STUDY OF INHERENT FREQUENCY OF HELMHOLTZ RESONATOR

ABSTRACT Self-resonating water jet has been the subject of interest for researchers. The inherent frequency of Helmholtz Resonator is one of the important parameters for design of self-resonating water jet nozzle device. A new parametric model for prediction of the inherent frequency of Helmholtz resonator used in the field of water jet technology was proposed in this paper. Development of the model was based on the assumptions that the length and the diameter of Helmholtz resonating chamber and the length of the straight pipe segment of water jet nozzle are in the same order of magnitude. In comparison with the existing parametric model, the assumptions are more reasonable and the physical model on which the new parametric model was developed is in good agreement with the real configuration of Helmholtz resonator found in self-resonating water jet. As a result, the new model was expected to be more accurate in prediction of the inherent frequency of Helmholtz resonator. Organized and Sponsored by the Water Jet Technology Associatio

Safety Profile of TiO2-Based Photocatalytic Nanofabrics for Indoor Formaldehyde Degradation

Anatase TiO2 nanoparticles (TNPs) are synthesized using the sol-gel method and loaded onto the surface of polyester-cotton (65/35) fabrics. The nanofabrics degrade formaldehyde at an efficiency of 77% in eight hours with visible light irradiation or 97% with UV light. The loaded TNPs display very little release from nanofabrics (~0.0%) during a standard fastness to rubbing test. Assuming TNPs may fall off nanofabrics during their life cycles, we also examine the possible toxicity of TNPs to human cells. We found that up to a concentration of 220 μg/mL, they do not affect viability of human acute monocytic leukemia cell line THP-1 macrophages and human liver and kidney cells

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations

New Method for a SEM-Based Characterization of Helical-Fiber Nonwovens

The lack of tools particularly designed for the quantification of the fiber morphology in nonwovens, especially the multi-level structured fibers, is the main reason for the limited research studies on the establishment of realistic nonwoven structure. In this study, two polymers, cellulose acetate (CA) and thermoplastic polyurethane (TPU), which have different molecular flexibility, were chosen to produce nonwovens with helical nanofibers. Focusing on the nonwovens with helical fibers, a soft package was developed to characterize fiber morphologies, including fiber orientation, helix diameter, and curvature of helix. The novelty of this study is the proposal of a method for the characterization of nanofibrous nonwovens with special fiber shape (helical fibers) which can be used for curve fibers. The characterization results for the helical-fiber nonwoven sample and the nonwoven sample with straight fibers were compared and analyzed

A Begomovirus DNAβ-Encoded Protein Binds DNA, Functions as a Suppressor of RNA Silencing, and Targets the Cell Nucleus

Our previous results demonstrated that the DNAβ satellite (Y10β) associated with Tomato yellow leaf curl China virus Y10 isolate (TYLCCNV-Y10) is essential for induction of leaf curl symptoms in plants and that transgenic expression of its βC1 gene in Nicotiana plants induces virus-like symptoms. In the present study, in vitro DNA binding activity of the βC1 proteins of Y10β and DNAβ (Y35β) found in the Tobacco curly shoot virus Y35 isolate (TbCSV-Y35) were studied following their expression as six-His fusion proteins in Escherichia coli. Electrophoretic mobility shift assays and UV cross-linking experiments revealed that βC1 proteins could bind both single-stranded and double-stranded DNA without size or sequence specificity. Suppression of green fluorescent protein (GFP) transgene silencing was observed with the new leaves of GFP-expressing Nicotiana benthamiana plants coinoculated by TYLCCNV-Y10 plus Y10β or by TbCSV-Y35 plus Y35β. In a patch agroinfiltration assay, the transiently expressed βC1 gene of Y10β or Y35β was able to suppress host RNA silencing activities and permitted the accumulation of high levels of GFP mRNA in the infiltrated leaf patches of GFP transgenic N. benthamiana plants. The βC1 protein of Y10β accumulated primarily in the nuclei of plant and insect cells when fused with β-glucuronidase or GFP and immunogold labeling showed that the βC1 protein is present in the nuclei of infected N. benthamiana plants. A mutant version of Y10β carrying the mutations within the putative nuclear localization sequence of the Y10 βC1 protein failed to induce disease symptoms, suppress RNA silencing, or accumulate in the nucleus, suggesting that nuclear localization of the βC1 protein is a key requirement for symptom induction and silencing suppression