### 168985 results

Contributors: BURHAN SHAMURAD

Date: 2020-01-19

... Mineral wastes utilised for pretreatment of OFMSW, followed by anaerobic digestion at mesophilic temperature of 37oC in lab scale continuous stirred reactor systems (CSTR). Genomic DNA of samples of digestate from these reactors were used for Illumina Hiseq 16S rRNA analysis.

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Contributors: Tianhua Wu, Yongtao Gao, Yu Zhou, Jianwang Li

Date: 2020-01-18

... Research data of this revised manuscript

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Contributors: thanh Pham Dinh, thang ta bao, Hoang Ngo Viet, Long Nguyen Binh

Date: 2020-01-18

... + The tested data for Inter-Domain Path Computation under Domain Uniqueness constraint (IDPCDU). + On account of no instances were available for IDPC-DU, we made up our minds to generate a set for test instances to evaluate the proposed algorithms. To generate an instance, we first passed three parameter: number of nodes, number of domains and number of edges. After that, we created an array of distinct nodes and an array of distinct domains that satisfied the number of nodes is greater than the length of domain array. Source nodes and terminal node are the first and the last nodes of the nodes array, respectively. With the above arrays, we merged them to make a valid solution called P. Each edge of P was set to the weight one, except the out-edge of the source node is set to the weight two. To add noise to the test instance, for every single node in P, we added some edges to random nodes not in P. Moreover, we created some one-weighed-edges between the the nodes not in P. These traps would make simple greedy algorithms get it harder to find optimal solution. Eventually, we randomly generated edges that have greater values of weight than the value of the length of P. This method guaranteed that P is the optimal solution of the instance. There were two set of instances created, a small set and a large set. + Filename idpc_xx.idpc First line of a file constains two intergers N and D, which are number of nodes and number of domains, respectively. Second line contains two integers s and t, which are the source node and terminal node. Every next line contains four integers u, v, w, d, represents an edge (u,v) has weight w and belong to domain d.

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Contributors: Maurizio Porfiri, Lorenzo Zino, Alessandro Rizzo

Date: 2020-01-18

... Matlab implementation of the proposed algorithm to estimate the model parameters

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Contributors: Xueguang Yuan, Cuicui Wang, Yang'an Zhang, Jinnan Zhang, Gong Chen

Date: 2020-01-18

... The raw data of the transparency of the fixed analyzer method for each fiber is provided. Six different optical fibers are used for test fibers. The optical power spectrum with and without the polarizer is measured by a high precision optical spectrum analyzer (OSA). The resolution of the OSA is 0.01nm. The type and length of the test fiber is used as the folder name. Each data is named as "x.txt" or "x_p.txt" , where "x" is the experiment number. "x.txt" is the data of optical power spectrum without the polarizer, and "x_p.txt" is the data of optical power spectrum without the polarizer.

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Contributors: Yongliang Bai

Date: 2020-01-18

... This data set provides the original input data for crustal thickness inversion, as well as codes and final calculation results

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Contributors: sandra poncet

Date: 2020-01-17

... This file contains the do files and datasets to reproduce the results from the Improving or Disappearing: Firm-Level Adjustments to Minimum Wages in China, published in the Journal of Development Economics, 2018, 135, 20-40.

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Contributors: Pourush Sood, Kaustav Brahma

Date: 2020-01-17

... Presented is an implementation of the SALAD algorithm for dream content analysis through word searching. Helper functions for constructing initial seed word dictionaries are provided in "hyponym_dictionary.py" which will also be used to construct the dictionaries from the seed words. "read_csv.py" reads and pre-processes the dream reports into a dictionary that captures the linguistic features of the words and sentences from the dreams. It also contains an implementation of the Improved Lesk Algorithm. The folder Series/ can be populated with data from any dream journal (you can take data from www.dreambank.net). The required data format is a csv file containing one dream in each row. The code "search_lemmas.py" performs the actual word search. The exact steps of SALAD and the parameters that need to be played around with to obtain the best results are described in the paper. The codes are written in Python 3.6 and can run on Python 3.6 and above.

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Contributors: Sergio Castro-García, Fernando Aragon Rodriguez, Rocío Arias Calderón, Rafael R. Sola-Guirado, Jesús Gil-Ribes

Date: 2020-01-17

... Raw data of the acceleration signals measured in the different vibration paths under different branch stages (Castro-Garcia, Aragon-Rodriguez, Arias-Calderon, Sola-Guirado & Gil-Ribes, Biosystems Engineering, 2020). 1. Description of each vibration path. Identification of the vibration input and output sensors, file name and sample frequency (data/second) for each vibration path. 2. Acceleration data set. Acceleration values in m/s2. The data of each acceleration sensor is shown in 3 columns, corresponding to the X, Y, Z axes.

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Contributors: Mei-Ling Huang, Ting-Yu Ling

Date: 2020-01-17

... The dataset combining four breast density categories with breast mass labeled benign or malignant, there are 8 categories in our classification task. The eight categories are: (1) The category of breast density is 1 and breast mass is benign (Density1+Benign) (2) The category of breast density is 1 and breast mass is malignant (Density1+Malignant) (3) The category of breast density is 2 and breast mass is benign (Density2+Benign) (4) The category of breast density is 2 and breast mass is malignant (Density2+Malignant (5) The category of breast density is 3 and breast mass is benign (Density3+Benign) (6) The category of breast density is 3 and breast mass is malignant (Density3+Malignant) (7) The category of breast density is 4 and breast mass is benign (Density4+Benign) (8) The category of breast density is 4 and breast mass is malignant (Density4+Malignant). In addition, the data contains images before and after data augmentation, and the image matrix was 224 x 224 or 227 x 227.

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