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The Linked Data Benchmark Council's Financial Benchmark (LDBC FinBench) is a new effort that defines a graph database benchmark targeting financial scenarios such as anti-fraud and risk control. The benchmark has one workload, the Transaction Workload, currently. It captures OLTP scenario with complex, simple read queries and write queries that continuously insert or delete data in the graph. Compared to the LDBC SNB, the LDBC FinBench differs in application scenarios, data patterns, and query patterns. This document contains a detailed explanation of the data used in the LDBC FinBench, the definition of transaction workload, a detailed description for all queries, and instructions on how to use the benchmark suite., Comment: For the source code of this specification, see the ldbc_finbench_docs repository on Github. arXiv admin note: substantial text overlap with arXiv:2001.02299

Indoor positioning based on the received signal strength index of ZigBee has received more and more attention due to its low cost, low hardware power consumption and easy implementation. However, due to the existence of multi-path and shadow effects, traditional positioning algorithms often cannot achieve better positioning results. Support vector regression can use these relative errors as the characteristics of the fingerprint database to establish a regression map between the RSSI values and the positioning coordinates. Based on this, this paper proposes a genetic algorithm optimization support vector regression (GA-SVR) method to solve the problem of low ZigBee positioning accuracy. The penalty vector $$C$$ , RBF kernel width $$\sigma$$ , and loss function variable $$\in$$ of support vector regression are optimized by genetic algorithm in the proposed method, so that support vector regression achieves the best position prediction performance. Firstly, the training data is clustered by Gaussian hybrid clustering algorithm to establish a fingerprint database. Then the GA-SVM classifier is used to classify the test points. Finally, the coordinates of the test points are regressed and predicted by the GA-SVR model. The simulation and experiment in the actual scene prove the effectiveness of the proposed method. The experimental results show that compared with PSO-SVR, GS-SVR, PLS-SVR, SVR and WKNN algorithms, the GA-SVR algorithm has higher positioning accuracy.

This paper reports a microelectromechanical systems (MEMS) optical fiber accelerometer with an integrated blazed grating as a wavelength modulation component. The accelerometer includes a V-shaped cantilever beam, a vertically symmetrical proof mass, and an integrated blazed grating. The accelerometer prototype is fabricated with bulk micromachining processes and is tested. The bandwidth of the accelerometer is 200 Hz. The optical sensitivity and nonlinearity of the measure range ±5.2 g are 1.63 nm/g at 100 Hz and 0.06%, respectively. The x- and y-axes cross-axis sensitivities are 2% and 4.5%, respectively. In the full temperature range of −20 °C–200 °C, the temperature sensitivity is 2.8 pm/°C. Thanks to the above-mentioned characteristics, the proposed MEMS accelerometer could have a wide range of applications, such as rail traffic and attitude heading sensing applications.

A series of SAPO-34/SAPO-18 molecular sieves with an intergrowth CHA/AEI crystal structure were synthesized under hydrothermal conditions in the presence of a single or a mixture of N,N-diisopropylethylamine (DIEA), triethylamine (TEA), and diethylamine (DEA) as template agents. The effects of different types, amounts, and combinations of templates on the structure, morphology, and surface acidity of SAPO-34/SAPO-18 molecular sieves were investigated by XRD, XRF, BET, SEM, and NH3-TPD. These results show that the intergrowth ratio of CHA and AEI structures in SAPO-34/SAPO-18 molecular sieves depends on the nature and composition of mixture template largely. For the methanol to olefins (MTO) reaction, the obtained SAPO-34/SAPO-18 with different intergrowth ratios of CHA and AEI structures exhibits different catalytic performances varying with the acid properties and structural differences. Sample S6 with hierarchical structure and suitable acidity exhibits higher selectivity to ethylene plus propylene and ...

At present, China is the world’s largest apple producer. After being picked, apples rely on its own nutrients to maintain life activities, and with the consumption of energy, the freshness will gradually decrease. Nowadays, the storage and preservation awareness, facilities and methods of the apple processing industry and households are weak and lacking. Apples are prone to decay and deterioration, which seriously affects the flavor, nutritional value and food safety. This paper studies the artificial intelligence recognition technology based on sensors array apple freshness characteristics. The main research contents include: artificial intelligence odor recognition theory research and construction of sensor array information acquisition system. Combined with theoretical experiments, the research establishes apple odor characteristic parameter evaluation model, uses dielectric features, odor recognition technology and node location information to achieve apple freshness artificial intelligence recognition. This project has important significance in both theoretical research and practical application.

Uncovering the genetic basis and optimization of root system architecture (RSA) traits are crucial for modern wheat breeding. Genome-wide association mapping has become a powerful approach to dissect the genetic architecture of complex quantitative traits. In the present study, RSA traits, viz. total root length (TRL), total root area, average root diameter and number of root tips in a diverse panel of 165 elite wheat cultivars from the Yellow and Huai River Valley Facultative Wheat Region of China were evaluated as seedlings in hydroponic culture and in the field to identify loci significantly associated with those traits. The diverse panel was genotyped using the wheat 90 K and 660 K SNP arrays, and a genome-wide association study using a mixed linear model identified 28 and 4 loci significantly associated with RSA traits in hydroponic culture and in the field, explaining 8.8–15.6% and 8.9–12.6% of phenotypic variances, respectively. Seven loci for RSA traits co-located with known genes or quantitative trait loci (QTL), whereas the other 22 were potentially new. Linear regression between favorable alleles and RSA traits suggested that QTL pyramiding should be effective in optimizing root systems. Two candidate genes for RSA traits were identified, including genes encoding calcium dependent protein kinase and E3-Ubiquitin protein ligase. This study provides novel insights into the genetic architecture of RSA traits.

