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1 – 8 of 8Han Jianyu, Chen Zhonghua, Tang Ying and Yu Fei
The purpose of this paper is to study the curing mechanisms, anticorrosive properties and protective mechanisms of three kinds of amine curing agents applied in a new kind of…
Abstract
Purpose
The purpose of this paper is to study the curing mechanisms, anticorrosive properties and protective mechanisms of three kinds of amine curing agents applied in a new kind of light colored water‐borne epoxy antistatic anticorrosive paint.
Design/methodology/approach
Using light color‐conductive mica, titanium oxides and environmentally‐friendly anticorrosive pigments in the two‐component water‐borne epoxy system, the light colored water‐borne antistatic anticorrosive paint was prepared. The molecular structure and curing mechanisms of the curing agents was analyzed by Fourier transform infra‐red spectroscopy, and the influence of the curing agents on anticorrosive properties and protective mechanisms was studied by electrochemical impedance spectroscopy.
Findings
The paints cured by the modified amine curing agent possessed optimal integrated properties with a coating surface resistivity of 106 Ω and the best anticorrosive performance.
Originality/value
A novel light colored water‐borne epoxy antistatic anticorrosive paint cured by the optimal curing agent could be used in corrosion protection for oil tanks to replace the traditional oil‐based antistatic anticorrosive paints.
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Xiaochun Tian, Jiabin Chen, Yongqiang Han, Jianyu Shang and Nan Li
This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise…
Abstract
Purpose
This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.
Design/methodology/approach
A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.
Findings
The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.
Originality/value
Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.
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Damla Yalçıner Çal and Erdal Aydemir
The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly…
Abstract
Purpose
The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.
Design/methodology/approach
Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.
Findings
In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.
Practical implications
It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.
Originality/value
Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.
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Jianyu Zhao, Anzhi Bai, Xi Xi, Yining Huang and Shanshan Wang
Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to…
Abstract
Purpose
Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to knowledge networks has important theoretical and practical significance. Despite the insights being offered by the growing research stream, few studies discuss the diverse responses of knowledge networks’ robustness to different target-attacks, and the authors lack sufficient knowledge of which forms of malicious attacks constitute greater disaster when knowledge networks evolve to different stages. Given the irreversible consequences of malicious attacks on knowledge networks, this paper aims to examine the impacts of different malicious attacks on the robustness of knowledge networks.
Design/methodology/approach
On the basic of dividing malicious attacks into six forms, the authors incorporate two important aspects of robustness of knowledge networks – structure and function – in a research framework, and use maximal connected sub-graphs and network efficiency, respectively, to measure structural and functional robustness. Furthermore, the authors conceptualize knowledge as a multi-dimensional structure to reflect the heterogeneous nature of knowledge elements, and design the fundamental rules of simulation. NetLogo is used to simulate the features of knowledge networks and their changes of robustness as they face different malicious attacks.
Findings
First, knowledge networks gradually form more associative integrated structures with evolutionary progress. Second, various properties of knowledge elements play diverse roles in mitigating damage from malicious attacks. Recalculated-degree-based attacks cause greater damage than degree-based attacks, and structure of knowledge networks has higher resilience against ability than function. Third, structural robustness is mainly affected by the potential combinatorial value of high-degree knowledge elements, and the combinatorial potential of high-out-degree knowledge elements. Forth, the number of high in-degree knowledge elements with heterogeneous contents, and the inverted U-sharp effect contributed by high out-degree knowledge elements are the main influencers of functional robustness.
Research limitations/implications
The authors use the frontier method to expose the detriments of malicious attacks both to structural and functional robustness in each evolutionary stage, and the authors reveal the relationship and effects of knowledge-based connections and knowledge combinatorial opportunities that contribute to maintaining them. Furthermore, the authors identify latent critical factors that may improve the structural and functional robustness of knowledge networks.
