Visitor Arrivals: Marine: North America: Canada data was reported at 0.503 Person th in Dec 2018. This records an increase from the previous number of 0.275 Person th for Nov 2018. Visitor Arrivals: Marine: North America: Canada data is updated monthly, averaging 0.038 Person th from Jan 1989 (Median) to Dec 2018, with 360 observations. The data reached an all-time high of 1.024 Person th in Feb 2009 and a record low of 0.000 Person th in Oct 2018. Visitor Arrivals: Marine: North America: Canada data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB003: No of Visitors Arrivals: by Mode of Transport. According Ministry of Tourism, the monthly Visitor Arrivals will release in annual basis due to Tourism Ministry receive once in the year some input data from the Federal Policy, based on these data they perform the estimation for all the months in the year

Visitor Arrivals: Air: Central America & Caribbean: Costa Rica data was reported at 0.920 Person th in Dec 2018. This records a decrease from the previous number of 0.925 Person th for Nov 2018. Visitor Arrivals: Air: Central America & Caribbean: Costa Rica data is updated monthly, averaging 0.433 Person th from Jan 1989 (Median) to Dec 2018, with 360 observations. The data reached an all-time high of 5.177 Person th in Jun 2014 and a record low of 0.033 Person th in Apr 1993. Visitor Arrivals: Air: Central America & Caribbean: Costa Rica data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB003: No of Visitors Arrivals: by Mode of Transport. According Ministry of Tourism, the monthly Visitor Arrivals will release in annual basis due to Tourism Ministry receive once in the year some input data from the Federal Policy, based on these data they perform the estimation for all the months in the year

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Successful leadership requires leaders to make their followers aware of expectations regarding the goals to achieve, norms to follow, and task responsibilities to take over. This awareness is often achieved through leader-follower communication. In times of economic globalization and digitalization, however, leader-follower communication has become both more digitalized (virtual, rather than face-to-face) and less frequent, making successful leader-follower-communication more challenging. The current research tested in four studies (three preregistered) whether digitalization and frequency of interaction predict task-related leadership success. In one cross-sectional (Study 1, N=200), one longitudinal (Study 2, N=305), and one quasi-experimental study (Study 3, N=178), as predicted, a higher frequency (but not a lower level of digitalization) of leader-follower interactions predicted better task-related leadership outcomes (i.e., stronger goal clarity, norm clarity, and task responsibility among followers). Via mediation and a causal chain approach, Study 3 and Study 4 (N=261) further targeted the mechanism; results showed that the relationship between (higher) interaction frequency and these outcomes is due to followers perceiving more opportunities to share work-related information with the leaders. These results improve our understanding of contextual factors contributing to leadership success in collaborations across hierarchies. They highlight that it is not the digitalization but rather the frequency of interacting with their leader that predicts whether followers gain clarity about the relevant goals and norms to follow and the task responsibilities to assume.

Brazil Visitor Arrivals: Road: Central America & Caribbean: Cuba data was reported at 0.152 Person th in Dec 2018. This records an increase from the previous number of 0.004 Person th for Nov 2018. Brazil Visitor Arrivals: Road: Central America & Caribbean: Cuba data is updated monthly, averaging 0.000 Person th from Jan 1989 (Median) to Dec 2018, with 360 observations. The data reached an all-time high of 0.152 Person th in Dec 2018 and a record low of 0.000 Person th in Dec 2007. Brazil Visitor Arrivals: Road: Central America & Caribbean: Cuba data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB003: No of Visitors Arrivals: by Mode of Transport. According Ministry of Tourism, the monthly Visitor Arrivals will release in annual basis due to Tourism Ministry receive once in the year some input data from the Federal Policy, based on these data they perform the estimation for all the months in the year

Annual international travel by non-Canadians visitors coming to Canada for a trip, by port of entry (e.g. airport, border crossing). This table includes breakdowns by mode of transportation (air, land, water), by arrival type and by duration (same-day, overnight). Data come from Frontier Counts, part of the Tourism Statistics Program.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Source: Trend visualization of census blocks showing the change in commute share that is non-automotive.

