Take Bitcoin into your portfolio:a novel ensemble portfolio optimization framework for broad commodity assets

The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.

Preparation of graphene on SiC by laser-accelerated pulsed ion beams

Laser-accelerated ion beams(LIBs) have been increasingly applied in the field of material irradiation in recent years due to the unique properties of ultra-short beam duration, extremely high beam current, etc. Here we explore an application of using laser-accelerated ion beams to prepare graphene. The pulsed LIBs produced a great instantaneous beam current and thermal effect on the SiC samples with a shooting frequency of 1 Hz. In the experiment, we controlled the deposition dose by adjusting the number of shootings and the irradiating current by adjusting the distance between the sample and the ion source. During annealing at 1100℃, we found that the 190 shots ion beams allowed more carbon atoms to self-assemble into graphene than the 10 shots case. By comparing with the controlled experiment based on ion beams from a traditional ion accelerator, we found that the laser-accelerated ion beams could cause greater damage in a very short time. Significant thermal effect was induced when the irradiation distance was reduced to less than 1 cm, which could make partial SiC self-annealing to prepare graphene dots directly. The special effects of LIBs indicate their vital role to change the structure of the irradiation sample.

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.

A novel catalytic application of heteropolyacids:chemical transformation of major ginsenosides into rare ginsenosides exemplified by Rg1

High performance liquid chromatography coupled with quadruple-time-of-flight mass spectrometry(HPLC-Q-TOF-MS)method was developed for analyzing the hydrolytic mixtures of ginsenoside R_(g1) in acidic conditions(pH 3). Three catalysts, a heteropolyacid(H_4SiW_(12)O_(40), SiW_(12) for short), its complex with γ-CD(SiW_(12)/γ-CD for short) and formic acid, were used for comparison. The chemical transformation products were identified based on the accurate mass measurement and the fragment ions obtained from tandem mass spectrometry. It was concluded that the catalytic efficiency of SiW_(12)(≈SiW_(12)/γ-CD) is ca. 410 times higher than that of formic acid, thus becoming the most efficient catalyst for chemical transformations of ginsenosides.

基于随机森林模型内脏脂肪等级相关指标分析

目的探讨基于随机森林模型分析内脏脂肪等级的相关指标。方法本研究为横断面研究,选取2021年3—9月在黑龙江省医院健康管理中心进行体检的医院职工(包括在职职工和退休职工)共617例的各项实验室指标以及体成分分析各项指标,按照2∶1的比例将样本分为训练集(411例)和测试集(206例),模型共纳入预测变量110个,使用训练集数据进行随机森林模型构建,测试集数据进行模型验证,选择最优节点数和决策树数目,对构建模型的预测性能进行评价,同时选取重要性在前10位的相对重要因子进行下一步的研究。按内脏脂肪等级,对617名研究对象再次进行分组:内脏脂肪等级正常组和内脏脂肪等级偏高组,进一步分析前10位相对重要因子在组间的差异。结果随机森林模型的最优节点数为39、决策树数目为300。模型在测试集上的准确率为83.3%、精确率为73.9%、灵敏度为89.4%、特异度为78.7%,其受试者工作特征曲线下面积为0.881(95%CI:0.832~0.931)。模型中前10位相对重要因子依次为:体重指数、性别、年龄、尿酸、红细胞计数、单核细胞计数、C肽、癌胚抗原、糖化血红蛋白、谷氨酰转肽酶。内脏脂肪等级偏高组的体重指数、年龄、尿酸、红细胞计数、单核细胞计数、C肽、癌胚抗原、糖化血红蛋白、谷氨酰转肽酶水平均高于内脏脂肪等级正常组(均P<0.05);内脏脂肪等级偏高的发生率男性大于女性(P<0.05)。结论本研究构建的内脏脂肪等级的随机森林预测模型表现良好,内脏脂肪与机体肝功能、胰岛功能、免疫功能的改变均有关系。

Using lithium isotopes to quantitatively decode continental weathering signal:A case study in the Changjiang(Yangtze River)Estuary

