Alexa, what's on my shopping list? Transforming customer experience with digital voice assistants

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model. 

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2063089?needAccess=true

Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis

Investigating the effect of advertising irritation on digital advertising effectiveness: A moderated mediation model

The Eureka moment in understanding luxury brand purchases! A non-linear fsQCA-ANN approach

Much of the literature has focused on marketing such as social commerce and customer intentions. We performed a literature survey and identified 73 articles that dealt with the application of the Structural Equation Modeling (SEM) with Artificial Neural Network (ANN) method. However, there is a gap in the literature that needs to be addressed. In this context, this research contributes to potential future work by extending the application of the mentioned techniques to more vital applied topics, such as entrepreneurship, family business, organization studies, and the health sector. This chapter describes the potential future work of SEM and ANN by highlighting issues that need to be further explored based on linear and nonlinear relations. 

Artificial Neural Network and Structural Equation Modeling in the Future

Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis


 CRUD depositions on fuel cladding are generated as a result of coolant chemistry and corrosion of primary system components during the normal operation of a nuclear reactor. These depositions can affect both reactor power peaking and fuel element structural integrity through CRUD Induced Power Shifts (CIPS) and CRUD Induced Local Corrosion (CILC) phenomena, respectively. The CIPS/CILC phenomena are typically assessed using the Boron-induced Offset Anomaly (BOA) computer code, which predicts the characterization of the distribution of CRUD within a reactor and the risk for occurrence of CIPS/CILC within a given cycle.
 Machine learning (ML) algorithms have often been applied towards understanding large data sets and acquiring useable information in many industries, including nuclear. In the nuclear engineering field, ML is increasingly being used as a method to improve nuclear power plant efficiency and reduce engineering costs. The application of machine learning to enhance the prediction of CIPS/CILC increases the possibility of reducing the cost associated with CRUD mitigation. In Westinghouse, exploratory analysis was performed to identify ways in which ML could be utilized to improve CIPS risk assessments. There were two paths identified, the first one was ML applications for CIPS with the standalone BOA code, and the second path was ML applications for CIPS calibration with coupled ANC/FUELUDTYDRV/VIPRE-W/BOA code. The exploratory study demonstrated that ML was capable of accurately predicting the boron mass in the cores of selected nuclear power plants as surrogate models of the BOA code.

Applying Machine Learning to Improve BOA Analysis of CIPS/CILC

In this paper, aiming at the performance of intercalated meltblown nonwoven materials, the relationship between its process parameters, structural variables and product performance is explored, and the product performance can be optimized by changing the process parameters through regression, planning and other methods. The results of grey correlation analysis showed that the intercalation rate had a significant effect on both structural variables and product properties. According to the results of the multivariate nonlinear regression model, the ideal maximum cleaning efficiency can reach 99.7%. Finally, in order to explore when the influencing factors such as acceptance distance, hot air speed, thickness, and compression rebound rate are limited, to achieve the goal of achieving the highest filtering efficiency and the smallest filtering resistance as possible, the machine learning method is used, and random forest is selected as the regression model, build samples and make predictions, and finally get the optimal value of the acceptance distance of 21 cm and the hot air speed of 1580 r/min.

/pdf/research-on-performance-control-of-intercalated-meltblown-1rah8wvx.pdf

Guoqiang Wang

Papers

Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis

Applying Machine Learning to Improve BOA Analysis of CIPS/CILC

Research on performance control of intercalated meltblown nonwovens based on neural network and grey relational analysis