Dynamic Portfolio Management with Reinforcement Learning

Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets within a portfolio to maximize the total return in a given period of time. With the recent advancement in machine learning and artificial intelligence, many efforts have been put in designing and discovering efficient algorithmic ways to manage the portfolio. This paper presents two different reinforcement learning agents, policy gradient actor-critic and evolution strategy. The performance of the two agents is compared during backtesting. We also discuss the problem set up from state space design, to state value function approximator and policy control design. We include the short position to give the agent more flexibility during assets redistribution and a constant trading cost of 0.25%. The agent is able to achieve 5% return in 10 days daily trading despite 0.25% trading cost

Geostatistical and geoarchaeological study of Holocene floodplains and site distributions on the Sha‐Ying River Basin, Central China

Geostatistics has become a powerful method for investigating complex spatial variations of prehistoric settlements in floodplains and other geomorphological settings. A geoarchaeological drilling program that covers most of the Sha‐Ying River Basin provides a rare opportunity with unusually detailed environmental data to contest and develop the geostatistics method, which proves to be essential, in combination with archaeological data, to understand long‐term (9000–2500 B.P.) patterns of human inhabitation and adaption to volatile floodplain environments in eastern Central China. We analysed the variography and multivariate ordination of the borehole data and explored the complexities of landform evolution, with reference to sedimentation processes and soil development in the floodplain of the Sha‐Ying River. The recurrent impact of river floods on regional landforms is manifested by spatial‐autocorrelated properties over distances up to 10 km, sometimes with directional trends. We then developed a model of landform evolution through kriging and compared the model with detailed reconstruction of archaeological settlement patterns. Our results illustrate long‐term socio‐environmental dynamics by which human communities first populated and then adapted in diverse ways to the changing floodplain environments from the early to middle Holocene. This improved method will have far‐reaching implications for future studies on similar geomorphological settings across vast floodplains of Central China and other global regions

Earliest ceramic drainage system and the formation of hydro-sociality in monsoonal East Asia

The earliest ceramic drainage system unearthed at the Pingliangtai site on the Central Plains of China represents an unprecedented social and environmental manipulation as societies faced surging environmental crises in the Late Holocene East Asian Monsoon region. Here we present results of excavation and a geoarchaeological survey of the water-management infrastructures and environment which reveal the operation and maintenance of a well-planned and regulated two-tiered drainage system. Rather than a ‘centralized hierarchy’, the drainage activities were mainly practised at household and communal levels, through which Pingliangtai society was drawn to more pragmatic aspects of social governance. Through their emphasis on spatial uniformity, cooperation in public affairs, and a series of technological innovations, water management at Pingliangtai gravitated to collective shared interest as the society responded to recurrent environmental contingencies. Such a pragmatic focus on public affairs constituted a previously unrecognized, alternative pathway to the development of power structure and social governance on the Central Plains regimes in late Neolithic and later times

Numerical investigation of Rayleigh waves in layered composite piezoelectric structures using the SIGA-PML approach

Existence of surface acoustic waves(SAW) on a piezoelectric layer with the half-infinite elastic layer is investigated. This structure belongs to an open waveguide with the unbounded boundary in the transverse direction. Except for trapped modes, leaky modes have often been considered in SAW applications, which requires waves of low attenuation in order to maximize the propagation distance. Therefore, we develop an another formulation of piezoelectric layer structures for the computation of trapped and leaky modes in open waveguides. This method combines the so-called semi-analytical isogeometric analysis and a perfectly matched layer technique (SIGA-PML). The comparison between semi-analytical finite element (SAFE-PML) and SIGA-PML is given, in order to show the effective and accuracy of SIGA-PML. Finally, we analyze propagation properties of Rayleigh waves and discuss the impact of the thickness of Cu films on the dispersive relationships

Unravelling the Influence of Surface Modification on the Ultimate Performance of Carbon Fiber/Epoxy Composites

Article asserts that the overall performance of polymer composites depends on not only the intrinsic properties of the polymer matrix and inorganic filler but also the quality of interfacial adhesion. The authors report carbon fiber (CF)/epoxy composites with improved interfacial adhesion by covalent bonding between CFs and the epoxy matrix, which leads to the improved ultimate mechanical properties and enhanced thermal aging performance

Risk factors associated with amyotrophic lateral sclerosis based on the observational study: a systematic review and meta-analysis

ObjectiveAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder affecting the upper and lower motor neurons. Though the pathogenesis of ALS is still unclear, exploring the associations between risk factors and ALS can provide reliable evidence to find the pathogenesis. This meta-analysis aims to synthesize all related risk factors of ALS to understand this disease comprehensively.MethodsWe searched the following databases: PubMed, EMBASE, Cochrane library, Web of Science, and Scopus. Moreover, observational studies, including cohort studies, and case-control studies, were included in this meta-analysis.ResultsA total of 36 eligible observational studies were included, and 10 of them were cohort studies and the rest were case-control studies. We found six factors exacerbated the progression of disease: head trauma (OR = 1.26, 95% CI = 1.13, 1.40), physical activity (OR = 1.06, 95% CI = 1.04, 1.09), electric shock (OR = 2.72, 95% CI = 1.62, 4.56), military service (OR = 1.34, 95% CI = 1.11, 1.61), pesticides (OR = 1.96, 95% CI = 1.7, 2.26), and lead exposure (OR = 2.31, 95% CI = 1.44, 3.71). Of note, type 2 diabetes mellitus was a protective factor for ALS. However, cerebrovascular disease (OR = 0.99, 95% CI = 0.75, 1.29), agriculture (OR = 1.22, 95% CI = 0.74, 1.99), industry (OR = 1.24, 95% CI = 0.81, 1.91), service (OR = 0.47, 95% CI = 0.19, 1.17), smoking (OR = 1.25, 95% CI = 0.5, 3.09), chemicals (OR = 2.45, 95% CI = 0.89, 6.77), and heavy metal (OR = 1.5, 95% CI = 0.47, 4.84) were not risk factors for ALS based on meta-analyses.ConclusionsHead trauma, physical activity, electric shock, military service, pesticides, and lead were risk factors for ALS onset and progression. But DM was a protective factor. This finding provides a better understanding of ALS risk factors with strong evidence for clinicians to rationalize clinical intervention strategies.INPLSY registration numberhttps://inplasy.com/inplasy-2022-9-0118/, INPLASY202290118

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants