Search results

Coastal zone ecological restoration project is of great significance to alleviate marine ecological degradation. Evaluating the effect of coastal ecological restoration projects and identifying the obstacle factors affecting their restoration level can provide an empirical basis for future Marine ecological restoration projects.

However, due to the initial stage of coastal zone ecological restoration projects, the actual monitoring data of coastal zone ecological restoration is relatively lacking. Based on the CRITIC-TOPSIS (combination of CRITIC method and TOPSIS method) method, combined with the subjective perception of the public and the actual data of the restoration project, this paper proposes an evaluation method of the coastal zone ecological restoration effect to obtain the specific implementation effect of the coastal zone ecological restoration project. The main obstacle factors affecting the evaluation of coastal ecological restoration effect are identified by using the obstacle degree model.

This paper conducted an empirical study on the restoration of sandy shoreline and coastal wetland in Qinhuangdao city. Based on the data of restoration projects and the subjective perception of ecological restoration by the public in Qinhuangdao city, the research results showed that the coastal zone ecological restoration effect of Qinhuangdao city was general. The quality of the restoration project and the public perception have an important influence on the evaluation of the restoration effect. Improving the quality of the restoration project, strengthening the public's participation in ecological restoration and allowing the public to better participate in the ecological restoration of the coastal zone can improve the effect of ecological restoration of the coastal zone in an all-round way.

The research results of this paper have a guiding role in the ecological restoration of coastal cities in the future, and also have a demonstration and reference role for the assessment of the effect of ecological restoration of coastal zones.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

The choices that international students make regarding abroad study destination selection or leave the host country after graduation are influenced by a variety of factors that are both related to positive and negative aspects of the host country.

This study builds on the push-pull factor theory and examines the factors that influence international students' decision to choose abroad study destination (Finland) or leave the country after their graduations. The data were collected through an online survey of 195 international students currently studying in Finland and were analysed using partial least squares structural equation modelling (PLS-SEM) technique. This method offers a flexible and robust approach to test relationships, particularly in situations where sample size and the conceptual model are small and complex.

The results show that international students' choice of study destination (Finland) is influenced by the host country's quality of life, academic excellence and economic factors such as salary and benefits. Unfamiliarity with the culture and language barriers have a negative impact on their decisions to stay in the host country after graduation.

By utilising a comprehensive analysis of both push and pull factors in relation to the host country, this study unveils a novel perspective in the field of international student mobility. The results provide insights to the institutional leaders and policymakers into how to attract and retain international students by focusing on the factors that matter most to international students. To attract more international students, higher education institutions (HEIs) should include career development activities, e.g. job fairs, language training, scholarships and internships in their curriculum. Moreover, it provides recommendations on how to create a welcoming and supportive environment that promotes academic excellence and career development.

There is a limited number of research work on critical success factors (CSFs) in public construction projects in Saudi Arabia. In response to this knowledge gap, the objective of this paper is to assess the impact of CSFs on the government construction projects in Saudi Arabia. The success factors are investigated from a broader consideration of failure criteria, from consideration of most effectiveness in successful project completion and also from consideration of the impact of implementing control processes for successful project completion.

This study has analysed the impact of success factors on construction projects in Saudi Arabia using a descriptive methodology. An exhaustive literature survey is undertaken to identify the success and failure factors related to government construction projects in Saudi Arabia. The survey data are sorted out and analysed by cost, schedule, technical, context and finance dimensions of the projects based on project types, engineering complexity, size, modality, jurisdictional control and funding approach. To evaluate the influence of success factors implementation, qualitative data were collected in a survey via a web-based questionnaire that was sent to officials working and occupying a responsible position in national project guidelines organizations and in government construction organizations in Saudi Arabia. In all, 28 CSFs were identified, ranked and evaluated for their impact on project success. The four identified factors belong to process categories of construction projects, nine factors belong to management of construction projects and 15 success factors are identified for impact assessment of implementation in construction projects.

The study's findings have identified and ranked the top five CSFs that significantly influence project outcomes, including meeting time targets, adhering to financial budgets, delivering desired outcomes for all stakeholders, effectively managing risks and assembling the appropriate team while optimizing resource allocation. Additionally, the research indicates that hindrances to projects primarily stem from execution, economic, human and political factors. The study advocates for strict controls over incomplete engineering designs and advises against contractors independently handling design work to ensure project success. Additionally, addressing contractors' qualifications and financial matters is crucial for project success. By highlighting these CSFs and challenges, the research provides actionable insights to enhance project management practices in the construction industry.

