Generalized matrix-based Bayesian network for multi-state systems

To achieve a resilient society, the reliability of core engineering systems should be evaluated accurately. However, this remains challenging due to the complexity and large scale of real-world systems. Such complexity can be efficiently modelled by Bayesian network (BN), which formulates the probability distribution through a graph-based representation. On the other hand, the scale issue can be addressed by the matrix-based Bayesian network (MBN), which allows for efficient quantification and flexible inference of discrete BN. However, the MBN applications have been limited to binary-state systems, despite the essential role of multi-state engineering systems. Therefore, this paper generalizes the MBN to multi-state systems by introducing the concept of composite state. The definitions and inference operations developed for MBN are modified to accommodate the composite state, while formulations for the parameter sensitivity are also developed for the MBN. To facilitate applications of the generalized MBN, three commonly used techniques for decomposing an event space are employed to quantify the MBN, i.e. utilizing event definition, branch and bound (BnB), and decision diagram (DD), each being accompanied by an example system. The numerical examples demonstrate the efficiency and applicability of the generalized MBN. The supporting source code and data can be download at https://github.com/jieunbyun/Generalized-MBN-multi-state

Efficient Optimization for Multi-Objective Decision-Making on Civil Systems Using Discrete Influence Diagram

The breakdown of civil systems, e.g. bridge networks and water distribution networks, has a significant social and economic impact, highlighting the importance of optimal decision-making on such systems. Modeling and optimization of probabilistic decision-making problems for civil systems, can be facilitated by graphical methodologies such as influence diagram (ID). However, the converging structure in IDs representing civil systems, which relates the random variables standing for component events and that for system event, results in the exponential increase in the number of modeling parameters and variables to be optimized as that of component events increases. In order to address these challenges, in this paper, the recently proposed matrix-based Bayesian network (MBN) is employed to quantify the IDs. To facilitate the optimization process, a proxy objective function is also proposed. The proxy func-tion not only significantly reduces the number of variables to be optimized, but also allows an efficient framework for multi-objective optimization in which the weighted sum of the objectives is optimized to obtain a set of non-dominated solutions. Three numerical examples demonstrate the performance of the proposed methodology.This research was supported by a grant (18SCIP-B146946-01) from Smart Civil Infrastructure Re-search Program funded by Ministry of Land, In-frastructure, and Transport of Korean government

Linear Programming by Delayed Column Generation for Bounds on Reliability of Larger Systems

In various efforts to secure the resilience of community, accurate reliability analysis of civil systems is critical considering their pivotal functions. As such systems generally consist of multiple components, their reliability analysis requires complete information to construct joint probabilistic dis-tributions of component events, which is rarely available in practice. In order to obtain the best estimates on the system reliability based on the available information, the linear programming (LP) bounds method was proposed (Song and Der Kiureghian 2003).The method obtains bounds on system reliability by solv-ing LP problems constructed by decomposing the event space into mutually exclusive and collectively exhaustive (MECE) events. Despite the optimality and flexibility of the LP bounds method, there is a limitation in the size of systems as the number of MECE events increases exponentially in regards to that of component events. In order to address this issue, this paper develops an alternative LP bounds formu-lation by employing delayed column generation, in which the LP is solved as an iteration of smaller binary integer programming (BIP). The BIP can be formulated by Boolean algebra that represents the inclusion relationships between component events, system event, and constraint events. The proposed formulation requires polynomial memory in regards to the number of constraints, allowing the evaluation of the LP bounds for larger systems and changing the major bottleneck from the number of components to that of constraint events incorporated into the LP. Four numerical examples are provided to illustrate and demonstrate the proposed method.This research was supported by a grant (18SCIP-B146946-01) from Smart Civil Infrastructure Re-search Program funded by Ministry of Land, In-frastructure, and Transport of Korean government

A general framework of Bayesian network for system reliability analysis using junction tree

