Output Metrics Research Articles

Development of a topology analysis tool for fifth-generation district heating and cooling networks

Fifth generation district heating and cooling networks are characterized by supply temperatures in the ambient range of 15–25 °C, which not only reduces heat loss but also allows for integrating various kinds of low-temperature waste heat sources. The ability of these networks to absorb waste heat that is normally unrecoverable makes them an attractive solution for the future energy supply of urban areas. To enhance the adoption of these networks, this paper describes the development of a software tool to analyze the feasibility of fifth-generation of district heating and cooling systems in both new and existing districts. The research attempts to answer the question: ”Which buildings should be connected to a low-temperature hydraulic network given both the incremental benefits and costs relative to the scenario of decentralized (dedicated) heating and cooling systems for each building?” Therefore, all possible network layouts of the buildings are considered where the heating and cooling demands of each building are met by the fifth-generation of district heating and cooling network. For the heating and cooling load characterization of the buildings, reduced-order models are used while renewable energy and waste heat sources are included in the network. The focus of the research lies in the development of a hydraulic model for flexible use in the urban energy modeling and the optimization of the fifth-generation of district heating and cooling network topology for a given urban district. Simulation results quantify the performance of the fifth-generation of district heating and cooling network based on various output metrics, including primary energy usage, carbon dioxide emissions, and network implementation cost.

Coordinated production target setting for production–pollutant control systems: A DEA two-stage bargaining game approach

Setting a production target for a two-stage production–pollutant control system can provide guidance for the system to achieve efficient performance in its future production. The existing studies have succeeded in targeting but have missed the consideration of coordination between the adjustments for the two stages’ input and output metrics. In this article, we propose an approach to address this problem using a non-parametric method: data envelopment analysis. First, we use the leader–follower analysis from game theory to propose slacks-based measure models for production target setting in situations where a dominance relationship exists between the two stages of the systems. Based on the results generated by the leader–follower models, we define each stage’s dissatisfaction degree with the set targets. Further, considering the coordination between the adjustments of the two stages’ inputs and outputs, we incorporate the bargaining game theory to propose a model which can generate the set of coordinated adjustments that is a bargaining solution taking account of both the dissatisfaction degrees of the two stages. The properties of the solution generated by the bargaining model feature fairness, which makes the target setting result coordinated between each system’s two stages. Finally, the proposed approaches are applied and tested in an empirical study of 30 provincial regions in China.

A Holistic Performance Management Approach in Business Networks

Companies want to achieve a sustainable financial success through process and performance improvements. Increasing the company’s performance is a good way for sustainable success. Typically, the success is measured by process output metrics. This output mirrors the performance of a company’s processes in the end. It is obvious that for being able to cope with complex business network structures in a derivative supply-demand-structure as well as collaboration cooperation incentives, performance needs to be managed across the whole network to strengthen the own network centrality. The present paper contributes to the development of a new holistic Performance Management in business networks. Thus, a Network Performance Wheel - for a more network oriented approach, and a Process Management Balanced Scorecard - to close the gap between internal and external impacts, are introduced. Also, a PM-VICTORY model is launched for implementing the whole network directed Performance Management.

LO10: Associations between ED crowding metrics and 72h-hour ED re-visits: Which crowding metrics are most highly associated with patient-oriented adverse outcomes?

Introduction: Emergency Department (ED) crowding is a pervasive problem and is associated with adverse patient outcomes. Yet, there are no widely accepted, universal ED crowding metrics. The objective of this study is to identify ED crowding metrics with the strongest association to the risk of ED revisits within 72 hours, which is a patient-oriented adverse outcome. Methods: Crowding metrics, patient characteristics and outcomes were obtained from administrative data for all ED encounters from 2011-2014 for three adult EDs in Calgary, AB. The data were randomly divided into three partitions for cross-validation, and further divided by CTAS category 1, 2/3 and 4/5. Twenty unique ED crowding metrics were calculated and assigned to each patient seen on each calendar day or shift, to standardize the exposure. Logistic regression models were fitted with 72h ED revisit as the dependent variable, and an individual crowding metric along with a common list of confounders as independent variables. Adjusted odds ratios (OR) for the 72h return visits were obtained for each crowding metric. The strength of associations between 72h revisits and crowding metrics were compared using Akaike's Information Criterion and Akaike weights. Results: This analysis is based on 1,149,939 ED encounters. Across all CTAS groups, INPUT metrics (ED census, ED occupancy, waiting time, EMS offload delay, LWBS%) were only weakly associated with the risk of 72h re-visit. Among THROUGHPUT metrics, ED Length of Stay and MD Care Time had similar adjusted ORs for 72h ED re-visit (range 0.99-1.15). Akaike weights ranging from 0.3/1.00 to 0.4/1.00 indicate that both THROUGHPUT metrics are reasonable predictors of 72h ED re-visits. All OUTPUT metrics (boarding time, # of boarded patients, % of beds occupied by boarded patients, hospital occupancy) had statistically significant ORs for 72h ED re-visits. The median boarding time had the highest adjusted OR for 72h ED re-visit (adjusted OR 1.40, 95% CI 1.33-1.47) and highest Akaike weight (0.97/1.00) compared to all other OUTPUT metrics, indicating that median boarding time had the strongest association with 72h re-visits. Conclusion: ED THROUGHPUT and OUTPUT metrics had consistent associations with 72h ED re-visits, while INPUT metrics had little to no association with 72h re-visits. Median boarding time is the strongest predictor of 72h re-visits, indicating that this may be the most meaningful measure of ED crowding.

