Effect of ultrasound on removal of persistent organic pollutants (POPs) from different types of soils

A new and promising technology is utilization of sonochemistry on decontamination of polluted soil. The feasibility of this technology on treatment of contaminated soils (synthetic clay, natural farm clay, and kaolin) was studied by using two target persistent organic pollutants (POPs): hexachlorobenzene (HCB) and phenanthrene (PHE). The soils were highly contaminated in 500 mg/kg. The laboratory experiments were conducted with various conditions (moisture, power, and time duration). The effects of these parameters on ultrasonication (as well as the removal of contaminants) were examined. The reasonable moisture ratio of the slurry could be in range of 2:1–3:1. The process did not change pH values of soils. Experimental results showed that ultrasonication has a potential to reduce the high concentrations of these POPs.     

The efficient lignocellulose-based sorbent for oil spill treatment from polyurethane and agricultural residue of Vietnam

In this study, new and efficient sorbent with density 0.2 g/cm³ was fabricated by incorporating rice straw into polyurethane matrix to get an open cell structure material with high oil uptake capacity. The influences of various important factors such as amount of adding rice straw, size of rice straw particles, and adsorption time on oil adsorption capacity of new sorbent material were investigated. The results showed that the oil absorption rate occurred fast in first 15–30 min, then slowed down and reached saturation level after about 2 h of treatment. Oil adsorption capacity of the new sorbent material was relatively high, up to 12.0 g/g. In comparison with pristine polyurethane or lignocellulosic materials, the new sorbents had higher oil adsorption capacity. Some characteristics of the as-obtained sorbent, such as surface shapes and porosity, were also studied by SEM analysis.     

Investigation of load transfer mechanisms in granular platforms reinforced by geosynthetics above cavities

Geosynthetic-reinforced soils constitute an interesting solution for bridging cavities. Many methods have been developed to analyze the stability of soil-geosynthetic-cavity systems, but none of them is able to take into account all the complexities of these mechanisms. Many researchers have assumed mechanisms developed in the reinforced granular platform when cavities appear, such as load transfer and expansion of materials. However, they are not fully understood because many factors can influence the design, such as the cavity opening processes, the type, and the density of the soil.In this study, a new laboratory apparatus is developed to simulate two different cavity opening procedures (trapdoor and progressive opening) for different geometric configurations. A series of tests is conducted for three granular soils with two different geosynthetic sheets. By measuring the shape of the surface soil settlement and the geosynthetic deflection, the expansion coefficient is calculated. A novel tactile pressure sensor is used to observe the load transfer during the cavity opening. The experimental data are analyzed and the influence of the experimental conditions (geometric and soil properties and the opening procedure) are also discussed. Correspondingly, elicited findings can be used to propose recommendations to improve the existing design methods.     

Secure Multi-hop Data Transmission in Cognitive Radio Networks Under Attack in the Physical Layer

Wireless Personal Communications - Cognitive radio networks (CRNs) have a shortcoming in that attackers can increase their ability to disturb secondary users (SUs). This paper focuses on jamming...     

Estimation and forecasting of machine health condition using ARMA/GARCH model

This paper proposes the hybrid model of autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH) to estimate and forecast the machine state based on vibration signal. The main idea in this study is to employ the linear ARMA model and the nonlinear GARCH model to explain the wear and fault condition of machine, respectively. The successful outcomes of the ARMA/GARCH prediction model can give obvious explanation for future states of machine, which enhance the worth of machine condition monitoring as well as condition-based maintenance in practical applications. The advance of the proposed model is verified in empirical results as applying for a real system of a methane compressor in a petrochemical plant.     

Greenhouse gas emission mitigation potential of rice husks for An Giang province, Vietnam

To evaluate the greenhouse gas (GHG) emission mitigation potential of rice husk utilization, a life cycle inventory analysis was conducted for 18 scenarios. The allocation of fuels, other than rice husks, was decided based on the current demand for and supply of rice husks. To prevent the bulky nature of rice husks, briquette production is also discussed. In the power generation scenarios, the differences between two capacities (5 MW and 30 MW) were analyzed. The results of analysis reveal that CH₄ and N₂O emissions from open burning contribute largely to the current GHG emissions. Therefore, ceasing open burning alone has a large GHG mitigation potential. The use of briquettes, even though GHG is emitted during the production stage, can still contribute to GHG emission mitigation as the production is more efficient than rice husk burning or dumping. In the power generation scenarios, most GHG emissions were derived from the combustion process. Therefore, gasification which has a little combustion process is the most efficient GHG mitigator. Both the replacement of grid electricity by generated electricity, and the replacement of diesel oil by pyrolyzed oil show larger GHG mitigation potentials than what could be derived from open burning cessation alone.     

Detecting Regular Visit Patterns

We are given a trajectory T\mathcal{T} and an area A\mathcal{A} . T\mathcal{T} might intersect A\mathcal{A} several times, and our aim is to detect whether T\mathcal{T} visits A\mathcal{A} with some regularity, e.g. what is the longest time span that a GPS-GSM equipped elephant visited a specific lake on a daily (weekly or yearly) basis, where the elephant has to visit the lake most of the days (weeks or years), but not necessarily on every day (week or year).     

Dexterous manipulation of an object by means of multi‐DOF robotic fingers with soft tips

This article analyzes the dynamics of motion of various setups of two multiple degree‐of‐freedom (DOF) fingers that have soft tips, in fine manipulation of an object, and shows performances of their motions via computer simulation. A mathematical model of these dynamics is described as a system of nonlinear differential equations expressing motion of the overall fingers‐object system together with algebraic constraints due to tight area contacts between the finger‐tips and surfaces of the object. First, problems of (1) dynamic, stable grasping and (2) regulation of the object rotational angle by means of a setup of dual two‐DOF fingers, are treated. Second, the problem of regulating the position of the object mass center by means of a pair of two‐DOF and three‐DOF fingers is considered. Third, a set of dual three‐DOF fingers is treated, in order to let it perform a sophisticated task, which is specified by a periodic pattern of the object posture and a constant internal force. In any case, there exist sensory‐motor coordinations, which are described by analytic feedback connections from sensing to actions at finger joints. In the cases of setpoint control problems, convergences of motion to secure grasping together with the specified object rotational angle and/or the specified object mass center position, are proved theoretically. A constraint stabilization method (CSM) is used for solving numerically the differential algebraic equations to show performances of the proposed sensory‐feedback schemes. © 2002 Wiley Periodicals, Inc.     

Blind separation of instantaneous mixture of sources via anindependent component analysis

In this paper, we introduce a procedure for separating a multivariate distribution into nearly independent components based on minimizing a criterion defined in terms of the Kullback-Leibner distance. By replacing the unknown density with a kernel estimate, we derive useful forms of this criterion when only a sample from that distribution is available. We also compute the gradient and Hessian of our criteria for use in an iterative minimization. Setting this gradient to zero yields a set of separating functions similar to the ones considered in the source separation problem, except that here, these functions are adapted to the observed data. Finally, some simulations are given, illustrating the good performance of the method