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1 – 10 of 18Kaizheng Zhang, Jian Di, Jiulong Wang, Xinghu Wang and Haibo Ji
Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual…
Abstract
Purpose
Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.
Design/methodology/approach
The authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.
Findings
The proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.
Originality/value
Infeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.
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Florian Rupp, Benjamin Schnabel and Kai Eckert
The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the…
Abstract
Purpose
The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.
Design/methodology/approach
In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.
Findings
The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.
Practical implications
Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.
Originality/value
With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.
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Hillal M. Elshehabey, Andaç Batur Çolak and Abdelraheem Aly
The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double…
Abstract
Purpose
The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.
Design/methodology/approach
The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.
Findings
The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.
Originality/value
A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.
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James Tarver, Kieran Nar and Candice Majewski
The purpose of this paper is to elucidate the extent to which the mechanisms of polymer melt viscous flow and finish layer powder particle adhesion influence the top surface…
Abstract
Purpose
The purpose of this paper is to elucidate the extent to which the mechanisms of polymer melt viscous flow and finish layer powder particle adhesion influence the top surface topographies of laser sintered polyamide (PA12) components.
Design/methodology/approach
Laser sintered specimens were manufactured at varying laser parameters in accordance with a full factorial design of experiments. Focus variation microscopy was used to ascertain insight into their top surface heights and peak/valley distributions. Subsequently, regression expressions were generated to model the former with respect to applied laser parameters. Auxiliary experimental analysis was also performed to validate the proposed mechanisms and statistical models.
Findings
Within the parameter range tested, this work found the root mean square (Sq) and skewness (Ssk) roughness responses of laser sintered PA12 top surfaces to be inversely related to one another, and both also principally influenced by beam spacing. Furthermore, it was demonstrated that using optimised laser parameters (to promote polymer melt dispersion) and building without finish layers (to avert subsequent powder particle adhesion) reduced the mean Sq roughness of resultant topographies by 30.8% and 47.9% relative to standard laser sintered PA12 top surfaces, respectively.
Practical implications
The scope to which laser sintered PA12 top surfaces can be modified was highlighted.
Originality/value
This research demonstrated the impact the mechanisms of polymer melt viscous flow and finish layer powder particle adhesion have on laser sintered PA12 top surfaces.
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Despite the well-recognized importance of recycled water, the study of industry-peer pressure on recycled water is relatively new. This study investigates how organizations…
Abstract
Purpose
Despite the well-recognized importance of recycled water, the study of industry-peer pressure on recycled water is relatively new. This study investigates how organizations experience and react to industry-peer pressure to set recycled water targets. Additionally, this study investigates the role of board chairs involved in sustainability committees in contributing to responses to industry-peer pressure.
Design/methodology/approach
Using Eviews 12, this study employed a pooled logistic regression model to analyze data from 1,346 firms on Taiwan and Taipei exchanges (2017–2020).
Findings
The findings revealed that frequency-based imitation drives recycled water target-setting diffusion. However, there is no direct relationship between outcome-based imitation and recycled water target-setting. Notably, outcome-based imitation drives the adoption of recycled water target-setting of firms with board-chair membership in sustainability committees.
Research limitations/implications
This study faces certain data limitations. First, this study primarily focuses on water recycling. Future research could explore other ways to reduce water usage, such as using water-efficient equipment. Second, this study gathered information solely on the presence or absence of a board chairperson on the sustainability committee. Future researchers could explore the impact of the composition of sustainability committee on recycled water target-setting. Lastly, the sample used in this study is restricted to Taiwan's corporations that existed during 2017–2020. Future researchers may consider adopting a longitudinal design in other economies to address this limitation.
Practical implications
The findings of this study offer several guidelines and implications for recycled water target-setting and the composition of sustainability committees. It responds to an urgent call for solutions to water shortages when pressure from governments and nongovernmental organizations is relatively absent. The number of industry peers that have already set recycled water targets is indispensable for motivating firms to set their own recycled water targets. In terms of insufficient water-related regulatory pressure and normative pressure, this study found evidence suggesting that the direct motivation for setting recycled water targets stems from mimetic pressures via frequency-based imitation. The evidence in this study suggests that policymakers should require companies to disclose their peers’ recycled water target information, as doing so serves as an alternative means to achieving SDG 6.3.