Originality/value
First, from the dynamic evolutionary perspective, the authors systematically examine structural and functional robustness to reveal the roles of the properties of knowledge element, and knowledge associations to maintain the robustness of knowledge networks. Second, the authors compare the damage of six forms of malicious attacks to identify the reasons for increased robustness vulnerability. Third, the authors construct the stock, power, expertise knowledge structure to overcome the difficulty of knowledge conceptualization. The results respond to multiple calls from different studies and extend the literature in multiple research domains.
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Guangkuan Deng, Jianyu Zhang and Ying Xu
Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both…
Abstract
Purpose
Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both technological and human – possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual).
Design/methodology/approach
Based on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform’s AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique.
Findings
The empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively.
Originality/value
This study contributes to the existing literature by exploring how e-commerce platforms’ AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.
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Guangkuan Deng, Jianyu Zhang, Naiyi Ye and Rui Chi
Drawing from the ancient Chinese philosopher Xunzi's insights on humanity, this study aims to address human nature's critical role in influencing and shaping consumers' shopping…
Abstract
Purpose
Drawing from the ancient Chinese philosopher Xunzi's insights on humanity, this study aims to address human nature's critical role in influencing and shaping consumers' shopping channel choices in the emerging artificial intelligence (AI) era and the implications for non-East Asian countries.
Design/methodology/approach
Based on the theory of planned behaviour and accessibility–diagnosticity theory, our approach created a holistic model conceptualising human nature, shopping orientations, channel choice intentions, subjective norms and perceived AI usefulness. A questionnaire survey method served to test the framework.
Findings
The results validated human nature's role in shaping and influencing consumers' channel choices through shopping orientation. Subjective norms weaken the positive relationship between human nature and shopping orientation, while the positive relationship between shopping orientation and online purchase intention is stronger when consumers perceived AI as highly useful.
Originality/value
This paper contributes to humanity hypotheses literature in management by introducing Xunzi's theory that views human nature as evil. Additionally, it enriches channel choice literature by introducing perceived AI usefulness.
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Libiao Bai, Zhiguo Wang, Hailing Wang, Ning Huang and Huijing Shi
Inadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of…
Abstract
Purpose
Inadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk.
Design/methodology/approach
A fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk.
Findings
Accurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately.
Originality/value
The authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables.
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Zhai Longzhen and ShaoHong Feng
The rapid evacuation of personnel in emergency situations is of great significance to the safety of pedestrians. In order to further improve the evacuation efficiency in emergency…
Abstract
Purpose
The rapid evacuation of personnel in emergency situations is of great significance to the safety of pedestrians. In order to further improve the evacuation efficiency in emergency situations, this paper proposes a pedestrian evacuation model based on improved cellular automata based on microscopic features.
Design/methodology/approach
First, the space is divided into finer grids, so that a single pedestrian occupies multiple grids to show the microscopic behavior between pedestrians. Second, to simulate the velocity of pedestrian movement under different personnel density, a dynamic grid velocity model is designed to establish a linear correspondence relationship with the density of people in the surrounding environment. Finally, the pedestrian dynamic exit selection mechanism is established to simulate the pedestrian dynamic exit selection process.
Findings
The proposed method is applied to single-exit space evacuation, multi-exit space evacuation, and space evacuation with obstacles, respectively. Average speed and personnel evacuation decisions are analyzed in specific applications. The method proposed in this paper can provide the optimal evacuation plan for pedestrians in multiple exit and obstacle environments.
Practical implications/Social implications
In fire and emergency situations, the method proposed in this paper can provide a more effective evacuation strategy for pedestrians. The method proposed in this paper can quickly get pedestrians out of the dangerous area and provide a certain reference value for the stable development of society.
Originality/value
This paper proposes a cellular automata pedestrian evacuation method based on a fine grid velocity model. This method can more realistically simulate the microscopic behavior of pedestrians. The proposed model increases the speed of pedestrian movement, allowing pedestrians to dynamically adjust the speed according to the specific situation.
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