Given the size, complexity and dynamic nature of many supply chains, there is a need to understand the impact of disruptions on the operation of the system. This paper presents a network-based modelling methodology to determine how changes or disruptions propagate in supply chains and how those changes or disruptions affect the supply chain system. Understanding the propagation of disruptions and gaining insight into the operational performance of a supply chain system under the duress of an unexpected change can lead to a better understanding of supply chain disruptions and how to lessen their effects. The modelling approach presented, Disruption Analysis Network (DA_NET), models how changes disseminate through a supply chain system and calculates the impact of the attributes by determining the states that are reachable from a given initial marking in a supply chain network. This ability will permit better management of the supply chain and thus will allow an organization to offer quicker response times to the customer, lower costs throughout the chain, and to the end customer higher levels of flexibility and agility, lower inventories throughout the chain (both with work-in-process and inventories), lower levels of obsolescence and a reduced bullwhip effect throughout the chain. This is of particular benefit in large-scale systems, since it can give the user the ability to perform detailed analysis of a dynamic system without the computational burden of a full-scale execution of the model. Consequently, the model may then be segmented to evaluate only the portions or sub-networks that are affected by changes in an initial marking.

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.

Predictive modeling of clinical time series data is challenging due to various factors. One such difficulty is the existence of missing values, which leads to irregular data. Another challenge is capturing correlations across multiple dimensions in order to achieve accurate predictions. Additionally, it is essential to take into account the temporal structure, which includes both short-term and long-term recurrent patterns, to gain a comprehensive understanding of disease progression and to make accurate predictions for personalized healthcare. In critical situations, models that can make multi-step ahead predictions are essential for early detection. This review emphasizes the need for forecasting models that can effectively address the aforementioned challenges. The selection of models must also take into account the data-related constraints during the modeling process. Time series models can be divided into statistical, machine learning, and deep learning models. This review concentrates on the main models within these categories, discussing their capability to tackle the mentioned challenges. Furthermore, this paper provides a brief overview of a technique aimed at mitigating the limitations of a specific model to enhance its suitability for clinical prediction. It also explores ensemble forecasting methods designed to merge the strengths of various models while reducing their respective weaknesses, and finally discusses hierarchical models. Apart from the technical details provided in this document, there are certain aspects in predictive modeling research that have arisen as possible obstacles in implementing models using biomedical data. These obstacles are discussed leading to the future prospects of model building with artificial intelligence in healthcare domain.

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. Convolutional neural network is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of Residual Inception Encoder-Decoder Neural Network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with 2 models from the literature: sCT-DCNN and Shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index (SSIM), mean absolute percent error (MAPE), and peak signal to noise ratio (PSNR).

• Propose a new deep learning framework to incorporate biological knowledge effectively. • Utilize surrogate biomarker Δage for improving MCI to AD conversion prediction. • Provide insight discussions on the use of composite parameters with Δage to help improve the prediction performance. • Achieved high AUC in predicting the MCI converter vs. non-converter.