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Advanced sensing and cloud systems propel the rapid advancements of service-oriented smart manufacturing. As a result, there is widespread generation and proliferation of data in the interest of manufacturing analytics. The sheer amount and velocity of data have also attracted a myriad of malicious parties, unfortunately resulting in an elevated prevalence of cyber-attacks whose impacts are only gaining in severity. Therefore, this article presents a new distributed cryptosystem for analytical computing on encrypted data in the manufacturing environment, with a case study on manufacturing resource planning. This framework harmonizes Paillier cryptography with the Alternating Direction Method of Multipliers (ADMM) for decentralized computation on encrypted data. Security analysis shows that the proposed Paillier-ADMM system is resistant to attacks from external threats, as well as privacy breaches from trusted-but-curious third parties. Experimental results show that smart allocation is more cost-effective than the benchmarked deterministic and stochastic policies. The proposed distributed cryptosystem shows strong potential to leverage the distributed data for manufacturing intelligence, while reducing the risk of data breaches. 

Physical sensing is increasingly implemented in modern industries to improve information visibility, which generates real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this article presents a new “virtual sensing” approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems. 

Potassium channels (Kv) are responsible for repolarizing the action potential in cardiomyocytes. There is a variety of Kv isoforms and corresponding currents (e.g. IKto, IKslow1, IKslow2) that contribute to different phases of repolarization. Because only the sum of their activities can be measured in the form of currents (IKsum), there is a need to delineate individual K+ currents. Most existing studies make inference of Kv activities via curve-fitting procedures but encounter certain limitations as follows: (1) curve-fitting decomposition only relies on the shape of K+ current traces, which does not discern the underlying kinetics; (2) IKsum traces can only be fitted for one clamp voltage at each time, and then analyzed in a population-averaged way later. This paper presents a novel concurrent data assimilation method to calibrate biophysics-based models and delineate kinetics of Kv isoforms with multiple voltage-clamp responses simultaneously. The proposed method is evaluated and validated with whole-cell IKsum recordings from wild-type and chronically glycosylation-deficient cardiomyocytes. Experimental results show that the proposed method effectively handles multiple-response data and describes glycosylation-conferred perturbations to Kv isoforms. Further, we develop a graphical-user-interface (GUI) application that provides an enabling tool to biomedical scientists for data-driven modeling and analysis of Kv kinetics in various heart diseases. 

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure and the primary reason for heart transplantation; upward of 70% of DCM cases are considered idiopathic. Our in-vitro experiments showed that reduced hybrid/complex N-glycosylation in mouse cardiomyocytes is linked with DCM. Further, we observed direct effects of reduced N-glycosylation on K v gating. However, it is difficult to rigorously determine the effects of glycosylation on K v activity, because there are multiple K v isoforms in cardiomyocytes contributing to the cardiac excitation. Due to complex functions of K v isoforms, only the sum of K + currents (I Ksum ) can be recorded experimentally and decomposed later using exponential fitting to estimate component currents, such as I Kto , I Kslow , and I Kss . However, such estimation cannot adequately describe glycosylation effects and K v mechanisms. Here, we propose a framework of simulation modeling of K v kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced glycosylation conditions through ablation of the Mgat1 gene (i.e., Mgat1KO). Calibrated models facilitate the prediction of K v characteristics at different voltages that are not directly observed in the in-vitro experiments. A model calibration procedure is developed based on the genetic algorithm. Experimental results show that, in the Mgat1KO group, both I Kto and I Kslow densities are shown to be significantly reduced and the rate of I Kslow inactivation is much slower. The proposed approach has strong potential to couple simulation models with experimental data for gaining a better understanding of glycosylation effects on K v kinetics.