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Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This paper aims to address how perceived social media agility can promulgate co-creation processes, such as co-production and value-in-use, and how it impacts brand-related outcomes. This study also addresses calls for marketing scholars to investigate the types of personality traits that affect these potential relationships by accounting for the impact of technology reflectiveness.

This paper conducted an online survey with 321 adult subjects. The direct, indirect and conditional (moderation) effects were assessed using multivariate regression, various PROCESS models and the Johnson–Neyman technique (to probe the interaction terms). Additional supplemental analyses were conducted via PROCESS models.

The results show that perceived social media agility directly and indirectly (through co-production and value-in-use) positively influences brand attachment and that the order of these two processes matters (co-production followed by value-in-use). Results also show that the positive impact of perceived social media agility on co-production and value-in-use deviates for customers high in technology reflectiveness but can be manipulated according to which process comes first.

This paper expounds on the new construct of perceived social media agility by uniquely linking perceived social media agility to two distinct value co-creation processes (co-production and value-in-use) and brand-related outcomes while highlighting how consumer-specific traits can affect this relationship in a social media setting.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

The purpose of this study is to determine the influence of project critical success factors (CSFs) on change management in the context of business process management (BPM). Despite widespread interest in BPM, the existing literature is insufficient in addressing the antecedents that contribute to change management in business process projects.

Key factors of change management success in BPM projects were initially identified in a systematic literature review (SLR) and were used as antecedents of change management through a structural equation modeling (SEM) with 464 business project stakeholders. Next, a neural network analysis allowed the key factors to be ranked non-linearly. Finally, a latent class analysis (LCA) was performed to determine the sample's heterogeneous groups based on their project management characteristics.

Project management, top management support and technological competencies were the main CSFs identified as having positive effects on change management. The most important factor is project management, followed by top management support, which plays a crucial mediating role in enabling change management. Although relevant, technological competencies were secondary in the study. Regarding project management CSF, four heterogeneous classes of individuals were determined.

Although this study provides an opportunity to observe CSFs, it does not address the need to analyze the phenomenon in different classifications of projects, regarding maturity, complexity, project management approach and other aspects that differentiate projects in a meaningful way.

The study allows practitioners to understand the critical factors underlying change management and take necessary actions to manage it, recognizing that individuals have heterogeneous profiles regarding project management.

This study pioneeringly discusses the CSFs of change management BPM projects to enable successful change management, ranking the main factors and mapping heterogeneous profiles.

This article aims to present the results of an exploratory pilot study that demonstrates the validity of a self-created implementation methodology to develop the students' level of perceived achievement of the social entrepreneurship competency and explain how this is equally valid in developing the perceived achievement of the complex thinking competency.

Based on a multivariate descriptive statistical analysis, this article offers the results of an educational intervention carried out on a sample group of students from a Mexican university before and after a training program in social entrepreneurship.

The favorable results showed that the proposed methodology is valid for scaling social entrepreneurship and complex thinking competencies and their subcompetencies.

These results are not only academically valuable, as they highlight the need to delve into the relationship between these two competencies, but they also allow us to appreciate the ample opportunities for practical implementation of entrepreneurship programs by universities and other institutions to work directly with social entrepreneurs and seek alternatives to develop skills through devising, proposing and developing social entrepreneurship projects.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

This study explored whether the critical incident management systems (CIMS) model can predict the EMS performance in the COVID-19 context. Past research has established the significance of early detection and response (ER) in the context of Ebola virus disease (EVD), prompting a question of whether the model can also be helpful in the COVID-19 context. Consequently, the authors assessed whether ER influences the impact of communication capacity (CC), reliable information channel (RC) and environment (EN) on COVID-19 EMS performance. Assessing these relationships will advance emerging infectious disease (EID) preparedness.

The authors employed standardized measurement instruments of the CIMS model (CC, ER, RC and EN) to predict the performance of COVID-19 EMS using structural equation modeling (SEM) in a study of 313 participants from frontline responders.

The results show that the relationship of ER and EN with COVID-19 EMS performance is positive, while that of EN on CC is negative. The relationship between EN and COVID-19 EMS performance was insignificant. Contrary to the hypothesis, CC was negatively significant to COVID-19 EMS performance due to poor communication capacities.

The authors acknowledge some limitations due to challenges faced in this study. First, Data collection was a significant limitation as these questionnaires were built and distributed in June 2020, but the response time was prolonged due to the recurring nature of the pandemic. The authors had wanted to implore the inputs of all stakeholders, and efforts were made to reach out to various Ministry of Health, the local CDC and related agencies in the region via repeated emails explaining the purpose of the study to no avail. The study finally used the frontline workers as the respondents. The authors used international students from various countries as the representatives to reach out to their countries' frontline workers. Second, since the study was only partially supported using the CIMS model, future studies may combine the CIMS model with other models or theories. Subsequent research reassesses this outcome in other contexts or regions. Consequently, further research can explore how CC can be improved with COVID-19 and another future EID in the region. This may improve the COVID-19 EMS performance, thereby expanding the lesson learned from the pandemic and sustaining public health EID response. Additionally, other authors may combine the CIMS model with other emergency management models or theories to establish a fully supported theoretical model in the context of COVID-19.

The findings have practical implications for incident managers, local CDCs, governments, international organizations and scholars. The outcome of the study might inform these stakeholders on future direction and contribution to EID preparedness. This study unfolds the impact of lessons learned in the region demonstrated by moderating early detection and responses with other constructs to achieve COVID-19 EMS performance. The findings reveal that countries that experienced the 2013–2016 Ebola outbreak, were not necessarily more prepared for an epidemic or pandemic, judging by the negative moderating impact of early detection and response. However, these experiences provide a foundation for the fight against COVID-19. There is a need for localized plans tailored to each country's situation, resources, culture and lifestyle. The localized plan will be to mitigate and prevent an unsustainable EID management system, post-epidemic fund withdrawals and governance. This plan might be more adaptable and sustainable for the local health system when international interventions are withdrawn after an epidemic. Public health EID plans must be adapted to each country's unique situation to ensure sustainability and constantly improve EID management of epidemics and pandemics in emergency response. The high to moderate importation risk in African countries shows Africa's largest window of vulnerability to be West Africa (Gilbert et al., 2020). Therefore, they should be in the spotlight for heightened assistance towards the preparedness and response for a future pandemic like COVID-19. The West African region has a low capacity to manage the health emergency to match the population capacities. The COVID-19 outbreak in West Africa undoubtedly inflicted many disruptions in most countries' economic, social and environmental circumstances. The region's unique challenges observed in this study with CC and reliable information channels as being negatively significant highlight the poor maintenance culture and weak institutions due to brain drain and inadequate training and monitoring. This outcome practically informs West African stakeholders and governments on aspects to indulge when trying to improve emergency preparedness as the outcomes from other regions might not be applicable.

This study explored the relevance of the CIMS model in the context of the COVID-19 pandemic, revealing different patterns of influence on COVID-19 EMS performance. In contrast to the extant literature on EVD, the authors found the moderating effects of ER in the COVID-19 context. Thus, the authors contribute to the COVID-19 EMS performance domain by developing a context-driven EMS model. The authors discuss the theoretical and practical implications.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.