Purpose: Tile layer utilized for visualization.

Contact Information: Charles Rudder (crudder@citiesthatwork.com)/ Alex Bell (abell@citiesthatwork.com)

In this paper, we explore the differences in item non-response that result from different modes of interview and find that mode makes a difference. The data are from an experiment in which we randomly assigned an adult population to an in-person or self-completed survey after subjects agreed to participate in a short poll. For nearly every topic and format of question, we find less item non-response in the self-complete mode. Furthermore, we find the difference across modes in non-response is exacerbated for respondents with low levels of cognitive abilities.

Here, we present experimental and analytical results of a preventive approach applied to a fault-mode phenomenon caused by electrodes or power-source failure in a no-insulation (NI) high-temperature superconducting REBa2Cu3O7−x (REBCO, RE = rare earth) magnet. It is generally agreed that the NI magnets, at least those of laboratory scale, are self-protected from overheating and, therefore, from quenching, chiefly because of turn-to-turn current bypassing unique to NI. However, these NI magnets do experience unexpected quenches, e.g., when the current through the magnet suddenly drops due to the aforementioned fault-mode phenomenon. Here, we report this phenomenon of a sudden-discharging-triggered quench of an NI REBCO coil, conduction-cooled, and operated at 4.2 K. We also present our preventive approach for this phenomenon that relies on an appropriately designed resistor shunted across the coil terminals. With this shunt resistor, a quench was prevented by suppressing the quench initiating turn-to-turn heat and induced overcurrent within the NI winding, and the coil current decayed safely.

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Visitor Arrivals: River: Africa data was reported at 0.041 Person th in 2018. This records an increase from the previous number of 0.015 Person th for 2017. Visitor Arrivals: River: Africa data is updated yearly, averaging 0.033 Person th from Dec 1989 (Median) to 2018, with 30 observations. The data reached an all-time high of 0.534 Person th in 2007 and a record low of 0.004 Person th in 1989. Visitor Arrivals: River: Africa data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

These visualisations feature Opal Trips for all modes of Public Transport by week, month and year. Visualisations for each of the modes show the number of ticketed trips based on operator, line, contract area (where applicable) and card type. An Opal trip describes where an Opal or Contactless card is used to tap-on and tap-off, including where a single tap-on or tap-off is recorded. All other travel is not included. As of 1 July 2024, the methodology for calculating trip numbers for individual Lines and Operators has changed to better reflect the services our passengers use on the transport network. The new approach applies to Train, Metro and Light Rail and will soon be extended to Ferry and Bus. Aggregations between Line, Agency and Mode levels are no longer valid as a passenger may use several lines on a single trip. Trip numbers at Line, Operator or Mode level should be used as provided without further combination. This dataset has reports based on both the new and old methodology with reports progressively moved to the new method in the coming months. Due to the change in method care should be taken when looking at longer trends that utilise both datasets.

Visitor Arrivals: River: Central America & Caribbean (CC) data was reported at 0.042 Person th in 2018. This stayed constant from the previous number of 0.042 Person th for 2017. Visitor Arrivals: River: Central America & Caribbean (CC) data is updated yearly, averaging 0.083 Person th from Dec 1989 (Median) to 2018, with 30 observations. The data reached an all-time high of 0.633 Person th in 1995 and a record low of 0.009 Person th in 1989. Visitor Arrivals: River: Central America & Caribbean (CC) data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

