Majid Abdolrazzagh-Nezhad and Shaghayegh Izadpanah
Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for…
Abstract
Purpose
Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for comparing a new patient’s information with the aggregated data to detect cancer. The main step in this process is data classification. There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data.
Design/methodology/approach
In this paper, a new fuzzy bio-inspired-based classification method is designed to classify the imbalance of medical data. The method consists of a new fuzzy draft of the Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that are called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA), which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time.
Findings
The experimental results were presented in five steps. Step1, the details of the average and the best results of the proposed methods were reported and compared. Step2, the quality of the detection methods with different numbers of hyper-planes were compared. The obtained weights of different numbers of hyper-planes were reported in Step3. Step4, the convergence process of the FCOA and the COA were shown. Step5, the best obtained results were compared with the best reported one in previous literature. The experimental results and the presented comparisons show that the proposed hybrid detection method is comparable to other methods and operates better than them in most cases.
Originality/value
A technical review has been done based on classifying the applied classification methods to cancer detection and analyzing advantages (+) and disadvantages (−) of the methods and their optimizer algorithms. A new fuzzy draft of COA has been designed to dynamically tuning the Egg Laying Radius based on a fuzzy inference system with four fuzzy rules. A novel hybridization of the hyper-planes classification method and the designed FCOA has been proposed to optimize the hyper-planes' weights. The effectiveness of the proposed hybridization has been examined in famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than 30 previous researches.
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Yawen Liu, Bin Sun, Tong Guo and Zhaoxia Li
Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to…
Abstract
Purpose
Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.
Design/methodology/approach
This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.
Findings
In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.
Originality/value
To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.
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Zhiqing Tian, Bin Xu, Xiaobing Fan, Bingli Pan, Shuang Zhao, Bingchan Wang and Hongyu Liu
This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving…
Abstract
Purpose
This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving tribological properties.
Design/methodology/approach
Pored PTFE (PPTFE) was prepared by mixing powder PTFE and citric acid and experienced a cold-press sintering molding process. Subsequently, textured surfaces were obtained with using a tattoo strategy. Surface-textured PPTFE was thus impregnated with polyethylene glycol 200, yielding oil-impregnated and pore-connected PPTFE.
Findings
This study found that oil-impregnated and surface-textured PPTFE exhibited excellent tribological performances with an 82% reduction in coefficient of friction and a 72.5% lowering in wear rate comparing to PPTFE.
Originality/value
This study shows an efficient strategy to improve the tribological property of PTFE using a tattoo-inspired surface texturing method.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0378/
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Jingru Lian, Xiaobing Fan, Bin Xu, Shan Li, Zhiqing Tian, Mengdan Wang, Bingli Pan and Hongyu Liu
This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.
Abstract
Purpose
This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.
Design/methodology/approach
PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.
Findings
It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.
Originality/value
This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/
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Shuai Yang, Bin Wang, Junyuan Tao, Zhe Ruan and Hong Liu
The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and…
Abstract
Purpose
The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.
Design/methodology/approach
First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.
Findings
Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.
Originality/value
A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.
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Mouad Sadallah, Saeed Awadh Bin-Nashwan and Abderrahim Benlahcene
The escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance…
Abstract
Purpose
The escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance levels. This paper aims to delve into academic performance within the context of the ChatGPT era by exploring the influence of several pivotal predictors, such as academic integrity, academic competence, personal best goals and perceived stress, as well as the moderating effect of ChatGPT adoption on academic performance.
Design/methodology/approach
This study uses a quantitative method to investigate the impact of essential variables on academic integrity, academic competence, perceived stress and personal best goals by analysing 402 responses gathered from ResearchGate and Academia.edu sites.
Findings
While affirming the established direct positive relationship between academic integrity and performance since adopting AI tools, this research revealed a significant moderating role of ChatGPT adoption on this relationship. Additionally, the authors shed light on the positive relationship between academic competence and performance in the ChatGPT era and the ChatGPT adoption-moderated interaction of competence and performance. Surprisingly, a negative association emerges between personal best goals and academic performance within ChatGPT-assisted environments. Notably, the study underscores a significant relationship between heightened performance through ChatGPT and increased perceived stress among academicians.
Practical implications
The research advocates formulating clear ethical guidelines, robust support mechanisms and stress-management interventions to maintain academic integrity, enhance competence and prioritise academic professionals’ well-being in navigating the integration of AI tools in modern academia.
Originality/value
This research stands out for its timeliness and the apparent gaps in current literature. There is notably little research on the use of ChatGPT in academic settings, making this investigation among the first to delve into how faculty and researchers in education use OpenAI.
