Influence of pre-electrophoresis on protein separations in polyacrylamide gels

Polyacrylamide gel electrophoresis (PAGE) is a widely used technique in protein separations. In this study, the effects of pre-electrophoresis (i.e., exposing polyacrylamide gels to an electrical field prior to loading samples) on the separation of a mixture of 10 standard proteins was investigated. Three different gel concentrations (based on amounts of acrylamide and bis-acrylamide, always at a 37.5:1 ratio) relative to the volume of the gel solution were tested: 6%, 9%, and 12%. For each concentration, multiple slab gels were subjected to pre-electrophoresis prior to separation of proteins, while multiple other (i.e., companion) slab gels were used but without applying the pre-electrophoresis technique. Separation results were investigated, showing that the application of the pre-electrophoresis technique had a significant effect on separations particularly for 6% and 9% gels.     

Review of Process Industry Accidents Analysis towards Safety System Improvement and Sustainable Process Design

Risk assessment is the tool for maintaining perfect safety management systems and aiding sustainable process design, with hazard identification as the critical step. This step can be executed by past accidents analysis (PAA) to achieve the mentioned objectives. Despite of available analyses, the recurring of accidents identifies the shortcomings in PAA and requires a detailed examination as reported in this review. The intensified exploration of accident information will strengthen both the safety management system at existing facilities and process designing in terms of sustainability.     

QAM equalization and symbol detection in OFDM systems using extreme learning machine

This paper presents a new learning-based framework to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing systems with quadrature amplitude modulation. The framework utilizes extreme learning machine (ELM), a recent addition to the class of supervised learning algorithms, to achieve fast training, high performance, and low error rates. The proposed ELM scheme employs infinitely differentiable nonlinear activation functions in least-square solution to learn the channel response, which is the equalization part. In addition to equalization, ELM performs symbol detection. Existing learning-based schemes require an additional symbol slicer for the symbol detection. The proposed framework does not experience training bottleneck imposed by gradient descent–based approaches. Simulation results show that the proposed framework outperforms other learning-based equalizers in terms of symbol error rate and training speeds.     

Minimum thickness of elliptical masonry arches

In this paper, we compute the minimum thickness and the location of the imminent intrados hinge of symmetric elliptical masonry arches when subjected to their weight. While this problem (Couplet’s problem) was solved rigorously for semicircular arches more than a century ago, no results have been available for elliptical arches. Motivated from the recent growing interest in identifying the limit equilibrium states of historic structures, this paper first computes two neighboring physically admissible thrust-lines which can just be located in elliptical arches by adopting either a polar or a cartesian coordinate system. These two distinguishable, physically admissible thrust-lines are neighboring thrust-lines to Hooke’s catenary which is not a physically admissible thrust-line as has been shown recently. Accordingly, the paper shows that the answer for the minimum thickness of symmetric elliptical masonry arches is not unique and that it depends on the coordinate system adopted and the associated stereotomy exercised. This result is confirmed by developing a variational formulation after selecting the appropriate directions of the rupture that initiates at the intrados hinge. The paper concludes that Hooke’s limiting catenary, although not a physically admissible thrust-line, offers a conservative value for the minimum thickness in most practical configurations.     

Selected mapping with symbol re‐mapping for OFDM/TDM using MMSE‐FDE

Orthogonal frequency division multiplexing (OFDM) signals have a problem with a high peak‐to‐average power ratio (PAPR). A distortionless selected mapping (SLM) has been proposed to reduce the PAPR, but a high computational complexity prohibits its application to an OFDM system with a large number of subcarriers. Recently, we proposed OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency‐domain equalization (MMSE‐FDE) to improve the bit error rate (BER) performance of conventional OFDM with a lower PAPR. The PAPR problem, however, cannot be completely eliminated. In this paper, we present an SLM combined with symbol re‐mapping for OFDM/TDM using MMSE‐FDE. Unlike the conventional OFDM, where SLM is applied over subcarriers in the frequency domain, we exploit both time and frequency dimensions of OFDM/TDM signal to improve the performance with respect to PAPR and BER. A mathematical model for PAPR distribution of OFDM/TDM with SLM is presented to complement the computer simulation results. It is shown that proposed SLM can further reduce the PAPR without sacrificing the BER performance with the same or reduced computational complexity. Copyright © 2010 John Wiley & Sons, Ltd.     