This project integrated a deep rated version of the Ion Sensitive Field Effect Transistor (ISFET)-based pH sensor, the Deep ISFET pH, into a Slocum Webb G2 glider. The pH sensor unit is complemented with existing glider sensors including a CTD, a WETLabs FLBBCD ECO puck configured for simultaneous chlorophyll fluorescence and optical backscatter measurements, and an Aanderaa Optode for measuring dissolved oxygen. This approximately 15 to 30 day deployment near Sandy Hook, NJ ran a cross-shelf transect to the shelf break north of Hudson Canyon to sample in Atlantic sea scallop habitat. Then the glider will turned and flew back to shore in a west-southwest direction to cover more sea scallop and Atlantic surfclam habitat with possible recovery targeted for Barneget, NJ. However, if pH data was still stable after 15 days (no increased time lag response due to biofouling), the glider turned southeast and headed back to the shelf break then flew back inshore toward Atlantic City. This deployment also supports the realtime data delivery of autonomous underwater gliders in the coastal ocean to better resolve and understand essential ocean features and processes that contribute to hurricane intensification or weakening prior to making landfall. This is a partnership between NOAA Ocean and Atmospheric Research (OAR) through the Atlantic Oceanographic and Meteorological Laboratory (AOML) and Integrated Ocean Observing System (IOOS) regional associations such as MARACOOS, SECOORA, CariCOOS and institutions including the University of Puerto Rico, University of the Virgin Islands, Skidaway Institute of Oceanography, University of Delaware, and Rutgers University. The goal of the project is to provide realtime data for ocean model validation and assimilation throughout hurricane season. This project is supported by the Disaster Recovery Act. The glider was deployed out of Tuckerton, NJ and transected E to an offshore waypoint north of Carteret Canyon, then transected SSW to Wilmington Canyon, then NW back to Tuckerton, NJ as the battery pack allowed. This dataset contains CTD, chlorphyll a, CDOM and optical backscatter measurements. Delayed mode dataset. _NCProperties=version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.3 acknowledgment=This deployment supported by NSF Oceanographic Technology and Interdisciplinary Coordination, and NOAA Oceanic and Atmospheric Research. cdm_data_type=Trajectory cdm_trajectory_variables=trajectory comment=Glider operated by the Rutgers University Center for Coastal Ocean Observing Leadership, Rutgers University, USA. Deployed by Grace Saba, Chip Haldeman, Nicole Waite, and the Rutgers Ocean Methods and Data A nalysis class aboard the R/V Rutgers, with shore support by Dave Aragon. contributor_name=Grace Saba, Liza Wright-Fairbanks, Travis Miles, Dave Aragon, Nicole Waite, Chip Haldeman, John Kerfoot, Laura Nazzaro contributor_role=Principal Investigator, Principal Investigator, Principal Investigator, Glider Pilot, Glider Pilot, Glider Pilot, Data Manager, Data Manager Conventions=CF-1.6, COARDS, ACDD-1.3 defaultGraphQuery=longitude,latitude,time&.draw=markers&.marker=6%7C3&.color=0xFFFFFF&.colorBar=Rainbow2%7C%7C%7C%7C%7C&.bgColor=0xffccccff deployment_name=ru30-20191015T1824 Easternmost_Easting=-71.81351833333333 featureType=Trajectory geospatial_bounds=POLYGON ((40.36737 -73.84600999999999, 40.36737 -73.84371, 40.36649833333333 -73.84371, 40.36649833333333 -73.84600999999999, 40.36737 -73.84600999999999)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=40.38173833333334 geospatial_lat_min=39.36295 geospatial_lat_resolution=0.00001 degree geospatial_lat_units=degrees_north geospatial_lon_max=-71.81351833333333 geospatial_lon_min=-73.85679 geospatial_lon_resolution=0.00001 degree geospatial_lon_units=degrees_east geospatial_verical_resolution=0 geospatial_vertical_max=188.9775 geospatial_vertical_min=-0.3770807 geospatial_vertical_positive=down geospatial_vertical_units=m history=2021-03-02T03:04:11Z: /tmp/tmpz2a830k8/TrajectoryNetCDFWriter.pyp6rjpp6e.nc created 2021-03-02T03:04:11Z: /home/kerfoot/code/glider-proc/scripts/proc_deployment_trajectories_to_nc.py /home/coolgroup/slocum/deployments/2019/ru30-20191015T1824/data/in/ascii/dbd/ru30_2019_309_1_0_dbd.dat

