Event-Triggered Differentially Private Average Consensus for Multi-agent Network

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems(MANSs). Based on the MANSs,a new distributed differentially private consensus algorithm(DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.

Novel multi‐domain attention for abstractive summarisation

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

On-road pollutant emission and fuel consumption characteristics of buses in Beijing

On-road emission and fuel consumption （FC） levels for Euro Ⅲ and Ⅳ buses fueled on diesel and compressed natural gas （CNG） were compared, and emission and FC characteristics of buses were analyzed based on approximately 28,700 groups of instantaneous data obtained in Beijing using a portable emissions measurement system （PEMS）. The experimental results revealed that NOx and PM emissions from CNG buses were decreased by 72.0% and 82.3% respectively, compared with Euro IV diesel buses. Similarly, these emissions were reduced by 75.2% and 96.3% respectively, compared with Euro III diesel buses. In addition, CO2, CO, HC, NOx, PM emissions and FC of Euro IV diesel buses were reduced by 26.4%, 75.2%, 73.6%, 11.4%, 79.1%, and 26.0%, respectively, relative to Euro Ⅲ diesel buses. The CO2, CO, HC, NOx, PM emissions and FC factors all decreased with bus speed increased, while increased as bus acceleration increased. At the same time, the emission/FC rates as well as the emission/FC factors exhibited a strong positive correlation with the vehicle specific power （VSP）. They all were the lowest when VSP 〈 0, and then rapidly increased as VSP increased. Furthermore, both the emission/FC rates and emission/FC factors were the highest at accelerations, higher at cruise speeds, and the lowest at decelerations for non-idling buses. These results can provide a base reference to further estimate bus emission and FC inventories in Beijing.

Monitoring and predicting the soil water content in the deeper soil profile of Loess Plateau,China

Estimation of soil water content(SWC)in deep soil profiles is of crucial importance for strategic management of water resource for sustainable land use in arid and semi-arid zones,as well as for soil and water conservation.Soil properties have a very important effect on SWC.This study aimed to analyze the influence of soil particle size on SWC,for the first time using soil particle size to estimate SWC in deep soil profiles.SWC was measured mainly in farmland,natural grasslands and plantations of Caragana from the surface to more than 20 m depth.The same soil samples were also tested for particle size.The results show that the soil desiccation is formed in the caragana forest in 3–18 m soil layers,but almost no formation in 18–24 m layers;water content of farmland and grassland is different in all soil profiles although they are both shallow rooted plants.Correlation analysis indicated that SWC could be well predicted by clay content and the close correlation between SWC and clay content yielded a coefficient of determination(R^(2))of 0.82 and 0.72,respectively,for farmland and grassland.After multiple regression analysis,a regression model was built using SWC,clay content and sand content data,giving R^(2)=0.66.The model provided reliable estimates of SWC profile based on textural class.This can assist in estimating water depletion by vegetation,by comparing moisture of farmland and grassland soils with that of plantation forests,and in selecting sustainable land use of arid land.