Predicting Transportation Carbon Emission with Urban Big Data

Transportation carbon emission is a significant contributor to the increase of greenhouse gases, which directly threatens the change of climate and human health. Under the pressure of the environment, it is very important to master the information of transportation carbon emission in real time. In the traditional way, we get the information of the transportation carbon emission by calculating the combustion of fossil fuel in the transportation sector. However, it is very difficult to obtain the real-time and accurate fossil fuel combustion in the transportation field. In this paper, we predict the real-time and fine-grained transportation carbon emission information in the whole city, based on the spatio-temporal datasets we observed in the city, that is taxi GPS data, transportation carbon emission data, road networks, points of interests (POIs), and meteorological data. We propose a three-layer perceptron neural network (3-layerPNN) to learn the characteristics of collected data and infer the transportation carbon emission. We evaluate our method with extensive experiments based on five real data sources obtained in Zhuhai, China. The results show that our method has advantages over the well-known three machine learning methods (Gaussian Naive Bayes, Linear Regression, and Logistic Regression) and two deep learning methods (Stacked Denoising Autoencoder and Deep Belief Networks)

Brusatol

The title compound, C26H32O11, is composed of an α,β-unsaturated cyclo­hexa­none ring (A), two cyclo­hexane rings (B and C), a six-membered lactone ring (D) and tetra­hydro­furan ring (E). Ring A exists in a half-chair conformation with a C atom displaced by 0.679 (2) Å from the mean plane through the remaining five atoms. Ring B exists in a normal chair conformation. Both rings C and D exist in a twisted-chair conformation due to the O-atom bridge and the carbonyl group in rings C and D, respectively. Ring E shows an envelope conformation with a C atom displaced by 0.761 (1) Å from the mean plane through the remaining five atoms. The ring junctions are A/B trans, B/C trans, C/D cis and D/E cis. An intra­molecular O—H⋯O hydrogen bond occurs. In the crystal, O—H⋯O hydrogen bonds involving the hy­droxy, lactone and ester groups and C—H⋯O inter­actions are observed

Deca : a garbage collection optimizer for in-memory data processing

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca,1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2× to 22.7× speedup in terms of execution time in cases without data spilling and 16× to 41.6× speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems

Effects of Transcutaneous Electrical Acustimulation on Refractory Gastroesophageal Reflux Disease

Objective. To investigate effects and possible mechanisms of transcutaneous electrical acustimulation (TEA) performed by a wearable watch-size stimulator for refractory gastroesophageal reflux disease (RGERD). Methods. Twenty patients diagnosed as RGERD were enrolled in the study and randomly divided into four groups: esomeprazole group (Group A), esomeprazole combined with TEA group (Group B), esomeprazole combined with sham-TEA group (Group C), and esomeprazole combined with domperidone group (Group D). HRM and 24 h pH-impedance monitoring and GerdQ score were used to measure related indexes before and after treatment. Results. (1) TEA significantly increased LESP, compared with PPI treatment only or PPI plus sham-TEA. After pairwise comparison, LESP of Group B was increased more than Group A (P=0.008) or Group C (P=0.021). (2) PPI plus TEA decreased not only the number of acid reflux episodes but also the number of weak acid reflux episodes (P=0.005). (3) Heartburn and reflux symptoms were improved more with PPI + TEA than with PPI treatment only or PPI plus sham-TEA (GerdQ scores, P=0.001). Conclusion. TEA can improve symptoms in RGERD patients by increasing LESP and decreasing events of weak acid reflux and acid reflux; addition of TEA to esomeprazole significantly enhances the effect of TEA