Predicting the returns of the US real estate investment trust market:evidence from the group method of data handling neural network

Purpose:The Group Method of Data Handling(GMDH)neural network has demon-strated good performance in data mining,prediction,and optimization.Scholars have used it to forecast stock and real estate investment trust(REIT)returns in some coun-tries and region,but not in the United States(US)REIT market.The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method.Design/methodology/approach:To forecast the return on the US REIT index,this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity(GARCH)model.In this test,the training samples,testing samples,and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach.Corresponding experiments were performed using the GARCH model,and the accuracies of these two approaches were compared.Findings:Compared with GARCH,GMDH’s accuracy is much higher,indicating that the machine learning approach can provide a highly accurate prediction of REIT prices.The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results.In particular,the kernel function has a signifi-cant impact on prediction accuracy.The linear and linear covariance kernel functions are simple to train and yield accurate predictions,whereas the quadratic function is difficult to train.Even with small training samples,GMDH can outperform GARCH in prediction accuracy.Research limitations/implications:Although GMDH shows good performance in predicting the US REIT return,it is still a black-box model,and the algorithm is difficult for financial analysts to develop and customize.The data used in this study come from the US REIT market,which is the world’s largest and most liquid market.Social implications:This research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns.Hence,investors can use the machine thus better investment decisions.Future investors and researchers may use GMDH to forecast the performance of REITs in other markets.Originality/value:This is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance.For example,this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network.It also includes short-term daily prediction returns that were not previously considered,making it a valuable reference for financial industry analysts.

A robust synthesis route of confined carbyne

The unique mechanical,optical,and electrical properties of carbyne,a one-dimensional allotrope of carbon,make it a highly promising material for various applications.It has been demonstrated that carbon nanotubes(CNTs)can serve as an ideal host for the formation of confined carbyne(CC),with the yield being influenced by the quality of the carbon nanotubes for confinement and the carbon source for carbyne growth.In this study,a robust synthesis route of CC within CNTs is proposed.C70 was utilized as a precursor to provide an additional carbon source,based on its ability to supply more carbon atoms than C60 at the same filling ratio.Multi-step transformation processes,including defect creation,were designed to enhance the yield of CC.As a result,the yield of CC was significantly increased for the C70 encapsulated single-walled CNTs by more than an order of magnitude than the empty counterparts,which also surpasses that of the double-walled CNTs,making it the most effective route for synthesizing CC.These findings highlight the importance of the additional carbon source and the optimal pathway for CC formation,offering valuable insights for the application of materials with high yield.

Solid polymer electrolytes:Ion conduction mechanisms and enhancement strategies

Solid polymer electrolytes(SPEs)possess comprehensive advantages such as high flexibility,low interfacial resistance with the electrodes,excellent film-forming ability,and low price,however,their applications in solid-state batteries are mainly hindered by the insufficient ionic conductivity especially below the melting temperatures,etc.To improve the ion conduction capability and other properties,a variety of modification strategies have been exploited.In this review article,we scrutinize the structure characteristics and the ion transfer behaviors of the SPEs(and their composites)and then disclose the ion conduction mechanisms.The ion transport involves the ion hopping and the polymer segmental motion,and the improvement in the ionic conductivity is mainly attributed to the increase of the concentration and mobility of the charge carriers and the construction of fast-ion pathways.Furthermore,the recent advances on the modification strategies of the SPEs to enhance the ion conduction from copolymer structure design to lithium salt exploitation,additive engineering,and electrolyte micromorphology adjustion are summarized.This article intends to give a comprehensive,systemic,and profound understanding of the ion conduction and enhancement mechanisms of the SPEs for their viable applications in solid-state batteries with high safety and energy density.

Attenuation ofβ-Amyloid Toxicity In Vitro and In Vivo by Accelerated Aggregation

Accumulation and aggregation of β-amyloid（Aβ） peptides result in neuronal death, leading to cognitive dysfunction in Alzheimer＇s disease. The self-assembled Aβ molecules form various intermediate aggregates including oligomers that are more toxic to neurons than the mature aggregates, including fibrils. Thus, one strategy to alleviate Aβ toxicity is to facilitate the conversion of Aβ intermediates to larger aggregates such as fibrils. In this study, we designed a peptide named A3 that significantly enhanced the formation of amorphous aggregates of Aβ by accelerating the aggregation kinetics. Thioflavin T fluorescence experiments revealed an accelerated aggregation of Aβ monomers, accompanying reduced Aβ cytotoxicity. Transgenic Caenorhabditis elegans over-expressing amyloid precursor protein exhibited paralysis due to the accumulation of Aβ oligomers, and this phenotype was attenuated by feeding the animals with A3 peptide. These findings suggest that the Aβ aggregation-promotion effect can potentially be useful for developing strategies to reduce Aβ toxicity.

Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates

Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems,which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption.Here,we report synaptic devices made from highly insulating ferroelectric LiNbO_(3)(LNO)thin films bonded to SiO_(2)/Si wafers.Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells,which are stimulated using positive/negative voltage pulses(synaptic plasticity),we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls.The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles,representing much better performance than that of random defect-based nonlinear memristors,which generally exhibit large-scale resistance dispersion.The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6%recognition accuracy for faces,thus approaching the theoretical yield of ideal neuromorphic computing devices.