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An Intellectual Capital Risk Measurement Model Using Bayesian Network

Journal of the Knowledge Economy 2025年 1-33页

作者： Shabankhah, Sanaz Afrazeh, Mohammadhossein Afrazeh, Abbas Esfahanipour, Akbar Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran Department of Industrial Engineering University of Tehran Tehran Iran

This research introduces a hybrid problem-solving method to enhance the understanding and management of intellectual capital risks in organizations. It aims to assess the current situation, identify the underlying causes, and propose suitable strategies. The study offers a fresh perspective on measuring and exploring intellectual capital risks using the Bayesian network methodology, consisting of four comprehensive steps. First risks are identified and classified based on relevant literature, and hypothesizing that person, organization, and environment are key root causes, using a validated questionnaire. Second, a Bayesian network model is constructed to evaluate the probability of risks occurring across different situations. Third, data analysis techniques are used to prioritize risks and identify internal strengths and weaknesses, as well as external opportunities and threats. Finally, a strategic planning method is proposed, including three stages of input, matching, and decision making. Through the application of this methodology to a case study, all risk factors within the organization are identified and the probability of intellectual capital risk in various conditions is determined. The results highlighted the significant impact of human risks in the case studied. Based on the strategic formulation method, the study provides effective strategies for mitigating intellectual capital risks, prioritizing and proposing the best approach, which includes enhancing the company's knowledge through training programs, attracting educated individuals, and establishing connections with successful organizations. This method enables organizations to proactively monitor and manage intellectual capital risks, enhancing their competitive advantage in the market. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bayesian network Intellectual capital risks Organizational strategy Risk management

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Analysis on the Influence of Supply Chain Optimization and IT Development on Chinese Real Estate 24

Analysis on the Influence of Supply Chain Optimization and I...

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2024 International Conference on Cloud Computing and Big Data, ICCBD 2024

作者： Zheng, Li Pornsing, Choosak Department of Industrial Engineering and Management Faculty of Engineering and Industrial Technology Silpakorn University Thailand

ISBN: (纸本)9798400710223

This article aims to thoroughly study the Chinese real estate industry, emphasizing its essential features, anticipated development patterns, and the use of engineering management concepts to grab potential possibilities. This research utilizes structural equation modeling and hierarchical regression analysis to examine survey data from 144 Chinese companies, aiming to determine the impact of supply chain collaborative innovation on the long-term growth of supply networks. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) are conducted to validate the findings, providing strong corroboration of the results .IT and supply chain advancements have transformed the Chinese real estate industry, enhancing project efficiency, reducing costs, and improving customer satisfaction. © 2024 ACM.

关键词： Customer satisfaction

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Improving Hornet Detection with the YOLOv7-Tiny Model:A Case Study on Asian Hornets

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Computers, Materials & Continua 2025年第5期83卷 2323-2349页

作者： Yung-Hsiang Hung Chuen-Kai Fan Wen-Pai Wang Department of Industrial Engineering&Management National Chin-Yi University of TechnologyTaichung411030TaiwanChina

Bees play a crucial role in the global food chain,pollinating over 75% of food and producing valuable products such as bee pollen,propolis,and royal ***,theAsian hornet poses a serious threat to bee populations by preying on them and disrupting agricultural *** address this issue,this study developed a modified YOLOv7tiny(You Only Look Once)model for efficient hornet *** model incorporated space-to-depth(SPD)and squeeze-and-excitation(SE)attention mechanisms and involved detailed annotation of the hornet’s head and full body,significantly enhancing the detection of small *** Taguchi method was also used to optimize the training parameters,resulting in optimal *** for this study were collected from the Roboflow platformusing a 640×640 resolution *** YOLOv7tinymodel was trained on this *** optimizing the training parameters using the Taguchi method,significant improvements were observed in accuracy,precision,recall,F1 score,andmean average precision(mAP)for hornet *** the hornet head label,incorporating the SPD attentionmechanism resulted in a peakmAP of 98.7%,representing an 8.58%increase over the original *** including the hornet head label and applying the SPD attention mechanism and Soft-CIOU loss function,themAP was further enhanced to 97.3%,a 7.04% increase over the original ***,the Soft-CIOU Loss function contributed to additional performance enhancements during the validation phase.