To perform the reliability analysis of complex and large-scale systems, Bayesian network (BN) can be useful as it facilitates modelling the causal relationship between multiple types of variables, e.g. hazards, material properties, and inspection results. However, its conventional approach shows limitations in handling large-scale systems and advanced inference tasks such as continuous distributions and approximate inference. On the other hand, these issues have been successfully addressed by system reliability analysis (SRA) theory, while the complexity of system reliability methods (SRMs) makes it challenging to handle multiple types of variables collectively. Accordingly, to facilitate the reliability analysis of real-world problems, this paper develops a general framework to implement BN for SRA by employing junction tree (JT). The connection between BN and SRA is further consolidated by summarizing common computational challenges and proposing heuristics to resolve them. While it provides a systematic way to implement SRMs within the BN framework, such generalization can also be used to enhance the functionality of the general-purpose software programs developed for BN as demonstrated by the companion Matlab®-based toolkit BNS-JT. The applicability and efficiency of the proposed framework are demonstrated by numerical examples

Effects of Tilting Pad Journal Bearing Design Parameters on the Pad-Pivot Friction and Nonlinear Rotordynamic Bifurcations

This study numerically analyzes and investigates the effects of the bearing design parameters of a tilting pad journal bearing (TPJB) on the pad-pivot friction-induced nonlinear rotordynamic phenomena and bifurcations. The bearing parameters were set to the pad preload, pivot offset, spherical pivot radius, and bearing length to diameter (L/D) ratio. The Stribeck curve model (SCM) model was applied at the contact surface between the pad and the pivot, which varied to the boundary-mixed-fluid friction state depending on the friction condition. The rotor-bearing model was set up with a symmetrical five-pad TPJB system supporting a Jeffcott type rigid rotor. The fluid repelling force generated in the oil film between each pad and the shaft was calculated using a finite element method. The simulation recurrently conducted the transient numerical integration to obtain the Poincaré maps and phase states of the journal and pad with various bearing design variables, then the nonlinear properties of each condition were analyzed by expressing the bifurcation diagrams. As a result, the original findings of this study are: (1) The pad preload and pivot offset significantly influenced the emergence of Hopf bifurcations and the associated limit cycles. In contrast, (2) the pivot radius and L/D ratio contributed relatively less to the friction-induced instability. Resultantly, (3) all the effects diminished when the rotor operated under the larger mass eccentricity of the disc

Photosensitizer-Trapped Gold Nanocluster for Dual Light-Responsive Phototherapy

Photoresponsive nanomaterials have recently received great attention in the field of cancer therapy. Here, we report a photosensitizer-trapped gold nanocluster that can facilitate dual light-responsive cancer therapy. We utilized methylene blue (MB) as a model photosensitizer, gold nanocluster as a model photothermal agent, and a polymerized DNA as the backbone of the nanocluster. We synthesized MB-intercalated gold DNA nanocluster (GMDN) via reduction and clustering of gold ions on a template consisting of MB-intercalated long DNA. Upon GMDN treatment, cancer cells revealed clear cellular uptake of MB and gold clusters; following dual light irradiation (660 nm/808 nm), the cells showed reactive oxygen species generation and increased temperature. Significantly higher cancer cell death was observed in cells treated with GMDN and dual irradiation compared with non-irradiated or single light-irradiated cells. Mice systemically injected with GMDN showed enhanced tumor accumulation compared to that of free MB and exhibited increased temperature upon near infrared irradiation of the tumor site. Tumor growth was almost completely inhibited in GMDN-treated tumor-bearing mice after dual light irradiation, and the survival rate of this group was 100% over more than 60 days. These findings suggest that GMDN could potentially function as an effective phototherapeutic for the treatment of cancer disease

DNA-based artificial dendritic cells for in situ cytotoxic T cell stimulation and immunotherapy

In immunotherapy, ex vivo stimulation of T cells requires significant resources and effort. Here, we report artificial dendritic cell-mimicking DNA microflowers (DM) for programming T cell stimulation in situ. To mimic dendritic cells, DNA-based artificial dendritic microflowers were constructed, surface-coated with polydopamine, and further modified with anti-CD3 and anti-CD28 antibodies to yield antibody-modified DM (DM-A). The porous structure of DM-A allowed entrapment of the T cell-stimulating cytokine, ineterleukin-2, yielding interleukin-2-loaded DM-A (DM-AI). For comparison, polystyrene microparticles coated with polydopamine and modified with anti-CD3 and anti-CD28 antibodies (PS-A) were used. Compared to PS-A, DM-AI showed significantly greater contact with T cell surfaces. DM-AI provided the highest ex vivo expansion of cytotoxic T cells. Local injection of DM-AI to tumor tissues induced the recruitment of T cells and expansion of cytotoxic T cells in tumor microenvironments. Unlike the other groups, model animals injected with DM-AI did not exhibit growth of primary tumors. Treatment of mice with DM-AI also protected against growth of a rechallenged distant tumor, and thus prevented tumor recurrence in this model. DM-AI has great potential for programmed stimulation of CD8+ T cells. This concept could be broadly extended for the programming of specific T cell stimulation profiles