Iterative decision‐directed estimation and compensation of nonlinear distortion effects for OFDM systems

Orthogonal frequency division multiplexing (OFDM) has been adopted for several wireless network standards due to its robustness against multipath fading. Main drawback of OFDM is its high peak‐to‐average power ratio (PAPR) that causes a signal degradation in a peak‐limiting (e.g., clipping) channel leading to a higher bit error rate (BER). At the receiver end, the effect of peak limitation can be removed to some extent to improve the system performance. In this paper, a joint iterative channel estimation/equalization and clipping noise reduction technique based on minimum mean square error (MMSE) criterion is presented. The equalization weight that minimizes the mean square error (MSE) between the signal after channel equalization and feedback signal after clipping noise reduction is derived assuming imperfect channel state information (CSI). The MSE performance of the proposed technique is theoretically evaluated. It is shown that the BER performance of OFDM with proposed technique can be significantly improved in a peak‐limited and doubly‐selective (i.e., time‐ and frequency‐selective) fading channel. Copyright © 2011 John Wiley & Sons, Ltd.     

Spatial and temporal trends in nitrate concentrations in the River Derwent, North Yorkshire, and its need for NVZ status

Long-term spatial and temporal variations in nitrate-N concentrations along the River Derwent have been examined using Environment Agency data to investigate the relative importance of impacts of atmospheric N deposition, land use, and changes in management. Where moorland and rough grazing dominate upstream of Forge Valley and Malton, over the 20 years since 1988 mean nitrate-N concentrations were initially increasing significantly, but are now levelling off, with peaks at ca. 4.5 mg N l⁻¹. As expected in a catchment in a nitrate vulnerable zone (NVZ), more agricultural land use increases mean nitrate concentrations and the occurrence of distinct winter maxima, though the latter have become markedly less pronounced since 2001. It is suggested that this improvement is a combined effect of imposition of NVZ designation in the lower reaches in 2002, animal number declines associated with the Foot & Mouth outbreak in the region in 2001, and the impact of farmers' responses to increasing fertilizer prices and to beneficial pollutant mineral N inputs from the atmosphere. Minima in nitrate-N concentrations in summer have become much less pronounced over the past decade and are typically ca. 60% higher in concentration than a decade earlier. This probably is attributable to the effects of pollutant-N leaching to depths in soil below the rooting zone when near surface biotic uptake is low in winter. The resultant N mineralization in summer enhances summer nitrate leaching. The Derwent is a relatively clean river; however, its entire catchment was designated justifiably as a NVZ in January 2009, apparently based upon a projected 95 percentile nitrate-N concentration > 11.29 mg l^− 1 for 2010 based upon forward projection of data from 1990 to 2004 for Derwent Bridge. A survey of water quality in March 2009 showed that some agricultural areas are still making a significant contribution to the total nitrate level well downstream, at the point responsible for implementation of NVZ status. At 3 of the 29 sites sampled, nitrate concentration exceeded 60 mg l^− 1.     

The growth of pine-leaf-like hierarchical SnO2 nanostructures

Pine-leaf-like SnO₂ hierarchical nanostructures (NSs) were grown by a two-step vapour transport deposition process with a combination of vapour–solid and vapour–liquid–solid mechanisms at the primary and secondary processes, respectively. This type of hierarchical structure consisted of SnO₂ trunk with homo-branching nanowires (NWs). The branched NWs connected the trunk NWs at included angles of 56° and 90° for two different types of hierarchical NSs. Based on the thermodynamic calculation, the formation of branched NWs at those angles are all energetically favourable.     

Exploring Data-Independent Dimensionality Reduction in Sparse Representation-Based Speaker Identification

The sparse representation classification (SRC) has attracted the attention of many signal processing domains in past few years. Recently, it has been successfully explored for the speaker recognition task with Gaussian mixture model (GMM) mean supervectors which are typically of the order of tens of thousands as speaker representations. As a result of this, the complexity of such systems become very high. With the use of the state-of-the-art i-vector representations, the dimension of GMM mean supervectors can be reduced effectively. But the i-vector approach involves a high dimensional data projection matrix which is learned using the factor analysis approach over huge amount of data from a large number of speakers. Also, the estimation of i-vector for a given utterance involves a computationally complex procedure. Motivated by these facts, we explore the use of data-independent projection approaches for reducing the dimensionality of GMM mean supervectors. The data-independent projection methods studied in this work include a normal random projection and two kinds of sparse random projections. The study is performed on SRC-based speaker identification using the NIST SRE 2005 dataset which includes channel matched and mismatched conditions. We find that the use of data-independent random projections for the dimensionality reduction of the supervectors results in only 3 % absolute loss in performance compared to that of the data-dependent (i-vector) approach. It is highlighted that with the use of highly sparse random projection matrices having \(\pm \) 1 as non-zero coefficients, a significant reduction in computational complexity is achieved in finding the projections. Further, as these matrices do not require floating point representations, their storage requirement is also very small compared to that of the data-dependent or the normal random projection matrices. These reduced complexity sparse random projections would be of interest in context of the speaker recognition applications implemented on platforms having low computational power.