2-Methyl-1-phenyl­sulfonyl-1H-indole-3-carbaldehyde

In the title compound, C16H13NO3S, the sulfonyl-bound phenyl ring forms a dihedral angle of 84.17 (6)° with the indole ring system. An intra­molecular C—H⋯O hydrogen bond generates an S(6) ring motif. The crystal structure exhibits weak inter­molecular C—H⋯O hydrogen bonds and π–π inter­actions between the five- and six-membered rings of the indole group [centroid–centroid distance = 3.6871 (9) Å]

Ethyl 1-benzene­sulfonyl-2-[(E)-2-(2-methyl­phen­yl)ethen­yl]indole-3-carboxyl­ate

In the title compound, C26H23NO4S, the phenyl, tolyl and ester groups make dihedral angles of 82.28 (5), 77.67 (6) and 8.52 (6)°, respectively, with the indole ring system. The S atom of the sulfonyl group is displaced by 0.1968 (4) Å from the indole mean plane. The mol­ecular structure is stabilized by weak intra­molecular C—H⋯O inter­actions. The crystal structure structure features short intramolecular C—H⋯O contacts and π–π stacking inter­actions between the phenyl and tolyl groups [centroid–centroid distance = 3.9448 (11) Å]

3-Iodo-2-methyl-1-phenyl­sulfonyl-1H-indole

In the title compound, C15H12INO2S, the sulfonyl-bound phenyl ring forms a dihedral angle 82.84 (9)° with the indole ring system. The mol­ecular structure is stabilized by a weak intra­molecular C—H⋯O hydrogen bond. The crystal structure exhibits weak inter­molecular C—H⋯π inter­actions and π–π inter­actions between the indole groups [centroid–centroid distance between the five-membered and six-membered rings of the indole group = 3.7617 (18) Å]

Detecting popular subjects and optimizing microblog performance using the Hybrid Hadoop Framework

Active users of social media networks all over may dynamically create intolerable stuff. Big data helps to sustain the enormous volume of information on social media channels. Big data is advanced on Hadoop-based cloud systems using its fault-tolerance and dependability. Hadoop is the basic platform for big data analytics. Using Hadoop has a main disadvantage in terms of handling the enormous number of configuration metrics handling. Driven by cloud-based Apache Spark, the hybrid Hadoop Framework is proposed in this paper to enhance big data processing by means of key parameter regulation including workload, response time, network bandwidth, and the hot topic detection mechanism especially tailored for the microblog into the big data. To manage the big volumes, we deliberately construct the MapReduce jobs to precisely identify hot subjects. According to the experimental results, the proposed system\u27s accuracy is quite high when compared to related methods

AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA & OPTICAL DATA

Abstract. This study was carried out to estimate the area of cotton and maize crops in Permabalur district of Tamil Nadu using microwave and optical data. Permabalur was selected as the study area, as it is the largest cotton and maize producing district in Tamil Nadu. The multi-temporal Sentinel-1A SAR data was acquired from 09th July, 2016 to 17th January, 2017 as it coincides with the crop calendar of these crops. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data were compared. The cloud free Landsat 8 data acquired on 7th October 2016 was fused with the Vertical–Vertical (VV) and Vertical-Horizontal (VH) polarized data of 13th October and classified. Unsupervised classification approach was adopted to classify the cotton and maize pixels. The highest accuracy of 72.73% and 76.24% were achieved in VV polarization + Landsat 8 data and VH polarization + Landsat 8 data respectively. The cotton and maize areas were estimated to be 20,218 ha and 28,032 ha respectively. It is also evident that VH polarization fused with optical data is better in discriminating the cotton and maize crop than VV polarization fused with optical data. </jats:p