Two-Layer Predictive Control of a Continuous Biodiesel Transesterification Reactor

A novel two-layer predictive control scheme for a continuous biodiesel transesterification reactor is presented. Based on a validated mechanistic model, the least squares (LS) algorithm is used to identify the finite step response (FSR) process model adapted in the controller. The two-layer predictive control method achieves the steady-state optimal setpoints and resolves the multivariable dynamic control problems synchronously. Simulation results show that the two-layer predictive control strategy leads to a significant improvement of control performance in terms of the optimal set-points tracking and disturbances rejection, as compared to conventional PID controller within a multiloop framework

Quantum reinforcement learning in continuous action space

Quantum mechanics has the potential to speedup machine learning algorithms, including reinforcement learning(RL). Previous works have shown that quantum algorithms can efficiently solve RL problems in discrete action space, but could become intractable in continuous domain, suffering notably from the curse of dimensionality due to discretization. In this work, we propose an alternative quantum circuit design that can solve RL problems in continuous action space without the dimensionality problem. Specifically, we propose a quantum version of the Deep Deterministic Policy Gradient method constructed from quantum neural networks, with the potential advantage of obtaining an exponential speedup in gate complexity for each iteration. As applications, we demonstrate that quantum control tasks, including the eigenvalue problem and quantum state generation, can be formulated as sequential decision problems and solved by our method.Comment: 9 pages, 8 figure

Frosting Weights for Better Continual Training

Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one appealing property is the additive nature of ensemble models. In this paper, we propose two generic ensemble approaches, gradient boosting and meta-learning, to solve the catastrophic forgetting problem in tuning pre-trained neural network models

Biosynthesis of thiocarboxylic acid-containing natural products.

Thiocarboxylic acid-containing natural products are rare and their biosynthesis and biological significance remain unknown. Thioplatensimycin (thioPTM) and thioplatencin (thioPTN), thiocarboxylic acid congeners of the antibacterial natural products platensimycin (PTM) and platencin (PTN), were recently discovered. Here we report the biosynthetic origin of the thiocarboxylic acid moiety in thioPTM and thioPTN. We identify a thioacid cassette encoding two proteins, PtmA3 and PtmU4, responsible for carboxylate activation by coenzyme A and sulfur transfer, respectively. ThioPTM and thioPTN bind tightly to β-ketoacyl-ACP synthase II (FabF) and retain strong antibacterial activities. Density functional theory calculations of binding and solvation free energies suggest thioPTM and thioPTN bind to FabF more favorably than PTM and PTN. Additionally, thioacid cassettes are prevalent in the genomes of bacteria, implicating that thiocarboxylic acid-containing natural products are underappreciated. These results suggest that thiocarboxylic acid, as an alternative pharmacophore, and thiocarboxylic acid-containing natural products may be considered for future drug discovery

Exploring the potentials of Sesuvium portulacastrum L. for edibility and bioremediation of saline soils

Sesuvium portulacastrum L. is a flowering succulent halophyte in the ice plant family Aizoaceae. There are various ecotypes distributed in sandy coastlines and salty marshlands in tropical and subtropical regions with the common name of sea purslane. These plants are tolerant to salt, drought, and flooding stresses and have been used for the stabilization of sand dunes and the restoration of coastal areas. With the increased salinization of agricultural soils and the widespread pollution of toxic metals in the environment, as well as excessive nutrients in waterbodies, S. portulacastrum has been explored for the desalination of saline soils and the phytoremediation of metals from contaminated soils and nitrogen and phosphorus from eutrophic water. In addition, sea purslane has nutraceutical and pharmaceutical value. Tissue analysis indicates that many ecotypes are rich in carbohydrates, proteins, vitamins, and mineral nutrients. Native Americans in Florida eat it raw, pickled, or cooked. In the Philippines, it is known as atchara after being pickled. S. portulacastrum contains high levels of ecdysteroids, which possess antidiabetic, anticancer, and anti-inflammatory activities in mammals. In this review article, we present the botanical information, the physiological and molecular mechanisms underlying the tolerance of sea purslane to different stresses, its nutritional and pharmaceutical value, and the methods for its propagation and production in saline soils and waterbodies. Its adaptability to a wide range of stressful environments and its role in the production of valuable bioactive compounds suggest that S. portulacastrum can be produced in saline soils as a leafy vegetable and is a valuable genetic resource that can be used for the bioremediation of soil salinity and eutrophic water

A Target Sequential Effect on the Forced-Choice Prime Visibility Test in Unconscious Priming Studies: A Caveat for Researchers

In unconscious priming studies, most researchers adopt a combination of subjective and objective measures to assess the visibility of the prime. Although some carry out the visibility test at the end of the experiment separately from the unconscious priming task, others suggest that the forced-choice visibility test should be conducted immediately after the response to the target within each trial. In the present study, the influence of prime and target on the forced-choice prime discrimination was assessed within each trial. The results showed that the target affected the response in the forced-choice prime visibility test. Participants tended to make the same response or avoid repeating the same response they made to the target as in Experiments 1 and 3 rather than randomly guessing. However, even when the forcedchoice visibility test was conducted separately from the priming experiment, the problem was not completely solved, because some participants tended to make one same response in the forced-choice visibility test as in Experiments 2. From another point of view, using these strategies in the forced-choice task can be seen as a helpless move by the participants when they are unaware of the stimuli. Furthermore, the results revealed that the forced-choice test performed immediately after the response to the target within each trial could possibly impair the unconscious priming as well as produce misleading visibility test results. Therefore, it is suggested that the forced-choice prime visibility test and the unconscious priming task may better be conducted separately

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection

Quality Assessment of Guidelines forVascular Cognitive Impairment Using the AGREEⅡ

BackgroundAs the second primary type of cognitive impairment worldwide, vascular cognitive impairment (VCI) is closely associated with cerebrovascular risks, which imposes a heavy burden on the society and families. Early diagnosis and treatment are important for intervening and reversing VCI. And formulating high-quality clinical guidelines is an effective way to improve diagnosis and treatment levels of VCI.ObjectiveTo assess the quality of guidelines for VCI, aiming at offering support for making clinical decisions for VCI.MethodsFrom August to November 2021, we searched literature databases and websites in China and abroad to identify guidelines for VCI, and assessed them using the Appraisal of Guidelines for Research & Evaluation (AGREE) Ⅱ.ResultsA total of 18 guidelines were enrolled, 12 of which are Chinese guidelines and 6 are foreign guidelines; 9 of which are evidence-based guidelines, and the other 9 are not. The intraclass correlation coefficient was 0.935, indicating a high degree of agreement between raters. The overall quality of these guidelines was relatively low, since in the six domains, only the average score of Clarity of Presentation was greater than 60% (64.04%) , and the average scores of Scope and Purpose (52.31%) and Editorial Independence (42.01%) were between 30% and 60%, and those for other three domains, Stakeholder Involvement (27.24%) , Rigor of Development (20.05%) and Applicability (13.83%) , were all less than 30%. The grade of recommendation for 6 guidelines was B, and that for other 12 guidelines was C.ConclusionThe overall quality of the included guidelines was rated relatively low, especially their average score for each of the three domains, Stakeholder Involvement, Rigor of Development, and Applicability, was below the average level. It is suggested to enhance the quality of VCI guidelines via improving the details of guidelines strictly under the evidence-based principle