Computational intelligent brain computer interaction and its applications on driving cognition

Driving is one of the most common attention-demanding tasks in daily life. Driver's fatigue, drowsiness, inattention, and distraction are reported a major causal factor in many traffic accidents. Due to the drivers lost their attention, they had markedly reduced the perception, recognition and vehicle control abilities. In recent years, many computational intelligent technologies were developed for preventing traffic accidents caused by driver's inattention. Driver's drowsiness and distraction related studies had become a major interest research topic in automotive safety engineering. Many researches had investigated the driving cognition in cognitive neuro-engineering, but how to utilize the main findings of driving-related cognitive researches in traditional cognitive neuroscience and integrate with computational intelligence technologies for augmenting driving performance will become a big challenge in the interdisciplinary research area. For this reason, we attempt to integrate the driving cognition for real life application in this study. The implications of the driving cognition in cognitive neuroscience and computational intelligence for daily applications are also demonstrated through two common attention-related driving studies: (1) cognitive-state monitoring of the driver performing the realistic long-term driving tasks in a simulated realistic-driving environment; and (2) to extract the brain dynamic changes of driver's distraction effect during dual-task driving. Experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.     

Particulate emission factors for mobile fossil fuel and biomass combustion sources

PM emission factors (EFs) for gasoline- and diesel-fueled vehicles and biomass combustion were measured in several recent studies. In the Gas/Diesel Split Study (GD-Split), PM_2.5 EFs for heavy-duty diesel vehicles (HDDV) ranged from 0.2 to ~ 2 g/mile and increased with vehicle age. EFs for HDDV estimated with the U.S. EPA MOBILE 6.2 and California Air Resources Board (ARB) EMFAC2007 models correlated well with measured values. PM_2.5 EFs measured for gasoline vehicles were ~ two orders of magnitude lower than those for HDDV and did not correlate with model estimates. In the Kansas City Study, PM_2.5 EFs for gasoline-powered vehicles (e.g., passenger cars and light trucks) were generally < 0.03 g/mile and were higher in winter than summer. EMFAC2007 reported higher PM_2.5 EFs than MOBILE 6.2 during winter, but not during summer, and neither model captured the variability of the measured EFs. Total PM EFs for heavy-duty diesel military vehicles ranged from 0.18 ± 0.03 and 1.20 ± 0.12 g/kg fuel, corresponding to 0.3 and 2 g/mile, respectively. These values are comparable to those of on-road HDDV. EFs for biomass burning measured during the Fire Laboratory at Missoula Experiment (FLAME) were compared with EFs from the ARB Emission Estimation System (EES) model. The highest PM_2.5 EFs (76.8 ± 37.5 g/kg) were measured for wet (> 50% moisture content) Ponderosa Pine needles. EFs were generally < 20 g/kg when moisture content was < 20%. The EES model agreed with measured EFs for fuels with low moisture content but underestimated measured EFs for fuel with moisture content > 40%. Average EFs for dry chamise, rice straw, and dry grass were within a factor of three of values adopted by ARB in California's San Joaquin Valley (SJV). Discrepancies between measured and modeled emission factors suggest that there may be important uncertainties in current PM_2.5 emission inventories.     

One-step synthesis of highly biocompatible multi-shaped gold nanostructures with fruit extract

In this study, the authors demonstrate the synthesis of various gold nanostructures through a one-step, green and complete bio-modulation approach. Nanoparticles were successfully synthesised by the addition of gold aqueous solution to fruit extracts, including orange, papaya, peach or lemon. The particles were of various shapes and sizes with high abundance, such as sphere, marigold, triangle and hexagon. The biocompatibility of the presented gold nanostructures was examined; haemolysis tests revealed a non-toxicity result in blood cell uptake of such gold nanostructures. This study opens the exciting possibility of synthesising various multi-shaped nanoparticles through a simple and green approach, as well as paving the way for future bio-applications.     

PM2.5 chemical composition in Hong Kong: urban and regional variations

Chemically speciated PM2.5 measurements were made at roadside, urban, and rural background sites in Hong Kong for 1 year during 2000/2001 to determine the spatial and temporal variations of PM2.5 mass and chemical composition in this highly populated region. Annual average PM2.5 concentrations at the urban and rural sites were 34.1 and 23.7 microg m(-3), respectively, approximately 50-100% higher than the United States' annual average National Ambient Air Quality Standard (NAAQS) of 15 microg m(-3). Daily PM2.5 concentrations exceeded the U.S. 24-h NAAQS of 65 microg m(-3) on 19 days, reaching 131+/-8 microg m(-3) at the roadside site on 02/28/2001. Carbonaceous aerosol is the largest contributor to PM2.5 mass (explaining 52-75% of PM2.5 mass at the two urban sites and 32% at the background site), followed by ammonium sulfate (ranging from 23% to 37% at the two urban sites and 51% at the background site). Ammonium sulfate and crustal concentrations showed more uniform spatial distributions, while the largest urban-rural contrasts found in carbonaceous aerosol (likely due to emissions from on-road gasoline and diesel vehicles). Marine influences accounted for 7% of the mass at the background site (more than twice as much as at the two urban sites). Ternary diagrams are utilized to illustrate the different spatial patterns.