Wave propagation in two-phase flow of magnetic fluid under a nonuniform magnetic field

The effect of nonuniform magnetic field on the linear and nonlinear wave propagation phenomena in two-phase pipe flow of magnetic fluid is investigated theoretically to realize the effective energy conversion system using boiling two-phase flow of magnetic fluid. Firstly, the governing equations of two-phase flow based on the unsteady thermal nonequilibrium two-fluid model are presented and the linear void wave propagation phenomena in boiling two-phase flow are numerically analyzed by using the finite volume method. Next, the nonlinear pressure wave propagation in gas-liquid two-phase flow is numerically analyzed by using the finite different method. According to these theoretical studies on the wave propagation phenomena in two-phase flow of magnetic fluid, it seems to be a reasonable proposal that the precise control of the wave propagation in two-phase flow is possible by effective use of the magnetic force.     

Can EC-IC bypass prevent brain ischemia from recurring?

The effectiveness of extracranial-intracranial arterial bypass (EC-IC bypass) surgery for patients with hemodynamic compromise still remains controversial. In the present study, we evaluated the correlation between the pre- and post-surgical cerebral hemodynamics and long-term prognosis. 28 patients and a subsequent 21 patients (41 men, eight women: mean age 59.9 [S.D. 8.6] years) with reduced cerebrovascular reserve due to steno-occlusive disease of the cerebral major arteries formed the study groups 1 and 2, respectively. Measurement of the mean hemispheric cerebral blood flow (mCBF) and the cerebral vasoreactivity (%mCVR) with an intravenous acetazolamide injection were performed by a 133Xe inhalation method and SPECT. Patients were treated with EC-IC bypass surgery and measurement of mCBF and %mCVR were made again about one month after surgery. The patients were observed for a long period (mean 44.3 months). During the follow-up period, 6 patients experienced recurrent ischemic strokes. The annual incidence of recurrent ischemic stroke was 4.4%. The patients with significantly reduced pre- and post-surgical resting mCBF of the affected hemisphere were at significantly higher risk of recurrent ischemic stroke than the patients with normal mCBF (p < 0.01). The %mCVR of the affected hemisphere rose after surgery.     

Catalyst Design of Pt-Modified Ni/Al2O3 Catalyst with Flat Temperature Profile in Methane Reforming with CO2 and O2

Pt(0.3)/Ni(10)/Al₂O₃, prepared by a sequential impregnation method, exhibited a more excellent performance in methane reforming with CO₂ and O₂ in terms of the catalytic activity and the temperature profile of the catalyst bed than Pt(0.3) + Ni(10)/Al₂O₃ prepared by a coimpregnation method, Ni(10)/Al₂O₃, Pt(0.3)/Al₂O₃, and Pt(10)/Al₂O₃. It is thought that this is because the surface Pt atoms on Ni catalyst can contribute to the enhancement of the catalyst reducibility.     

Managing a Very Connected World: A Report on APNOMS2015

This article presents a report on APNOMS2015, which was held August 19–21, 2015 in Busan, Korea. The theme of APNOMS2015 was “Managing a Very Connected World.”     

Mean–variance relationship of the number of flows in traffic aggregation and its application to traffic management

We consider the mean–variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean–variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean–variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean–variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean–variance relationship for estimating the processing capability required in each network equipment.     

Autonomic load balancing of flow monitors

In monitoring flows at routers for flow analysis or deep packet inspection, the monitor calculates hash values from the flow ID of each packet arriving at the input port of the router. Therefore, the monitors must update the flow table at the transmission line rate, so high-speed and high-cost memory, such as SRAM, is used for the flow table. This requires the monitors to limit the monitoring target to just some of the flows. However, if the monitors randomly select the monitoring targets, multiple routers on the route will sometimes monitor the same flow, or no monitors will monitor a flow. To maximize the number of monitored flows in the entire network, the monitors must select the monitoring targets while maintaining a balanced load among them. We propose an autonomous load-balancing method where monitors exchange information on monitor load only with adjacent monitors. Numerical evaluations using the actual traffic matrix of Internet2 show that the proposed method improves the total monitored flow count by about 50% compared with that of independent sampling. Moreover, we evaluate the load-balancing effect on 36 backbone networks of commercial ISPs.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Optimally designing caches to reduce P2P traffic

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks; therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks and investigate the main factors determining cache efficiency. We also analyze the property of network structure in which deploying caches are effective in reducing P2P traffic for transit ISPs. We show that transit ISPs can reduce the P2P traffic within their networks by about 50-85% by optimally designing caches at the transit links to the lower networks.     

Efficient semi-supervised learning on locally informative multiple graphs

We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene networks, in which informative parts vary over networks. We present a powerful, time-efficient approach for this problem by combining soft spectral clustering with label propagation for multiple graphs. We demonstrate the effectiveness and efficiency of our approach using both synthetic and real biological datasets.     

Structural and hydration properties of amorphous tricalcium silicate

Mechanical milling was carried out to synthesize amorphous tricalcium silicate (Ca₃SiO₅) sample, where Ca₃SiO₅ is the most principal component of Portland cement. The partial phase transformation from the crystalline to the amorphous state was observed by X-ray and neutron diffractions. Moreover, it was found that the structural distortion on the Ca-O correlation exists in the milled Ca₃SiO₅. The hydration of the milled Ca₃SiO₅ with D₂O proceeds as follows: the formation of hydration products such as Ca(OD)₂ rapidly occurs in the early hydration stage, and then proceeds slowly after about 15 h. The induction time for the hydration of the milled Ca₃SiO₅ is approximately one half shorter than that for the hydration of the unmilled one. This result means that the mechanical milling brings about the chemical activity of Ca₃SiO₅ for hydration, and may be particularly useful for increasing the reactivity in the early hydration stage.