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About 10,000 papers for “Occupancy”
Ayub Khan, Stella Thebridge · 2022
Building occupancy is an area of planning that is often forgotten. Once the building phase is over, there is other work to be done. There needs to be careful preparation and planning for moving in and a robust postoccupancy evaluation plan. This chapter explores this process more fully and concludes with some examples of success factors cited by librarians in recent new builds.
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Robert D. Zeigler, Barak A. Cohen · 2013
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Haitham Hassanieh, Lixin Shi, Omid Abari et al. · 2013
Abstract – The goal of this paper is to make sensing and decoding GHz of spectrum simple, cheap, and low power. Our thesis is simple: if we can build a technology that captures GHz of spectrum using commodity Wi-Fi radios, it will have the right cost and power budget to enable a variety of new applications such as GHzwidedynamicaccessandconcurrentdecodingofdiversetechnologies.Thisvisionwillchangetoday’ssituationwhereonlyexpensive power-hungryspectrumanalyzerscancaptureGHz-widespectrum. Towards this goal, the paper harnesses the sparse Fourier transform to compute the frequency representation of a sparse signal without sampling it at full bandwidth. The paper makes the followingcontributions.First,itpresentsBigBand,areceiver thatcan senseanddecodeasparsespectrumwiderthanitsowndigitalbandwidth. Second, it builds a prototype of its design using 3 USRPs thateachsamplesthespectrumat50MHz,producingadevicethat captures 0.9 GHz — i.e., 6 × larger bandwidth than the three US-RPs combined. Finally, it extends its algorithm to enable spectrum sensinginscenarios where thespectrum isnot sparse.
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Murat Alanyali, Maxim Dashouk · 2014
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Jordana L. Maisel, Aldo Steinfeld, Megan Basnak et al. · 2017
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
Cited by 0Open access
Irving Stackpole · 1934
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Paul Schuette A, Aaron P. Wagner B, Meredith E. Wagner C et al. · 2011
is a sample cover image for this issue. The actual cover is not yet available at this time.) This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
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Vincent Miele, Claude Thermes · 2008
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Vincent Miele, Claude Thermes · 2008
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Allan F. O’Connell, Larissa L. Bailey · 2010
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kiko Gracia (18517575) · Figshare · 2024
<p dir="ltr"><b>At a glance:</b></p><ul><li><a href="https://www.cd-genomics.com/longseq/resource-pore-occupancy.html#_What_is_Pore_Occupancy?" target="_blank">What is Pore Occupancy?</a></li><li><a href="https://www.cd-genomics.com/longseq/resource-pore-occupancy.html#_Why_is_Pore_Occupancy_Low?" target="_blank">Why is Pore Occupancy Low?</a></li><li><a href="https://www.cd-genomics.com/longseq/resource-pore-occupancy.html#_Strategies_to_Improve_Pore_Occupancy" target="_blank">Strategies to Improve Pore Occupancy</a></li></ul><h3>What is Pore Occupancy?</h3><p dir="ltr"><a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">Pore occupancy</a> is a key determinant of sequencing efficiency and directly impacts the speed and success of data acquisition in the <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">Oxford Nanopore Sequencing Technology Platform</a>. Expressed as a percentage, it represents the proportion of active sequencing channels used during a sequencing run.</p><p dir="ltr">Technically, <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">pore occupancy</a> is defined as the ratio of lanes in sequencing to total lanes (i.e. lanes in sequencing plus lanes in wells) multiplied by 100. This measurement provides a quantitative view of the amount of "threadable" DNA or RNA, including adapters, that is present on the flow cell.</p><h3>Why is Pore Occupancy Low?</h3><p dir="ltr">Although it is important, maintaining optimal <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">pore occupancy</a> can be challenging. If you observe pore occupancy significantly below the 70% threshold within the first hour of a run, it is unlikely that the situation will improve over time. There are several factors that may contribute to suboptimal pore occupancy:</p><ul><li>Inaccurate conversion between mass and moles.</li><li>Underestimation of input mass and fragment length, both of which can affect the final splice joining step and thus recovery.</li><li>The quality and integrity of the starting materials and reagents used also significantly affects <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">pore occupancy</a>.</li><li>Contaminant residues, including those from incompatible buffers, can reduce pore occupancy.</li><li>For some sequencing technologies (e.g., Cas9 Targeted Enrichment Sequencing), the average pore occupancy is inherently low, typically between 5-15%.