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Ha Kyung Lee, Woo Bin Kim and Ho Jung Choo
Shopping through e-commerce platforms has become a primary daily activity. However, research on consumer engagement within e-commerce platform contexts remains scarce. We examine…
Abstract
Purpose
Shopping through e-commerce platforms has become a primary daily activity. However, research on consumer engagement within e-commerce platform contexts remains scarce. We examine the relationship between consumer engagement on online shopping platforms and their subjective well-being, considering self-expansion and self-extension as mediators.
Design/methodology/approach
We investigate the role of consumer engagement by dividing it into two experiences (crowdsourcing and crowdsending). Using validated measurement scales to analyze data from 440 South Korean consumers, we examine how these engagement experiences affect self-expansion and self-extension, ultimately leading to higher subjective well-being.
Findings
Crowdsourcing and crowdsending play different and complementary roles in improving self-concept. Furthermore, self-expansion and self-extension are key variables influencing consumer engagement and well-being on the platform.
Originality/value
This study provides a new perspective of consumer online shopping behavior, revealing the self-related mechanisms that influence the relationship between consumer engagement experiences and subjective well-being.
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Shuai bin Guan and Xingjian Fu
This study aims to optimize control strategies for multi-unmanned aerial vehicle (UAV) systems by integrating differential game theory with sliding mode control and neural…
Abstract
Purpose
This study aims to optimize control strategies for multi-unmanned aerial vehicle (UAV) systems by integrating differential game theory with sliding mode control and neural networks. This approach addresses challenges in dynamic and uncertain environments, enhancing UAV system coordination, operational stability and precision under varying flight conditions.
Design/methodology/approach
The methodology combines sliding mode control, differential game theory and neural network algorithms to devise a robust control framework for multi-UAV systems. Using a nonsingular fast terminal sliding mode observer and Nash equilibrium concepts, the approach counters external disturbances and optimizes UAV interactions for complex task execution.
Findings
Simulations demonstrate the effectiveness of the proposed control strategy, showcasing enhanced stability and robustness in managing multi-UAV operations. The integration of neural networks successfully solves high-dimensional Hamilton–Jacobi–Bellman equations, validating the precision and adaptability of the control strategy under simulated external disturbances.
Originality/value
This research introduces a novel control framework for multi-UAV systems that uniquely combines differential game theory, sliding mode control and neural networks. The approach significantly enhances UAV coordination and operational stability in dynamic environments, providing a robust solution to high-dimensional control challenges. The use of neural networks to solve complex Hamilton–Jacobi–Bellman equations for real-time multi-UAV management represents a groundbreaking advancement in autonomous aerial vehicle research.
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Obaid Gulzar, Muhammad Imran Malik, Faisal Nawaz and Osama Bin Shahid
The study aims to investigate the relationship between internal knowledge dissemination and employee-based brand equity (EBBE) through the lens of inclusive marketing among…
Abstract
Purpose
The study aims to investigate the relationship between internal knowledge dissemination and employee-based brand equity (EBBE) through the lens of inclusive marketing among university faculty members. The study also examines the role of employee absorptive capacity and brand knowledge as mediators.
Design/methodology/approach
A sample of 362 faculty members from Pakistani universities was considered for analysis using a quantitative study design. A questionnaire was used to measure the variables under study, and structural equation modeling was used to examine the direct and indirect relationships.
Findings
There exists a positive and significant relationship between internal knowledge dissemination and EBBE among faculty members. Moreover, it is noteworthy to highlight that employee absorptive capacity and brand knowledge play pivotal roles as mediators.
Practical implications
The research findings have significant implications for the universities. Universities can strengthen their EBBE by properly disseminating knowledge among faculty members, which in turn fosters a sense of belongingness toward them. By improving the absorptive capacity of faculty members, universities can better prepare them to contribute successfully to the university’s brand and image. Developing brand knowledge among faculty members can help in fostering a unified and coherent brand image that deeply resonates with stakeholders such as colleagues, students and the academic community as a whole. Furthermore, promoting an inclusive culture within the organization will emphasize diversity and equity in internal knowledge dissemination practices, thereby further enhancing EBBE.
Originality/value
This study contributes to the prevailing knowledge-base by exploring the role of internal knowledge dissemination in developing EBBE among university faculty members. The research not only enriches the understanding of brand management in universities but also provides practical guidelines for the expansion of effective branding initiatives. Moreover, this study adds value by examining the association between internal knowledge dissemination and EBBE from the perspective of inclusive marketing strategies. It highlights the significance of encouraging a culture of diversity, inclusion and equity within organizations, leading toward significant outcomes in terms of enhanced brand equity among employees.
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Linwei Dang, Xiaofan He, Dingcheng Tang, Hao Xin and Bin Wu
Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is…
Abstract
Purpose
Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm.
Design/methodology/approach
In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods.
Findings
The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06.
Originality/value
The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.