Particle-scale investigation of PAH desorption kinetics and thermodynamics from sediment

Dredged sediment from Milwaukee Harbor showed two primary classes of particles in the <2 mm size range: a lighter-density coal- and wood-derived fraction with 62% of total PAHs and a heavier-density sand, silt, and clay fraction containing the remaining 38% of the PAHs. Room-temperature PAH desorption kinetic studies on separated sediment fractions revealed slow desorption rates for the coal-derived particles and fast desorption rates for the clay/silt particles. The effect of temperature on PAH release was measured by thermal program desorption mass spectrometry to investigate the desorption activation energies for PAHs on the different sediment particles. Three activated diffusion-based models and an activated first-order rate model were used to describe the thermal desorption of PAHs for four molecular weight classes. PAH binding with the coal-derived particles was associated with high activation energies, typically in the range of 115-139 kJ/mol. PAHs bound to the clay/silt material had much lower activation energy, i.e., in the range of 37-41 kJ/ mol for molecular weight 202. Among the desorption models tested, a spherical diffusion model with PAHs located like a rind on the outer 1-3 microm region best described the PAH thermal desorption response for coal-derived particles. This internal PAH distribution pattern on coalderived particles is based on prior direct measurement of PAH locations at the subparticle scale. These studies reveal that heterogeneous particle types in sediment exhibit much different amounts and binding of PAHs. PAHs associated with coal-derived particles aged over several decades in the field appear to be far from reaching an equilibrium sorption state due to the extremely slow diffusivities through the polymer-like coal matrix. These results provide an improved mechanistic perspective for the understanding of PAH mobility and bioavailability in sediments.     

Geometry and Control of Human Eye Movements

In this paper, we study the human oculomotor system as a simple mechanical control system. It is a well known physiological fact that all eye movements obey Listing's law, which states that eye orientations form a subset consisting of rotation matrices for which the axes are orthogonal to the normal gaze direction. First, we discuss the geometry of this restricted configuration space (referred to as the Listing space). Then we formulate the system as a simple mechanical control system with a holonomic constraint. We propose a realistic model with musculotendon complexes and address the question of controlling the gaze. As an example, an optimal energy control problem is formulated and numerically solved     

Role of Direct Reduced Iron Fines in Nitrogen Removal from Electric Arc Furnace Steel

Electric arc furnace steel contains about 70‐120 ppm nitrogen. There is no suitable method for nitrogen removal from electric arc furnace steel up to the level desired for good quality bars and flat rolled products (30 ppm max). The existing process based on vacuum degassing can remove only up to 20% of nitrogen in steel. In the present study DRI fines have been injected into a steel bath which can drift out nitrogen in steel through production of fine CO bubbles in‐situ on reaction with residual FeO in DRI fines and C in bath. For high and medium carbon steel, nitrogen got reduced to 30 ppm and 60 ppm respectively where initial nitrogen was 150 – 200 ppm in steel. Nitrogen removal also depends upon bath depth and addition level of DRI.     

Strength Characteristics of Class F Fly Ash Modified with Lime and Gypsum

This paper presents the shear strength characteristics of a low lime class F fly ash modified with lime alone or in combination with gypsum. Unconfined compression tests were conducted for both unsoaked and soaked specimens cured up to 90 days. Addition of a small percentage of gypsum (0.5 and 1.0%) along with lime (4–10%) enhanced the shear strength of modified fly ash within short curing periods (7 and 28 days). The gain in unsoaked unconfined compressive strength (qu) of the fly ash was 2,853 and 3,567% at 28 and 90 days curing, respectively, for addition of 10% lime along with 1% gypsum to the fly ash. The effect of 24?h soaking showed reduction of qu varying from 30 to 2% depending on mix proportions and curing period. Unconsolidated undrained triaxial tests with pore-pressure measurements were conducted for 7 and 28 days cured specimens. The cohesion of the Class F fly ash increased up to 3,150% with addition of 10% lime along with 1% gypsum to the fly ash and cured for 28 days. The modified fly ash shows the values of Skempton’s pore-pressure parameter, Af similar to that of over consolidated soils. The effects of lime content, gypsum content, and curing period on the shear strength parameters of the fly ash are highlighted herein. Empirical relationships are proposed to estimate the design parameters like deviatoric stress at failure, and cohesion of the modified fly ash. Thus, this modified fly ash with considerable shear strength may find potential use in civil engineering construction fields.     

