The acquisition of an artificial logographic script and bilingual working memory : Evidence for L1-specific orthographic processing skills transfer in Chinese-English bilinguals

Studies of orthographic skills transfer between languages focus mostly on working memory (WM) ability in alphabetic first language (L1) speakers when learning another, often alphabetically congruent, language. We report two studies that, instead, explored the transferability of L1 orthographic processing skills in WM in logographic-L1 and alphabetic-L1 speakers. English-French bilingual and English monolingual (alphabetic-L1) speakers, and Chinese-English (logographic-L1) speakers, learned a set of artificial logographs and associated meanings (Study 1). The logographs were used in WM tasks with and without concurrent articulatory or visuo-spatial suppression. The logographic-L1 bilinguals were markedly less affected by articulatory suppression than alphabetic-L1 monolinguals (who did not differ from their bilingual peers). Bilinguals overall were less affected by spatial interference, reflecting superior phonological processing skills or, conceivably, greater executive control. A comparison of span sizes for meaningful and meaningless logographs (Study 2) replicated these findings. However, the logographic-L1 bilinguals’ spans in L1 were measurably greater than those of their alphabetic-L1 (bilingual and monolingual) peers; a finding unaccounted for by faster articulation rates or differences in general intelligence. The overall pattern of results suggests an advantage (possibly perceptual) for logographic-L1 speakers, over and above the bilingual advantage also seen elsewhere in third language (L3) acquisition.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Recombination, decreased host specificity and increased mobility may have driven the emergence of maize streak virus as an agricultural pathogen

Maize streak virus (MSV; family Geminiviridae, genus Mastrevirus), the causal agent of maize streak disease, ranks amongst the most serious biological threats to food security in subSaharan Africa. Although five distinct MSV strains have been currently described, only one of these - MSV-A - causes severe disease in maize. Due primarily to their not being an obvious threat to agriculture, very little is known about the 'grass-adapted' MSV strains, MSV-B, -C, -D and -E. Since comparing the genetic diversities, geographical distributions and natural host ranges of MSV-A with the other MSV strains could provide valuable information on the epidemiology, evolution and emergence of MSV-A, we carried out a phylogeographical analysis of MSVs found in uncultivated indigenous African grasses. Amongst the 83 new MSV genomes presented here, we report the discovery of six new MSV strains (MSV-F to -K). The non-random recombination breakpoint distributions detectable with these and other available mastrevirus sequences partially mirror those seen in begomoviruses, implying that the forces shaping these breakpoint patterns have been largely conserved since the earliest geminivirus ancestors. We present evidence that the ancestor of all MSV-A variants was the recombinant progeny of ancestral MSV-B and MSV-G/-F variants. While it remains unknown whether recombination influenced the emergence of MSV-A in maize, our discovery that MSV-A variants may both move between and become established in different regions of Africa with greater ease, and infect more grass species than other MSV strains, goes some way towards explaining why MSV-A is such a successful maize pathogen. © 2008 SGM.

The impact of anxiety on processing efficiency : implications for the attentional control theory

The Attentional Control Theory (ACT) proposes that high-anxious individuals maintain performance effectiveness (accuracy) at the expense of processing efficiency (response time), in particular, the two central executive functions of inhibition and shifting. In contrast, research has generally failed to consider the third executive function which relates to the function of updating. In the current study, seventy-five participants completed the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory in order to explore the effects of anxiety on attention. Results indicated that anxiety lead to decay in processing efficiency, but not in performance effectiveness, across all three Central Executive functions (inhibition, set-shifting and updating). Interestingly, participants with high levels of trait anxiety also exhibited impaired performance effectiveness on the n-back task designed to measure the updating function. Findings are discussed in relation to developing a new model of ACT that also includes the role of preattentive processes and dual-task coordination when exploring the effects of anxiety on task performance.

Lignocellulosics as a renewable feedstock for chemical industry : chemical hydrolysis and pretreatment processes

Lignocellulosic materials including agricultural, municipal and forestry residues, and dedicated bioenergy crops offer significant potential as a renewable feedstock for the production of fuels and chemicals. These products can be chemically or functionally equivalent to existing products that are produced from fossil-based feedstocks. To unlock the potential of lignocellulosic materials, it is necessary to pretreat or fractionate the biomass to make it amenable to downstream processing. This chapter explores current and developing technologies for the pretreatment and fractionation of lignocellulosic biomass for the production of chemicals and fuels.

Partial melt processing and electrical properties of Bi-Sr-Ca-Cu-o superconducting thick films on (100) MgO substrates

Superconducting thick films of Bi2Sr2CaCu2Oy (Bi-2212) on single-crystalline (100) MgO substrates have been prepared using a doctor-blade technique and a partial-melt process. It is found that the phase composition and the amount of Ag addition to the paste affect the structure and superconducting properties of the partially melted thick films. The optimum heat treatment schedule for obtaining high Jc has been determined for each paste. The heat treatment ensures attainment of high purity for the crystalline Bi-2212 phase and high orientation of Bi-2212 crystals, in which the c-axis is perpendicular to the substrate. The highest Tc, obtained by resistivity measurement, is 92.2 K. The best value for Jct (transport) of these thick films, measured at 77 K in self-field, is 8 × 10 3 Acm -2.

