Search results

The purpose of this paper is to improve the performance of the genetic algorithm-based compliant robot path planning (GACRPP) in complex dynamic environment by proposing an improved bidirectional rapidly exploring random tree (Bi-RRT)-based population initialization method.

To achieve GACRPP in complex dynamic environment with high performance, an improved Bi-RRT-based population initialization method is proposed. First, the grid model is adopted to preprocess the working space of mobile robot. Second, an improved Bi-RRT is proposed to create multi-cluster connections between the starting point and the goal point. Third, the backtracking method is used to generate the initial population based on the multi-cluster connections generated by the improved Bi-RRT. Subsequently, some comparative experiments are implemented where the performances of the improved Bi-RRT-based population initialization method are compared with other population initialization methods, and the comparison results of the improved genetic algorithm (IGA) combining with the different population initialization methods are shown. Finally, the optimal path is further smoothed with the help of the technique of quadratic B-spline curves.

It is shown in the experiment results that the improved Bi-RRT-based population initialization method is capable of deriving a more diversified initial population with less execution time and the IGA combining with the proposed population initialization method outperforms the one with other population initialization methods in terms of the length of optimal path and the execution time.

In this paper, the Bi-RRT is introduced as a population initialization method into the GACRPP problem. An improved Bi-RRT is proposed for the purpose of increasing the diversity of initial population. To characterize the diversity of initial population, a new notion of breadth is defined in terms of Hausdorff distance between different paths.

The purpose of this paper is to analyze the historical pattern of environmental cost due to grain production in China and to provide further implications of technologies and policies for the transformation of China’s agricultural development toward sustainable intensification.

The data sets about grain production, arable land and chemical fertilizer use in China were collected from FAO, NBSC, and IFA. Greenhouse gas emissions were estimated using life cycle assessments. The policies concerning grain production and the environment were collected from the Ministry of Agriculture, and the State Council of China.

China has produced enough food to feed its growing population, but has neglected the resource-environmental costs of grain production since 1978. Consequently, China’s grain production is always accompanied with a high cost of resource and environment sustainability. However, from 2006 to 2015, the growth rate of grain production has surpassed that of chemical fertilizer consumption, resulting in improvement in nutrient use efficiency and decreasing trends of environmental cost for grain production. This could be partially attributed to technology innovations, such as Soil-Testing and Fertilizer-Recommendations (STFR), soil quality and crop management improvement, and so on, and policy supports (policies of STFR, soil quality improvement, and high-yield construction). This indicated that China’s grain production is starting to transform from high-input and high-output model to “less for more.”

This study is the first to determine the detailed, historical role of technological innovation and agri-environmental policy on the sustainability of grain production in China. The findings should have significant implications for technology and policy for the transformation of China’s agriculture development to sustainable intensification.

The purpose of this paper is to improve the accuracy of the facial expression recognition by using genetic algorithm (GA) with an appropriate fitness evaluation function and Pareto optimization model with two new objective functions.

To achieve facial expression recognition with high accuracy, the Haar-like features representation approach and the bilateral filter are first used to preprocess the facial image. Second, the uniform local Gabor binary patterns are used to extract the facial feature so as to reduce the feature dimension. Third, an improved GA and Pareto optimization approach are used to select the optimal significant features. Fourth, the random forest classifier is chosen to achieve the feature classification. Subsequently, some comparative experiments are implemented. Finally, the conclusion is drawn and some future research topics are pointed out.

The experiment results show that the proposed facial expression recognition algorithm outperforms ones in the existing literature in terms of both the actuary and computational time.

The GA and Pareto optimization algorithm are combined to select the optimal significant feature. To improve the accuracy of the facial expression recognition, the GA is improved by adjusting an appropriate fitness evaluation function, and a new Pareto optimization model is proposed that contains two objective functions indicating the achievements in minimizing within-class variations and in maximizing between-class variations.

This paper aims to find optimal emission reduction investment strategies for the manufacturer and examine the effects of carbon cap-and-trade policy and uncertain low-carbon preferences on emission reduction investment strategies.

This paper studied a supply chain consisting of one manufacturer and one retailer, in which the manufacturer is responsible for emission reduction investment. The manufacturer has two emission reduction investment strategies: (1) invest in traditional emission reduction technologies only in the production process and (2) increase investment in smart supply chain technologies in the use process. Then, three different Stackelberg game models are developed to explore the benefits of the manufacturer in different cases. Finally, this paper coordinates between the manufacturer and the retailer by developing a revenue-sharing contract.