A Single-Phase Boost Rectifier System for Wide Range of Load Variations

Converters operated in discontinuous-conduction-mode (DCM) and in continuous-conduction-mode (CCM) are suitable for lighter and higher loads, respectively. A new, constant switching frequency based single-phase rectifier system is proposed, which operates in DCM and in CCM for outputs less than and greater than 50% rated load, respectively, covering a wide range of load variation. The power circuit and the control circuit of the proposed rectifier are easily configurable for DCM and CCM operations. The measured load current is used to select the desired operating mode. The peak device current under DCM is limited to rated device current under CCM without using a device of higher current rating. The input current shaping under CCM and DCM are based on the comparison of measured input current with linear and nonlinear carriers, respectively. A load current feedforward scheme is presented to improve the system dynamic performance and also to ensure a smooth transition between the two operating modes. All the necessary control operations are performed without using multiplication, division and square-root operation. The proposed rectifier shows improved input current characteristics over the existing CCM converters for the above load range. This is validated on a 600-W rectifier prototype. Simulation and experimental results are presented     

Midwavelength Infrared Avalanche Photodiode Using InAs–GaSb Strain Layer Superlattice

InAs-GaSb strain layer superlattice p⁺-n^--n avalanche photodiodes (APDs) are fabricated using a newly introduced electron-beam aided zinc sulfide deposition. Temperature-dependent measurements were performed on 300 times 300 mum² mesa etched APDs. The effect of passivation was also studied on the diode characteristics and APD performances. Temperature-dependent gain strongly correlates with avalanche mechanism.     

Notice of Violation of IEEE Publication PrinciplesLDPC decoder: A cognitive radio perspective for next generation (XG) communication

With the rapid growth of multimedia communication systems during the last decade, there has been an increasing demand for improved technology for Error Correcting Code (ECO to enable the communication systems to have a reliable transmission over noisy channels. Low Density Parity Check (LDPC) codes are the best known ECC codes that can achieve data rates very close to the Shannon limit. In addition, superior error correction performance and parallelizable decoding algorithms have made LDPC codes a powerful competitor to turbo codes for reliable high speed communication applications. Recently, Cognitive Radio (CR) has been proposed as a promising technology to solve today's spectrum scarcity problem. CR promises to alleviate this spectrum shortage problem by dynamically accessing free spectrum resources. This implies that the radio has to work in multi-band, cope with various wireless channels and support various services such as voice, data and video. The basic requirement for CR is that it has a reconfigurable architecture to support multi-band and frequency adaptive operations. One of the ambitious design goals of future wireless systems, including 4G, IEEE 802.11n/802.16 standards, is to provide reliably very high data rate transmission in hostile environments: for example, around 100 Mb/s peak rate for downlink and around 30 Mb/s sum rate for uplink transmission with a low frame error rate (FER), typically less than 5 times10^-4. To ensure reliable nd error-free communication, there is a demand to consider implementing LDPC decoders in CR and frequency agile environments. In this article we discuss the design of adaptable as well as efficient LDPC decoders with low bit-error rate (BER) in low signal-to-noise ratio (SNR) channels for CR environments.     

CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS

The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.     

CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes

ABSTRACT

Every production process consists of a large number of dependent and independent variables, which substantially influence the quality of the machined parts. Due to the large impact of process variabilities, it is difficult to design optimal models for the machining processes. Mathematical or numerical models for production processes are resource driven, which are not cost effective approaches in terms of computation and economical production. In this paper, a new artificial neural network (ANN) based predictive model is introduced, which exploits particle swarm optimization (PSO) algorithm to minimize the root mean square errors (RMSE) for the network training. This approach can effectively obtain an optimized predictive model that can calculate precise output responses for the production processes. In order to verify the proposed approach, two case studies are considered from literature and shown to produce significant improvements. Furthermore, the proposed model is validated on abrasive water jet machining (AWJM) with industrial garnet abrasives and optimal machining conditions have been obtained with optimized responses, which are substantially improved while compared with gray relational analysis (GRA).