18 research outputs found
Optimal design of unmodeled linear systems using control-based continuation
This thesis describes the use of control-based continuation for design optimization, in the presence of constraints and without access to a model, of the response of a linear system to harmonic input. A proof of concept of this paradigm is presented in the context of an armature-controlled DC motor.
Specifically, three design problems are formulated with the objective function equal to the maximum angular velocity response to a harmonic torque disturbance, and a constraint that is imposed on each of three distinct stability margins, respectively. The analysis shows that the simulation model for the DC motor may be treated analogously to an actual experiment with all information drawn from real-time measurements, rather than from the model itself. The control-based continuation paradigm is formulated in terms of a non-invasive, yet locally stabilizing control scheme, which can be tuned to accelerate convergence to the steady state response. The numerical analysis uses the matlab-compatible continuation platform coco to determine the implicit relationship between model parameters that results from the constraint, and to evaluate the objective function along the corresponding constraint manifold. A comparison between a scheme that relies on finite differences for approximating the problem Jacobian and an algorithm based on the Broyden update is also included
Exploring causal effects of hormone- and radio-treatments in an observational study of breast cancer using copula-based semi-competing risks models
Breast cancer patients may experience relapse or death after surgery during
the follow-up period, leading to dependent censoring of relapse. This
phenomenon, known as semi-competing risk, imposes challenges in analyzing
treatment effects on breast cancer and necessitates advanced statistical tools
for unbiased analysis. Despite progress in estimation and inference within
semi-competing risks regression, its application to causal inference is still
in its early stages. This article aims to propose a frequentist and
semi-parametric framework based on copula models that can facilitate valid
causal inference, net quantity estimation and interpretation, and sensitivity
analysis for unmeasured factors under right-censored semi-competing risks data.
We also propose novel procedures to enhance parameter estimation and its
applicability in real practice. After that, we apply the proposed framework to
a breast cancer study and detect the time-varying causal effects of hormone-
and radio-treatments on patients' relapse-free survival and overall survival.
Moreover, extensive numerical evaluations demonstrate the method's feasibility,
highlighting minimal estimation bias and reliable statistical inference.Comment: Contact: [email protected]
Reliability-based co-design and its applications to wind energy and mobile energy storage systems
Autonomous systems, such as autonomous driving vehicles, unmanned aerial vehicles (UAVs), and field robots, received much attentions recently. The performance of autonomous systems relies on both its physical design and the appropriate control strategies, which often takes place at an early stage of design. The plant design and the control design are strongly coupled. Neglecting this coupling effect may cause an imbalance in the feasible design spaces of plant design and control design, such as over-constrained operation conditions, over design, or requirement of skilled operators, which hinders the development of autonomous systems. On the other hand, the products are manufactured goods and usually operate in environments with uncertainty. Reliable operation of such systems ask for balanced physical design and feasible control decisions to address the parametric uncertainty and stochastic environmental disturbances.
While integrated physical and control system co-design has been demonstrated successfully on several engineering system design applications, it has been primarily applied in a deterministic manner without considering uncertainties. An opportunity exists to study non-deterministic co-design strategies, taking into account various uncertainties in an integrated co-design framework. While significant advancements have been made in co-design and RBDO separately, little is known about methods where reliability-based dynamic system design and control design optimization are considered jointly. In this research, we investigate optimal design and control of dynamical systems with model parametric uncertainties, which presumably operate in uncertain environments. Techniques in control co-design (CCD) and reliability-based design optimization (RBDO) are adapted and integrated to solve the proposed problem. Since the proposed method adopts the idea of multi-disciplinary design optimization, it can improve the performance of autonomous systems without leveraging the difficulty in design and control for systems with uncertainties.
First, the problem formulation and strategies to solve the reliability-based control co-design problem is presented. A comparison of accuracy and efficiency is made using numerical and simple engineering case studies. The method is then applied to a horizontal axis wind turbine. The uncertain wind load and model parameters of a wind turbine are compensated through active control or endured by a reliable design regarding its aerodynamics and structural dynamics. Different strategies of reliability assessment are also compared, which provides insights on their advantages and limits under different cases.
