Combinative effects of Thanh Hao Miet Giap Thang (sweetwormwood and tortoise shell decoction) ingredients on antioxidative activity in vitro

Background: Traditional formulae usually exhibit therapeutic effects through the combinations of different ingredients. The purpose of this study was to investigate in vitro anti-oxidative activity of Thanh Hao Miet Giap Thang (THMGT) (Sweet Wormwood and Tortoise Shell Decoction) formula and the interactions of its ingredients leading to the overall anti-oxidative effect.Materials and Methods: We prepared 31 combinations containing two to four of the five ingredients including Herba Artemisia apiacea L (HbA),Carapax Trionycis (Tryonix sinensis) (CT), Rhizoma Anemarrhenae (Anemarrhena asphodeloides) (RzA), Radix Rehmanniae (Rehmannia glutinosaLibosch) (RdR), Moutan Cortex (Paeonia suffruticosa) (MC). These  combinations were tested for anti-oxidative activity using DCFH-DA and DPPH assays on Hep G2 cells. We also analyzed changes in expression of genes involved in antioxidant defense system including Nuclear Factor Erythroid-Derived 2-Like 2 (NFE2L2), catalase (CAT), heme oxygenase-1 (HO-1), glutathione peroxidase (GPx), cytoplasmic superoxide dismutase (SOD1), mitochondrial superoxide dismutase (SOD2).Results: The complete formula and all combinations containing Moutan Cortex showed high antioxidant activity in both radical solution-basedchemical assay and cellular-based assay. On the contrary, Carapax Trionycis displayed inhibitory effect on the overall antioxidant activity whenpresent in a combination, an effect clearly emphasized in cellular-based assay. Hep G2 cells treated with the formula showed increased geneexpression of HO-1 and SOD2 while expression of CAT, SOD1, GPx was unchanged.Conclusion: Our results suggested that THMGT had anti-oxidative activity essentially through intrinsic reducing capacities and the overall activity ofthe formula resulted from enhancing and inhibiting interactions of  ingredients.Key words: Thanh Hao Miet Giap Thang, Sweet Wormwood and Tortoise Shell Decoction, antioxidant, traditional formulaAbbreviations: THMGT, Thanh Hao Miet Giap Thang; HbA, Herba Artemisia apiacea; CT, Carapax Tryonicis; RzA, Rhizoma Anemarrhenae; MC,Moutan Cortex; RdR, Radix Rehmanniae; ROS, Reactive oxygen species; NFE2L2, Nuclear Factor Erythroid-Derived 2-Like 2; CAT, catalase; GPx,glutathione peroxidase; SOD1, cytoplasmic superoxide dismutase; SOD2, mitochondrial superoxide dismutase; HO-1, heme oxygenase-1

Effect of Dissolved Silicon on the Removal of Heavy Metals from Aqueous Solution by Aquatic Macrophyte Eleocharis acicularis

Silicon (Si) has been recently reconsidered as a beneficial element due to its direct roles in stimulating the growth of many plant species and alleviating metal toxicity. This study aimed at validating the potential of an aquatic macrophyte Eleocharis acicularis for simultaneous removal of heavy metals from aqueous solutions under different dissolved Si. The laboratory experiments designed for determining the removal efficiencies of heavy metals were conducted in the absence or presence of Si on a time scale up to 21 days. Eleocharis acicularis was transplanted into the solutions containing 0.5 mg L−1 of indium (In), gallium (Ga), silver (Ag), thallium (Tl), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) with various Si concentrations from 0 to 4.0 mg L−1. The results revealed that the increase of dissolved Si concentrations enhanced removal efficiencies of E. acicularis for Ga, Cu, Zn, Cd, and Pb, while this increase did not show a clear effect for In, Tl, and Ag. Our study presented a notable example of combining E. acicularis with dissolved Si for more efficient removals of Cu, Zn, Cd, Pb, and Ga from aqueous solutions. The findings are applicable to develop phytoremediation or phytomining strategy for contaminated environment.</jats:p

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems

Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this paper, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties

Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators

Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-specific demands helps to improve the robustness and to increase the success rate when driving the manipulator from its current configuration towards a desired pose. This is necessary, especially in high-dynamic tasks like catching objects in mid-flights. To compute a suitable target configuration in the joint space for a given target pose in the trajectory planning context, various factors such as travel time or manipulability must be considered. However, these factors increase the complexity of the overall problem which impedes real-time implementation. In this paper, a real-time framework to compute the analytical inverse kinematics of a redundant robot is presented. To this end, the analytical IK of the redundant manipulator is parameterized by so-called redundancy parameters, which are combined with a target pose to yield a unique IK solution. Most existing works in the literature either try to approximate the direct mapping from the desired pose of the manipulator to the solution of the IK or cluster the entire workspace to find IK solutions. In contrast, the proposed framework directly learns these redundancy parameters by using a neural network (NN) that provides the optimal IK solution with respect to the manipulability and the closeness to the current robot configuration. Monte Carlo simulations show the effectiveness of the proposed approach which is accurate and real-time capable ($\approx$ \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820

Real-time seat allocation for minimizing boarding/alighting time and improving quality of service and safety for passengers

Rail is considered as one of the most important ways of transferring passengers. High passenger loads has implications on train punctuality. One of the important parameters affecting punctuality is the average boarding/alighting time. Organizing boarding/alighting flows not only reduces the risk of extended dwell time, but also minimizes the risk of injuries and improves the overall service quality. In this paper, we investigate the possibility of minimizing the boarding/alighting time by maintaining a uniform load on carriages through systematic distribution of passengers with flexible tickets, such as season or anytime tickets where no seat information are provided at the time of reservation. To achieve this, the proposed algorithm takes other information such as passenger final destination, uniform load of luggage areas, as well as group travelers into account. Moreover, a discrete event simulation is designed for measuring the performance of the proposed method. The performance of the proposed method is compared with three algorithms on different test scenarios. The results show the superiority of the proposed method in terms of minimizing boarding/alighting time as well as increasing the success rate of assigning group of seats to group of passengers

A search for technosignatures from 14 planetary systems in the Kepler field with the Green Bank Telescope at 1.15-1.73 GHz

Analysis of Kepler mission data suggests that the Milky Way includes billions of Earth-like planets in the habitable zone of their host star. Current technology enables the detection of technosignatures emitted from a large fraction of the Galaxy. We describe a search for technosignatures that is sensitive to Arecibo-class transmitters located within ~420 ly of Earth and transmitters that are 1000 times more effective than Arecibo within ~13 000 ly of Earth. Our observations focused on 14 planetary systems in the Kepler field and used the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. Each source was observed for a total integration time of 5 minutes. We obtained power spectra at a frequency resolution of 3 Hz and examined narrowband signals with Doppler drift rates between +/-9 Hz/s. We flagged any detection with a signal-to-noise ratio in excess of 10 as a candidate signal and identified approximately 850 000 candidates. Most (99%) of these candidate signals were automatically classified as human-generated radio-frequency interference (RFI). A large fraction (>99%) of the remaining candidate signals were also flagged as anthropogenic RFI because they have frequencies that overlap those used by global navigation satellite systems, satellite downlinks, or other interferers detected in heavily polluted regions of the spectrum. All 19 remaining candidate signals were scrutinized and none were attributable to an extraterrestrial source.Comment: 15 pages, 5 figures, accepted for publication in the Astronomical Journa

Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of largescale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decompositionbased coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bilevel resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems

Real Talk: A Realist Dialogic Approach in a Realist Evaluation

Realist evaluation is a method increasingly adopted to provide an understanding of how and why a program works, for whom, and under what circumstances. Initial program theories (IPT) are the crucial starting point of any realist evaluation, however descriptions about how they are developed in practice remain under-reported in the published literature. This article argues for the value of genuine research-group conversations using David Bohm’s concept of dialogue in realist research. We label it the realist dialogic approach. We draw out the relational qualities as well as the contextual circumstances of dialogue through our development of IPT and interview guides for a research study on the implementation and scaling of a large-system value-based program to transform complex health services. We selected the relevant middle-range theories, conducted a literature review, and drew on informal discussions with key stakeholders, to develop IPT through research-group conversations. The benefits of this approach were: 1) development of rigorous, novel, deep and well-tailored IPT, 2) detailed understanding of the complex intervention under investigation and development of rapport and networks with participants, 3) empirically grounded Context-Mechanism-Outcome (CMO) configurations, predicated on suitable abstract and contextually-contingent middle-range theories, and 4) productive research team interactions which supported the entire research process. The challenges of this approach include: 1) establishing and retaining a sense of humility across the research team, 2) contextual circumstances can hinder dialogic relationship, and 3) time and resource heavy. This paper uses middle-range theory and ethnographic insights to advance the existing practice of realist evaluations and offer transferable lessons to other scholars considering similar approaches. Moreover, we content that the use of middle-range theory to extend the methodological literature is a novel contribution to realist work