By controlling the amorphous-to-crystalline relative volume,chalcogenide phase-change memory materials can provide multi-level data storage(MLS),which offers great potential for high-density storageclass memory and ne...By controlling the amorphous-to-crystalline relative volume,chalcogenide phase-change memory materials can provide multi-level data storage(MLS),which offers great potential for high-density storageclass memory and neuro-inspired computing.However,this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase(‘‘drift")in the amorphous phase,which limits the number of attainable storage levels.Here,we report a new type of MLS system in yttriumdoped antimony telluride,utilizing reversible multi-level phase transitions between three states,i.e.,amorphous,metastable cubic and stable hexagonal crystalline phases,with ultralow power consumption(0.6–4.3 p J)and ultralow resistance drift for the lower two states(power-law exponent<0.007).The metastable cubic phase is stabilized by yttrium,while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms.Finally,the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance.This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.展开更多
Objective To gain a better understanding of gene expression changes in the brain following microwave exposure in mice. This study hopes to reveal mechanisms contributing to microwave-induced learning and memory dysfun...Objective To gain a better understanding of gene expression changes in the brain following microwave exposure in mice. This study hopes to reveal mechanisms contributing to microwave-induced learning and memory dysfunction. Methods Mice were exposed to whole body 2100 MHz microwaves with specific absorption rates (SARs) of 0.45 W/kg, 1.8 W/kg, and 3.6 W/kg for 1 hour daily for 8 weeks. Differentially expressing genes in the brains were screened using high-density oligonucleotide arrays, with genes showing more significant differences further confirmed by RT-PCR. Results The gene chip results demonstrated that 41 genes (0.45 W/kg group), 29 genes (1.8 W/kg group), and 219 genes (3.6 W/kg group) were differentially expressed. GO analysis revealed that these differentially expressed genes were primarily involved in metabolic processes, cellular metabolic processes, regulation of biological processes, macromolecular metabolic processes, biosynthetic processes, cellular protein metabolic processes, transport, developmental processes, cellular component organization, etc. KEGG pathway analysis showed that these genes are mainly involved in pathways related to ribosome, Alzheimer's disease, Parkinson's disease, long-term potentiation, Huntington's disease, and Neurotrophin signaling. Construction of a protein interaction network identified several important regulatory genes including synbindin (sbdn), Crystallin (CryaB), PPP1CA, Ywhaq, Psap, Psmb1, Pcbp2, etc., which play important roles in the processes of learning and memory. Conclusion Long-term, low-level microwave exposure may inhibit learning and memory by affecting protein and energy metabolic processes and signaling pathways relating to neurological functions or diseases.展开更多
In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that ar...In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that are capable of capturing three key features of time series: long range dependence, volatility?and level shift. The main concern is on detection of mean and volatility level shift in a fractionally integrated time series with volatility. We will denote such a time series as level shift autoregressive fractionally integrated moving average (LS-ARFIMA) and level shift generalized autoregressive conditional heteroskedasticity (LS-GARCH). Test statistics that are useful to examine if mean and volatility level shifts are present in an autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity (ARFIMA-GARCH) model are derived. Quasi maximum likelihood estimation of the model is also considered.展开更多
基金the National Key Research and Development Program of China(2017YFB0701700)the National Natural Science Foundation of China(51872017)the High-Performance Computing(HPC)Resources at Beihang University。
文摘By controlling the amorphous-to-crystalline relative volume,chalcogenide phase-change memory materials can provide multi-level data storage(MLS),which offers great potential for high-density storageclass memory and neuro-inspired computing.However,this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase(‘‘drift")in the amorphous phase,which limits the number of attainable storage levels.Here,we report a new type of MLS system in yttriumdoped antimony telluride,utilizing reversible multi-level phase transitions between three states,i.e.,amorphous,metastable cubic and stable hexagonal crystalline phases,with ultralow power consumption(0.6–4.3 p J)and ultralow resistance drift for the lower two states(power-law exponent<0.007).The metastable cubic phase is stabilized by yttrium,while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms.Finally,the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance.This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.
基金supported by the Foundation of Astronaut Research and Training Center of China(No.SN 02-3)
文摘Objective To gain a better understanding of gene expression changes in the brain following microwave exposure in mice. This study hopes to reveal mechanisms contributing to microwave-induced learning and memory dysfunction. Methods Mice were exposed to whole body 2100 MHz microwaves with specific absorption rates (SARs) of 0.45 W/kg, 1.8 W/kg, and 3.6 W/kg for 1 hour daily for 8 weeks. Differentially expressing genes in the brains were screened using high-density oligonucleotide arrays, with genes showing more significant differences further confirmed by RT-PCR. Results The gene chip results demonstrated that 41 genes (0.45 W/kg group), 29 genes (1.8 W/kg group), and 219 genes (3.6 W/kg group) were differentially expressed. GO analysis revealed that these differentially expressed genes were primarily involved in metabolic processes, cellular metabolic processes, regulation of biological processes, macromolecular metabolic processes, biosynthetic processes, cellular protein metabolic processes, transport, developmental processes, cellular component organization, etc. KEGG pathway analysis showed that these genes are mainly involved in pathways related to ribosome, Alzheimer's disease, Parkinson's disease, long-term potentiation, Huntington's disease, and Neurotrophin signaling. Construction of a protein interaction network identified several important regulatory genes including synbindin (sbdn), Crystallin (CryaB), PPP1CA, Ywhaq, Psap, Psmb1, Pcbp2, etc., which play important roles in the processes of learning and memory. Conclusion Long-term, low-level microwave exposure may inhibit learning and memory by affecting protein and energy metabolic processes and signaling pathways relating to neurological functions or diseases.
文摘In this paper, we introduce the class of autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity?(ARFIMA-GARCH) models with level shift type intervention that are capable of capturing three key features of time series: long range dependence, volatility?and level shift. The main concern is on detection of mean and volatility level shift in a fractionally integrated time series with volatility. We will denote such a time series as level shift autoregressive fractionally integrated moving average (LS-ARFIMA) and level shift generalized autoregressive conditional heteroskedasticity (LS-GARCH). Test statistics that are useful to examine if mean and volatility level shifts are present in an autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity (ARFIMA-GARCH) model are derived. Quasi maximum likelihood estimation of the model is also considered.