Fabrication of perovskite solar cell with high short-circuit current density (J(SC)) using moth-eye structure of SiOX

The performance of solar cells is determined by three factors: the open-circuit voltage (V-OC), short-circuit current density (J(SC)), and fill factor (FF). The V-OC and FF are determined by the material bandgap and the series/shunt resistance, respectively. However, J(SC) is determined by the amount of incident light in addition to the bandgap of the material. In this study, a moth-eye pattern was formed on a glass surface via direct printing to increase the amount of incident light and thus increase J(SC). The moth-eye pattern is a typical antireflection pattern that reduces the reflection by gradually increasing the refractive index. A flat perovskite solar cell (F-PSC) and a moth-eye patterned perovskite solar cell (M-PSC) had J(SC) values of 23.70 and 25.50 mA/cm(2), respectively. The power-conversion efficiencies of the F-PSC and M-PSC were 19.81% and 21.77%, respectively

DNA-cloaked nanoparticles for tumor microenvironment-responsive activation

© 2022Although progress has been made in developing tumor microenvironment-responsive delivery systems, the list of cargo-releasing stimuli remains limited. In this study, we report DNA nanothread-cloaked nanoparticles for reactive oxygen species (ROS)-rich tumor microenvironment-responsive delivery systems. ROS is well known to strongly induce DNA fragmentation via oxidative stress. As a model anticancer drug, hydrophobic omacetaxine was entrapped in branched cyclam ligand-modified nanoparticles (BNP). DNA nanothreads were prepared by rolling-circle amplification and complexed to BNP, yielding DNA nanothread-cloaked BNP (DBNP). DBNP was unmasked by DNA nanothread-degrading ROS and culture supernatants of LNCaP cells. The size and zeta potential of DBNP were changed by ROS. In ROShigh LNCaP cells, but not in ROSlow fibroblast cells, the uptake of DBNP was higher than that of other nanoparticles. Molecular imaging revealed that DBNP exhibited greater distribution to tumor tissues, compared to other nanoparticles. Ex vivo mass spectrometry-based imaging showed that omacetaxine metabolites were distributed in tumor tissues of mice treated with DBNP. Intravenous administration of DBNP reduced the tumor volume by 80% compared to untreated tumors. Profiling showed that omacetaxine treatment altered the transcriptional profile. These results collectively support the feasibility of using polymerized DNA-masked nanoparticles for selective activation in the ROS-rich tumor microenvironment.N

The Effect of Excessive Sulfate in the Li-Ion Battery Leachate on the Properties of Resynthesized Li[Ni1/3Co1/3Mn1/3]O2

In order to examine the effect of excessive sulfate in the leachate of spent Li-ion batteries (LIBs), LiNi1/3Co1/3Mn1/3O2 (pristine NCM) and sulfate-containing LiNi1/3Co1/3Mn1/3O2 (NCMS) are prepared by a co-precipitation method. The crystal structures, morphology, surface species, and electrochemical performances of both cathode active materials are studied by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and charge-discharge tests. The XRD patterns and XPS results identify the presence of sulfate groups on the surface of NCMS. While pristine NCM exhibits a very dense surface in SEM images, NCMS has a relatively porous surface, which could be attributed to the sulfate impurities that hinder the growth of primary particles. The charge-discharge tests show that discharge capacities of NCMS at C-rates, which range from 0.1 to 5 C, are slightly decreased compared to pristine NCM. In dQ/dV plots, pristine NCM and NCMS have the same redox overvoltage regardless of discharge C-rates. The omnipresent sulfate due to the sulfuric acid leaching of spent LIBs has a minimal effect on resynthesized NCM cathode active materials as long as their precursors are adequately washed