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Factories use many manufacturing processes that consume a lot of energy and highly contribute to greenhouse gas emissions. The introduction of the concept of Industrial Internet in USA and Industry 4.0 in Europe offers many opportunities to reduce energy consumption in these factories. Introducing and utilizing smart techniques for the applications pertinent to manufacturing processes within the Industry 4.0 domain can offer many benefits for reducing energy consumption in smart factories. This paper investigates and discusses these opportunities and benefits. This paper also discusses the roles of Industry 4.0 technologies in enabling these opportunities. Consequently, introducing these capabilities will help significantly reduce both production costs and greenhouse gas emissions. This paper then provides a benefit analysis that shows the advantages of such leverage. In addition, this paper offers an enabling architecture and its components that include a cyber-physical system manufacturing services' layer, a fog manufacturing services' layer, a cloud manufacturing services' layer, and a blockchain-based service-oriented middleware to support such opportunities.

Background: Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.

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Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes' efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.

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