Inhibition of return (IOR) refers to an increase in reaction times to targets that appeared at a previously cued location relative to an uncued location, often investigated using a spatial cueing paradigm. Despite numerous studies that have examined many aspects of IOR, the neurophysiological mechanisms underlying IOR are still in dispute. The objective of the current research is to investigate the plausible mechanisms by manipulating the cue and target types between central and peripheral stimuli in a traditional cue-target paradigm with saccadic responses to targets. In peripheral-cueing conditions, we observed inhibitory cueing effects across all cue-target onset asynchronies (CTOAs) with peripheral targets, but IOR was smaller and arose later with central targets. No inhibition was observed in central-cueing conditions at any CTOAs. Empirical data were simulated using a two-dimensional dynamic neural field model. Our results and simulations support previous work demonstrating that, at short CTOAs, behavioral inhibition is only observed with repeated stimulation-an effect of sensory adaptation. With longer CTOAs, IOR is observed regardless of target type when peripheral cueing is used. Our findings suggest that behaviorally exhibited inhibitory cueing effects can be attributed to multiple mechanisms, including both attenuation of visual stimulation and local inhibition in the superior colliculus.

Recent studies reveal that driving without sufficient sleep would increase the risk of road traffic accident. With the aim to facilitate a safer driving experience, eye activity detection algorithm were studied actively. Though the use of wired multi-channel brain computer interface (BCI) to monitor driver's mental state has shown promising results, but the actual practicality were limited by its inconvenience. Consequently, we examined the effectiveness of a wireless and wearable singlechannel BCI in detecting driver's eye-states. Using the NeuroSky MindWave headset that entailed a single-electrode for prefrontal cortex, we observed an increment of low alpha activity during the transition from eyes-open to eyes-closed state. A monitoring system to keep drivers awake by means of alarm notifications is then implemented using adaptive percentage threshold algorithm for alarm-triggering purpose. Through simulation, our algorithm has demonstrated an EEG eye-states recognition system with: adequate detection rate of 31% per second, negligible false alarm rate of 0.5%, and minimum latency of 2 seconds.

This work comprised of the comparative study of arsenic (As) uptake efficiency by Pteris vittata and Pityrogramma calomelanos grown in (i) As amended soils (0-600 ppm) and (ii) As tainted water (40 ppb) using a new compact continuous flow phytofiltration system in a tropical greenhouse. The As hyperaccumulation efficiency was dependent on the growth medium for the two fern species. The highest level of As detected in the fronds of P. vittata was 19,300+/-190 ppm (dry weight basis) and 11,600+/-230 ppm for Pityrogramma calomelanos, after growing for 78 days in soils amended with As. In the compact continuous flow As phytofiltration system experiments, Pityrogramma calomelanos was found to perform better than P. vittata in phytofiltrating As contaminated water under waterlogged conditions. During the 167 h of phytofiltration experiment, the removal efficiency was approximately 99% and 67% for Pityrogramma calomelanos and P. vittata systems respectively, based on an initial 40 ppb As. Pityrogramma calomelanos also required a shorter acclimatization time than P. vittata under waterlogged conditions.

Abstract-This paper demonstrates electroencephalogram (EEG) analysis in MATLAB environment with the objective to investigate effectiveness of cognitive stress recognition algorithm using EEG from single-electrode BCI. 25 subjects' EEG were recorded in MATLAB with the use of Stroop color-word test as stress inducer. Questionnaire on subjects' self-perceived stress scale during Stroop test were gathered as classification's target output. The main analysis tool used were MATLAB, coupled with the use of Discrete Cosine Transform (DCT) as dimension reduction technique to reduce data size down to 2% of the origin. Three pattern classification algorithms' -Artificial Neural Network (ANN), k-Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) were trained using the resulted 2% DCT coefficients. Our study discovered the use of DCT along with KNN offers highest average classification rate of 72% compared to ANN and LDA.

The orchid genus Vanda includes more than 70 monopodial species and numerous hybrids. The aim of this study was to develop microsatellite markers for this horticultural valuable genus. Thirteen polymorphic microsatellite markers were isolated from the variety Vanda Miss Joaquim and were characterized in four Vanda species, 11 Vanda hybrids, and one Aranda intergenus hybrid. Populations from three species were also analysed. Number of alleles ranged from two to 19. For the V. sumatrana population, the expected heterozygosity ranged from 0 to 0.76 (mean 0.31) and the observed heterozygosity ranged from 0 to 1 (mean 0.36). All the varieties tested were distinct from one another (similarity index < 0.8). These microsatellites could be used for studying genetic diversity and population structure of wild populations within the orchid genus Vanda, as well as for distinguishing cultured Vanda varieties.