Financial overview and grant giving statistics of Foundation Acd Inc.

This repo contains the data for S2O: Static to Openable Enhancement for Articulated 3D Objects. See the code on GitHub and the paper for details. Please cite S2O [1] if you use ACD. We provide the mesh, point cloud, and metadata for the two datasets used in S2O.

PM-Openable - This is a subset of 648 openable objects from full PartNet-Mobility [2]. We use a train/val/test split of 460/95/93 objects.
Articulated Container Dataset (ACD) [1] - We take openable container objects from HSSD [3]… See the full description on the dataset page: https://huggingface.co/datasets/3dlg-hcvc/s2o.

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.

Information processing in neuronal networks involves the recruitment of selected neurons into coordinated spatiotemporal activity patterns. This sparse activation results from widespread synaptic inhibition in conjunction with neuron-specific synaptic excitation. We report the selective recruitment of hippocampal pyramidal cells into patterned network activity. During ripple oscillations in awake mice, spiking is much more likely in cells in which the axon originates from a basal dendrite rather than from the soma. High-resolution recordings in vitro and computer modeling indicate that these spikes are elicited by synaptic input to the axon-carrying dendrite and thus escape perisomatic inhibition. Pyramidal cells with somatic axon origin can be activated during ripple oscillations by blocking their somatic inhibition. The recruitment of neurons into active ensembles is thus determined by axonal morphological features.

Financial overview and grant giving statistics of The Three Angels Memorial Fund For Acd Research Inc

No description is available. Visit https://dataone.org/datasets/d86895d542934b7407a7a66b76bd036b for complete metadata about this dataset.

The deposited table comprises all molecular data analyzed within the study "Morphomolecular pathobiology of the capillary network in alveolar capillary dysplasia".

Protein-Protein, Genetic, and Chemical Interactions for ACD (Homo sapiens) curated by BioGRID (https://thebiogrid.org); DEFINITION: adrenocortical dysplasia homolog (mouse)

2014 Global exporters importers export import shipment records of Acd acid with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Automatic Call Distributor Market Outlook

The global Automatic Call Distributor (ACD) market size was valued at USD 2.3 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.3% during the forecast period. The growth of this market is primarily driven by the rising demand for efficient customer service management systems and the increasing adoption of cloud-based solutions.

One of the primary growth factors contributing to the expansion of the ACD market is the surge in demand for customer service automation. Businesses across various sectors are increasingly recognizing the importance of delivering exceptional customer experiences. ACD systems play a crucial role in enhancing customer satisfaction by efficiently routing calls to the most appropriate agent or department, reducing wait times, and ensuring prompt issue resolution. This trend is particularly evident in industries such as BFSI, healthcare, and IT and telecommunications, where customer interactions are frequent and critical.

Another significant driver of market growth is the increasing adoption of cloud-based ACD solutions. Cloud-based deployment offers several advantages, including scalability, cost-effectiveness, and ease of integration with other communication tools and customer relationship management (CRM) systems. Small and medium enterprises (SMEs) are particularly benefiting from cloud-based ACD solutions, as they can access advanced call distribution features without the need for substantial upfront investments in hardware and infrastructure. This shift towards cloud deployment is expected to continue accelerating, further propelling market growth.

Moreover, technological advancements in artificial intelligence (AI) and machine learning (ML) are playing a pivotal role in shaping the future of the ACD market. AI-powered ACD systems can analyze customer data and interaction patterns to provide more accurate call routing, personalized customer experiences, and predictive analytics. These capabilities are highly valued by businesses aiming to enhance their customer support services and gain a competitive edge in the market. As AI and ML technologies continue to evolve, their integration into ACD systems is anticipated to drive significant advancements and market growth.

Regionally, North America holds a prominent share in the ACD market, driven by the high adoption rate of advanced communication technologies and the presence of key market players. The Asia Pacific region is also witnessing substantial growth, fueled by the rapid digital transformation initiatives, increasing internet penetration, and the rising demand for efficient customer service solutions in emerging economies such as India and China. Europe is another significant market, characterized by the growing emphasis on customer experience management and regulatory requirements for data protection and privacy.

In the realm of emergency response and public safety, Computer Aided Dispatch (CAD) systems are becoming increasingly vital. These systems are designed to streamline communication between dispatchers and emergency services, ensuring that help is dispatched quickly and efficiently. By integrating with ACD systems, CAD can enhance the speed and accuracy of call routing, particularly in critical situations where every second counts. This integration allows for real-time data sharing and coordination, improving response times and resource allocation. As public safety agencies continue to adopt advanced technologies, the synergy between CAD and ACD systems is expected to play a crucial role in enhancing emergency response capabilities.

Component Analysis

The Automatic Call Distributor market can be segmented by component into software, hardware, and services. Each of these components plays a critical role in the overall functionality and effectiveness of ACD systems, and their demand and adoption rates vary based on organizational needs and technological advancements.

