Efficient Multi-way Theta-Join Processing Using MapReduce

Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the (key,value) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.Comment: VLDB201

A Comparative Study of Supergrid and Superblock Urban Structure in China and Japan Rethinking the Chinese Superblocks: Learning from Japanese Experience

The Supergrid and Superblock together constitute a grid-and-cell urban structure that is especially evident in China and Japan. The Supergrid is a large-scale net of wide roads that defines a series of cells or Superblocks, each containing a network of narrower streets. While common in both countries, there are no comparative morphological studies. As a crucial contribution to urban design, this thesis places the structures in their cultural contexts and examines them against a group of post-1960 theories that focus on interrelationships between urban structure and functions. Here, they are synthesized as ‘Interconnection theory’ and the source of qualitative and quantitative methods (including Space Syntax) used to examine form-function interrelationships by understanding levels of Integration, Connection and Interaction in two Superblocks in each country. Particular emphasis is on the relationships between street networks and distribution of functions/activities. Primary research findings indicate that 1) Supergrid/Superblock systems are strongly rooted in Eastern culture, with Supergrid systems providing multi-directional global movement across wide urban areas in both countries. 2) However, the work reveals different types of street structures and functional patterns within Superblocks, with (Chinese) ‘wall’ and (Japanese) ‘floor’ spatial conceptions underlying differences. 3) These differing internal structures within the Superblocks have a deterministic impact on the spatial distribution of human activities. Clear but divergent patterns are displayed in the Superblocks with strong interrelationships between the street network and distribution of activities in the Japanese cases but less distinct ones in the Chinese: this is linked to China’s wall and gate structure that is absent in Japan

Low-carbon scenario analysis on urban transport of one metropolitan in China in 2020

Purpose: This paper discussed possible ways of implementing effective energy conservation and GHG emission reduction measures by providing: the forecasts of mid-to-long term city-wide carbon emission rate; and the analysis of potential low-carbon transport solutions. Design/methodology/approach: According to the characteristics of the transport system in Beijing, based on the review and application analysis of existing transport energy and GHG emission calculation models, the comprehensive carbon emission calculation model established. Existing data were utilized with regression analysis to project the prospective traffic data in the baseline scenario at the target year of 2020 to calculate the emission amount. Four low-carbon scenarios were set in accordance with the goal of “low carbon transportation, green trip”, and the effectiveness of each low-carbon scenario was evaluated by comparing them with the baseline scenario in terms of the respective GHG emission rate. Findings: Under the current developing trend in policy environment and technical specifications, the total projected GHG (CO2) emissions from transport sector at 2020 in Beijing will reach 24.69 million t CO2; private-vehicle is the major contributor among all transport modes at 15.96 million t CO2. Practical implications: Limiting the growth in private-vehicle ownership, reducing the frequency of mid-to-long range travel and the average trip distance, and prompting the public transit oriented policies are all possible solutions to reduce carbon emission. The most effective practice involves a shift in public travel behavior. Originality/value: This paper presents a method to forecast the mid-to-long term city-wide carbon emission rate; and provides some potential low-carbon transport solutions.Peer Reviewe

Analysis of frequent trading effects of various machine learning models

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy

Constructing entanglement witnesses for infinite-dimensional systems

It is shown that, every entangled state in an infinite-dimensional composite system has a simple entanglement witness of the form $\alpha I+T$ with $\alpha$ a nonnegative number and $T$ a finite rank self-adjoint operator. We also provide two methods of constructing entanglement witness and apply them to obtain some entangled states that cannot be detected by the PPT criterion and the realignment criterion.Comment: 15 page

A human health risk assessment of rare earth elements in soil and vegetables from a mining area in Fujian Province, Southeast China

AbstractContaminated food through dietary intake has become the main potential risk impacts on human health. This study investigated concentrations of rare earth elements (REEs) in soil, vegetables, human hair and blood, and assessed human health risk through vegetables consumption in the vicinity of a large-scale mining area located in Hetian Town of Changting County, Fujian Province, Southeast China. The results of the study included the following mean concentrations for total and bio-available REEs of 242.92±68.98 (135.85–327.56)μgg−1 and 118.59±38.49 (57.89–158.96)μgg−1 dry weight (dw) in agricultural soil, respectively, and total REEs of 3.58±5.28 (0.07–64.42)μgg−1 dw in vegetable samples. Concentrations of total REEs in blood and hair collected from the local residents ranged from 424.76 to 1274.80μgL−1 with an average of 689.74±254.25μgL−1 and from 0.06 to 1.59μgg−1 with an average of 0.48±0.59μgg−1 of the study, respectively. In addition, a significant correlation was observed between REEs in blood and corresponding soil samples (R2=0.6556, p<0.05), however there was no correlation between REEs in hair and corresponding soils (p>0.05). Mean concentrations of REEs of 2.85 (0.59–10.24)μgL−1 in well water from the local households was 53-fold than that in the drinking water of Fuzhou city (0.054μgL−1). The health risk assessment indicated that vegetable consumption would not result in exceeding the safe values of estimate daily intake (EDI) REEs (100−110μgkg−1d−1) for adults and children, but attention should be paid to monitoring human beings health in such rare earth mining areas due to long-term exposure to high dose REEs from food consumptions