Increasing the Durability of Piezoelectric Impact-based Micro Wind Generator in Real Application

AbstractThe purpose of this study is to increase the durability of piezoelectric impact-based micro wind generator (PIMWG) in real application. Using new PIMWG design, numerical simulation, and experimental comparison analysis, we improved the durability of PIMWGs in real application. The experimental results show that the optimized PIMWG generated 2.4 mW (RMS value), and it did not crack within 40h. In this study, we improved the durability of PIMWGs for real application

Phytohormone abscisic acid control RNA-dependent RNA polymerase 6 gene expression and post-transcriptional gene silencing in rice cells

RNA-dependent RNA polymerase 6 (RDR6) catalyses dsRNA synthesis for post-transcriptional gene silencing (PTGS)-associated amplification and the generation of endogeneous siRNAs involved in developmental determinations or stress responses. The functional importance of RDR6 in PTGS led us to examine its connection to the cellular regulatory network by analyzing the hormonal responses of RDR6 gene expression in a cultured cell system. Delivery of dsRNA, prepared in vitro, into cultured rice (Oryza sativa cv. Japonica Dongjin) cells successfully silenced the target isocitrate lyase (ICL) transcripts. Silencing was transient in the absence of abscisic acid (ABA), while it became persistent in the presence of ABA in growth medium. A transcription assay of the OsRDR6 promoter showed that it was positively regulated by ABA. OsRDR6-dependent siRNA(ICL) generation was also significantly up-regulated by ABA. The results showed that, among the five rice OsRDR isogenes, only OsRDR6 was responsible for the observed ABA-mediated amplification and silencing of ICL transcripts. We propose that ABA modulates PTGS through the transcriptional control of the OsRDR6 gene

‘Evidence of an auxin signal pathway, microRNA167-ARF8-GH3, and its response to exogenous auxin in cultured rice cells’

MicroRNA167 (miR167) was shown to cleave auxin responsive factor 8 (ARF8) mRNA in cultured rice cells. MiR167 level was found to be controlled by the presence of auxin in the growth medium. When cells grew in auxin-free medium, miR167 level decreased, resulting in an increase in the level of ARF8 mRNA. Cells growing in the normal growth medium containing auxin showed a reversed trend. It was also shown that expression of OsGH3-2, an rice IAA-conjugating enzyme, was positively regulated by ARF8. Delivery of synthesized miR167 into cells led to decrease of both ARF8 mRNA and OsGH3-2 mRNA. This study provides an evidence in which the exogeneous auxin signal is transduced to OsGH3-2 through miR167 and ARF8 in sequence. This proposed auxin signal transduction pathway, auxin-miR167-ARF8-OsGH3-2, could be, in conjunction with the other microRNA-mediated auxin signals, an important one for responding to exogeneous auxin and for determining the cellular free auxin level which guides appropriate auxin responses

Mathematical Modeling of Smart Space for Context-Aware System: Linear Algebraic Representation of State-Space Method Based Approach

A smart space is embedded with several components such as sensors, actuators, and computing devices that enable the sensing and control of the environment, and the inhabitants interact with the devices in the smart space whenever they need to. To model a smart space, a dynamic relationship needs to be established among the elements of the space whereby the interactions with devices are considered a dynamic-process state. In this paper, a linear model of a smart space is presented using a state equation, where the two coefficient matrices L and H need to be defined to model the smart space, and the coefficient matrix L is used to determine the states of the devices; similarly, the situation of the smart space is determined using coefficient H. An algorithm is presented to make a linear model from the logical functions that are used to describe the system. This model is flexible in terms of the control of the smart-space environment because the environmental factors are represented by a matrix element. This linear smart-space model is helpful for the control of a context-aware system, and we use an example to illustrate the effectiveness of the proposed model