A charge sharing–based switching scheme for SAR ADCs

This paper presents an energy-efficient switching scheme for successive approximation register (SAR) analogue-to-digital converter (ADC). The proposed scheme employs charge recycling method to keep the capacitor arrays free of transitional energy between bit generations except reset phase. In comparison with the conventional switching scheme, the proposed one achieves 100% transitional energy saving without considering reset phase. In addition, configuration of a 10-bit SAR ADC shows that the proposed switching scheme reduces the capacitor area by 25% compared with the conventional switching scheme.     

Beta-lactamase-producing mutants of Streptococcus cremoris

Penicillin-resistant mutants were isolated for six strains of Streptococcus cremoris used in commercial Cheddar cheese manufacture after treatment with N-methyl-N-nitro-N-nitrosoguanidine. The resistant mutants had an elevated minimal growth inhibitory concentration, 2.5 micrograms (4.13 units)/ml, for penicillin G and other beta-lactam antibiotics as compared with the penicillin-susceptible parent strains, which were each sensitive to .05 micrograms (.08 units)/ml. Penicillin resistance was due to the production of beta-lactamase. Plasmid DNA was not demonstrated in partially purified lysates of four mutants. Mutants had normal cellular morphology but altered phage sensitivity patterns. All except one strain were able to support complete phage adsorption. Resistance was retained after 20 passages in absence of penicillin.     

Temporal and Spatial Assessment of Supply and Demand of the Water-yield Ecosystem Service for Water Scarcity Management in Arid to Semi-arid Ecosystems

Water scarcity is one of the problems affecting people’s livelihoods in arid and semi-arid areas, requiring a sustainable balance between water demands and water resources. This study was carried out to assess temporal and spatial distribution of water supply and demand in order to help managers to overcome water scarcity in Jiroft basin, southeastern Iran. Spatial supply and demand of water were mapped and standardized rainfall index (SPI) was used to assess drought for a 20 years period (1994–2014). Supply and demand of water were matched in 23% of the basin area, mostly concentrated in the cold zones. Water supply was reduced up to 80% during dry years, declining water supply-demand matching to 5% of the basin area. Shrub-grass rangelands and deciduous woodlands were the most valuable land covers for conservation with $ 1,100 and $ 936 per hectare water prices respectively. Water value dropped more than 72% in mismanaged ecosystems (p?<?0.01). Our finding showed that water supply-demand ratio can be used as a proxy of ecosystem health and water-yield, which can provide a good information for water resources managers to reduce the threats of water scarcity in arid and semi-arid regions.

     

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

The Journal of Supercomputing - During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have...     

Design,Facile Synthesis and Characterization of Porphyrin-Zirconium-Ferrite@SiO2 Core-Shell and Catalytic Application in Cyclohexane Oxidation

Silicon - In this study, a new magnetic ZrFe2O4@SiO2-TCPP nanocatalyst with high efficiency was used for the oxidation of cyclohexane to cyclohexanone (Ke) and cyclohexanol (Al). The mesoporous...     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Effect of fiber angle on LYP steel shear walls behavior简

Steel shear wall（SSW） was properly determined using numerical and experimental approaches.The properties of SSW and LYP（low yield point） steel shear wall（LSSW） were measured.It is revealed that LSSW exhibits higher properties compared to SSW in both elastic and inelastic zones.It is also concluded that the addition of CFRP（carbon fiber reinforced polymers） enhances the seismic parameters of LSSW（stiffness,energy absorption,shear capacity,over strength values）.Also,stress values applied to boundary frames are lower due to post buckling forces.The effect of fiber angle was also studied and presented as a mathematical equation.     

A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

Analytics of learning tactics and strategies in an online learnersourcing environment

Background

The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.

Objectives

To contribute to filling this gap, this study explores how students engage with learnersourcing tasks across a range of course and assessment designs.

Methods

We conducted an exploratory study on trace data of 1279 students across three courses, originating from the use of a learnersourcing environment under different assessment designs. We employed a new methodology from the learning analytics (LA) field that aims to represent students' behaviour through two theoretically-derived latent constructs: learning tactics and the learning strategies built upon them.

Results

The study's results demonstrate students use different tactics and strategies, highlight the association of learnersourcing contexts with the identified learning tactics and strategies, indicate a significant association between the strategies and performance and contribute to the employed method's generalisability by applying it to a new context.

Implications

This study provides an example of how learning analytics methods can be employed towards the development of effective learnersourcing systems and, more broadly, technological educational solutions that support learner-centred and data-driven learning at scale. Findings should inform best practices for integrating learnersourcing activities into course design and shed light on the relevance of tactics and strategies to support teachers in making informed pedagogical decisions.