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This paper aims to propose a conceptual scale model of mobile drilling robot according to the actual drilling rig and working conditions to improve the safety and automation of drilling in tunnel construction and coal mining applications.

A couple of pinion and rack serves as the support mechanism driven by a motor with low rotation speed at high power, and these components are assembled in the center of the robot to tightly fasten the whole body together. The drilling rod and the sleeve are connected through a hole with screw thread so that the rod feeds and rotates simultaneously along with the sleeve. The robot model is automatically controlled by a single-chip microcomputer, and the anti-disturbance circuit is designed as well. A five-step rule obstacle avoidance method is proposed to ensure safe and reliable movement.

The results of simulation experiments on drilling operation do indicate that the mechanism and control method are feasible and effective.

The robot is nearly complete but indeed remains only an experimental machine.

The design of the mechanism structure for the conceptual robot is novelty. The method of five-step rule obstacle avoidance can improve reliability of obstacle avoidance according to the experimental results, which can meet the requirements of complex working conditions underground coal mine.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

Poverty alleviation is a global challenge. Human society has never ceased to fight against poverty. China was once the developing country with the largest rural poor population in the world. Remarkable achievements have been made in China’s antipoverty program over the past decades, shaping a unique poverty reduction strategy with Chinese characteristics. The purpose of this paper is to first review the history of China’s rural reform and antipoverty, and then analyze the related policy systems, mechanism innovations and future challenges in poverty alleviation and development. At last, some specific policy implications were provided.

Literature on China’s antipoverty history was reviewed and mechanism innovations on targeted poverty alleviation strategy were investigated.

Along with the deepening of the rural reform, the poverty alleviation and development in new China have undergone six stages, and experienced a transformation from relief-oriented to development-oriented poverty alleviation. The object of poverty alleviation has gradually targeted with a transformation from poor counties/areas to villages/households, and the effectiveness of poverty alleviation is also gradually improved. However, the increase in the difficulty of antipoverty, fragile ecological environment, rapid population aging and rural decline poses challenges to the construction of a well-off society in an all-round way in China. Specific antipoverty measures were put forward based on the investigation. Finally, the authors emphasize the importance of strengthening the study of poverty geography.

This study investigates the history of China’s antipoverty policy and analyzes the future challenges for implementing targeted poverty alleviation policy. These findings will lay a foundation for the formulation of China’s antipoverty policies after 2020, and provide experience for poverty alleviation in other developing countries around the world.

The purpose of this study was to measure the innovative performance of a managed and owned mainland Chinese family business. The objective of the study was to assist an inheritor and/or successor of a family business and to find management problems in innovative activity.

To improve the innovative technical efficiency (TE) of the business, the study offers methods that enhance the allocation of resources to provide outcomes that improve the core competitiveness of the business and realize the sustainable development of the business. Innovation performance is a well-organized and efficient way of turning innovation input into innovation output. Human input, research and development expenditures measure innovation input. Patent output and other outputs, which include total labor productivity and asset liability ratios, measure innovation output. To complete the study’s task, the innovative performance of 46 Chinese listed family run and owned businesses were evaluated based on the data envelopment analysis and the Banker, Charnes and Cooper model.

The results of the study show that the overall TE of innovation in a Chinese family run and owned business is low and that the returns to scale of most such businesses is decreasing, and furthermore, that the overall innovation performance of is low.

The implications from the study further suggest that for business efficiency and increased profit a beneficiary of a Chinese family-owned business should optimize the firm’s size and resource allocation.

The present paper tries to give a new vision on the firm's future evolution forecasting. By taking into account some of the current values of its symptoms and applying one of the most used models in the grey systems theory, namely the GM(1,1), the predictions related to its future symptoms' values can be determined. Having these projected values and the grey economic-financial matrix, K, the future diseases that can hit a company can be depicted along with their causes. The paper aims to discuss these issues.

Forecasting the future development of a firm is always an important issue in firm's survival in nowadays economy. Most of all, it is extremely important to be aware all the time about the inner and outer factors than can make a difference between a successful and a bankrupt firm. For this, here the authors have used three GM(1,1) models for forecasting the future symptoms (expressed through financial indicators) and performance indicator of a firm. Each time, based on the determined accuracy rate, a specific GM model has been chosen for every indicator's forecasting.

Considering some previous researches and findings in bankruptcy modelling and diagnosis, this paper enlarges their applicability by adding the possibility to make future predictions on the indicators' evolution and to observe and to better manage their causes. As it was expected, the GM(1,1) models used for the forecasting of the various time series variables taken into account were different from one case to another, choosing the best-specific model for each variable case conducted to more accurate data-fit, with direct results in the causes hierarchy.

By knowing the main causes that determine a certain state in firms' development and understanding them, the manager can action upon them in a manner that can make the difference between a bankrupt and a real successful firm.

The paper succeeds in enlarging the view regarding bankruptcy forecasting by adding a dynamic view over the considered variables. If, in most of the cases when facing with financial forecasting, a single model is used for predictions, here the best GM model has been chosen for each variable based on the obtained accuracy rate. The results are concluding.