Intelligent control of a class of nonlinear systems

The objective of this study is to improve and propose new fuzzy control algorithms for a class of nonlinear systems. In order to achieve the objectives, novel stability theorems as well as modeling techniques are also investigated. Fuzzy controllers in this work are designed based on the fuzzy basis function neural networks and the type-2 Takagi-Sugeno fuzzy models. For a class of single-input single-output nonlinear systems, a new stability condition is derived to facilitate the design process of proportional-integral Mamdani fuzzy controllers. The stability conditions require a new technique to calculate the dynamic gains of nonlinear systems represented by fuzzy basis function network models. The dynamic gain of a fuzzy basis function network can be approximated by finding the maximum of norm values of the locally linearized systems or by solving a non-smooth optimal control problem. Based on the new stability theorem, a multilevel fuzzy controller with self-tuning algorithm is proposed and simulated in a tower crane control system. For a class of multi-input multi-output nonlinear systems with measurable state variables, a new method for modeling unstructured uncertainties and robust control of unknown nonlinear dynamic systems is proposed by using a novel robust Takagi-Sugeno fuzzy controller. First, a new training algorithm for an interval type-2 fuzzy basis function network is presented. Next, a novel technique is derived to convert the interval type-2 fuzzy basis function network to an interval type-2 Takagi-Sugeno fuzzy model. Based on the interval type-2 Takagi-Sugeno and type-2 fuzzy basis function network models, a robust controller is presented with an adjustable convergence rate. Simulation results on an electrohydraulic actuator show that the robust Takagi-Sugeno fuzzy controller can reduce steady-state error under different conditions while maintaining better responses than the other robust sliding mode controllers can. Next, the study presents an implementation of type-2 fuzzy basis function networks and robust Takagi-Sugeno fuzzy controllers to data-driven modeling and robust control of a laser keyhole welding process. In this work, the variation of the keyhole diameter during the welding process is approximated by a type-2 fuzzy-basis-function network, while the keyhole penetration depth is modelled by a type-1 fuzzy basis function network. During the laser welding process, a CMOS camera integrated with the welding system was used to provide a feedback signal of the keyhole diameter. An observer was implemented to estimate the penetration depth in real time based on the adaptive divided difference filter and the feedback signal from the camera. A robust Takagi-Sugeno fuzzy controller was designed based on the fuzzy basis function networks representing the welding process with uncertainties to adjust the laser power to ensure that the penetration depth of the keyhole is maintained at a desired value. Experimental results demonstrated that the fuzzy models provided an accurate estimation of both the welding geometry and its variations due to uncertainties, and the robust Takagi-Sugeno fuzzy controller successfully reduced the penetration depth variation and improved the quality of the welding process

The Dynamics of Income and Neighborhood Context for Population Health: Do Long Term Measures of Socioeconomic Status Explain More of the Black/White Health Disparity than Single-Point-In-Time Measures?

Socioeconomic status, though a robust and strong predictor of health, has generally been unable to fully explain the health gap between blacks and whites in the Untied States. However, at both the individual and neighborhood levels, socioeconomic status is often treated as a static factor with only single-point-in-time measurements. These cross-sectional measures fail to account for possible heterogeneous histories within groups who may share similar characteristics at a given point in time. As such, ignoring the dynamic nature of socioeconomic status may lead to the underestimation of its importance in explaining health and racial health disparities. In this study, I use national longitudinal data to investigate the relationship between neighborhood poverty and respondent-rated health, focusing on whether the addition of a temporal dimension reveals a stronger relationship between neighborhood poverty and health, and a greater explanatory power for the health gap between blacks and whites. Results indicated that long-term neighborhood measures are stronger predictors of health outcomes and explain a greater amount of the black/white health gap than single-point measures

ViFi: accurate detection of viral integration and mRNA fusion reveals indiscriminate and unregulated transcription in proximal genomic regions in cervical cancer.

The integration of viral sequences into the host genome is an important driver of tumorigenesis in many viral mediated cancers, notably cervical cancer and hepatocellular carcinoma. We present ViFi, a computational method that combines phylogenetic methods with reference-based read mapping to detect viral integrations. In contrast with read-based reference mapping approaches, ViFi is faster, and shows high precision and sensitivity on both simulated and biological data, even when the integrated virus is a novel strain or highly mutated. We applied ViFi to matched genomic and mRNA data from 68 cervical cancer samples from TCGA and found high concordance between the two. Surprisingly, viral integration resulted in a dramatic transcriptional upregulation in all proximal elements, including LINEs and LTRs that are not normally transcribed. This upregulation is highly correlated with the presence of a viral gene fused with a downstream human element. Moreover, genomic rearrangements suggest the formation of apparent circular extrachromosomal (ecDNA) human-viral structures. Our results suggest the presence of apparent small circular fusion viral/human ecDNA, which correlates with indiscriminate and unregulated expression of proximal genomic elements, potentially contributing to the pathogenesis of HPV-associated cervical cancers. ViFi is available at https://github.com/namphuon/ViFi

Determinant of HIV-1 mutational escape from cytotoxic T lymphocytes.

CD8+ class I-restricted cytotoxic T lymphocytes (CTLs) usually incompletely suppress HIV-1 in vivo, and while analogous partial suppression induces antiretroviral drug-resistance mutations, epitope escape mutations are inconsistently observed. However, escape mutation depends on the net balance of selective pressure and mutational fitness costs, which are poorly understood and difficult to study in vivo. Here we used a controlled in vitro system to evaluate the ability of HIV-1 to escape from CTL clones, finding that virus replicating under selective pressure rapidly can develop phenotypic resistance associated with genotypic changes. Escape varied between clones recognizing the same Gag epitope or different Gag and RT epitopes, indicating the influence of the T cell receptor on pressure and fitness costs. Gag and RT escape mutations were monoclonal intra-epitope substitutions, indicating limitation by fitness constraints in structural proteins. In contrast, escape from Nef-specific CTL was more rapid and consistent, marked by a polyclonal mixture of epitope point mutations and upstream frameshifts. We conclude that incomplete viral suppression by CTL can result in rapid emergence of immune escape, but the likelihood is strongly determined by factors influencing the fitness costs of the particular epitope targeted and the ability of responding CTL to recognize specific epitope variants

Design of a genetic muller C-element

Journal ArticleSynthetic biology uses engineering principles to design circuits out of genetic materials that are inserted into bacteria to perform various tasks. While synthetic combinational Boolean logic gates have been constructed, there are many open issues in the design of sequential logic gates. One such gate common in most asynchronous circuits is the Muller C-element, which is used to synchronize multiple independent processes. This paper proposes a novel design for a genetic Muller C-element using transcriptional regulatory elements. The design of a genetic Muller C-element enables the construction of virtually any asynchronous circuit from genetic material. There are, however, many issues that complicate designs with genetic materials. These complications result in modifications being required to the normal digital design procedure. This paper presents two designs that are logically equivalent to a Muller C-element. Mathematical analysis and stochastic simulation, however, show that only one functions reliably

Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study. Our method achieved a remarkable accuracy of 0.9999986\% on the test suite, surpassing the performance of the previous method. From this result, we use 3D printing technology to illustrate the shape of the wound filling. The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design. By automating facial wound segmentation and improving the accuracy of wound-filling extraction, our approach can assist in carefully assessing and optimizing interventions, leading to enhanced patient outcomes. Additionally, it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants. Our source code is available at \url{https://github.com/SIMOGroup/WoundFilling3D}