This statistic displays the monthly rent for a mid-range two-bedroom apartment in Buenos Aires, Argentina in 2018 and 2019. In 2019, the monthly rent of a two-bedroom apartment in Buenos Aires amounted to approximately *** U.S. dollars, down from *** U.S. dollars the previous year.

In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preprocessing phase has been to divide the data by the standard deviation along each dimension. Like division by the standard deviation, the great majority of scaling techniques can be said to have roots in some sort of statistical take on the data. Here we explore the use of multidimensional shapes of data, aiming to obtain scaling factors for use prior to clustering by some method, like k-means, that makes explicit use of distances between samples. We borrow from the field of cosmology and related areas the recently introduced notion of shape complexity, which in the variant we use is a relatively simple, data-dependent nonlinear function that we show can be used to help with the determination of appropriate scaling factors. Focusing on what might be called “midrange” distances, we formulate a constrained nonlinear programming problem and use it to produce candidate scaling-factor sets that can be sifted on the basis of further considerations of the data, say via expert knowledge. We give results on some iconic data sets, highlighting the strengths and potential weaknesses of the new approach. These results are generally positive across all the data sets used.

As of July 2025, the Radeon Ryzen 5 7535HS achieved the best PassMark performance score among high mid-range video cards with a score of *****. The majority of the top 10 high mid-range video cards are either provided by Radeon and FirePro, or by Nvidia, such as GeForce and GRID.

The αk’s are the ones leading to the highest values of ARIfnc in the intervals on the rightmost column of Table 2.

The Midrange Speakers market plays a pivotal role in the audio industry, offering a critical balance of sound quality that captures the rich, detailed frequencies between bass and treble. As an essential component in sound systems ranging from home theaters to professional audio setups, midrange speakers excel at de

As of May 2025, the AMD Ryzen 3 PRO 5450U achieved the best PassMark performance score among high mid-range CPUs with a score of 11,044

5% Annual Exceedance Probability which can also be expressed as the 20 Year Return Period and as 20:1 odds of occurrence in any given year. 1% (Medium Probability) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year. The Mid-Range Future Scenario extents where generated taking in the potential effects of climate change using an increase in rainfall of 20%. Data has been produced for catchments greater than 5km2 in areas for which flood maps were not produced under the National CFRAM Programme and should be read in this context. River reaches that have been modelled are indicated by the NIFM Modelled River Centrelines dataset. Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources). The purpose of the Flood Maps is not to designate individual properties or point locations at risk of flooding, or to replace a detailed site-specific flood risk assessment. Lineage: The indicative fluvial flood maps were developed using hydrodynamic modelling, based on calculated design river flows, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods, etc.). The process may vary for particular areas or maps. The National Indicative Fluvial Maps provide an indication of areas that may flood during a flood of an estimated probability of occurring. As detailed in the Technical Data, a number of assumptions have been made in order to produce a dataset suitable for national level flood risk assessments. The National Indicative Fluvial Maps are not the best achievable representation of flood extents and they are not as accurate as the Flood Maps produced under the National Catchment Flood Risk Assessment and Management (CFRAM) Programme. The maps should not be used to assess the flood risk associated with individual properties or point locations, or to replace a detailed site-specific flood risk assessment. Flood levels and depths are derived from the hydrodynamic models for the various event probabilities and scenarios. Flood extents are derived from the raster flood depth maps and vectorised to produce the final vector outputs. Purpose: The data has been developed to inform a national assessment of flood risk that in turn will inform a review of the Preliminary Flood Risk Assessment required to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods.

