This Dataset contains Year and Registrars of Companies (RoC)-wise statistics of total number of Indian, Foreign and limited liability partnership (LLP) Companies registered in India

Note: Data is not available in MCA Website for Indian and LLP Companies during 2021 November and 2022 March

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for master of computer applications mca in the U.S.

Master of Computer Applications, Maulana Abul Kalam Azad University of Technology |

High Frequency Indicator: The dataset contains year-, month- and state-wise compiled data from the year 2016 to till date on the number of Indian, Foreign and Limited Liability Partnership (LLP) companies registered in India

Note: Data is not available in MCA Website for Indian and LLP Companies during 2021 November and 2022 March

Explore the progression of average salaries for graduates in Master Of Computer Application Mca from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Master Of Computer Application Mca relative to other fields. This data is essential for students assessing the return on investment of their education in Master Of Computer Application Mca, providing a clear picture of financial prospects post-graduation.

Master of Computer Applications (2 Years), Maulana Abul Kalam Azad University of Technology |

Explore the progression of average salaries for graduates in Master In Computer Application (M.C.A) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Master In Computer Application (M.C.A) relative to other fields. This data is essential for students assessing the return on investment of their education in Master In Computer Application (M.C.A), providing a clear picture of financial prospects post-graduation.

This dataset includes peptidase hydrolysis rates from sediments to measure microbial enzyme activities. Links to archived CTD data are also provided. Five substrates linked to a 7-amido-4-methyl coumarin (MCA) fluorophore, one amino acid \u2013 leucine \u2013 and four oligopeptides \u2013 the chymotrypsin substrates alanine-alanine-phenylalanine (AAF) and alanine-alanine-proline-phenylalanine (AAPF), and the trypsin substrates glutamine-alanine-arginine (QAR) and glutamic acid-gylcine-arginine (EGR) \u2013 were used to measure exo- and endo-acting peptidase activities, respectively. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt acquisition_description=Surficial sediments were collected using a multi-corer.

Five substrates linked to a 7-amido-4-methyl coumarin (MCA) fluorophore, one amino acid \u2013 leucine \u2013 and four oligopeptides \u2013 the chymotrypsin substrates alanine-alanine-phenylalanine (AAF) and alanine- alanine-proline-phenylalanine (AAPF), and the trypsin substrates glutamine- alanine-arginine (QAR) and glutamic acid-gylcine-arginine (EGR) \u2013 were used to measure exo- and endo-acting peptidase activities, respectively. Hydrolysis rates of the substrates were measured as an increase in fluorescence as the fluorophore was hydrolyzed from the substrate over time [as in Hoppe, 1993; Obayashi and Suzuki, 2005]. For sediment measurements, peptidase activities were measured in a 1:2 (vol: vol) sediment: autoclaved seawater slurry. 100 micromolar concentrations of substrate were added to triplicate live and single autoclaved killed control incubations. 2 ml of slurry was centrifuged at each timepoint (0, 1h ,2h, and 4 h), filtered, and diluted with 1 ml borate buffer; fluorescence was measured in a 4 ml cuvette.\u00a0 Hydrolysis rates were calculated from the rate of increase of fluorescence in the incubation over time relative to a set of standards of known concentration of fluorophore.