Software is a key component of ACD systems, encompassing the applications and platforms that facilitate call routing, management, and reporting. The software segment is experiencing robust growth due to the increasing demand for advanced features such as AI-driven call routing, real-time analytics, and integration with CRM systems. Businesses are increasingly opting for software-based ACD solutions that offer flexibility, scalability, and ease of updates. Additionally,

This dataset presents the footprint of cancer incidence data in Australia for all cancers combined, and six selected cancers (female breast cancer, colorectal cancer, cervical cancer, lung cancer, melanoma of the skin, and prostate cancer) with their respective ICD-10 codes. The data spans the years 2009 to 2013 and is aggregated to the 2015 Primary Health Network (PHN) geographic areas based on the 2011 Australian Statistical Geography Standard (ASGS). The source of the incidence data is the 2014 Australian Cancer Database (ACD). The ACD is compiled by the Australian Institute of Health and Wellbeing (AIHW) from data provided by the state and territory population-based cancer registries. For further information about this dataset, please visit: AIHW - Cancer Incidence and Mortality in Australia Data Tables 2014 Australian Cancer Database Data Quality Statement Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Where records are null, data was not publishable because of small numbers, confidentiality or other concerns about the quality of the data.

An important outcome of Arctic Coastal Dynamics I was the segmentation and characterization of the entire circum-Arctic coastline by regional experts which is presented in this dataset. This dataset contains data on coastal morphology, composition, dominant processes, ground ice, and environmental forcing parameters such as wind speed, storm counts, melt season, and wave energy. A listing of the variables included in the coastal classification can be found in Appendix A of the ACD II Science and Implementation Plan (2006). This information is available for over 800 segments, covering the coastline of all eight regional seas of the Arctic Ocean. The length of individual segments is variable (median length is 38 km), and depends on classification parameters and data availability. The segmentation format is scalable, allowing the adoption of future digital coastlines and the integration of additional data at higher spatial resolution. An assessment of the data quality for the more important quantitative variables has just been completed and the data will be publicly released on an internet map server (IMS). The goal of the IMS will be to allow individual users to prepare their own maps displaying the region and variables of interest. The ACD Classification was conceived as a broad enough framework to encompass existing classification schemes while capturing fundamental information for the assessment of climate change impacts and coastal processes. The implementation of the classification was done by so-called "regional experts", who, based on digital and paper products and personal knowledge provided information which was subsequently gathered into a circum-Arctic coastal database. The classification was primarily geomorphological in nature and considered: (1) the shape or form of the subaerial part of the coastal tract, (2) the marine processes acting upon the coast, (3) the shape or the form of the subaqueous part of the coastal tract and (4) the lithofacies of the materials constituting the coastal zone The beta version of the classification is made of 1331 segments each characterized by a series of geomorphological quantitative and qualitative variables. The classification is stored as an ISO 19115-compliant personal geodatabase and is therefore mappable in off-the-shelf Geographical Information Systems (GIS)

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.

The ACD (Acid-Citrate-Dextrose) Blood Collection Tube market plays a vital role in the healthcare and laboratory sectors, facilitating the safe and efficient collection, preservation, and transport of blood samples for various tests, including blood banking and clinical diagnostics. These tubes contain anticoagulant

Protein-Protein, Genetic, and Chemical Interactions for ACD-4 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: ACid-sensitive Degenerin

This dataset contains output of FLEXPART, a Lagrangian Particle Dispersion Model (LPDM), including an updated version of the plots/ICARTT data. It provides information on the history of air sampled along the C130 flight track by releasing "particles" (infinitesimally small parcels of air) from the plane location and following its path backwards in time. Advection, turbulence and convection are the processes considered. A particle itself is "inert", processes like deposition, emission or chemical conversion are not considered. Thereby the result could be called "airmass history", as the particles behave like air. Its location back in time and space since released is called "backtrajectory". The chaotic nature of the atmosphere and uncertainties in model estimates of wind vector, turbulence and convection requires a statistical approach to estimate all probable backtrajectories. Hence not only one, but a large number of particles (1e5 - 1e6) are released over a short time period (1 hr) and followed back in time. While the exact trajectory for each particle is used internally, the main model result is the number of particles and the time they spent within a 3D lat/lon/altitude grid, called a "sensitivity" or "residence time" field. This is calculated at discrete intervals (every hour) in time since release. These fields are the raw output of the model calculations. For ease of use, deterministic backtrajectories as mass weighted mean of the plume at each hour, and 5 plume cluster centroids (Stohl et al., Atm. Env., 2002) are calculated as well. NOTE: Narrowing your order by date will allow you to select from a smaller group of files. Please make sure to properly acknowledge this dataset in publications or presentations, or consider offering co-authorship if you deem it to be a substantial contribution to your work.

Historical Dataset of University Heights Preparatory Acd is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2006-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2006-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (2007-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2007-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2007-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2015),Math Proficiency Comparison Over Years (2010-2015),Overall School Rank Trends Over Years (2010-2015),Graduation Rate Comparison Over Years (2011-2015)

Historical Dataset of Crown Elementary Comm Acd Fine Arts Center is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1988-2021),Two or More Races Student Percentage Comparison Over Years (2022-2023),Diversity Score Comparison Over Years (1990-2023),Free Lunch Eligibility Comparison Over Years (2003-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2012),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2010-2021),Overall School Rank Trends Over Years (2011-2022)

ACD/Labs Vilnius, UAB financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

The Automatic Call Distributor (ACD) market has emerged as a critical component within the telecommunications landscape, streamlining call management and enhancing customer experience for various industries. ACD systems auto-route incoming calls to the most suitable agent or department based on predefined criteria,