This study is limited to the infrastructure projects constructed by governmental bodies with the participation of officials from government organizations. Further study, including private projects and officials working on private projects, may be needed to generalized the research outcome.

Numerous studies have investigated CSFs in construction projects, but few have examined their relevance to Saudi Arabian government projects. This study aims to fill this gap by identifying key CSFs specific to Saudi Arabian public sector construction projects and assessing their impact on project success. It advocates for stringent controls in the Saudi Arabian construction sector, emphasizing the importance of preventing incomplete or altered engineering designs by contractors to increase the success rate of public sector projects. This research offers practical insights to stakeholders, advancing project management practices in Saudi Arabia's construction sector for improved outcomes and resource utilization.

Data represents a critical resource that enables construction companies’ success; thus, its management is very important. The purpose of this study is to assess the benefits of construction data risks management (DRM) in the construction industry (CI).

This study adopted a quantitative method and collected data from various South African construction professionals with the aid of an e-questionnaire. These professionals involve electrical engineers, quantity surveyors, architects and mechanical, as well as civil engineers involved under a firm, or organisation within the province of Gauteng, South Africa. Standard deviation, mean item score, non-parametric Kruskal–Wallis H test and exploratory factor analysis were used to analyse the retrieved data.

The findings revealed that DRM enhances project and company data availability, promotes confidentiality and enhances integrity, which are the primary benefits of DRM that enable the success of project delivery.

The research was carried out only in the province of Gauteng due to COVID-19 travel limitations.

The construction companies will have their data permanently in their possession and no interruption will be seen due to data unavailability, which, in turn, will allow long-term and overall pleasant project outcomes.

This study seeks to address the benefits of DRM in the CI to give additional knowledge on risk management within the built environment to promote success in every project.

This research aimed to investigate the mediating role of social media engagement in the relationship between differentiation-oriented content and purchase intentions. Additionally, this research studies the moderating impact of entrepreneurial social media skills in the relationship between social media engagement and purchase intentions.

The research proposes a positivist research philosophy, deductive research approach and survey research strategy. Data were collected from followers of social media pages of small and medium businesses operating in the fields of groceries, food items, apparel and supplies in Pakistan. Respondents were selected randomly. The descriptive statistics were calculated first, followed by reliability and validity analysis as part of the measurement model. Finally, mediation and moderation analyses were run by using structural equation modeling.

Results of the study confirm that differentiation-oriented content has a positive relationship with purchase intentions and social media engagement mediates this relationship. Results further confirm that the social media skills of entrepreneurs moderate the relationship between social media engagement and purchase intentions.

From a practical point of view, this study will potentially help entrepreneurs in Pakistan unveil the undiscovered potential of social media and understand the importance of social media marketing campaigns in crisis situations. It will unlock the importance of entrepreneurial training and development to better adapt to the dynamic and vibrant world of social media.

This is the first study that investigates the relationship between differentiation-oriented content and purchase intentions. Additionally, the current study adds to existing knowledge by proposing entrepreneurial social media skills as moderators in the relationship of social media engagement with purchase intentions.

The purpose of this study is to examine the impact of the characteristics and competencies of entrepreneurs on business performance within the creative industry in Indonesia. By creative industry, is refer to market-oriented enterprises that use creativity and intellectual capital to create, produce, distribute and disseminate creative goods and services through media channels.

This study measured six entrepreneurial characteristics: creative and innovative, risk-taking, need for achievement, leadership, autonomy and proactiveness. In addition, it measured five entrepreneurial competencies: opportunity, strategy, relationship, organization and learning. Data collection was conducted using a questionnaire administered to 294 entrepreneurs operating businesses within the creative industry in Java, Sumatra and Kalimantan, Indonesia.

The findings of this study demonstrated that entrepreneurial characteristics and competencies had a significantly positive effect on business performance. Creativity and innovation emerged as the most influential characteristics, whereas strategy stood out as the most influential competency.

This study aimed to examine whether the personal qualities of entrepreneurs as identified within conventional industries − specifically their entrepreneurial characteristics and competencies − had an impact on business performance in the creative industry in Indonesia. Conventional industries, unlike creative industries, are characterized by the production and distribution of physical goods or basic services, with less emphasis on creative innovation as the core driver.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.