace groups’ negotiation of their relational race and class positioning in the new context (Ong, 1999) that is often invisible in the processes of social reproduction. Third, we describe how both groups must negotiate national, community, and schooling conceptions of the model minority concept (Lee, 1996) that shapes Asian-American’s lived realities in the United States; yet the continuing salience of their immigrant experience, home culture, and access to cultural capital (Bourdieu, 2007) means that they enact the “model minority” concept differently. The findings suggest the complexity of Chinese immigrants’ accommodation of and resistance to normative ideologies and local structures that cumulatively contribute to social reproduction on the basis of class.

This paper discusses the applicability of different occupational health risk assessment (OHRA) methods in assessing noise hazards during the production phase of assembled precast concrete (PC) components and makes targeted recommendations based on the assessment results from multiple perspectives to reduce noise hazards in this phase.

In this paper, the noise levels of various plant operations are measured on-site and the actual working conditions of plant workers are investigated. Then, four distinct occupational health risk assessment (HRA) models are used to estimate the risk of noise hazards during the production of PC components. Finally, the results obtained from the various models are analyzed and discussed, and then the most appropriate method for assessing noise hazards at this stage is chosen accordingly.

The noise exposure levels of workers in the four processes of steel processing, concrete mixing, concrete vibrating and mold removal exceeded occupational exposure limits. Similarly, the risk associated with these four processes is relatively elevated. For risk assessment (RA) of noise hazards in the production phase of assembled PC components, both the Australian RA model and the occupational hazard risk index method can be used, with the latter being more applicable.

The assessment results acquired in this paper can serve as a reference for the government and other relevant agencies when determining inspection priorities. In addition, the measures and recommendations outlined in this paper serve as a guide for businesses and government agencies to strengthen the noise management in the production stage of PC components, thereby reducing the noise hazards in the production stage of assembled PC components.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

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.