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The purpose of this paper is to identify and analyse factors that affect contractors’ behavioural strategies in resolving disputed claims.

Factors were explored by a literature review and an open-ended questionnaire survey. In total, 9 hypotheses involving 12 factors were developed accordingly. Then a structured questionnaire survey was conducted, and 248 valid questionnaires were received from Chinese contractors. Partial least squares structural equation modelling was employed to test the hypotheses.

Factors that have the largest impacts on the contractual approach and the relational approach regarding obliging and compromising are favourability of evidence, time pressure and reputation, respectively. Unexpected results show that obliging behaviours are negatively correlated with procedural fairness but positively correlated with occurrence time of the dispute.

The results are based on correlation, although the research design improves the internal validity. Furthermore, this study belongs to single-level research. In the future, researchers can conduct multilevel research to enrich theories.

The findings not only enhance practitioners’ understanding of the factors influencing contractors’ behavioural strategies when dealing with disputed claims, but also offer insights into both parties’ ex ante focus of attention on specific factors to facilitate the subsequent dispute resolution.

This study furnishes a nuanced picture of multiple factors’ impacts on contractors’ behavioural strategies of claim-related dispute resolution, and thus supplements the relevant construction dispute management literature. From the perspective of contractual governance, it is one of those exploring drivers of contract application in problem situations. It extends the body of knowledge on this topic and hopefully will encourage more research on contractual governance from the reactive perspective.

How to depict conflict characteristics? Previous literature has overwhelmingly used intensity and frequency of conflict, resulting in an incomplete understanding of conflict itself and its impacts. To fill this knowledge gap, this paper aims to develop a comprehensive theoretical framework for conflict attributes.

Through a systematic and integrative literature review, this study has achieved the objectives by synthesizing the current state of knowledge on conflict and borrowing insights from event system theory.

A total of 16 conflict attributes were identified to constitute the event-oriented conceptualization of conflict, describing conflict from three dimensions – strength, time and space. Four promising areas for future conflict inquiry are proposed: linking the effectiveness of conflict to its attributes; exploring the interplay and configuration of multiple conflict attributes; progressing from variance- to process-oriented conflict theories; and developing symmetric/asymmetric views of conflict.

This paper conceptually clarifies conflict attributes from the event perspective and offers a nuanced understanding of conflict, which contributes to the current fragmented knowledge of conflict attributes. Scholars can build on this study’s findings to fill gaps and move conflict research forward. It also enhances researchers’ awareness of time and space and thus encourages more longitudinal exploration into the dynamics of conflict.

The learning ability on critical bargaining information contributes to accelerating construction claim negotiations in the win-win situation. The purpose of this paper is to study how to apply Zeuthen strategy and Bayesian learning to simulate the dynamic bargaining process of claim negotiations with the consideration of discount factor and risk attitude.

The authors first adopted certainty equivalent method and curve fitting to build a party’s own curve utility function. Taking the opponent’s bottom line as the learning goal, the authors introduced Bayesian learning to refine former predicted linear utility function of the opponent according to every new counteroffer. Both parties’ utility functions were revised by taking discount factors into consideration. Accordingly, the authors developed a bilateral learning model in construction claim negotiations based on Zeuthen strategy.

The consistency of Zeuthen strategy and the Nash bargaining solution model guarantees the effectiveness of the bilateral learning model. Moreover, the illustrative example verifies the feasibility of this model.

As the authors developed the bilateral learning model by mathematical deduction, scholars are expected to collect empirical cases and compare actual solutions and model solutions in order to modify the model in future studies.

Negotiators could refer to this model to make offers dynamically, which is favorable for the parties to reach an agreement quickly and to avoid the escalation of claims into disputes.

The proposed model provides a supplement to the existing studies on dynamic construction claim negotiations.

Remaining useful life (RUL) prediction for power transformer maintenance is a challenging task on heterogeneous data. Monitoring data of power transformers are not always compatible or in an identical format; therefore, RUL predictions traditionally work separately on different data. Moreover, chemical molecules used in RUL prediction can be transformed into each other under different conditions, thus forming a complete graph with uncertain adjacency matrix (UAM). This study aims to find and evaluate a new model to achieve better results of RUL prediction than the other baselines.

In this work, the authors propose a spatiotemporal complete graph convolutional network (STCGCN) for RUL prediction in two branches, in which daily and hourly features are extracted from correlated heterogeneous data separately. This study provides a thorough evaluation of the proposed model on real-world data and compare the proposed model with state-of-the-art RUL prediction models.

By using the multibranch structure and EucCos similarity aggregation, STCGCN was able to capture the dynamic spatiotemporal patterns on a variety of heterogeneous data and obtain more accurate prediction results, compared to other time series prediction methods.

In this work, the authors propose a novel multibranch structure to compute feature maps from two heterogeneous data sources efficiently and a novel similarity aggregation method to compute the spatial UAM within the complete graph. Compared with traditional time series prediction models, the model pays attention to the spatial relationships in time series data.

Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.

Methods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.

The simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.

The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.

The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices.

The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022.

The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices.

This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency.

The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

Determine the elements of young adult consumers' attitudes toward food safety using a food safety attitude (FSA) questionnaire and identify the factors influencing them.

This study adopts a descriptive and explanatory perspective to the research problem. Determination of students' attitudes was carried out by direct survey using a questionnaire. The ABC model of attitude was used to construct the statements in the questionnaire. The respondents' answers were analyzed using statistical methods.

The proposed questionnaire has proven to be a useful tool for assessing food safety attitudes and has identified important new elements in consumers' attitudes. Students' attitudes toward food safety are shaped by sociodemographic and psychosocial factors such as customer type, attitude toward risk, and how they make food purchasing decisions.

Information about students' attitudes was obtained only from surveys. The survey results provide valuable insights for business practice.

Findings can be used to increase the effectiveness of efforts by various organizations aimed at changing consumer attitudes and behavior and to help understand why consumers implement some food safety behaviors and not others.

The research results will help more effectively target efforts to change consumer attitudes, which could translate into a reduction in cases of illness caused by eating unsafe food or following proper practices when shopping and at the home preparation stage.

Development of a reliable tool for the study of attitudes. Identify the new elements of young adult consumers' attitudes and the factors that shape them.