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The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

The increasing popularity of XML has generated a lot of interest in query processing over graph‐structured data. To support efficient evaluation of path expressions structured indexes have been proposed. Extending the proposed indexes to work with large XML graphs and to support intra‐ or inter‐document links requires a lot of computing power for the creation process and a lot of space to store the indexes. Moreover, the efficient evaluation of ancestors‐descendants queries over arbitrary graphs with long paths is a severe problem. This paper aims to propose a scalable path index which is based on the concept of 2‐hop covers as introduced by Cohen et al.

The problem of efficiently managing and querying XML documents poses interesting challenges on database research. The proposed algorithm for index creation scales down the original graph size substantially. As a result a directed acyclic graph with a smaller number of nodes and edges will emerge. This reduces the number of computing steps required for building the index. Thus, computing time and space will be reduced as well. The index also permits ancestors‐descendants relationships to be efficiently evaluated. Moreover, the proposed index has a nice property in comparison to most other work; it is optimized for descendants‐or‐self queries on arbitrary graphs with link relationships.

In this paper, a scalable path index is proposed. It can efficiently address the problem of querying large XML documents that contain links and have cycles. Cycles in the graph stress path‐indexing algorithms. An overview about 2‐hop cover and the algorithms that used to build the index are given.

This paper works on the updating problem. Since the construction of the index is quite complex its construction make sense for some time. However, this means it is currently dealing with the problem of updating XML‐documents.

This paper presents an efficient path index that can test the reachability between two nodes and evaluate ancestors‐descendants queries over arbitrary graphs with long paths.

The determination of parameters of Duhem model that can describe piezoelectric hysteresis is usually a great challenge. The purpose of this paper is to find a way to identify the parameters of Duhem model by using a modified bee colony algorithm.

The promising bee colony algorithm has great potential to identify hysteresis nonlinearity, but has not yet been used to identify the Duhem-type hysteresis in the literatures. To explore this possibility, the classical bee colony algorithm is modified to enhance its performance regarding both searching capability and convergence speed. In the modification, the current optimal solution is used to guide the search direction, which can balance the local and global searching ability. Moreover, a new searching formula for scout bees is proposed to enhance the convergence ability of the algorithm.

Through a series of experiments, the modified algorithm can attain the optimal parameters with a 0.61 µm peak valley error and a 0.12 µm root-mean-square error. Compared to the particle swarm optimization and classical bee colony algorithms, the modified bee colony algorithm can reach higher parameter identification accuracy. Based on 50 trials, the robustness of the posed algorithm was also proved.

The well-performed modified bee colony algorithm is a good candidate in parameter identification of Duhem-type hysteresis nonlinear systems. As there is no work studying the parameter identification of Duhem model using a bee colony algorithm in the literatures, this work closed this gap and explored the ability of bee colony algorithm to identify piezoelectric hysteresis with superb accuracy and robustness.

This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.

A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.

The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.

The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.

The purpose of this paper is to synthesize polypyrrole/SiO₂ composite coating on 316 stainless steel (316SS) by cyclic voltammogram and preliminary do research about the valuable effects of SiO₂ particle incorporation within the polymer matrix.

This study is based on elaboration of coating by electrochemical process and of SiO₂ by a sol-gel process.

Electrochemical impedance studies revealed that compared with polypyrrole (PPy), PPy-SiO₂ coating acts as a more protective layer on 316SS against corrosion in 3.5 per cent NaCl. Scanning electron microscopy studies revealed that the PPy-SiO₂-coated 316SS showed more uniform and compact morphology.

To fully disperse SiO₂, a sol-gel method is used. Hydroxyl group is generated on the surface of inorganic particle by the sol-gel method, which improves the inorganic particle dispersion.

The vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.

MNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.

A real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.

From the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

This paper aims to investigate whether and how air pollution affects auditor behavior and audit quality. Specifically, the authors draw from studies of behavioral economics and psychology to develop a new prediction that air pollution-induced negative mood causes pessimistic bias in auditors’ risk assessments of client firms, which motivates them to put more effort into achieving higher audit quality.

This study uses a sample of Chinese public firms for the period 2013 to 2018 and an ordinary least squares model to examine the effects of air pollution on audit quality.