The manufacturer's optimal emission reduction strategy is dynamic. When consumers' low-carbon preferences are low and the government implements a carbon cap-and-trade policy, the manufacturer can obtain the highest profit by increasing the emission reduction investment in the use process. The carbon cap-and-trade policy can encourage the manufacturer to reduce emissions only when the initial carbon emission is low. The emission reduction, order quantity and the manufacturer's profit increase with the consumers' low-carbon preferences. And the manufacturer can adjust the emission reduction investment according to the emission reduction cost coefficient in two processes.

This paper considers the investment of emission reduction technologies in different processes and provides theoretical guidance for manufacturers to make a low-carbon transformation. Furthermore, the paper provides suggestions for governments to effectively implement carbon cap-and-trade policy.

The reinforced concrete open-web sandwich slab is composed of upper rib, lower rib, surface plate and shear key and was applied to long-span structure crossing at 18–30 m. The shear-bearing capacity of shear key, having vital effects on the slab’s bearing capacity, is analysed to present its calculation formula used for the engineering application of the slab.

The shear-bearing capacity of shear key is analysed by the strut-and-tie model and the benchmark model established by the finite element method. Furthermore, the design formula of its shear capacity is given by the parametric analysis of FEM to adjust the result of the strut-and-tie model, using multivariate linear regression analysis of these parameters.

The calculation result of the benchmark model is compared with those of the strut-and-tie model and the standard formula, which indicates that the result of the strut-and-tie model is closer to that of the benchmark model than that of the standard formula. Moreover, the parametric analysis of the finite element model indicates that the volume–stirrup ratio of the shear key and the compression strength of the concrete have lesser effect on the shear capacity compared with the longitudinal reinforcement ratio and the shear-to-span ratio of the shear key and the relative section height of the rib.

The shear capacity of the shear key is provided in the paper by combining the finite element method and the strut-and-tie model, which is different from the calculation of the shear key in local codes and Chinese code, based on the theory of short corbel and the experiment of member. Furthermore, the formula of the shear capacity could be employed in the design and construction of the RC open-web sandwich slab, mainly used in the public and industrial multi-story building with long span to save the dwindling land resource currently.

The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results.

This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion.

The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression.

To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

Literature about transparency in public-sector organizations has been attracting the attention of scholars for the last two decades. This study reviews the existing literature with the intention of creating a description of the state of the art, categorized by geographical areas, levels of government, topics, and methodologies.

The authors have developed a structured literature review following a rigorous protocol. The initial search was launched on 25 April 2022 on Scopus and Web of Science, resulting in 3,217 articles. After removing duplicates and studies that did not meet all the inclusion criteria specified in the review protocol, the final sample includes 956 articles from 1991 to 2021.

The analyses show a considerable increase in studies since 2005, especially in the last two years, when 30% of the publications have been produced. Most of the studies analyze the national/central level of government. Many authors compare different countries, while other scholars focus on specific countries, overall, the USA and the UK. The local level of government has also been widely studied, especially in the Spanish and Chinese contexts. The most frequently used methodologies are quantitative and empirical techniques, and the most common topics are those associated with accountability.

This study uses a huge sample (956 articles over the period 1991–2021), which has never been used before, to examine the literature on transparency. The structured literature review facilitates the identification of gaps that can be filled by future studies. These include analyzing transparency in specific geographical areas like Africa, Asia, and Latin America, studying transparency at different levels of government, especially at the regional and federal levels, and providing comparative studies and case study collections.

The purpose of this paper is to study the effect of two positive organizational factors: the perceived supervisor support (PSS) and the self-efficacy (SE) on nurses' burnout (BO), which concurrently affect the turnover intention (TI) and the mediating role of BO in this relationship.

A cross-sectional survey-based study of a sample of 552 Lebanese registered nurses from 19 Lebanese hospitals was conducted.

The authors’ findings confirm that PSS and SE both reduce the level of BO and the turnover intention significantly. The higher the perceived supervisors' support and the nurses' SE, the less they experience BO. BO has partially mediated the relationship of the PSS and SE on TI. This study reveals that supervisors' support is well perceived by Lebanese nurses, whose s is relatively high, while their levels of BO are considered moderate. However, BO levels vary proportionally with demographic variables, namely age, work experience, gender, marital status and education.

This study provides new evidence on the relationship between PSS, SE and BO and turnover intention of Lebanese nurses. It is unique in studying the role of nurses' SE with regard to BO and TI and improving the quality of nurses' work life. It shows the significance of the supervisors' role in supporting the psychological state of nurses. The context of the study, Lebanon, is also novel as it differs from advanced economies institutionally, culturally and in legal frameworks that govern the employee–supervisor relationships.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.