Search results

By analyzing the shortcomings of existing insulator robots, a novel ultra high voltage (UHV) insulator climbing robot, which could transfer between adjacent insulator strings, is proposed for operation on 800KV multiple-string insulators. An extended inchworm-like configuration was chosen and a stable gripping claw suitable for the insulator string was designed to enable the robot to multiple-string insulators. Then a set of nonheuristic structural parameters that can influence energy consumption was chosen to formulate a nonlinear optimization problem based on the configuration, which improved the energy efficiency of the robot during progressing along a string of insulator.

The purpose of this paper is to design an insulator climbing robot for operation on UHV multiple-string insulators, which could transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency.

A physical prototype was constructed that can operate at the speed of six pieces per minute (approximately 1.44 meters per minute) on a single string and complete transference between adjacent strings in 45 s. The energy consumption of joints during progressed along a string of insulator had been reduced by 38.8% with the optimized parameters, demonstrating the consistency between the experimental and simulation results.

An insulator climbing robot for operation on UHV multiple-string insulators has been developed with energy consumption optimization design. The robot can transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency. The CLIBOT could be expanded to detect or clean the insulators with similar specification.

This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

Risk analysis plays a vital role in controlling and managing cost overruns in complex construction projects, particularly where uncertainty is high. This study attempts to address an important issue of cost overrun that encountered by metropolitan rapid transit projects in relation to the significance of risk involved under high uncertainty.

In order to solve cost overrun problems in metropolitan transit projects and facilitate the decision-makers for effective future budgeting, a cost-risk contingency framework has been designed using fuzzy logic, analytical hierarchy process and Monte Carlo simulation.

Initially, a hierarchical breakdown structure of important complexity-driven risk factors has been conceptualized herein using relative importance index. Later, a proposed cost-risk contingency framework has investigated the expected total construction cost in order to consider the additional budgeted cost required to mitigate the risk consequences for particular project activity. The results of cost-risk analysis imply that poor design issues, an increase in material prices and delays in relocating facilities show higher dependency and increase the risk of cost overrun in metropolitan transit projects.

The findings and implication for project managers could possibly be achieved by assuming the proposed cost-risk contingency framework under high uncertainty of cost found in this research. Furthermore, this procedure may be used by experts from other engineering domains by replacing and considering the complex relationship between complexity-risk factors.

This study contributes to the body of knowledge by providing a practical contingency model to identify and evaluate the additional risk cost required to compute total construction cost for getting stability in future budgeting.

This paper aims to explore the applicability and strengths of proposing the three-paradigm hybrid simulation (HS) approach to developing and analysing strategies. The objective of the modelling effort is to conceptually illustrate its use in strategic planning by combining with the threat-opportunity-weakness-strength (TOWS) matrix, which builds a bridge between strategic management with the operations research community. The authors also aim to introduce a modelling framework to help model designers to apply HS to their own business issues.

The paper presents a process to develop a HS model associated with the development of strategies using the TOWS matrix.

After developing the model and testing four strategies, the best option for the supermarket to increase market share and sales is implementing the strength–opportunity strategy, which involves online shopping to adapt to the digital world.

First, some modelling assumptions are used to simplify the development process, but they need further validation. Second, the real data collection is limited. Third, the personal learning edition of the simulation software is not a comprehensive version and has some limitations.

The hybrid model and the scenario planning introduced, in this study, could allow decision makers to rehearse the potential strategy before actual implementation. The framework is easy to implement to other business and industry.

This study links HS with strategic management, which has not been performed previously and evaluates the capability of HS in strategic planning. The functionality of the modelling platform has been tested for simulating a completely dynamic system.

The global prevalence of social media and its potential to cause polarization are highly debated and impactful. The previous literature often assumes that the ideological bias of any media outlet remains static and exogenous to the polarization process. By studying polarization as a whole from an ecosystem approach, the authors aim to identify policies and strategies that can help mitigate the adverse effects of polarization and promote healthier online discourse.

To investigate online polarization, the authors perform a systematic review and analysis of approximately 400 research articles to explore the connection between cognitive bias and polarization, examining both causal and correlational evidence. The authors extensively evaluate and integrate existing research related to the correlation between online polarization and crucial factors such as public engagement, selective exposure and political democracy. From doing so, the authors then develop a PolarSphere ecosystem that captures and illustrates the process of online polarization formation.

The authors' review uncovers a wide range of associations, including ideological cognition, bias, public participation, misinformation and miscommunication, political democracy, echo chambers and selective exposure, heterogeneity and trust. Although the impact of bias on social media polarization depends on specific environments and internal/external conditions, certain variables exhibit strong associations across multiple contexts. The authors use these observations as a basis from which to construct PolarSphere, an ecosystem of bias-based polarization on social media, to theorize the process of polarization formation.

Based on the PolarSphere ecosystem, the authors argue that it is crucial for governments and civil societies to maintain vigilance and invest in further research to gain a deep comprehension of how cognitive bias affects online polarization, which could lead to ways to eliminate polarization.

