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We analyzed the effects of different combinations of organic soil amendments (OSAs) and chemical fertilizers on agrifood production, focusing on banana yields in China, the second-largest producer of bananas globally.

We computed these combinations by dividing the expenditures on OSAs by those on chemical fertilizers and called them OSA-CF ratios. First, we classified farmers based on quintiles of expenditures on chemical fertilizers. Then, we studied the association between OSA-CF ratios and banana yields for each quintile. We also considered an alternate specification in which farmers were grouped along the OSA-CF ratio continuum. The first group comprised farmers not using OSAs. Their OSA-CF ratio was zero. Farmers applying low, medium, and high OSA-CF ratios constituted groups two, three, and four; the groups were delineated based on the OSA-CF ratio tertiles, and the associations between tertiles of OSA-CF ratios and banana yields for each quintile were analyzed. The data used in this study were collected by surveying 616 households in three major banana-producing provinces (Guangdong, Hainan, and Yunnan) of China. Standard linear regressions and the two-stage predictor substitution method were employed to complete the analysis.

There were variations in the effects of OSA-CF ratios on banana yields obtained by farmers iifferent quintiles. For the first and second quintiles, low, medium, and high OSA-CF ratios improved banana yields relative to not using OSAs. For farmers in the first quintile using only chemical fertilizers, applying a low OSA-CF ratio was associated with an improvement of 792 kg/mu in banana yields. For their counterparts in the second quintile, the same transition was associated with a gain of 534 kg/mu. For the fifth quintile, comprising farmers spending 320 yuan/mu or more on chemical fertilizers, applying a high OSA-CF ratio instead of using only chemical fertilizers was associated with a 401 kg/mu decline in banana yields. Even so, for this group, no differences were observed between the yields of farmers not applying OSAs and those using low and medium OSA-CF ratios.

Banana farmers in southern China, using only chemical fertilizers, can improve yields by combining them with OSAs if their chemical fertilizer expenditures are less than 66.67 yuan/mu. Those using only chemical fertilizers and spending between 68 yuan/mu and 300 yuan/mu on them can maintain yields by applying OSAs in conjunction with chemical fertilizers. However, yields may decline for farmers using only chemical fertilizers and spending 320 yuan/mu or more on them if they incorporate OSAs such that the OSA-CF ratio reaches 0.78 or higher. Overall, combining OSAs with chemical fertilizers can improve yields while attenuating the adverse effects of chemical fertilizers on the environment. Policymakers should inform farmers of these benefits and accelerate the transition to sustainable agriculture through educational and awareness programs.

Farmers apply OSAs such as organic fertilizers and farmyard manure to adjust and remedy soil nutrition to improve farm productivity. However, little is known about how combining OSAs with chemical fertilizers affects banana yields. This study provided the first attempt to explore the associations between OSA-CF ratios and banana yields using cross-sectional data on farming households.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

Farm income in the agricultural sector is susceptible to natural and market risks. A large body of literature has studied the effects of cooperative membership on household welfare, technical efficiency, productivity and production behavior, yet little has been known about the impact of cooperative membership on farm income volatility. This paper aims to fill this research gap by investigating the relationship between cooperative membership and farm income volatility of Chinese pig farmers and drawing policy implications.

This paper examines the effect of cooperative membership on farm income volatility, using data from a two-round survey of pig farmers in China. The authors employ an endogenous switching regression model to address the selection bias issues associated with unobserved factors simultaneously affecting farmers' participation in agricultural cooperatives and income earning activities.

Using household panel from a two-round survey of 193 pig farmers in China, this analysis highlights two key findings: (1) agricultural cooperative membership has significant and positive effect on farm income stability and (2) the impact of cooperative membership on farm income stability varies with production scale.

This research makes two contributions to the literature. First, this study contributes to the scant literature exploring the relationship between agricultural cooperatives and farm income stability. Second, to the best of the authors' knowledge, this is the first study that explores such relationship in a livestock sector. The pig sector in China and around the developing world has been increasingly challenged by multifaceted risks (e.g. price fluctuations, epidemic diseases, environmental regulations), and understanding the role of agricultural cooperatives in farm income stability of pig farmers is of great practical and policy significance.

