Passivity Analysis and Passivation in the Design of Cyber-Physical Systems

This dissertation focuses on the analysis and control of cyber-physical systems (CPS) using dissipativity and passivity theory. Cyber-physical systems, as a new generation of systems with integrated computational and physical capabilities, present significant challenges in control design and analysis, due to non-traditional modeling, uncertain environment and highly coupled discrete-event and continuous-time dynamics. On the other hand, it is well known that passive and dissipative systems have modeling, compositionality advantages and stability-guaranteed performance, which are desirable requirements in CPS design. However, it is not straightforward to apply dissipativity and passivity theory to CPS directly in general. The main contribution of this dissertation is to provide systematic and computational methods of passivity analysis and passivation for continuous, networked and hybrid dynamical systems, which provide modeling foundations for CPS. These methods are originally developed for classical nonlinear systems. They include passivity analysis and passivation for interconnected systems using passivity indices and a transformation-based passivation scheme for individual systems. Later, it is shown that the proposed methods can address the issues in the design of CPS, by considering hybrid systems and networked control systems (NCS), respectively. For hybrid systems, the transformation-based passivation scheme provides valuable results on preserving passivity of switched systems under quantization. For networked control systems, the problems of passivity analysis and passivation using passivity indices for interconnected event-triggered feedback systems are investigated. The co-design of passivity levels and event-triggering conditions demonstrates how the trade off between required passivity levels and communication resource utilization can be achieved in NCS. Overall, this dissertation provides new approaches to passivity analysis and passivation of CPS with the focus being on hybrid systems and networked control systems. Numerical simulations and relevant examples are also provided to demonstrate the practical applications of these methods

Microstructural evolution in austenitic stainless steels for extended-life power station applications

Microstructural evolution in austenitic stainless steels for extended life power station application

Flame spread behaviors over thick solid materials near quenching limits

ISCP201

Nickel-Catalyzed Cross-Coupling of Aryl Fluorides and Organozinc Reagents

Ni­(PCy₃)₂Cl₂ was demonstrated to effectively catalyze cross-coupling of aryl fluorides and organozinc reagents. Both electron-poor and -rich aryl fluorides can react effectively with nucleophiles including aryl-, methyl-, and benzylzinc chlorides. A wide range of substituents and functional groups are tolerated. In the presence of a directing group, PhC­(O), the reaction is selective for cleavage of the C–F bond <i>ortho</i> to the carbonyl substituent in a difluoroarene

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics

Palladium-Catalyzed Coupling of Azoles or Thiazoles with Aryl Thioethers via C–H/C–S Activation

Palladium-catalyzed cross-coupling via the C_sp²–S bond activation of aryl thioethers and the C–H bond activation of azoles or thiazoles was carried out. Electron-deficient and -rich aryl methyl thioethers and diaryl thioethers can be employed as the coupling partners and the reaction tolerates a range of functional groups including MeO, CF₃, CN, PhCO, CONEt₂, and Py groups

Image1_Multi-omic analysis of glycolytic signatures: exploring the predictive significance of heterogeneity and stemness in immunotherapy response and outcomes in hepatocellular carcinoma.TIF