</li></ul><p dir="ltr">Suboptimal pore occupancy can trigger a number of undesirable consequences. The most immediate effects are electrolyte utilization and the number of "good" pores available for sequencing. As pore occupancy decreases, the number of open-state pores increases, leading to accelerated electrolyte utilization. In addition, the number of good pores decreases rapidly, reducing the total output of the run and the lifetime of the flow cell.</p><h3>Strategies to Improve Pore Occupancy</h3><ul><li>Stop the run and prepare more libraries. Ensure that quantitation is accurate by using a fluorometric assay (e.g., Qubit) and loading the recommended amount of fmol onto the flow cell.</li><li>Ensure efficient conversion between mass and moles. For example, the sequencing junction ligation step of the <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">MinION library</a> is optimized for 0.2 pmol end-prep DNA.</li><li>Accurately estimate input mass and fragment length.</li><li>Pay attention to the quality of reagents and starting materials. Good practice includes checking the integrity of reagents and maintaining the quality of starting materials. Another prevention strategy is to minimize contaminant residues. The use of incompatible buffers and other sources of contaminants can reduce <a href="https://www.cd-genomics.com/longseq/oxford-nanopore-sequencing-technology.html" target="_blank">pore occupancy</a>.</li></ul><p></p>
Cited by 0Open access
Bill Bordass, Adrian Leaman · 2006
Editorial comment Post-occupancy performance evaluation tries to answer four broad questions: ‘How is this building working?’, ‘Is it intended?’, ‘How can it be improved?’ and ‘How can future buildings be improved?’ Answers to these vital questions can add value both to the buildings assessed, and to client, industry and management procedures for future building construction and renovation. In spite of the significant accumulation of knowledge based on user feedback studies and post-occupancy evaluation (POE), those who procure, design and construct buildings are still often reluctant to engage in or carry out POEs on a systematic basis.
Cited by 17
Ben O’Neill · 2021
The classical and extended occupancy distributions occur in cases where balls are randomly allocated to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating the first four central moments of the distributions are implemented as described in O’Neill (2019) <<a href="https://doi.org/10.1080%2F00031305.2019.1699445" target="_top">doi:10.1080/00031305.2019.1699445</a>>.
Cited by 0Open access
Zhao, Meng, Pandharipande, Ashish Vijay; id_orcid 0000-0002-7920-3964, Verberkt, Mark Henricus et al. · TU/e Research Portal · 2020
Some embodiments are directed to an occupancy sensor calibration device (100) arranged to repeatedly detect an occupancy in vision data and a concurrent occupancy detection in the occupancy data, determine a location of the detected occupancy in the vision data, and store the location as part of the occupancy sensing region.
Cited by 0Open access
Data Archiving and Networked Services (DANS) · 2020
Some embodiments are directed to an occupancy sensor calibration device (100) arranged to repeatedly detect an occupancy in vision data and a concurrent occupancy detection in the occupancy data, determine a location of the detected occupancy in the vision data, and store the location as part of the occupancy sensing region.
Cited by 0Open access
Toll Operations, Brian L. Smith, Ph. D et al. · 2007
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Control And Manual, J. Mcdonald, Reinhart C et al. · 2007
A wide range of events in buildings occur at sub-hourly frequencies. Notable examples include daylight-sensing control and manual adjustment of blinds and lights in response to sub-hourly illuminance variations. Such short-term changes can produce notable shifts in instantaneous solar and equipment loads, in turn affecting electrical energy demand. Although sub-hourly time discretization is currently available in several whole building energy simulation programs, it remains challenging to model this level of complexity as traditional hourly utilization profiles describing occupancy, lighting and equipment loads remain the basic input data model. This paper provides a background review of building energy simulation models of stochastic occupancy prediction, occupancy-sensing control and stochastic manual control, and outlines the current addition of a few investigated models to ESP-r.
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MacStravic Rs · PubMed · 1982
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Ivo Herman · Digital Repository (National Repository of Grey Literature) · 2010
This thesis deals with theoretical analysis and practical use of different sensor models for occupancy grids. In the first part was theoretically derived one-dimensional sensor model with Gaussian noise, which was subsequently converted into second dimension. In the second part were compared several of the sensor models: the model by R. Murphy, maps generated by the EM algorithm and also by our implementation of 2D model. Another result of the thesis is computer programme, which allows measurement generating for models in simulated world and incorporating real--sensor data.
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