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Purpose – An agile supply chain (ASC) includes companies that are operationally linked to each other, such as supply, design, manufacturing and distribution centers that respond and react quickly and effectively to change markets. Information systems and technology have a main role in achieving this objective. Therefore, the purpose of this paper is to examine the relationship between information integration, information infrastructure flexibility and the ASC in the Iranian power plant industry (IPPI). Design/methodology/approach – The quantitative method was employed in this study. Survey questionnaires were sent to 87 managers in the IPPI to examine the relationship between information integration, information infrastructure flexibility, and the ASC. Findings – The final results indicated that information sharing and responsibility were strongly related with the ASC; accessibility and connectivity had important relations with the ASC; while the relationships between compatibility and adaptableness as IT flexibility variables and ASC were positive but not significant. Research limitations/implications – This study focussed on the impact of IT on the IPPI specifically companies that manufacture boilers, electronic control tools, turbines, turbine blades, generators and other power plant-related components. Practical implications – A new research model was developed to assess the impact of the interrelationships among IT capabilities and the ASC and results should assist managers as well as academicians. Originality/value – An investigation was carried out through this study based on the current situation in IPPI to empirically examine and evaluate the effect of IT integration and flexibility on ASC. Besides, a very limited number of studies have been done on the implementation of information technology in the IPPI.

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, threestream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce imageclassified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89%, with a receiver operating characteristic of 93%. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.

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