As the key link connecting the earth’s spheres,continental weathering plays an important role in regulating the global biogeochemical cycle and long-term climate change.Siliciclastic sediments derived from large river basins can record continental weathering and erosion signals,and are thus widely used to investigate weathering processes.However,sediment grain size,hydrodynamic sorting and sedimentary recycling complicate the interpretation of sediment weathering proxies.This study presents elemental and lithium isotope compositions of estuarine surface sediments(SS)and suspended particulate matters(SPM)collected from the Changjiang(Yangtze River)Estuary.Based on a simple mass balance model,the proportions of different end-members(i.e.,igneous rocks,modern weathering products and inherited weathering products)in sediments were quantitatively calculated and thus the silicate weathering process can be estimated.Overall,the sediments in the Changjiang Estuary are mainly eroded from un-weathered rock fragments(>60%),while modern weathering products account for less than 40%.The fine-grained SPM contain more shale components(52–66%),and the modern weathering products account for 21–40%.Comparatively,the coarse-grained surface sediments contain more un-weathered igneous rock fragments(63–84%)and less modern weathering products(only 4–18%).The comparison ofδ^(7)Li values with the weathering proxy(Chemical Index of Alteration,CIA)suggests that sediment weathering intensity declines with increasing proportion of un-weathered igneous rock fragments.Additionally,the occurrence of inherited weathering products(i.e.,shale)in modern sediments makes it a challenge to simply use CIA andδ^(7)Li as indicators of weathering intensity.This study confirms that fine-grained particles are more suitable for tracing contemporary weathering process,albeit with the influence of sedimentary recycling.Lithium isotopes combining with the mass balance model can quantitatively constrain the continental weathering processes in large river basins.

电离毛细管等离子体在激光加速领域的应用

激光加速是近年提出的一种新型加速机制,它以等离子体为加速介质,受到等离子体性质的强烈影响.电离毛细管装置作为一种能稳定产生等离子体通道和调制等离子体参数的可靠工具,既可以承载激光等离子体尾波的高梯度加速场,又能提供放电电流驱动的高梯度横向聚焦磁场,在紧凑型激光加速器、辐射源及等离子体透镜的研究中发挥着重要作用.本文介绍了电离毛细管等离子体的原理及特性,归纳了它在激光加速领域的主要应用,包括作为加速段进一步提高激光等离子体加速粒子束的能量及质量、作为束流传输元件匹配加速粒子束的实际应用需求.在此基础上,对毛细管等离子体未来的发展趋势进行了展望,并简要介绍了北京大学电离毛细管等离子体平台的建设情况.

Measurements of plasma density profile evolutions with a channel-guided laser

The discharged capillary plasma channel has been extensively studied as a high-gradient particle acceleration and transmission medium.A novel measurement method of plasma channel density profiles has been employed,where the role of plasma channels guiding the advantages of lasers has shown strong appeal.Here,we have studied the high-order transverse plasma density profile distribution using a channel-guided laser,and made detailed measurements of its evolution under various parameters.The paraxial wave equation in a plasma channel with high-order density profile components is analyzed,and the approximate propagation process based on the Gaussian profile laser is obtained on this basis,which agrees well with the simulation under phase conditions.In the experiments,by measuring the integrated transverse laser intensities at the outlet of the channels,the radial quartic density profiles of the plasma channels have been obtained.By precisely synchronizing the detection laser pulses and the plasma channels at various moments,the reconstructed density profile shows an evolution from the radial quartic profile to the quasi-parabolic profile,and the high-order component is indicated as an exponential decline tendency over time.Factors affecting the evolution rate were investigated by varying the incentive source and capillary parameters.It can be found that the discharge voltages and currents are positive factors quickening the evolution,while the electron-ion heating,capillary radii and pressures are negative ones.One plausible explanation is that quartic profile contributions may be linked to plasma heating.This work helps one to understand the mechanisms of the formation,the evolutions of the guiding channel electron-density profiles and their dependences on the external controllable parameters.It provides support and reflection for physical research on discharged capillary plasma and optimizing plasma channels in various applications.

Regional ecosystem health assessment using the GA-BPANN model:a case study of Yunnan Province,China

Background:Regional ecosystem health assessments are the basis for the sustainable development of society.However,an ecosystem is a complex integration of ecosystem mosaics and subsystems that influence each other,making it difficult to evaluate them using traditional assessment methods of linear and explicit functions.We introduce a back-propagation neural network model optimized by a genetic algorithm to evaluate ecosystem health in 16 districts in Yunnan Province.Result:(1)The model required fewer inputs to evaluate complex and nonlinear systems,avoided the need for subjective weights,and performed well in this practical application to regional ecosystem health assessment.(2)The ecosystem health in Yunnan Province was increasing,and there was a significant positive spatial autocorrelation during 2000-2020,showing that districts with high Ecosystem Health cluster together and the ecological protection policy of the region has produced a diffusion effect,leading to continuous improvement of the ecological health of the surrounding areas.High-low outlier areas of ecosystem health should be paid more attention,because of the increasing instability of local health levels.Conclusion:This study provides a methodological exploration for assessing spatial mosaics of different ecosystems at a regional scale.

Complex system management in the post-COVID world:Five research directions

A complex system is composed of many interrelated elements,and the interaction between these elements makes the overall performance of the system greater than the sum of member performance[1-5].In the context of management,various forms of organizations,from micro enterprises to macroeconomic systems,can be seen as systems formed by a large number of interactive individuals acting on their own limited information.