Search

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. [ABSTRACT FROM AUTHOR]

Abstract The highly sensitive hydrogen sulfide (H 2 S) gas sensor was developed from a semiconductor composite structure composed of flower-like WO 3 microspheres whose surface was modified with CuO nanoparticles. The composites were prepared by a simple two-step hydrothermal method. The morphology and structure of the samples were examined by XRD, SEM, TEM and BET. The sensing properties of the samples on H 2 S at different operating temperatures were examined, and the performance of WO 3 /CuO with different molar ratios was also investigated. Gas-sensing tests show that WO 3 /CuO composites exhibit high response, excellent selectivity and fast response to ppm-level H 2 S at low operating temperature of 80 °C. Beyond that, WO 3 /CuO composites with 7:1 M ratio was responsive to 5 ppm H 2 S at room temperature (28 °C), and its sensitivity could be reached 16. In addition, the gas sensing mechanism of flower-like WO 3 microspheres modified with CuO nanoparticles was also thoroughly discussed. The enhanced sensing properties of composites were mainly attributed to the morphology structure and p-n heterojuction between flower-like WO 3 and CuO nanoparticles. Graphical abstract Image 1 Highlights • Flower-like WO 3 /CuO composite was synthesized by a facile two-step hydrothermal method. • Flower-like WO 3 /CuO composite microspheres could detect H 2 S gas at room temperature. • Highly sensitive and selective H 2 S gas sensor with fast response obtained at optimized low operating temperature (80 °C). • The gas sensing mechanism by the formation of p-n junction is thoroughly discussed in the manuscript. [ABSTRACT FROM AUTHOR]

• A novel MOF-derived method is used for the synthesis of bamboo-like CuO/In 2 O 3 for H 2 S detection. • The pristine In2O3 based sensor derived from MOFs structure reduces the optimized working temperature to 70 °C. • The CuO/In 2 O 3 (3.5 wt%) based sensor exhibits outstanding gas sensing performances toward H 2 S. • The mechanism of enhanced gas sensing performance is thoroughly discussed in the manuscript. Hydrogen sulfide (H 2 S) sensors with excellent response and selectivity are in great demand to monitor its concentration changes in the real environment, especially at an ultra-trace level. This work presents a metal-organic framework (MOF)-derived metal oxide prepared via the solvothermal method and the developed sensors based on such materials exhibits enhanced gas-sensing performance. The bamboo-like CuO/In 2 O 3 derived from Cu2+-impregnated CPP-3 were investigated through structural analyses, and it confirms that the n-type In 2 O 3 and p-type CuO were successfully combined and heterojunctions were formed at CuO/In 2 O 3 interfaces. The gas-sensing properties of CuO/In 2 O 3 towards H 2 S were evaluated, and the sensor based on CuO/In 2 O 3 with 3.5 wt% of Cu to CPP-3(In) is found to exhibit excellent H 2 S response (R air /R gas = 229.3–5 ppm), which are 8.5 times higher than that of with pristine In 2 O 3. It also discloses low detection limits (200 ppb), low operating temperature (70 °C) and superior selectivity against other interfering gases. The gas sensing mechanism is thoroughly discussed and CuO/In 2 O 3 could be considered as a novel and promising material for the practical application to selectively detect H 2 S at low operating temperature. [ABSTRACT FROM AUTHOR]