关键词： Computer vision object detection YOLOv7tiny SE SPD Asian hornet

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A simulation evacuation framework for effective disaster preparedness strategies and response decision making

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European Journal of Operational Research 2024年第2期313卷 733-746页

作者： Chang, Kuo-Hao Wu, Ying-Zheng Su, Wen-Ray Lin, Lee-Yaw Department of Industrial Engineering and Engineering Management National Tsing Hua University Taiwan National Science and Technology Center for Disaster Reduction Taiwan

The damage and destruction of homes, buildings, and other infrastructure after a major earthquake makes it necessary for a segment of the population to be evacuated to emergency shelters. However, since areas prone to earthquakes face a major strike at highly unpredictable and infrequent times, disaster and evacuation management can and should be enhanced through the development and effective utilization of useful simulation models. We utilize the Stochastic Pedestrian Cell Transmission Model (SPCTM), a real data-based pedestrian evacuation simulation framework built in collaboration with the National Science and technology Center for Disaster Reduction (NCDR) in Taiwan to carry out various statistical analyses to gain useful insights for disaster management decision makers. In particular, SPCTM is used to (1) examine how evacuee compliance rate with the current government evacuation protocol (as opposed to SPCTM) affects the total evacuation time (2) compare the results of the evacuation process for different neighborhoods and compliance rates in Da'an district, and (3) determine how allocating different subsets of shelters to be opened affects the evacuation response time. The analysis results are insightful for government officials to formulate effective evacuation guidelines and strategies in the preparedness or response phases. © 2023 Elsevier B.V.

关键词： Earthquakes

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A risk assessment model with dependent failure modes for the manufacturing and design processes of medical equipment

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International Journal on Interactive Design and Manufacturing 2024年第6期18卷 4059-4074页

作者： Lo, Huai-Wei Gul, Muhammet Yucesan, Melih Liaw, Ching-Fang Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan School of Transportation and Logistics Istanbul University Istanbul Turkey Department of Emergency Aid and Disaster Management Munzur University Tunceli Turkey Department of Industrial Engineering and Management Chaoyang University of Technology Taichung Taiwan

The reliability of medical equipment is paramount for surgical success. Manufacturers are continuously exploring risk assessment and improvement strategies to prevent equipment malfunctions during surgery. Failure mode and effects analysis (FMEA) can primarily be divided into two types: DFMEA (Design FMEA) and PFMEA (Process FMEA). Traditional FMEA serves as a practical tool for identifying and prioritizing key failure modes (FMs). However, past FMEA research has overlooked the interdependencies among FMs, treating them as independent entities, which does not align with real-world operations. This paper introduces a groundbreaking risk dependency assessment model aimed at addressing the influential relationships among FMs. The modified technique for defining the influential relationship of failure modes (modified TEDIR-FMs) is developed to compute the relative risk scores of different FMs accurately. At its core, this paper integrates concepts from multiple criteria decision-making (MCDM) techniques into FMEA, with a particular focus on the interdependencies of failure modes in the manufacturing and design manufacturing processes of medical equipment. The model’s application is demonstrated through a case study on a medical device for hernia surgery. The introduction of modified TEDIR-FMs not only enhances the accuracy of risk assessments but also offers a fresh perspective on risk management within the design and manufacturing processes. The research results indicate that the three failure modes with the highest risk are "adhesive does not deploy or only partially deploys from the dispenser," "the mesh does not adhere to the adhesive," and "the adhesive drips from the tip." In the manufacturing and design processes of this device, greater attention should be given to improving the adhesive components. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024.

关键词： Risk assessment

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Enhancing the effectiveness of output projection in wafer fabrication using an Industry 4.0 and XAI approach

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International Journal of Advanced Manufacturing technology 2024年第1-2期134卷 113-125页

作者： Chen, Tin-Chih Toly Wang, Yu-Cheng Lin, Chi-Wei Department of Industrial Engineering and Management National Yang Ming Chiao Tung University University Road Hsinchu1001 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung City Taiwan Department of Industrial Engineering and Systems Management Feng Chia University Taichung Taiwan

Industry 4.0 and deep learning methods have been widely used for output projection in wafer fabrication. Although such applications appeared to be effective, they were difficult to understand and/or improve. Therefore, in this study, an Industry 4.0 and explainable artificial intelligence (XAI) approach is proposed to enhance the interpretability of a deep learning application for output projection. First, a deep neural network (DNN) is constructed to estimate the output times of jobs based on real-time job, machine, and factory information collected using Industry 4.0 technologies. Then, the DNN is approximated by a random forest (RF) to generate interpretable decision rules. SHAP analysis is also performed to generate local explanations. The proposed methodology has been applied to a real case from a wafer fab. Through the synergy of Industry 4.0 and deep learning, the proposed methodology achieved a satisfactory output projection performance. The MAE was only about two wafer lots. Additionally, user communication was also facilitated since the output projection mechanism using deep learning has been explained using XAI techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Industry 4.0