Agricultural biodiversity, farm level technical efficiency and conservation benefits : an empirical investigation

The issues involved in agricultural biodiversity are important and interesting areas for the application of economic theory. However, very little theoretical and empirical work has been undertaken to understand the benefits of conserving agricultural biodiversity. Accordingly, the main objectives of this PhD thesis are to: (1) Investigate farmers’ valuation of agricultural biodiversity; (2) Identify factors influencing farmers’ demand for agricultural biodiversity; (3) Examine farmers’ demand for biodiversity rich farming systems; (4) Investigate the relationship between agricultural biodiversity and farm level technical efficiency. This PhD thesis investigates these issues by using primary data in small-scale farms, along with secondary data from Sri Lanka. The overall findings of the thesis can be summarized as follows. Firstly, owing to educational and poverty issues of those being interviewed, some policy makers in developed countries question whether non-market valuation techniques such as Choice Experiment (CE) can be applied to developing countries such as Sri Lanka. The CE study in this thesis indicates that carefully designed and pre-tested nonmarket valuation techniques can be applied in developing countries with a high level of reliability. The CE findings support the priori assumption that small-scale farms and their multiple attributes contribute positively and significantly to the utility of farm families in Sri Lanka. Farmers have strong positive attitudes towards increasing agricultural biodiversity in rural areas. This suggests that these attitudes can be the basis on which appropriate policies can be introduced to improve agricultural biodiversity. Secondly, the thesis identifies the factors which influence farmers’ demand for agricultural biodiversity and farmers’ demands on biodiversity rich farming systems. As such they provide important tools for the implementation of policies designed to avoid the loss agricultural biodiversity which is shown to be a major impediment to agricultural growth and sustainable development in a number of developing countries. The results illustrate that certain key household, market and other characteristics (such as agricultural subsidies, percentage of investment of owned money and farm size) are the major determinants of demand for agricultural biodiversity on small-scale farms. The significant household characteristics that determine crop and livestock diversity include household member participation on the farm, off-farm income, shared labour, market price fluctuations and household wealth. Furthermore, it is shown that all the included market characteristics as well as agricultural subsidies are also important determinants of agricultural biodiversity. Thirdly, it is found that when the efficiency of agricultural production is measured in practice, the role of agricultural biodiversity has rarely been investigated in the literature. The results in the final section of the thesis show that crop diversity, livestock diversity and mix farming system are positively related to farm level technical efficiency. In addition to these variables education level, number of separate plots, agricultural extension service, credit access, membership of farm organization and land ownerships are significant and direct policy relevant variables in the inefficiency model. The results of the study therefore have important policy implications for conserving agricultural biodiversity in Sri Lanka.

Assessing eco-environmental performance of agricultural production in OECD countries : combination of soil surface, soil system and farm gate methods of nutrient auditing

Nitrogen balance is increasingly used as an indicator of the environmental performance of agricultural sector in national, international, and global contexts. There are three main methods of accounting the national nitrogen balance: farm gate, soil surface, and soil system. OECD (2008) recently reported the nitrogen and phosphorus balances for member countries for the 1985 - 2004 period using the soil surface method. The farm gate and soil system methods were also used in some international projects. Some studies have provided the comparison among these methods and the conclusion is mixed. The motivation of this present paper was to combine these three methods to provide a more detailed auditing of the nitrogen balance and flows for national agricultural production. In addition, the present paper also provided a new strategy of using reliable international and national data sources to calculate nitrogen balance using the farm gate method. The empirical study focused on the nitrogen balance of OECD countries for the period from 1985 to 2003. The N surplus sent to the total environment of OECD surged dramatically in early 1980s, gradually decreased during 1990s but exhibited an increasing trends in early 2000s. The overall N efficiency however fluctuated without a clear increasing trend. The eco-environmental ranking shows that Australia and Ireland were the worst while Korea and Greece were the best.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Abstract: Texture enhancement is an important component of image processing, with extensive application in science and engineering. The quality of medical images, quantified using the texture of the images, plays a significant role in the routine diagnosis performed by medical practitioners. Previously, image texture enhancement was performed using classical integral order differential mask operators. Recently, first order fractional differential operators were implemented to enhance images. Experiments conclude that the use of the fractional differential not only maintains the low frequency contour features in the smooth areas of the image, but also nonlinearly enhances edges and textures corresponding to high-frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we applied the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other fractional differential operators, our new algorithms provide higher signal to noise values, which leads to superior image quality.