In the new economy, governed by technological progress and informational abundance, e-government service represents one of the drivers of the digital economy and society. The government and its institutions have the role of stimulating, leading, and controlling the process of transition to the digital society, which is a key component for the future prosperity and resilience of the European Union (EU). With focus on a better functioning of society by improving the citizens' access and use of e-government services, in this work we aim to identify the factors that influence the online interaction of individuals with public authorities in the EU member states. We used panel data for the EU member states in the period 2013–2021 to investigate the determinants of individuals' interaction with public authorities through institutional websites, using clustering regression with fixed effects, which allows both the clustering of the states and obtaining different slope parameters for each cluster. The results indicated the grouping of the EU states in an optimal number of two clusters, and the fixed effects regression clustering pointed out different coefficients for the two clusters, indicating distinct patterns. The main factors that influence the online interaction of citizens with public authorities are related to internet use, education, and government effectiveness, but the impact is different for the two clusters, depending on the specifics of the component countries.

The purpose of this paper is to present a visual detection approach to predict the poses of target objects placed in arbitrary positions before completing the corresponding tasks in mobile robotic manufacturing systems.

A hybrid visual detection approach that combines monocular vision and laser ranging is proposed based on an eye-in-hand vision system. The laser displacement sensor is adopted to achieve normal alignment for an arbitrary plane and obtain depth information. The monocular camera measures the two-dimensional image information. In addition, a robot hand-eye relationship calibration method is presented in this paper.

First, a hybrid visual detection approach for mobile robotic manufacturing systems is proposed. This detection approach is based on an eye-in-hand vision system consisting of one monocular camera and three laser displacement sensors and it can achieve normal alignment for an arbitrary plane and spatial positioning of the workpiece. Second, based on this vision system, a robot hand-eye relationship calibration method is presented and it was successfully applied to a mobile robotic manufacturing system designed by the authors’ team. As a result, the relationship between the workpiece coordinate system and the end-effector coordinate system could be established accurately.

This approach can quickly and accurately establish the relationship between the coordinate system of the workpiece and that of the end-effector. The normal alignment accuracy of the hand-eye vision system was less than 0.5° and the spatial positioning accuracy could reach 0.5 mm.

This approach can achieve normal alignment for arbitrary planes and spatial positioning of the workpiece and it can quickly establish the pose relationship between the workpiece and end-effector coordinate systems. Moreover, the proposed approach can significantly improve the work efficiency, flexibility and intelligence of mobile robotic manufacturing systems.

The purpose of this study is to improve vehicles’ brake stability, the problem of constraint control for an antilock braking system (ABS) with asymmetric slip ratio constraints is concerned. A nonlinear control method based on barrier Lyapunov function (BLF) is proposed not only to track the optimal slip ratio but also to guarantee no violation on slip ratio constraints.

A quarter vehicle braking model and Burckhardt’s tire model are considered. The asymmetric BLF is introduced into the controller for solving asymmetric slip ratio constraint problems.

The proposed controller can implement ABS zero steady-state error tracking of the optimal wheel slip ratio and make slip ratio constraints flexible for various runway surfaces and runway transitions. Simulation and experimental results show that the control scheme can guarantee no violation on slip ratio constraints and avoid self-locking.

The slip rate equation with uncertainties is established, and BLF is introduced into the design process of the constrained controller to realize the slip rate constrained control.

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

During the COVID-19 pandemic, an infodemic erupted on social media, leading to a surge in negative disclosure behaviors such as expressing dissatisfaction and releasing negative emotions. By extending the elaboration likelihood model and the Big Five personality theory to the domain of online self-disclosure, we aimed to identify the factors that influence negative disclosure behavior.

We investigated how the features of negative information content, information sources and recipients’ social perceptions influence how social media users disclose negative information. We also examined the moderating roles of personality traits in this process. To validate the model and test our hypotheses, we collected cross-sectional data from 456 social media users.

Empirical results reveal that (1) information overload, topic relevance, attractiveness of information sources, peer approval of negative disclosure and social influence on negative information strengthen the intention to disclose negative information. (2) The perception of social risk weakens the intention to disclose negative information. (3) Openness to experience, extraversion and neuroticism strengthen the relationship between the intention to disclose negative information and actual disclosure behavior.

Our results not only provide new perspectives on the decision-making mechanisms behind negative disclosure behavior but also extend personality research within the context of the dissemination of negative information. Furthermore, it offers insights into negative information dissemination on social media platforms, with significant implications for various stakeholders.

This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects.

This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions.

Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud.

This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.