scholarly journals Daylight And Seating Preference In Open-Plan Library Spaces

2017 ◽  
Vol 17 ◽  
pp. 12-20 ◽  
Author(s):  
Zeynep Keskin ◽  
Yunhao Chen ◽  
Steve Fotios
Keyword(s):  
Performance Metrics ◽  
Time Varying ◽  
University Libraries ◽  
Quantitative Measurements ◽  
Wide Range ◽  
Annual Dynamic ◽  
Open Plan ◽  
Daylight Performance ◽  
Sky Conditions ◽  
Common Understanding

Daylight factor has long been the predominant metric to evaluate daylight performance. Recently, the profession has moved toward annual dynamic daylight metrics such as useful daylight illuminance and daylight autonomy, which are based on absolute values of time varying daylight illuminance for a period of full year. As opposed to static daylight metrics that only concentrate on individual sky conditions, such as the widely used daylight factor, these metrics provide a more comprehensive way to measure illuminance for a wide range of sun positions and sky conditions. Although there is a growing consensus assigning importance to dynamic daylight metrics, there is no common understanding of how to integrate the preference and behaviour of building occupants in assessing the applicability of these metrics. In fact, it is when these occupancy observations and quantitative measurements are taken together that the importance of daylight performance metrics is fully realized. This study seeks to investigate the extent to which the influence of daylight on behaviour can be predicted, and for this the behaviour investigated is seating preferences of occupants in open plan, hot-desking spaces in two university libraries in Sheffield: Western Bank Library and the Information Commons. The results suggest that the association between daylight and seat choice may not be strong, and that any effect is better associated with daylight factor than with useful daylight illuminance or daylight autonomy.

Understanding Distressed Residential Transaction Discounts Across Price Segments and Market Conditions

2020 ◽  
Vol 42 (1) ◽  
pp. 151-182
Author(s):  
Ramya Rajajagadeesan Aroul ◽  
J. Andrew Hansz ◽  
Mauricio Rodriguez
Keyword(s):  
Transaction Costs ◽  
Time Varying ◽  
Market Segment ◽  
Market Conditions ◽  
Large Market ◽  
Space And Time ◽  
Wide Range ◽  
Time Periods ◽  
Market Areas

In the literature, there is a wide range of discounts associated with foreclosures. Comparisons across studies are difficult as they use different methodologies across large areas over different time periods. We employ a consistent methodology across space and time. We find modest discounts, within the range of typical transaction costs, in all but the highest priced market segment. Higher priced segments could explain prior findings of substantial discounts. We find that discounts are time-varying, with discounts increasing with market distress. A one-size-fits-all approach is not appropriate when estimating distressed transaction discounts across large market areas or under changing market conditions.

scholarly journals Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

2021 ◽  
Vol 11 (13) ◽  
pp. 5859
Author(s):  
Fernando N. Santos-Navarro ◽  
Yadira Boada ◽  
Alejandro Vignoni ◽  
Jesús Picó
Keyword(s):  
Gene Expression ◽  
Gene Transcription ◽  
Performance Metrics ◽  
Cell Mass ◽  
Gene Copy ◽  
Substrate Uptake ◽  
Promoter Strength ◽  
Expression Levels ◽  
Trade Offs ◽  
Wide Range

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

Evaluation methods of the daylight performance and potential energy saving of tubular daylight guide systems: A review

2021 ◽  
pp. 1420326X2199241
Author(s):  
Hanlin Li ◽  
Dan Wu ◽  
Yanping Yuan ◽  
Lijun Zuo
Keyword(s):  
Potential Energy ◽  
Energy Saving ◽  
Energy Savings ◽  
Performance Metrics ◽  
Field Measurements ◽  
Building Design ◽  
Estimation Methods ◽  
The Past ◽  
Potential Energy Saving ◽  
Daylight Performance

In the past 30 years, tubular daylight guide systems (TDGSs) have become one of the most popular ways to transport outdoor natural light into the inner space in building design. However, tubular daylight guide systems are not widely used because of the lack of methods to evaluate methods on the suitability of the TDGSs. This study therefore summarizes the daylight performance metrics of TDGSs and presents the estimation methods in terms of field measurements, simulation and empirical formulae. This study focuses on the daylight performance and potential energy savings of TDGSs. Moreover, this study will be helpful for building designers to build healthy, comfortable and energy-saving indoor environment.

