Pretreatment of Miscanthus giganteus with Lime and Oxidants for Biofuels

ACKNOWLEDEGMENTS The authors are grateful to the Energy Biosciences Institute, University of California, Berkeley, Berkeley, CA, for financial support, Dr. Stefan R. Bauer, Valerie D. Mitchell, and Ana Belen Ibanez Zamora for technical assistance, and Jason Cai for fruitful discussions. The authors thank the China Scholarship Council for financial assistance to Fuxin Yang during his stay at University of California, Berkeley.Peer reviewedPostprin

Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation

In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with dual capability of exploiting current estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CL-DCEE algorithm are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

Time-delay control (TDC) is widely recognized as a robust and straightforward model-free control approach for complex systems. However, the transient performance and settling time are often given less consideration in most TDC-based controllers. In this article, we propose an integrated control protocol that combines fixed-time prescribed performance control with time-delay estimation techniques for autonomous ground vehicles. The proposed control paradigm offers the advantages of being model-free while ensuring that the preview error converges to a neighborhood of zero within a fixed time, adhering to predefined constraint functions. To overcome the limitations of commonly used exponential decay boundaries, a prescribed performance function that remains independent of the initial conditions is employed. Furthermore, a high-order model-free fixed-time differentiator is constructed to observe the high-order dynamics of the preview error, which are essential for estimating unknown model dynamics. Finally, the simulations and practical experiments have been conducted to demonstrate the superiority of our proposed control protocol

Cooperative Active Learning based Dual Control for Exploration and Exploitation in Autonomous Search

In this paper, a multi-estimator based computationally efficient algorithm is developed for autonomous search in an unknown environment with an unknown source. Different from the existing approaches that require massive computational power to support nonlinear Bayesian estimation and complex decision-making process, an efficient cooperative active learning based dual control for exploration and exploitation (COAL-DCEE) is developed for source estimation and path planning. Multiple cooperative estimators are deployed for environment learning process, which is helpful to improving the search performance and robustness against noisy measurements. The number of estimators used in COAL-DCEE is much smaller than that of particles required for Bayesian estimation in information-theoretic approaches. Consequently, the computational load is significantly reduced. As an important feature of this study, the convergence and performance of COAL-DCEE are established in relation to the characteristics of sensor noises and turbulence disturbances. Numerical and experimental studies have been carried out to verify the effectiveness of the proposed framework. Compared with existing approaches, COAL-DCEE not only provides convergence guarantee, but also yields comparable search performance using much less computational power

Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning

The quest for optimal operation in environments with unknowns and uncertainties is highly desirable but critically challenging across numerous fields. This paper develops a dual control framework for exploration and exploitation (DCEE) to solve an auto-optimisation problem in such complex settings. In general, there is a fundamental conflict between tracking an unknown optimal operational condition and parameter identification. The DCEE framework stands out by eliminating the need for additional perturbation signals, a common requirement in existing adaptive control methods. Instead, it inherently incorporates an exploration mechanism, actively probing the uncertain environment to diminish belief uncertainty. An ensemble based multi-estimator approach is developed to learn the environmental parameters and in the meanwhile quantify the estimation uncertainty in real time. The control action is devised with dual effects, which not only minimises the tracking error between the current state and the believed unknown optimal operational condition but also reduces belief uncertainty by proactively exploring the environment. Formal properties of the proposed DCEE framework like convergence are established. A numerical example is used to validate the effectiveness of the proposed DCEE. Simulation results for maximum power point tracking are provided to further demonstrate the potential of this new framework in real world applications

Risk of hepatitis B virus reactivation and its effect on survival in advanced hepatocellular carcinoma patients treated with hepatic arterial infusion chemotherapy and lenvatinib plus programmed death receptor-1 inhibitors

BackgroundHepatitis B virus (HBV) reactivation is a common complication in hepatocellular carcinoma (HCC) patients treated with chemotherapy or immunotherapy. This study aimed to evaluate the risk of HBV reactivation and its effect on survival in HCC patients treated with HAIC and lenvatinib plus PD1s.MethodsWe retrospectively collected the data of 213 HBV-related HCC patients who underwent HAIC and lenvatinib plus PD1s treatment between June 2019 to June 2022 at Sun Yat-sen University, China. The primary outcome was the risk of HBV reactivation. The secondary outcomes were overall survival (OS), progression−free survival (PFS), and treatment−related adverse events.ResultsSixteen patients (7.5%) occurred HBV reactivation in our study. The incidence of HBV reactivation was 5% in patients with antiviral prophylaxis and 21.9% in patients without antiviral prophylaxis, respectively. The logistic regression model indicated that for HBV reactivation, lack of antiviral prophylaxis (P=0.003) and tumor diameter (P=0.036) were independent risk factors. The OS and PFS were significantly shorter in the HBV reactivation group than the non-reactivation group (P=0.0023 and P=0.00073, respectively). The number of AEs was more in HBV reactivation group than the non-reactivation group, especially hepatic AEs.ConclusionHBV reactivation may occur in HCC patients treated with HAIC and lenvatinib plus PD1s. Patients with HBV reactivation had shorter survival time compared with non-reactivation. Therefore, HBV-related HCC patients should undergo antiviral therapy and HBV-DNA monitoring before and during the combination treatment

Survival benefit of neoadjuvant hepatic arterial infusion chemotherapy followed by hepatectomy for hepatocellular carcinoma with portal vein tumor thrombus

Background/purpose: The prognosis of hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) is generally poor and hepatectomy is optional for these patients. This study aims to explore the survival benefits of neoadjuvant hepatic arterial infusion chemotherapy (HAIC) for resectable HCC with PVTT.Methods: This retrospective study included 120 resectable HCC patients with PVTT who underwent hepatectomy, from January 2017 to January 2021 at Sun Yat-sen University Cancer Center. Of these patients, the overall survival (OS) and recurrence-free survival (RFS) of 55 patients who received hepatectomy alone (Surgery group) and 65 patients who received neoadjuvant HAIC followed by hepatectomy (HAIC-Surgery group) were compared. Logistic regression analysis was conducted to develop a model predicting the response to neoadjuvant HAIC.Results: The OS rates for the HAIC-Surgery group at 1, 3, and 5 years were 94.9%, 78%, and 66.4%, respectively, compared with 84.6%, 47.6%, and 37.2% in the Surgery group (p < 0.001). The RFS rates were 88.7%, 56.2%, and 38.6% versus 84.9%, 38.3%, and 22.6% (p = 0.002). The subgroup analysis revealed that the survival benefit of neoadjuvant HAIC was limited to patients who responded to it. The logistic model, consisting of AFP and CRP, that predicted the response to neoadjuvant HAIC performed well, with an area under the ROC curve (AUC) of 0.756.Conclusion: Neoadjuvant HAIC followed by hepatectomy is associated with a longer survival outcome than hepatectomy alone for HCC patients with PVTT and the survival benefit is limited to patients who respond to neoadjuvant FOLFOX-HAIC