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Performance Investigation of a Vacuum-Enhanced Direct Contact Membrane Distillation Coupled with a Photovoltaic-Thermal System 5th

Performance Investigation of a Vacuum-Enhanced Direct Contac...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Maqbool, Faisal Harijan, Khanji Kumar, Laveet Soomro, Mujeeb Iqbal Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan Department of Mechanical Engineering Mehran University of Engineering and Technology SZAB Campus Khairpur Mir’s 66020 Sindh Pakistan Department of Mechanical Engineering Balochistan University of Engineering and Technology Khuzdar 89100 Balochistan Pakistan Department of Mechanical Engineering & Industrial Engineering College ofEngineering Qatar University Doha Qatar

ISBN: (纸本)9789819790364

The need for desalination has increased as freshwater supplies decline. Low-grade heat sources such as solar photovoltaic thermal can operate membrane distillation. In this article, solar thermal sources, and vacuum-enhanced direct contact membrane distillation configurations are analyzed to assess how well photovoltaic thermal fits the process. Integrating photovoltaic-thermal systems with vacuum-enhanced direct contact membrane distillation provides an innovative, efficient, and technically better option. The research analyzed and simulated this integrated system’s performance in MATLAB. According to the findings, the efficiency of vacuum-enhanced direct contact membrane distillation systems driven by photovoltaic thermal technology is greatly enhanced when the feed water temperature is raised. The efficiency of evaporation and permeate flow showed a significant improvement, increasing from 43 to 48%. At the same time, there was a significant decrease in the specific thermal energy consumption from 1170 kWh/m3 to 890 kWh/m3 because the feed water temperature was increased from 36 °C to 48 °C and energy from a photovoltaic system was used. The results indicate that the vacuum-enhanced direct contact membrane distillation system has an increase in performance compared to the direct contact membrane distillation system under identical operating conditions for freshwater production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Desalination

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A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

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Multimedia Tools and Applications 2023年第28期82卷 44359-44385页

作者： Rezaei, Seyed Reza Ahmadi, Abbas Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Tumors

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Multiple objective optimal design for an electrically assisted bicycle under dynamic finite element analysis

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2024年第3期47卷 253-264页

作者： Lee, Cheng-Kang Liu, Hung-Chin Cheng, Yung-Chang Department of Industrial Engineering and Management Cheng Shiu University Kaohsiung Taiwan Department of Mechatronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

Electrically assisted bicycles have become increasingly popular in modern cities. This article presents an optimization design process to enhance the strength of an electrically assisted bicycle frame (EABF) model. This process includes several techniques such as uniform design (UD), Kriging interpolation (KGI), entropy weighting analysis (EWA), gray relational analysis (GRA), and genetic algorithm (GA). The EN 15,194 test standard is used to calculate the maximum deformation of the fork in an EABF, through falling mass (FM) and falling frame (FF) impact simulations with ANSYS/LS-DYNA software. Six geometry characteristics of an EABF are selected as control factors. The UD generates multiple simulation tests, as each control factor in the design space is continuous. Dynamic finite element analysis (FEA) is conducted to determine the maximum impact deformation (ID) of each experiment in the UD. Applying a multiple objective optimization process, the best design for an EABF is obtained. The newly designed model offers a maximum improvement of 4.20% over the original design for the largest distortion. In conclusion, the multiple objective optimization technique improves the maximum ID of an EABF. © 2024 The Chinese Institute of Engineers.

关键词： Bicycles

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A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

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Computers, Materials & Continua 2024年第6期79卷 3519-3547页

作者： Chao-Lung Yang Melkamu Mengistnew Teshome Yu-Zhen Yeh Tamrat Yifter Meles Department of Industrial Management National Taiwan University of Science and TechnologyTaipie106Taiwan Department of Industrial Engineering University of Gondar Institute of TechnologyGondar196Ethiopia

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective *** to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station *** model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and *** core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator *** method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple *** empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator *** findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost ***,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational *** research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction ***,the methodologies and principles developed in this study have pote

关键词： Corrective maintenance multi-objective optimization non-dominated sorting genetic algorithmⅢ operator allocation maintenance station location

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