scholarly journals Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dieter M. Tourlousse ◽  
Koji Narita ◽  
Takamasa Miura ◽  
Mitsuo Sakamoto ◽  
Akiko Ohashi ◽  
...  
Keyword(s):  
Dna Extraction ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Collaborative Study ◽  
Human Microbiome ◽  
Second Phase ◽  
Intermediate Precision ◽  
Fecal Microbiome ◽  
Library Construction ◽  
Wide Range

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

Abstract TP132: Selection and Activation of Sites in a Large Multi-Center Randomized Clinical Trial

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Bart M Demaerschalk ◽  
Robert D Brown ◽  
Virginia J Howard ◽  
MeeLee Tom ◽  
Mary E Longbottom ◽  
...  
Keyword(s):  
Site Selection ◽  
Performance Metrics ◽  
Interventional Neuroradiology ◽  
Interquartile Range ◽  
Carotid Revascularization ◽  
Training Requirements ◽  
Medical Centers ◽  
Wide Range ◽  
The Times ◽  
And Training

Introduction: Careful selection and timely activation of clinical sites in multicenter clinical trials is critical for successful enrollment, subject safety, and generalizability of results. Methods: In the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST-2), a multidisciplinary Site Selection Committee evaluated applicants referred via participation in CREST, CREST principal investigators (PIs) and other investigators, StrokeNet and industry partners. Data for consideration included performance metrics in CREST and other carotid trials and a site selection questionnaire containing information on the investigators as well as quantitative data on carotid procedures performed. Any FDA warning letters were reviewed. Results: The Committee met bi-weekly for 36 months (n=64 meetings). Applications from 176 sites between March 2014 and July 2016 were evaluated: 153 were approved, 7 are under Committee review, 5 were approved but withdrew, 5 were placed on a waiting list, and 6 were rejected. One-hundred-four sites have completed the regulatory and training requirements to randomize: 51 (49%) academic medical centers, 31 (30%) private hospital-based centers, 16 (15%) private office-based practices, and 6 (6%) Veterans Administration medical centers. The mean times from application-to- approval was 5.2 weeks (interquartile range, 1.9, 6.2), and from approval-to-randomization status was 46.7 weeks (interquartile range, 35.4, 51.7). Specialties of the 104 site PIs are vascular surgery for 35 (33.7%), cardiology for 30 (28.8%), neurology for 25 (24%), neurosurgery for 8 (7.7%), interventional radiology for 4 (3.8%), and interventional neuroradiology for 2 (1.9%). Conclusions: Careful site selection is time-consuming for prospective sites and for trial leadership. Times from application-to-site-approval were modest (mean = 5.2 weeks), in contrast to the times for completing regulatory and training requirements (mean = 46.7 weeks). However, subject enrollment by teams from a wide range of medical centers led by a multi-disciplinary cohort of PIs will promote the generalizability of trial results.

scholarly journals Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3143 ◽  
Author(s):  
Ignacio Acosta ◽  
Miguel Ángel Campano ◽  
Samuel Domínguez-Amarillo ◽  
Carmen Muñoz
Keyword(s):  
Energy Efficiency ◽  
Net Present Value ◽  
Energy Savings ◽  
Performance Metrics ◽  
Control Strategies ◽  
Window Size ◽  
Weather Conditions ◽  
Worst Case ◽  
Daylight Performance ◽  
Dynamic Metrics

Daylight performance metrics provide a promising approach for the design and optimization of lighting strategies in buildings and their management. Smart controls for electric lighting can reduce power consumption and promote visual comfort using different control strategies, based on affordable technologies and low building impact. The aim of this research is to assess the energy efficiency of these smart controls by means of dynamic daylight performance metrics, to determine suitable solutions based on the geometry of the architecture and the weather conditions. The analysis considers different room dimensions, with variable window size and two mean surface reflectance values. DaySim 3.1 lighting software provides the simulations for the study, determining the necessary quantification of dynamic metrics to evaluate the usefulness of the proposed smart controls and their impact on energy efficiency. The validation of dynamic metrics is carried out by monitoring a mesh of illuminance-meters in test cells throughout one year. The results showed that, for most rooms more than 3.00 m deep, smart controls achieve worthwhile energy savings and a low payback period, regardless of weather conditions and for worst-case situations. It is also concluded that dimming systems provide a higher net present value and allow the use of smaller window size than other control solutions.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

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