id=ru30-20191015T1824 infoUrl=https://rucool.marine.rutgers.edu institution=Rutgers University instrument=In Situ/Laboratory Instruments > Profilers/Sounders > CTD instrument_vocabulary=NASA/GCMD Instrument Keywords Version 8.5 keywords_vocabulary=NASA/GCMD Earth Sciences Keywords Version 8.5 naming_authority=edu.rutgers.rucool ncei_template_version=NCEI_NetCDF_Trajectory_Template_v2.0 Northernmost_Northing=40.38173833333334 platform=In Situ Ocean-based Platforms > AUVS > Autonomous Underwater Vehicles platform_type=Slocum Glider platform_vocabulary=NASA/GCMD Platforms Keywords Version 8.5 processing_level=Raw Slocum glider dataset from the native data file format. No quality control provided. Delayed mode dataset. program=Collaborative Research: Developing a profiling glider pH sensor for high resolution coastal ocean acidification monitoring,Sustained Underwater Glider Observations for Improving Atlantic Tropical Cyclone Intensity Forecast - AOML/OAR project=OTIC-pH,Sustained Underwater Glider Observations for Improving Atlantic Tropical Cyclone Intensity Forecast sea_name=Mid-Atlantic Bight source=Observational Slocum glider data from source dba file ru30-2019-309-1-0-dbd(03550000) sourceUrl=(local files) Southernmost_Northing=39.36295 standard_name_vocabulary=CF Standard Name Table v27 subsetVariables=source_file time_coverage_duration=PT20M28.83746S time_coverage_end=2019-11-06T17:23:58Z time_coverage_resolution=PTS time_coverage_start=2019-10-15T18:24:23Z uuid=4a17ee50-29e9-4fe1-8acf-1c273e75d413 Westernmost_Easting=-73.85679 wmo_id=4801946

Visitor Arrivals: Marine: CC: Others data was reported at 0.037 Person th in 2018. This records an increase from the previous number of 0.013 Person th for 2017. Visitor Arrivals: Marine: CC: Others data is updated yearly, averaging 0.288 Person th from Dec 1989 (Median) to 2018, with 30 observations. The data reached an all-time high of 1.455 Person th in 2005 and a record low of 0.009 Person th in 2016. Visitor Arrivals: Marine: CC: Others data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

Visitor Arrivals: River: Europe: Portugal data was reported at 0.068 Person th in 2018. This records an increase from the previous number of 0.056 Person th for 2017. Visitor Arrivals: River: Europe: Portugal data is updated yearly, averaging 0.059 Person th from Dec 1989 (Median) to 2018, with 30 observations. The data reached an all-time high of 0.098 Person th in 2008 and a record low of 0.003 Person th in 1993. Visitor Arrivals: River: Europe: Portugal data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

Visitors Arrivals: Annual: Air: Asia: Philippines data was reported at 5.942 Person th in 2017. This records a decrease from the previous number of 7.182 Person th for 2016. Visitors Arrivals: Annual: Air: Asia: Philippines data is updated yearly, averaging 0.000 Person th from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 7.489 Person th in 2015 and a record low of 0.000 Person th in 2014. Visitors Arrivals: Annual: Air: Asia: Philippines data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

Visitor Arrivals: River: Europe: Austria data was reported at 0.037 Person th in 2018. This records a decrease from the previous number of 0.042 Person th for 2017. Visitor Arrivals: River: Europe: Austria data is updated yearly, averaging 0.054 Person th from Dec 1989 (Median) to 2018, with 30 observations. The data reached an all-time high of 0.197 Person th in 2007 and a record low of 0.003 Person th in 1992. Visitor Arrivals: River: Europe: Austria data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.

Visitors Arrivals: Annual: Air: Asia: Malaysia data was reported at 2.906 Person th in 2017. This records a decrease from the previous number of 3.666 Person th for 2016. Visitors Arrivals: Annual: Air: Asia: Malaysia data is updated yearly, averaging 0.000 Person th from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 4.535 Person th in 2015 and a record low of 0.000 Person th in 2014. Visitors Arrivals: Annual: Air: Asia: Malaysia data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Brazil Premium Database’s Tourism Sector – Table BR.QB004: No of Visitors Arrivals: by Mode of Transport: Annual.