The results suggest that auditors exposed to higher levels of air pollution are more likely to put more effort into their audits, resulting in higher audit quality. Furthermore, the impacts of air pollution on audit quality are more pronounced when an auditor has a higher level of education, a major in accounting or a related subject and a position as a partner. A series of identification tests and sensitivity tests further support the main findings.

This study provides deeper insight into how air pollution affects auditors’ decision-making through its effect on mood.

The findings have broad potential implications for auditing and other high-skill professions. Because air pollution-induced negative mood is a common occurrence and numerous psychological experiments have demonstrated the potentially adaptive and beneficial role of negative mood in decision-making for professions like auditing that need a more conservative, alert and detail-oriented cognitive style, negative mood may to some extent facilitate decision-making. Professionals may benefit from paying closer attention to the adaptive benefits of different moods.

Few studies empirically discuss the effects of auditors’ psychology on audit outcomes. This study responds to this research gap with analyzes of how air pollution-induced negative mood can affect auditors’ professional judgment and audit outcomes. Further, this study adds to the growing literature that examines how air pollution affects various aspects of the economy and enriches the literature on behavioral economics, providing empirical evidence from a large sample of the effects of an environmental stressor on individual auditors’ professional judgment.

Previous literature tends to combine postponement and modularity or view them as parallel factors to achieve mass customization (MC) while ignoring the sequence of a firm to design and implement operations and supply chain strategy. Based on a customer-oriented strategy and theories of organizational information processing theory, three-dimensional (3D) concurrent engineering and resource dependency, the purpose of this paper is to propose a sequential model reflecting the sequence of practices as well as an overview picture for a firm to achieve MC.

The model links three company antecedents – postponement orientation, operational alignment and information sharing, to three company supply chain practices – product and process modularity and supplier segmentation. These practices, in turn, lead to the company’s MC capabilities. The proposed model is tested with a data set collected from automotive suppliers in China and in the USA. Structural equation modeling is used to analyze the data and test the model.

The results suggest that, for suppliers to achieve MC, postponement orientation and operational alignment are vital antecedents. The results also reveal the important responsibility and role of information sharing practices in coordinating suppliers’ modularity practices.

This research provides three findings that are of value to both academicians and practitioners of supply chain management. First, this study originally proposed and empirically tested that a postponement orientation is an antecedent of product and process modularity and supplier segmentation to achieve MC in the automotive sector, contrary to the traditional view of parallel relationships for both. Second, it developed and verified measures of operational alignment and supplier segmentation for future research use. Third, the vital role of information sharing to coordinate internal and external supply chain practices to achieve MC is empirically supported.

The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market.

The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research.

The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models.

The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures.

The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

The purpose of this paper is to examine the significance of the direct and indirect effects (through country and firm’s specific advantages) of government policies for export promotion (GPEP) on the export performance of small and medium-size enterprise (SME) Cocoa exporters in Cameroon.

To test the proposed model, data were obtained through self-administered questionnaires using snowball sampling technique to 101 SME Cocoa exporters. This was analyzed using structural equation modeling (SEM) techniques to examine both the direct and indirect effects of GPEP on the export performance of SME Cocoa exporters in the South and Centre Regions of Cameroon.

The findings suggest that GPEP had both direct and indirect effects on the export performance of SME Cocoa exporters. Direct effect was on the usage of GPEP which reduces operating cost and increase performance. The indirect effects were through the provision of country and firms specific advantages. However, the only significant path was through the provision of export marketing information.

The research is limited to one country, one sector, and two regions and does not take into consideration other factors that may influence the effect of GPEP, country, and firms specific advantages on export performance. Moreover, the non-significant paths should be interpreted with caution and further testing required in a different context.

Empirical findings are relevant for the government and SME Cocoa exporters. It informs the government about the effectiveness of GPEP and the need to disseminate marketing information using every possible medium best understood by the SMEs. It suggests an opportunity for engagement of both SMEs and government authorities in accessing the outcome of GPEP which will increase transparency, awareness, usage, and export performance.

The research has successfully developed and tested a model for analyzing the direct and indirect effects of GPEP on export performance based on the resource-based view and SEM in a context where there is a call for more empirical and theoretical work on export performance due to limited studies. The framework reveals positive effects of GPEP, country, and firms’ specific advantages as determinants of export performance.