All incubations were conducted at 0 C in the dark. awards_0_award_nid=712358 awards_0_award_number=OCE-1332881 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332881 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Henrietta N Edmonds awards_0_program_manager_nid=51517 cdm_data_type=Other comment=Microbial Activities: sediment MCA Polarstern ARK27-3
P.I.: C. Arnosti (UNC-CH) version: 2018-07-24 NOTE: These data were published in Balmonte et al (2018) Envir. Microbiol. Fig. 4 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 dataset_current_state=Final and no updates defaultDataQuery=&time<now doi=10.26008/1912/bco-dmo.743018.1 Easternmost_Easting=130.5795 geospatial_lat_max=88.809 geospatial_lat_min=79.6502 geospatial_lat_units=degrees_north geospatial_lon_max=130.5795 geospatial_lon_min=31.2277 geospatial_lon_units=degrees_east geospatial_vertical_max=4378.0 geospatial_vertical_min=349.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/743018 institution=BCO-DMO instruments_0_acronym=CTD instruments_0_dataset_instrument_nid=743025 instruments_0_description=The Conductivity, Temperature, Depth (CTD) unit is an integrated instrument package designed to measure the conductivity, temperature, and pressure (depth) of the water column. The instrument is lowered via cable through the water column and permits scientists observe the physical properties in real time via a conducting cable connecting the CTD to a deck unit and computer on the ship. The CTD is often configured with additional optional sensors including fluorometers, transmissometers and/or radiometers. It is often combined with a Rosette of water sampling bottles (e.g. Niskin, GO-FLO) for collecting discrete water samples during the cast. This instrument designation is used when specific make and model are not known. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/130/ instruments_0_instrument_name=CTD profiler instruments_0_instrument_nid=417 instruments_1_acronym=Fluorometer instruments_1_dataset_instrument_nid=743026 instruments_1_description=A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ. instruments_1_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/113/ instruments_1_instrument_name=Fluorometer instruments_1_instrument_nid=484 instruments_1_supplied_name=mini fluorometer instruments_2_acronym=Multi Corer instruments_2_dataset_instrument_nid=743046 instruments_2_description=The Multi Corer is a benthic coring device used to collect multiple, simultaneous, undisturbed sediment/water samples from the seafloor. Multiple coring tubes with varying sampling capacity depending on tube dimensions are mounted in a frame designed to sample the deep ocean seafloor. For more information, see Barnett et al. (1984) in Oceanologica Acta, 7, pp. 399-408. instruments_2_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/51/ instruments_2_instrument_name=Multi Corer instruments_2_instrument_nid=532 instruments_3_dataset_instrument_nid=743047 instruments_3_description=A machine with a rapidly rotating container that applies centrifugal force to its contents, typically to separate fluids of different densities (e.g., cream from milk) or liquids from solids. instruments_3_instrument_name=Centrifuge instruments_3_instrument_nid=629890 metadata_source=https://www.bco-dmo.org/api/dataset/743018 Northernmost_Northing=88.809 param_mapping={'743018': {'Latitude': 'master - latitude', 'depth_m': 'flag - depth', 'Longitude': 'master - longitude', 'ISO_DateTime_UTC': 'flag - time'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/743018/parameters people_0_affiliation=University of North Carolina at Chapel Hill people_0_affiliation_acronym=UNC-Chapel Hill people_0_person_name=Carol Arnosti people_0_person_nid=661940 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Nancy Copley people_1_person_nid=50396 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=Patterns of activities projects_0_acronym=Patterns of activities projects_0_description=NSF Award Abstract: Heterotrophic microbial communities are key players in the marine carbon cycle, transforming and respiring organic carbon, regenerating nutrients, and acting as the final filter in sediments through which organic matter passes before long-term burial. Microbially-driven carbon cycling in the ocean profoundly affects the global carbon cycle, but key factors determining rates and locations of organic matter remineralization are unclear. In this study, researchers from the University of North Carolina at Chapel Hill will investigate the ability of pelagic microbial communities to initiate the remineralization of polysaccharides and proteins, which together constitute a major pool of organic matter in the ocean. Results from this study will be predictive on a large scale regarding the nature of the microbial response to organic matter input, and will provide a mechanistic framework for interpreting organic matter reactivity in the ocean. Broader Impacts: This study will provide scientific training for undergraduate and graduate students from underrepresented groups. The project will also involve German colleagues, thus strengthening international scientific collaboration. projects_0_end_date=2017-07 projects_0_geolocation=Atlantic Ocean, Arctic Ocean, Pacific Ocean, Greenland projects_0_name=Latitudinal and depth-related contrasts in enzymatic capabilities of pelagic microbial communities: Predictable patterns in the ocean? projects_0_project_nid=712359 projects_0_start_date=2013-08 sourceUrl=(local files) Southernmost_Northing=79.6502 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=depth_no,comments time_coverage_end=2012-09-22T17:31Z time_coverage_start=2012-08-09T19:25Z version=1 Westernmost_Easting=31.2277 xml_source=osprey2erddap.update_xml() v1.5

The processed subset of the NKI-RS dataset for evaluation of various perturbation modes when studying instabilities. Linked to the pre-print found here, can be visualized using the plotting code here, and generated with various scripts and launch configurations found here.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Computer Applications Mca. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Computer Applications Mca. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This linear chart displays the number of PERM cases filed for graduates in Master Of Computer Applications Mca from 2020 to 2023, highlighting the trends and changes in sponsorship over the years. It provides a deep dive into how graduates in this specific major have engaged with potential employers for permanent residency in the U.S., illustrating the major’s effectiveness in connecting students with career opportunities that lead to permanent residency

Question Paper Solutions of chapter Introduction to Data Warehousing of Data Warehousing and Data Mining, 3rd Semester , Master of Computer Applications (2 Years)

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This linear chart displays the number of PERM cases filed for graduates in Master In Computer Application (M.C.A) from 2020 to 2023, highlighting the trends and changes in sponsorship over the years. It provides a deep dive into how graduates in this specific major have engaged with potential employers for permanent residency in the U.S., illustrating the major’s effectiveness in connecting students with career opportunities that lead to permanent residency

Question Paper Solutions of chapter Classification and Prediction of Data Warehousing and Data Mining, 3rd Semester , Master of Computer Applications (2 Years)

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