Background: Hepatocellular carcinoma (HCC) is a global health challenge with complex pathophysiology, characterized by high mortality rates and poor early detection due to significant tumor heterogeneity. Stemness significantly contributes to the heterogeneity of HCC tumors, and glycolysis is crucial for maintaining stemness. However, the predictive significance of glycolysis-related metabolic genes (GMGs) in HCC remains unknown. Therefore, this study aimed to identify critical GMGs and establish a reliable model for HCC prognosis.Methods: GMGs associated with prognosis were identified by evaluating genes with notable expression changes between HCC and normal tissues retrieved from the MsigDB database. Prognostic gene characteristics were established using univariate and multivariate Cox regression studies for prognosis prediction and risk stratification. The “CIBERSORT” and “pRRophetic” R packages were respectively used to evaluate the immunological environment and predict treatment response in HCC subtypes. The HCC stemness score was obtained using the OCLR technique. The precision of drug sensitivity prediction was evaluated using CCK-8 experiments performed on HCC cells. The miagration and invasion ability of HCC cell lines with different riskscores were assessed using Transwell and wound healing assays.Results: The risk model based on 10 gene characteristics showed high prediction accuracy as indicated by the receiver operating characteristic (ROC) curves. Moreover, the two GMG-related subgroups showed considerable variation in the risk of HCC with respect to tumor stemness, immune landscape, and prognostic stratification. The in vitro validation of the model’s ability to predict medication response further demonstrated its reliability.Conclusion: Our study highlights the importance of stemness variability and inter-individual variation in determining the HCC risk landscape. The risk model we developed provides HCC patients with a novel method for precision medicine that enables clinical doctors to customize treatment plans based on unique patient characteristics. Our findings have significant implications for tailored immunotherapy and chemotherapy methods, and may pave the way for more personalized and effective treatment strategies for HCC.</p

Table1_Multi-omic analysis of glycolytic signatures: exploring the predictive significance of heterogeneity and stemness in immunotherapy response and outcomes in hepatocellular carcinoma.xlsx

Background: Hepatocellular carcinoma (HCC) is a global health challenge with complex pathophysiology, characterized by high mortality rates and poor early detection due to significant tumor heterogeneity. Stemness significantly contributes to the heterogeneity of HCC tumors, and glycolysis is crucial for maintaining stemness. However, the predictive significance of glycolysis-related metabolic genes (GMGs) in HCC remains unknown. Therefore, this study aimed to identify critical GMGs and establish a reliable model for HCC prognosis.Methods: GMGs associated with prognosis were identified by evaluating genes with notable expression changes between HCC and normal tissues retrieved from the MsigDB database. Prognostic gene characteristics were established using univariate and multivariate Cox regression studies for prognosis prediction and risk stratification. The “CIBERSORT” and “pRRophetic” R packages were respectively used to evaluate the immunological environment and predict treatment response in HCC subtypes. The HCC stemness score was obtained using the OCLR technique. The precision of drug sensitivity prediction was evaluated using CCK-8 experiments performed on HCC cells. The miagration and invasion ability of HCC cell lines with different riskscores were assessed using Transwell and wound healing assays.Results: The risk model based on 10 gene characteristics showed high prediction accuracy as indicated by the receiver operating characteristic (ROC) curves. Moreover, the two GMG-related subgroups showed considerable variation in the risk of HCC with respect to tumor stemness, immune landscape, and prognostic stratification. The in vitro validation of the model’s ability to predict medication response further demonstrated its reliability.Conclusion: Our study highlights the importance of stemness variability and inter-individual variation in determining the HCC risk landscape. The risk model we developed provides HCC patients with a novel method for precision medicine that enables clinical doctors to customize treatment plans based on unique patient characteristics. Our findings have significant implications for tailored immunotherapy and chemotherapy methods, and may pave the way for more personalized and effective treatment strategies for HCC.</p

Glycosyl Cross-Coupling with Diaryliodonium Salts: Access to Aryl <i>C</i>‑Glycosides of Biomedical Relevance

A stereospecific cross-coupling reaction of anomeric nucleophiles with diaryliodonium triflates resulting in the synthesis of aryl C-glycosides is reported. This process capitalizes on a stereoretentive reaction of configurationally stable C1 stannanes and is promoted by a palladium catalyst in the presence of a bulky phosphine ligand that suppresses the undesired β-elimination. The utility of this reaction has been demonstrated in the preparation of a series of C-glycosides derived from common saccharides resulting in exclusive transfer of anomeric configuration from the anomeric nucleophile to the product, and in the synthesis of empagliflozin, a commercial antidiabetic drug