Selective determination of urea using urease immobilized on ZnO nanowires

Well-aligned zinc oxide (ZnO) nanowire arrays were fabricated on gold-coated plastic substrates using a low-temperature aqueous chemical growth (ACG) method. The ZnO nanowire arrays with 50–130 nm diameters and ∼1 μm in lengths were used in an enzyme-based urea sensor through immobilization of the enzyme urease that was found to be sensitive to urea concentrations from 0.1 mM to 100 mM. Two linear sensitivity regions were observed when the electrochemical responses (EMF) of the sensors were plotted vs. the logarithmic concentration range of urea from 0.1 mM to 100 mM. The proposed sensor showed a sensitivity of 52.8 mV/decade for 0.1–40 mM urea and a fast response time less than 4 s was achieved with good selectivity, reproducibility and negligible response to common interferents such as ascorbic acid and uric acid, glucose, K⁺ and Na⁺ ions.     

Trading Off Cache Capacity for Low-Voltage Operation

Two proposed techniques let microprocessors operate at low voltages despite high memory-cell failure rates. They identify and disable defective portions of the cache at two granularities: individual words or pairs of bits. Both techniques use the entire cache during high-voltage operation while sacrificing cache capacity during low-voltage operation to reduce the minimum voltage below 500 mV.     

Customized kernel execution on reconfigurable hardware for embedded applications

To conserve space and power as well as to harness high performance in embedded systems, high utilization of the hardware is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable systems in order to realize various customized kernels as execution proceeds. Fortunately, the encountered reconfiguration overheads can be estimated. Therefore, if the scheduling of time-consuming kernels considers also the reconfiguration overheads, an overall performance gain can be obtained. We present our policy, experiments, and performance results of customizing and reconfiguring Field-Programmable Gate Arrays (FPGAs) for embedded kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and MiBench embedded benchmark kernels show high performance using our main policy, when considering reconfiguration overheads. Our policy reduces the required reconfigurations by more than 50% as compared to brute-force solutions, and performs within 25% of the ideal execution time while conserving 60% of the FPGA resources. Alternative strategies to reduce the reconfiguration overhead are also presented and evaluated.     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Identifying high perceived value practices of CMMI level 2: An empirical study

ObjectiveIn this paper, we present findings from an empirical study that was aimed at identifying the relative “perceived value” of CMMI level 2 specific practices based on the perceptions and experiences of practitioners of small and medium size companies. The objective of this study is to identify the extent to which a particular CMMI practice is used in order to develop a finer-grained framework, which encompasses the notion of perceived value within specific practices.MethodWe used face-to-face questionnaire based survey sessions as the main approach to collecting data from 46 software development practitioners from Malaysia and Vietnam. We asked practitioners to choose and rank CMMI level 2 practices against the five types of assessments (high, medium, low, zero or do not know). From this, we have proposed the notion of ‘perceived value’ associated with each practice.ResultsWe have identified three ‘requirements management’ practices as having a ‘high perceived value’. The results also reveal the similarities and differences in the perceptions of Malaysian and Vietnamese practitioners with regard to the relative values of different practices of CMMI level 2 process areas.ConclusionsSmall and medium size companies should not be seen as being “at fault” for not adopting CMMI – instead the Software Process Improvement (SPI) implementation approaches and its transition mechanisms should be improved. We argue that research into “tailoring” existing process capability maturity models may address some of the issues of small and medium size companies.     

Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

Sulphur dioxide loadings over megacity Lahore (Pakistan) and adjoining region of Indo-Gangetic Basin

This article presents spatial and temporal variations of planetary boundary layer (PBL) sulphur dioxide (SO₂) over megacity Lahore and adjoining region, a typical representative area in the Indo-Gangetic Basin (IGB) largely influenced by transported volcanic SO₂ from Africa, Middle East, and southern Europe, by using data retrieved from satellite-based Ozone Monitoring Instrument (OMI) during October 2004–September 2015. We find a positive trend of 2.4% per year (slope 0.01 ± 0.005 with y-intercept 0.35 ± 0.03 Dobson Unit (DU), correlation coefficient r = 0.55 and 2-tailed p-value at 0.1) of OMI-SO₂ column with the average value of 0.4 ± 0.05 DU. Strong seasonality of OMI-SO₂ column is observed over the region linked with local meteorology, patterns of anthropogenic emissions, crop residue burning, and vegetation cover. There exists a seasonal high value in winter 0.56 ± 0.24 DU with a peak in December 0.67 ± 0.26 DU. The seasonal lowest value is observed to be 0.29 ± 0.11 DU in wet summer with minimum value in July 0.25 ± 0.06 DU. High growth rates of OMI-SO₂ column over the study region have been observed in January, June, October, and December ranging from 5.7% to 11.6% per year. Satellite data show elevated OMI-SO₂ columns in 2007, 2008, 2011, and 2012 largely contributed by trans-boundary volcanic SO₂. A detailed analysis of volcanic SO₂ transported from Africa and Middle East (Jabal Al-Tair, Dalaffilla, and Nabro volcanoes) over the study area is presented. Air mass trajectories suggest the presence of long-range transported volcanic SO₂ at high altitude levels over Lahore and IGB region during the volcanic episodes. The SO₂ enhancements in PBL during winter season are generally due to significant vertical downdraft of high-altitude volcanic SO₂. For the first time, we present significant influence of volcanic SO₂ from southern Europe (Mt. Etna volcano) reaching over the study area. Daily mean OMI-SO₂ levels up to 21.4, 10.0, 5.6, and 2.4 DU have been noticed due to the eruptions from Dalaffilla, Mt. Etna, Nabro, and Jabal Al-Tair volcanoes, respectively.     

Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh

The environmental and societal impacts of tropical cyclones could be reduced using a range of management initiatives. Remote sensing can be a cost effective, accurate, and potential tool for mapping the multiple impacts caused by tropical cyclones using high-to-moderate spatial resolution (5–30 m) satellite imagery to provide data on the following essential parameters – evacuation, relief, and management of natural resources. This study developed and evaluated an approach for assessing the impacts of tropical cyclones through object-based image analysis and moderate spatial resolution imagery. Pre- and post-cyclone maps of artificial and natural features are required for assessing the overall impacts in the landscape that could be acquired by mapping specific land cover types. We used the object-based approach to map land-cover types in pre- and post-cyclone Satellite Pour l’Observation de la Terre (SPOT) 5 image data and the post-classification comparison technique to identify changes in the particular features in the landscape. Cyclone Sidr (2007) was used to test the applicability of this approach in Sarankhola Upazila in Bangladesh. The object-based approach provided accurate results for classifying features from pre- and post-cyclone satellite images with an overall accuracy of 95.43% and 93.27%, respectively. Mapped changes identified the extent, type, and form of cyclone induced impacts. Our results indicate that 63.15% of the study area was significantly affected by cyclone Sidr. The majority of mapped damage was found in vegetation, cropped lands, settlements, and infrastructure. The damage results were verified through the high spatial resolution satellite imagery, reports and pictures that were taken after the cyclone. The methods developed may be used in future to assess the multiple impacts caused by tropical cyclones in Bangladesh and other similar environments for the purposes of tropical cyclone disaster management.