General scheduling framework in computational Grid based on Petri net

A general scheduling framework (GSF) for independent tasks in computational Grid is proposed in this paper, which modeled by Petri net and located on the layer of Grid scheduler. Furthermore, a new mapping algorithm aimed at time and cost is designed on the basis of this framework. The algorithm uses weighted average fuzzy applicability to express the matching degree between available machines and independent tasks. Some existent heuristic algorithms are tested in GSF, and the results of simulation and comparison not only show good flexibility and adaptability of GSF, but also prove that, given a certain aim, the new algorithm can consider the factors of time and cost as a whole and its performance is higher than those mentioned algorithms.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

General modeling and optimization technique for real-world earth observation satellite scheduling

Over the last two decades,many modeling and optimization techniques have been developed for earth observation satellite(EOS)scheduling problems,but few of them show good generality to be engineered in realworld applications.This study proposes a general modeling and optimization technique for common and real-world EOS scheduling cases;it includes a decoupled framework,a general modeling method,and an easy-to-use algorithm library.In this technique,a framework that decouples the modeling,constraints,and optimization of EOS scheduling problems is built.With this framework,the EOS scheduling problems are appropriately modeled in a general manner,where the executable opportunity,another format of the well-known visible time window per EOS operation,is viewed as a selectable resource to be optimized.On this basis,10 types of optimization algorithms,such as Tabu search and genetic algorithm,and a parallel competitive memetic algorithm,are developed.For simplified EOS scheduling problems,the proposed technique shows better performance in applicability and effectiveness than the state-of-the-art algorithms.In addition,a complicatedly constrained real-world benchmark exampled by a four-EOS Chinese commercial constellation is provided,and the technique is qualified and outperforms the in-use scheduling system by more than 50%.

Scenario of China Reaching Carbon Peaking ahead of Schedule and Its Effect on Macro Economy

At the 75th session of the United Nations General Assembly(UNGA)in 2020,China put forward the goal of peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060,a move to lead global response to climate change that has attracted wide attention and hot comments at home and abroad.Therefore,it is of great practical significance and academic value to explore ways of achieving carbon peaking ahead of schedule and study the macroeconomic effect.This paper,based on Energy,Environment and Economy recursive dynamic computable general equilibrium model(TECGE),a dynamic computable general equilibrium model,carries out a quantitative analysis of the effect of strengthening carbon peaking commitment on China's future macro economy.By setting up four scenarios,namely carbon peaking of 10.8 billion tons,10.7 billion tons,10.58 billion tons and 10.36 billion tons in 2030,2027,2025,and 2023,it examines the effects of carbon peaking ahead of schedule and carbon peaking in 2030 on macro economy.The findings show that,compared with the 2030 benchmark,the more ahead of schedule carbon peaking is achieved,the higher the carbon tax prices,and that though GDP and other macroeconomic variables,such as aggregate consumption,aggregate imports and exports decline,the share of the tertiary industry increases.That is,the more ahead of schedule carbon peaking is achieved,the more macroeconomic variables decline,and the more the share of the tertiary industry rises.This paper,using computable general equilibrium(CGE)model to conduct a quantitative analysis of the macroeconomic effect,makes policy recommendations for carbon peaking ahead of schedule and high-quality economic development.

Clinical, Hematological and Biochemical Health Benefit Effects of Hibiscus sabdariffa Lin Dried Calyces Beverage in Human

Background/Objective: This study was carried out to investigate the health benefit effects of Hibiscus sabdariffa Lin (H. sabdariffa L.) dried calyces beverage on some clinical, biochemical and hematological parameters in humans. Methods: The dried calyces were harvested in the two regions (Adamaoua and West) of Cameroon. The proximate, mineral composition and phytochemical screening were evaluated. A standardized extraction procedure was set up and from the calyces;we prepared a drink for 32 male volunteers’ subjects aged from 21 to 32 years, specially recruited for the experiment. Each participant consumed 500 mL twice a day (in the morning and in the evening) as supplement beverage during two weeks. The anthropometrics (age, height, weight, body mass index (BMI)), clinical (systolic and diastolic blood pressure), hematological (RBC, Hb, PCV, MCV, MCH, MCHC, WBC, Lymphocytes, MID cells, Granulocytes, platelet and MPV) and biochemical (TC, HDL-C, LDL-C, TG, serum iron, blood glucose, creatinine, urea, ASAT and ALAT) parameters were determined in the blood on days 0 and at the end of each week. Results: Crude protein, lipid, fiber and ash content of calyx ranged respectively from 4.57 - 5.98, 10.10 - 11.33, 20.39 - 22.30 and 9.15% - 10.38% while the levels of minerals were ranged from 512.0 - 740.6, 77.8 - 177.7, 52.84 - 52.85, 1.10 - 2.10, 41.2 - 119.5, 3.25 - 8.20 and 0.56 - 17.5 mg/100g respectively for Ca, Mg, K, Na, P, Fe and Zn. The phytochemical screening tests revealed the presence of alkaloids, flavonoids, tannins, phenols and anthocyanins on methanol and aqueous extracts. A significant increase of RBC, Hb, PCV, MPV, HDL-C, TG and creatinine and a significant decrease of WBC, MID cells, LDL-C and TC (p < 0.05) were observed during the study period. Furthermore, there was no significant change on BMI, MCV, MCH, MCHC, lymphocyte, granulocyte, platelet, serum iron, blood glucose, ASAT, ALAT and urea levels. Conclusion: H. sabdariffa L. dried calyces from Cameroon are rich sources of crude fibers and minerals. The H. Sabdariffa L. dried calyces drink can be safely used for people suffering for anemia. It also revealed good cholesterol lowering potential. No hepatoxicity and no kidney damage have been observed as far as serum enzymes were concerned.

Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning capabilities.While traditional scheduling methods cannot meet these needs due to their rigidity.Self-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error characteristics.Self-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control characteristics.This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling model.Secondly,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model.Lastly,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement learning.The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.

A Physicoempirical Model for Soil Water Simulation in Crop Root Zone

To predict soil water variation in the crop root zone, a general exponential recession （GER） model was developed to depict the recession process of soil water storage. Incorporating the GER model into the mass balance model for soil water, a GER-based physicoempirical （PE-GER） model was proposed for simulating soil water variation in the crop root zone. The PE-GER model was calibrated and validated with experimental data of winter wheat in North China. Simulation results agreed well with the field experiment results, as well as were consistent with the simulation results from a more thoroughly developed soil water balance model which required more detailed parameters and inputs. Compared with a previously developed simple exponential recession （SER） based physicoempirical （PF^SER） model, PE-GER was more suitable f0r application in a broad range of soil texture, from light soil to heavy soil. Practical application of PE-GER showed that PE-GER could provide a convenient way to simulate and predict the variation of soil water storage in the crop root zone, especially in case of insufficient data for conceptual or hydrodynamic models.