In the second application, reliability-based control co-design is applied to Lithium-ion battery. The electrode and charging current are optimized to minimize its charging time while regulating its aging effect for reasonable cycle life. The multi-scale nature of the problem requires first principle model to preserve the coupling effect between electrode design at the micro scale and the charging control at the macro scale. However, it is not feasible to use the first principle model for control optimization. A hybrid physics and machine learning strategy is proposed in this work, which extends the applicability of reliability-based control co-design to multi-scale problems.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste
From efficiency to resilience: unraveling the dynamic coupling of land use economic efficiency and urban ecological resilience in Yellow River Basin
Abstract This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales
Unveiling the Spatio-Temporal Evolution and Key Drivers for Urban Green High-Quality Development: A Comparative Analysis of China’s Five Major Urban Agglomerations
Faced with the dual challenges of ecological degradation and economic deceleration, promoting urban green high-quality development (UGHQD) is pivotal for achieving economic transformation, ecological restoration, and regional sustainable development. While the existing literature has delved into the theoretical dimensions of UGHQD, there remains a notable dearth of empirical studies that quantitatively assess its developmental levels, spatio-temporal evolution, and driving factors. This study examines 107 cities of China’s five major urban agglomerations from 2003 to 2020, constructing a comprehensive evaluation indicator system for UGHQD. By employing methodologies, including the Dagum Gini coefficient, Kernel density estimation, Markov chain, and geographical detector, this study extensively assesses the spatial difference, dynamic evolution, and underlying driving forces of UGHQD in these urban agglomerations. The findings indicate: (1) The UGHQD level of the five major urban agglomerations has witnessed a consistent year-over-year growth trend, with coastal agglomerations like the Pearl River Delta (PRD) and Yangtze River Delta (YRD) outperforming others. (2) Pronounced regional differences exist in UGHQD levels across the urban agglomerations, with inter-regional differences primarily contributing to these differences. (3) The dynamic evolution of UGHQD distribution generally transitions from a centralized to a decentralized pattern, with a marked “club convergence” characteristic hindering cross-type leaps. (4) While a range of factors drive UGHQD in these agglomerations, technological innovation stands out as the principal factor inducing spatial differentiation. The comprehensive analysis and findings presented in this research not only contribute to academic knowledge but also hold practical implications for policymakers and practitioners striving for environmentally conscious land use planning and urban management
From Imbalance to Synergy: The Coupling Coordination of Digital Inclusive Finance and Urban Ecological Resilience in the Yangtze River Economic Belt
In the context of rapid urbanization and digitalization, scientifically assessing the spatio-temporal interaction between digital inclusive finance (DIF) and urban ecological resilience (UER) is crucial for promoting the coordinated development of the regional ecology and economy. This study investigates the spatiotemporal evolution of the coupled coordination degree (CCD), the decoupling phenomenon, and its hindering factors in the Yangtze River Economic Belt (YREB) by utilizing the kernel density analysis, standard deviation ellipse, decoupling model, and obstacle degree analysis. Through systematic analyses, this paper aims to elucidate the development disparities among regions within the YREB, identify problematic areas, and propose targeted improvement measures. The results show that (1) The CCD between DIF and UER in the YREB has increased annually from 2011 to 2020. However, there are persistent imbalances, with an overall low level of coordination and uneven spatial development, and a trend of “higher coordination in the east and lower coordination in the west”. (2) The overall CCD of the YREB has reached at least the primary coordination level, with the coupling enhancement speed ranked as “downstream > midstream > upstream”, and regional differences decreasing. (3) The decoupling analysis reveals a predominant decoupling trend between DIF and UER, indicating that the digitization of financial services has not concurrently increased ecological pressures. (4) The obstacle degree analysis identifies resilience and digitalization as major barriers hindering CCD. This study provides a scientific basis and analytical framework for understanding the current spatiotemporal interaction between DIF and UER in the YREB, offering an important reference for formulating more effective policies
Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea
In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases
A Central Amygdala-Substantia Innominata Neural Circuitry Encodes Aversive Reinforcement Signals
Summary: Aversive stimuli can impact motivation and support associative learning as reinforcers. However, the neural circuitry underlying the processing of aversive reinforcers has not been elucidated. Here, we report that a subpopulation of central amygdala (CeA) GABAergic neurons expressing protein kinase C-delta (PKC-δ+) displays robust responses to aversive stimuli during negative reinforcement learning. Importantly, projections from PKC-δ+ neurons of the CeA to the substantia innominata (SI) could bi-directionally modulate negative reinforcement learning. Moreover, consistent with the idea that SI-projecting PKC-δ+ neurons of the CeA encode aversive information, optogenetic activation of this pathway produces conditioned place aversion, a behavior prevented by simultaneous ablating of SI glutamatergic neurons. Taken together, our data define a cell-type-specific neural circuitry modulating associative learning by encoding aversive reinforcement signals. : Cui et al. show that central amygdala PKC-δ+ neurons can modulate negative reinforcement learning by transmitting aversive signals to the substantia innominata. Keywords: central amygdala, negative reinforcement learning, substantia innominate, aversive signal
Characterization of regio- and stereo-selective sulfation of bufadienolides: exploring the mechanism and providing insight into the structure-sulfation relationship by experimentation and molecular docking analysis
Bufadienolides are a major class of bioactive compounds derived from amphibian skin secretion. Recent studies demonstrate that bufadienolides have a promising role in targeted cancer chemotherapy. However, extensive metabolism and inactivation strongly restrict the clinical applications of bufadienolides. This study aimed to systematically characterize the sulfation of six representative bufadienolides (including bufalin, resibufogenin, cinobufagin, bufotalin, telocinobufagin and deacetylcinobufagin) in amphibian skin secretion and to provide insight into the structure-sulfation relationship by experimentation and molecular docking analysis with series of bufadienolides and derivatives. For all the six representative bufadienolides, one corresponding monosulfate was detected in the incubation mixtures. The sulfates were accurately identified as bufadienolides 3-O-sulfates by NMR and HPLC-MSn techniques. Reaction phenotyping studies using human recombinant sulfotransferase (SULT) and liver S9 demonstrated that SULT2A1 mediated the formation of bufadienolide 3-O-sulfate with a high specific selectivity. Further kinetic evaluation demonstrated that deacetylcinobufagin could be used as a preferred probe of SULT2A1. The regio-and stereo-selective sulfation properties of SULT2A1 and the structural variation effects of bufadienolides were investigated by docking analysis, which revealed the significance of appropriate molecule orientation and hydrophobic interactions of motifs with SULT2A1 His99 residues. Additionally, significant differences between humans and animal species were observed in the sulfation of bufalin and resibufogenin. This study provided important data for elucidating the mechanisms of bufadienolide sulfation and leads to a better understanding of the bufadienolide-SULT interaction which can be further used in preclinical development and rational use of bufadienolides
Tethering of cellulose synthase complex to the plasma membrane relies on the isoform of EXO70A1 in Arabidopsis
Abstract In yeast and mammals, the EXO70 subunit of the exocyst complex plays a key role in mediating the tethering of exocytic vesicles to the plasma membrane (PM). In plants, however, the role of EXO70 in regulating vesicle tethering during exocytosis remains unclear. In land plants, EXO70 has undergone significant evolutionary expansion, resulting in multiple EXO70 paralogues that may allow the exocyst to form various isoforms with specific functions. Previous research in Arabidopsis has shown that generally disrupting exocyst function leads to various defects in cellulose synthase (CESA) complex (CSC) trafficking. In this study, we utilized real-time imaging combined with genetic approaches to explore the role of EXO70A1, a member of the EXO70 family in Arabidopsis, in CSC trafficking. The exo70a1 mutant exhibited a decrease in crystalline cellulose content and a reduced density of functional CSCs in the PM. Moreover, the delivery of tdTomato-CESA6 from the cortex to the PM was compromised in the mutant, leading to the accumulation of CSC vesicles at the cell cortex. However, the velocity of tdTomato-CESA6 in the PM was unaffected in exo70a1. These findings suggest that EXO70A1 has a specific role in tethering CSCs to the PM