Social implications
Recycled water target-setting might be challenging. Water recycling practices may face strong resistance and require substantial additional resources (Zhang and Tang, 2019; Gao et al., 2019; Gu et al., 2023). Therefore, this study suggests that firms should ensure the mindfulness of board members in promoting the welfare of the natural environment when making recycled water target-setting decisions. To reap the second-mover advantage, firms must consider the conditions in which board members can more effectively play their role. Corporations may help their chairpersons in setting recycled water targets by recruiting them as members of sustainability committees. Meanwhile, chairpersons tend to activate accurate mental models when the water conservation performance of pioneering industry peers is strong enough to indicate the potential benefits of adopting recycled water target-setting. Investors’ and stakeholders’ understanding of how the composition of sustainability committees is related to recycled water target-setting may help to identify the potential drivers of firms’ water responsibility. Investors and stakeholders should distinguish firms in terms of the board chair’s membership of their sustainability committee and focus on water-use reduction outcomes in the industry. This study provides insights into circumstances whereby chairpersons help to restore the water ecosystem.
Originality/value
This study explains how frequency-based and outcome-based imitation are two prominent mechanisms underlying the industry-peer pressure concerning recycled water target-setting. Moreover, this study fills literature gaps related to the moderating roles of board-chair membership in sustainability committees concerning industry-peer pressure on recycled water target-setting.
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Rongen Yan, Depeng Dang, Hu Gao, Yan Wu and Wenhui Yu
Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different…
Abstract
Purpose
Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.
Design/methodology/approach
This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.
Findings
The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.
Originality/value
The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.
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Jianping Zhang, Leilei Wang and Guodong Wang
With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the…
Abstract
Purpose
With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.
Design/methodology/approach
Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.
Findings
The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.
Originality/value
The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.
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Ismail Abiodun Sulaimon, Hafiz Alaka, Razak Olu-Ajayi, Mubashir Ahmad, Saheed Ajayi and Abdul Hye
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully…
Abstract
Purpose
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.
Design/methodology/approach
To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM2.5, PM10, NO2 and O3 in the atmosphere at various times of the day.
Findings
The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.
Research limitations/implications
This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.
Practical implications
This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.
Originality/value
The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.
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This study aims to examine whether and how the experience of specialized external governance mechanisms mandated by the Employee Retirement Income Security Act of 1974 – the…
Abstract
Purpose
This study aims to examine whether and how the experience of specialized external governance mechanisms mandated by the Employee Retirement Income Security Act of 1974 – the actuary and auditor – affect pension plan funding.
Design/methodology/approach
This study uses data from annual pension plan regulatory reports (Form 5500), Form 10-K filings, Form DEF 14A filings (company proxy statements) and publicly available data sources. The hand-collected data include information related to the pension plan’s actuary and auditor and various pension plan data disclosed in the company’s financial statement footnotes.
Findings
The author finds that more experienced actuaries and auditors are associated with better funded pension plans, especially when the company has higher financial risk or lower board independence. Additional analyses indicate that companies with more experienced actuaries and pension plan auditors are more likely to make higher annual pension plan contributions and hold fewer Level 3 fair value assets.
Originality/value
The dearth of pension plan governance research generally focuses on whether and how internal governance mechanisms affect pension plan funding. To the best of the author’s knowledge, this is the first empirical study of the relationship between external pension plan governance mechanisms and pension plan funding.
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Cori Crews, John Abernathy, Jimmy Carmenate, Divesh Sharma and Vineeta Sharma
The purpose of this study is to investigate the association between nonaudit services (NAS) and out-of-period adjustments (OOPAs). Over the years, the number of OOPAs has risen…
Abstract
Purpose
The purpose of this study is to investigate the association between nonaudit services (NAS) and out-of-period adjustments (OOPAs). Over the years, the number of OOPAs has risen while the number of restatements has decreased. This could indicate an improvement in financial reporting quality. It could also indicate the use of a type of stealth restatement for opportunistic purposes. These less prominent restatements are more likely to go undetected and could perpetuate opportunistic disclosure and mitigate the likelihood of unfavorable market reactions.
Design/methodology/approach
The authors use a two-stage multivariate regression analysis to examine the relationship between NAS and the reporting of an OOPA. The authors use prior research on NAS to guide the model development. The authors perform several robustness checks including different types of NAS and different characteristics of OOPAs.
Findings
The results indicate that NAS has a significantly negative association with the existence of OOPAs. The core findings suggest that NAS does not impair auditor independence. Rather, greater amounts of NAS may contribute to knowledge spillover, which leads to higher financial reporting and audit quality. The results are robust to several additional tests.
Research limitations/implications
The results raise interesting implications for regulators, executives, auditors, investors and future research. The authors provide insight into the relationship between NAS and auditor independence.
Originality/value
To the best of the authors’ knowledge, prior research has not considered the effect of NAS on OOPAs. The authors contribute to the literature by providing evidence that OOPAs, a form of stealth restatements, is an important consideration in audit quality research.
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