Abstract: This data shows the model nodes, indicating water level only and/or flow and water levels along the centre-line of rivers that have been modelled to generate the CFRAM flood maps. The nodes estimate maximum design event flood flows and maximum flood levels. Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time. The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed, expressed in terms of Annual Exceedance Probability (AEP), and identifies their parallels under other forms of expression: 10% (High Probability) Annual Exceedance Probability which can also be expressed as the 10 Year Return Period and as a 10:1 odds of occurrence in any given year. 1% (Medium Probability - Fluvail/River Flood Maps) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.5% (Medium Probability - Coastal Flood Maps) Annual Exceedance Probability which can also be expressed as the 200 Year Return Period and as 200:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year. The Mid-Range Future Scenario extents where generated taking in in the potential effects of climate change using an increase in rainfall of 20% and sea level rise of 500mm (20 inches). Data has been produced for the 'Areas of Further Assessment' (AFAs), as required by the EU 'Floods' Directive [2007/60/EC] and designated under the Preliminary Flood Risk Assessment, and also for other reaches between the AFAs and down to the sea that are referred to as 'Medium Priority Watercourses' (MPWs). River reaches that have been modelled are indicated by the CFRAM Modelled River Centrelines dataset. Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources). The purpose of the Flood Maps is not to designate individual properties at risk of flooding. They are community-based maps. Lineage: Fluvial and coastal flood map data is developed using hydrodynamic modelling, based on calculated design river flows and extreme sea levels, surveyed channel cross-sections, in-bank / bank-side / coastal structures, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods for model calibration, etc.). The process may vary for particular areas or maps. Technical Hydrology and Hydraulics Reports set out full technical details on the derivation of the flood maps. For fluvial flood levels, calibration and verification of the models make use of the best available data, including hydrometric records, photographs, videos, press articles and anecdotal information. Subject to the availability of suitable calibration data, models are verified in so far as possible to target vertical water level accuracies of approximately +/-0.2m for areas within the AFAs, and approximately +/-0.4m along the MPWs. For coastal flood levels, the accuracy of the predicted annual exceedance probability (AEP) of combined tide and surge levels depends on the accuracy of the various components used in deriving these levels i.e. accuracy of the tidal and surge model, the accuracy of the statistical data and the accuracy for the conversion from marine datum to land levelling datum. The output of the water level modelling, combined with the extreme value analysis undertaken as detailed above is generally within +/-0.2m for confidence limits of 95% at the 0.1% AEP. Higher probability (lower return period) events are expected to have tighter confidence limits. v101 (March 2025) The section of map near Oranmore Galway updated following a map review process see https://www.floodinfo.ie/map-review/ for further information, Map Review Code: MR019. v102 (July 2025) The section of map near Claregalway updated following a map review process see https://www.floodinfo.ie/map-review/ for further information, Map Review Code: MR057. Purpose: The data has been developed to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods.

Weather Source offers the full European Centre for Medium-Range Weather Forecasts (ECMWF) suite which is known as the best forecast model in the world. The products include (i) historical data back to 2000; (ii) short/mid-range forecast (i.e., up to 360-hour or 15 days); (iii) sub-seasonal forecast out to 46 days (iv) and a seasonal forecast in monthly format out to 7 months. We also offer historical forecasts in pristine format.

In addition, we also have the raw and statistically analyzed ensembles and we summarize the ensemble members by deciles and quartiles which are incredibly valuable to understand the potential of forecast variance (i.e., are the ensemble members tightly wound around the forecast mean which tells me the skill score of the forecast is very high or do they expose a bi-modal distribution which indicates I should plan for possible variance in the forecast.).

As of July 2025, Samsung MZNTY256HDHP-000 achieved the best PassMark performance score among low mid-range hard drives with a score of *****.

The Mid-Range Mechanical Lidar market has emerged as a pivotal segment in the evolving landscape of sensor technology, offering precise distance measuring solutions essential for a variety of industries, from automotive to robotics and urban planning. As businesses seek innovative ways to enhance safety, efficiency,

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

As of July 2025, the Qualcomm Technologies, Inc SDM720G achieved the best PassMark performance score among low mid-range CPUs with a score of *****.

The Mid Range Mirrorless Cameras market has emerged as a pivotal sector within the photography industry, bridging the gap between entry-level and professional-grade equipment. With their compact design, advanced features, and superior image quality, these cameras cater to both enthusiasts and budding professionals s

The Low- to Mid-Range Intelligent Driving Chips market is gaining significant momentum as the automotive industry increasingly shifts towards advanced driver-assistance systems (ADAS) and autonomous vehicle technologies. These specialized chips play a crucial role in enabling smart functionalities such as collision

The Mid-range LiDAR market is rapidly evolving, driven by advancements in technology and increasing demand for precise geospatial data across various industries. LiDAR, which stands for Light Detection and Ranging, is a remote sensing method that uses laser light to measure distances, creating detailed three-dimensi

As of May 2025, the Radeon R7 + R5 Dual graphic card achieved the best PassMark performance score among high mid-range video cards, with a score of 889. Radeon brand belongs to AMD while the GeForce falls under Nvidia

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time. The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed; 5% Annual Exceedance Probability which can also be expressed as the 20 Year Return Period and as 20:1 odds of occurrence in any given year. 1% (Medium Probability) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year. The Mid-Range Future Scenario extents where generated taking in the potential effects of climate change using an increase in rainfall of 20%. Data has been produced for catchments greater than 5km2 in areas for which flood maps were not produced under the National CFRAM Programme and should be read in this context. River reaches that have been modelled are indicated by the NIFM Modelled River Centrelines dataset. Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources). The purpose of the Flood Maps is not to designate individual properties or point locations at risk of flooding, or to replace a detailed site-specific flood risk assessment. Purpose:The data has been developed to inform a national assessment of flood risk that in turn will inform a review of the Preliminary Flood Risk Assessment required to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods.

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