Analyzing the Variability in Low-Flow Projections under GCM CMIP5 Scenarios

Water Resources Management - The present study considered the impacts of global climate model (GCM) selection in the Couple Model Intercomparison Phase 5 (CMIP5) scenarios on the low-flow...     

Performance of RSS Based Cooperative Localization in Millimeter Wave Wireless Sensor Networks

Wireless Personal Communications - Localization in wireless sensor networks (WSNs) is a necessity as there is a vital need to have location information combined with the measured quantities. The...     

Accelerating video encoding using cluster computing

The great advance and variety of multimedia applications such as video streaming, TV broadcasting, and video conferencing stimulated research to enhance video encoding, where a video is reduced in size and possibly transformed to numerous formats for portability. This paper is concerned with solving the problem of the huge processing time taken by the serial video encoding approaches by proposing a hybrid-parallel video encoding technique to speed up the process. In this work, the Joint Scalable Video Model (JSVM 9.19.14) is chosen as the basic serial video encoding algorithm for building different parallel video encoding architectures. The proposed technique exploits the triple-step nature of JSVM and intelligently determines the best task organization to achieve speedup and increase the efficiency on a cluster computing platform. Moreover, a dynamic load sharing scheme is proposed to redistribute load among different machines for additional parallelism. The remarkable feature of our approach is that, both the granularity of load partitioning among the cluster machines and all the associated overheads are considered. The experimental results are applied on a compact library of 160 mp4 encoded videos and two other bench mark datasets. The results proves a significant improvement in performance in comparison to the sequential version; which ranges from 64.2% to 95.3%, for a cluster with a number of machines ranging from 2 to 20 respectively.

     

Prenatal exposure to the fluoride containing psychiatric drug fluoxetine and anti-oxidative alterations in the neonatal rat brain

Fluoride is a key ingredient of many psychiatric drugs like fluoxetine (Prozac^®, Fluoxetine^®). Pregnant women frequently use this drug as they suffer from depression and anxiety disorders during this period. Fluoxetine is able to reach the fetus through the placenta and passes to the newborn through milk. In the present study, female Wistar rats were treated with 5, 10, and 20 mg/L fluoxetine (containing 94% fluorides) from pregnancy day 10 to day 20. After delivery, the levels of the enzymatic antioxidants in the brain of their offspring at postnatal day 2 were measured. The results showed that, in all fluoxetine exposed groups compared with the control group, there was a significant decrease (P < 0.01) in the glutathione, catalase, glutathione S-transferases and potassium and a non- significant increase (P > 0.05) in the activity of malondialdehyde and creatine kinase. The results suggest that fluoxetine may be a developmental neurotoxicant due to presence of fluoride hence must be used carefully during pregnancy.     

Photocatalytic Synthesis of p-Anisaldehyde in a Mini Slurry-Bubble Reactor under Solar Light Irradiation

Dense photocatalyst slurry was employed for the synthesis of p-anisaldehyde under solar light irradiation. An Fe-modified rutile TiO ₂ (Fe-TiO _2, 34.5 m ²/g) photocatalyst was used as a visible-light-responsive photocatalyst. A conventional TiO ₂ (P25, 35 m ²/g) photocatalyst was also examined as a reference catalyst. XRD patterns and diffuse reflectance spectra showed that Fe-TiO ₂ consists of 100 % rutile phase and absorbs more visible light compared to P25, respectively. The catalyst powder was suspended in an ethyl acetate solution of p-methoxytoluene in the mini-reactor, with oxygen bubbling, under a solar simulator, visible light, and UV LEDs. p-anisaldehyde, as a reaction product, was analyzed by sampling using gas-chromatograph. Regardless of the light source, Fe-TiO ₂ always outperformed P25 in terms of both generation rates (GR) of p-anisaldehyde and energy requirements (ER). It was demonstrated that the highly dense Fe-TiO ₂ slurry was efficient for the synthesis under solar light owing to the small size of the reactor. The small amount of Pt and ZrO ₂ cocatalysts significantly enhanced the GR under solar light. By adopting a visible light responsive Fe-TiO ₂ photocatalyst, the mini slurry-bubble reactor under solar light achieved a high GR per catalyst mass (CM), which is one to two orders higher than that reported by most previous studies with high-power lamps.     

Design of a continuous-flow reactor for in situ x-ray absorption spectroscopy of solids in supercritical fluids

This paper presents the design and performance of a novel high-temperature and high-pressure continuous-flow reactor, which allows for x-ray absorption spectroscopy or diffraction in supercritical water and other fluids under high pressure and temperature. The in situ cell consists of a tube of sintered, polycrystalline aluminum nitride, which is tolerant to corrosive chemical media, and was designed to be stable at temperatures up to 500 °C and pressures up to 30 MPa. The performance of the reactor is demonstrated by the measurement of extended x-ray absorption fine structure spectra of a carbon-supported ruthenium catalyst during the continuous hydrothermal gasification of ethanol in supercritical water at 400 °C and 24 MPa.     

Improved Thermoelectric Performance of Eco‐Friendly β‐FeSi2–SiGe Nanocomposite via Synergistic Hierarchical Structuring,Phase Percolation,and Selective Doping

A β‐FeSi₂–SiGe nanocomposite is synthesized via a react/transform spark plasma sintering technique, in which eutectoid phase transformation, Ge alloying, selective doping, and sintering are completed in a single process, resulting in a greatly reduced process time and thermal budget. Hierarchical structuring of the SiGe secondary phase to achieve coexistence of a percolated network with isolated nanoscale inclusions effectively decouples the thermal and electrical transport. Combined with selective doping that reduces conduction band offsets, the percolation strategy produces overall electron mobilities 30 times higher than those of similar materials produced using typical powder‐processing routes. As a result, a maximum thermoelectric figure of merit ZT of ≈0.7 at 700 °C is achieved in the β‐FeSi₂–SiGe nanocomposite.     

Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model

Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application.     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...