A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simultaneously computes centerline distance maps and detects branch endpoints. The method generates single-pixel-wide centerlines with no spurious branches. It handles arbitrary tree-structured object with no prior assumption regarding depth of the tree or its bifurcation pattern. It is also robust to substantial scale changes across different parts of the target object and minor imperfections of the object's segmentation mask. To the best of our knowledge, this is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object. The proposed method is validated in coronary artery centerline extraction on a dataset of 620 patients (400 of which used as test set). This application is challenging due to the large number of coronary branches, branch tortuosity, and large variations in length, thickness, shape, etc. The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask. Compared to a state-of-the-art traditional minimal path approach, our method improves patient-level success rate of centerline extraction from 54.3% to 88.8% according to independent human expert review., Comment: Accepted by the international conference on Information Processing in Medical Imaging (IPMI) 2019

Pesticide residue is a major problem in food safety. It is particularly important to detect pesticides in fruits and vegetables quickly and effectively The near infrared spectroscopy (NIR) technique was explored as a technique to predict organochlorine pesticides in Mesona chinensis Benth. Fourty-three samples were sacaned powder in a Antaris II FT-NIR. Samples were split into two sets, one set used as calibration (n=35) where the remaining samples (n=S) were used as validation set. The hexachlorocyclohexane (BHC). dichlorodiphenyltricliloroethane (DDT) and organochlorine residues were determined by gas chromatography as the reference ratio. The prediction model of total BHC. DDT and organochlorine of mesona chinensis benth was established by partial least square (PLS) method, which were optimized by the optimal spectral band range, spectral pretreatment and regression. Moreover, the prediction ability of the model was verified by validation set. The models gave the results that RMSEC. Rc. RMSEP and Rp of BHC. DDT and organochlorine residues respectively were 0.996. 0.9384, 0.151 and 0.9964; 1.21, 0.9889. 0.409. 0.9852: 2.21. 0.9423, 0.481, 0.9953. The relative errors of the predicted values are within 0.68% - 0.38% and - 0.34% - 6.88% and - 0.46% - 0.58% of the total validation set. It can be seen that the three models have good prediction effect, high precision and high stability, that is. NIR can be used as a simple, non-destructive, reliable and rapid method for the determination of organochlorine pesticide residues in mesona chinensis benth.

The effectiveness of perforated liner with bias flow on the control of combustion instability is investigated. Combustion instabilities result from the coupling between acoustic waves and unsteady combustion heat release. Sometimes the phenomenon happens in afterburners of aeroengine and rocket engine, and it always causes damage to flame holders, liner sections and other engine components. Passive methods, such as perforated liner, are often used to suppress such instabilities in application. In this article, first, a burner testbed is built in order to study the characteristic of this phenomenon. The unstable frequencies and unstable area are investigated experimentally. Then an analytical model, based on “transfer element method”, is developed and the numerical results are compared with those from experiments. At last the perforated liner is applied to the burner to suppress the instabilities. The results show that the sound pressure can be greatly reduced by the perforated liner.

Intermittent hydrothermal treatment was introduced into the synthesis of SAPO-34 molecular sieves to control the nucleation and the growth in the crystallization. The effect of the crystallization time, the order of long-time and short-time crystallization in two-stage crystallization, and frequency in multi-stage crystallization on synthesis, physicochemical properties and catalytic performance for conversion of methanol to light olefins (MTO) has been studied. The results show that pure SAPO-34 can be obtained with increasing crystallization time. The interruption of the initial crystallization is more beneficial for improving the Si distribution and the MTO catalytic performance of SAPO-34 molecular sieves. The sample obtained by repeatedly alternating heating and cooling during crystallization shows smaller particle size, higher acidity, longer lifetimes and higher yields of ethylene than that obtained by the conventional continuous crystallization at high temperature.

Load curve data is a type of time series data which records the electric energy consumptions at time points and plays an important role in operation and planning of power systems. Unfortunately, load curves always contain abnormal, noisy, unrepresentative and missing data due to various random factors. It is crucial to power systems to identify and repair corrupted and unrepresentative data before load curve data can be used for planning and modeling. In this thesis we present a new class of X-outliers that have abnormal power consumption levels related to periodicity (X-axis) and propose a novel solution to detect these outliers. The underlying assumption is that the data set follows a periodicity and the length (not the pattern) of the periodicity is known. This is the case for most real load curve data collected at BC Hydro. In the above the periodicity is assumed to be known for X-outlier detection. In some other applications, however, the periodicity needs to be discovered. The latter is the case when the periodicity evolves, when a new time series is collected, or when conditions that affect time series have changed. Periodicity detection for time series has important applications in forecasting, planning, trend detection, and outlier detection. For time series with unknown periodicity, X-outlier detection could still be performed after the periodicity is detected. Thus X-outlier detection and periodicity detection are highly related and periodicity detection could be considered as a pre-processing step of X-outlier detection for time series with unknown periodicity. Therefore, in this thesis, we also propose a trend based periodicity detection algorithm for time series data with unknown periodicity. This approach is trend preserving and noise resilient. Real load curve data in the BC Hydro system is used to demonstrate the eectiveness and accuracy of the proposed methods.