The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team psychological empowerment and information systems development (ISD) team performance.

A survey approach was employed to collect time-lagged, multi-source data for testing the proposed model of this study (N = 514 responses from 88 teams). PROCESS macro was used to analyze the data to generate empirical results.

The results suggest that instrumental AI use indirectly influences ISD team performance by enhancing team psychological empowerment. Additionally, it moderates the effects of team-level LMX on team psychological empowerment and ISD team performance. Furthermore, the results demonstrate that the interaction effect of LMX and instrumental AI use on ISD team performance is mediated by team psychological empowerment.

While research on ISD consistently demonstrates that teams, data, and technology collectively contribute to the success of these projects. What is less known, however, is how the exchange relationship between ISD teams and their leader, as well as technological factors, contribute to ISD projects. This study draws on LMX theory to propose how team-level LMX and the instrumental use of AI by team members influence team psychological empowerment and ISD team performance. The study puts forth a mediated moderation model to develop a set of hypotheses. It offers valuable contributions to AI and LMX, along with implications for ISD team management.

COVID-19, an infectious disease first identified in China, has resulted in an ongoing pandemic all over the world. Most of the countries have been experiencing a difficult period during the fighting of this pandemic. The purpose of this study is to explore the effect of privacy concerns and cultural differences on public opinion related to the pandemic. The authors conducted a comparative analysis of public opinion in the US and in China as a case study, in order to determine the results.

National policies on important issues faced during the COVID-19 pandemic in the US and in China were examined through a comparative analysis. The authors used text clustering and visualization to mine public opinion on two popular social media platforms, Twitter and Weibo. From the perspectives of concern for privacy and of national culture, this study combines qualitative and quantitative analysis to discover the acceptance level of national policies by the public in the two countries.

The anti-pandemic policies and measures of the US and China reflect the different characteristics of their respective political systems and national cultures. When considering the culture of the US, it is hard to establish and enforce a rigorous regulation on either mask wearing in public or home quarantine on the national level. The opinions of US people are diverse, regarding national COVID-19 policies, but they are rather unified on privacy issues. On the other hand, Chinese people show a high acceptance of national policies based on their mask-wearing customs and their culture of collectivism.

Prior studies have paid insufficient attention to the ways in which user privacy and cultural difference affect public opinion on national policies between the US and China. This case study that compares public opinion on current and topical issues which are closely bound up with public life shows originality, as it innovatively provides a cross-cultural perspective on the research of public opinion dissemination during emergencies by considering the ongoing COVID-19 pandemic.

Poor performance remains a challenge for the construction industry worldwide. One of the key performance indicators of the construction industry is the timely delivery of projects. Despite the recent methodological and technological advances in the field, project-overrun remains a significant challenge for the industry. This paper seeks to propose practical solutions that allow overcoming the challenges and promote the opportunities for improving the performance of the construction projects in Dubai.

This study focussed on the construction projects in Dubai; therefore, this research adopted a sequential mixed approach in two stages. The first stage involved face-to-face interviews with seven carefully selected construction professionals. Their answers were analysed to provide with the literature study “the informed-basis for the development of the online questionnaire”. The second stage involved an online survey administrated to 425 carefully selected construction organisations working in Dubai. Accordingly, a meticulous analysis for the prime causes of project overruns has also been undertaken. This analysis assisted proposing the most suitable solutions-based technologies that enabled alleviating overruns in the construction projects.

The findings revealed that, there was a consensus agreement on the formidable opportunities for improving the performance of the construction industry in general and in particular in Dubai. These opportunities are intrinsically linked with the adoption of the latest technologies such as building information modelling, augmented reality, virtual reality and the artificial intelligence (AI). Whereas, adopting AI has already assisted two public authorities to release No Objection Certificates and work permits effectively within one day instead of 14 working days, which has saved 90% of the time and cost. Likewise, adoption of the AI has assisted delivering the construction project with a 9% of time saving and a 6% of cost saving due to embracing an automated system that enabled them to instantly detect and report the delays, once occurred.

The main limitation of this study is that the study was limited to the construction industry in the Emirate of Dubai. Therefore, future research could target the whole United Arab Emirates construction industry to propose the practical solution on the country level.

The literature study is replete with solutions, which tend to be theoretical more than practical. Therefore, the proposed practical recommendations will significantly assist the construction industry to improve its suboptimal performance to rescind the sovereignty of the irrelevant involvements. The research recommended establishing independent entity to lead the change in the construction industry; this entity will have the power of enacting rules and legislations. Furthermore, this independent entity will have the power and authority of dictations and impose sanctions on the non-committed organisations that are reluctant to adopt the recommended technologies and approaches.

Based on the findings of the study, this paper draws a road map for the construction industry by determining practical solutions for improvements starting with an establishment of an independent authority that selects and tests the most appropriate technologies and approaches to contribute to performance improvements.