Identification and characterization of iPS clones via automated live-imaging and in-process analysis for quality control using high throughput robotic system

Background & Aim Induced pluripotent stem (iPS) cells are being developed for a broad range of research and therapeutic applications. However, reprogrammed iPS clones are inevitably heterogeneous. Current value judgements are made by visual inspection and manual selection. These methods are prone to large variation and lack the standardization or scalability needed for clinical translation. The goal of this work is to develop and validate Cell XTM, a robust robotic platform that will enable automated, and standardized iPS cell fabrication. Methods, Results & Conclusion We used DF6-9-9T.B hiPSC cell line, reprogrammed skin fibroblasts and peripheral blood mononuclear cells to develop our image processing parameters and picking and weeding protocols. To define and quantify critical quality attributes (CQAs) of “completely” reprogrammed iPS cells in systematic repeatable and reproducible manner, the Cell XTMPlatform is integrated with ColonyzeTM4.0, a very user-friendly WindowsTM-based automated quantitative cell and colony analysis software. The principles and nomenclature enabling this approach are outlined in ASTM Standard F2944-12. To automate manipulation of iPS cells in rapid, precise, repeatable and rigorously documented manner, the Cell XTMdevice integrates imaging, cell-manipulation (biopsy, pick, weed), fluid handling, and motion control systems. Using the Cell XTMgraphical user-interface, imaging, media changes and automated image processing were performed as illustrated in Figure 1. Picking operations were effective, and transplanted cells continued to proliferate effectively with morphology that is unchanged from that of the original colony. Automated image processing will display output metrics (colony area, the x,y,z-centroid for each colony, colony density, roundness, perimeter etc), as well as highlight the periphery of individual colonies or cell boundaries in the montage image. Based on these metrics, one can quantitatively define the cells or colonies of interest and selectively pick and transfer the cells for future experiments. The Cell XTMPlatform provides an integrated system of automated quantitative cell and colony imaging and analysis as well as tools for precision manipulation (biopsy, picking and weeding) driven by quantitative protocols that objectively determine the CQAs for repeatable, reproducible and quality iPS cell manufacturing.

Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation

To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect of multiple views. Data augmentation was performed during training. The Inception V3, Resnet, and Xception convolutional neural networks were constructed utilizing the Python programming language with Tensorflow as the framework. Training was performed using single radiographic views. Measured output metrics were accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. Model outputs were evaluated using both one and three radiographic views. Ensembles were created from a combination of CNNs after training. A voting method was implemented to consolidate the output from the three views and model ensemble. For single radiographic views, the ensemble of all 5 models produced the best accuracy at 76%. When all three views for a single case were utilized, the ensemble of all models resulted in the best output metrics with an accuracy of 81%. Despite our small dataset size, by utilizing an ensemble of models and 3 views for each case, we achieved an accuracy of 81%, which was in line with the accuracy of other models using a much higher number of cases with pre-trained models and models which implemented manual feature extraction.

SLA-Based Adaptation Schemes in Distributed Stream Processing Engines

With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naïve Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).

Changing Stakeholder Expectations of Library Value

ABSTRACTGiven the on-going emphasis placed on evaluation, measurement and Return on Investment (ROI), many public libraries have struggled to successfully integrate evaluation practices into operational workflows. Unfortunately, it appears that for some libraries who have implemented evaluation, they have focused on metrics that do little to inform the library’s practice, do not demonstrate how a library contributes to its community, and do not assist public libraries in the enhancement of their operations. While traditional output metrics such as circulation, program attendance and technology use provide data about library activities, these metrics are simple counts that fall short in providing insight into why people visited the library, if they accomplished their goals during their visit, or what would have improved their visitor experience. In addition, this reliance on “empty metrics” has had the negative effect of leading stakeholders, policy-makers and funders to believe libraries are nothing more than book depositories. Empty metrics reinforce old and inaccurate stereotypes about public libraries and the role of librarians. We contend that we are at a turning point: evaluation practices are now successfully being embedded into day-to-day library operations and have become a part of library culture. The next step is to ensure libraries develop meaningful metrics that demonstrate the library’s social contribution, and then engage stakeholders to recognize the necessity of adopting these new metrics.

Electrokinetic flow in the U-shaped micro-nanochannels

U-shaped micro-nanochannels can generate significant flow disturbance as well as locally amplified electric field, which gives itself potential to be microfluidic mixers, electrokinetic pumps, and even cell lysis process. Numerical simulation is utilized in this work to study the hidden characteristics of the U-shaped micro-nanochannel system, and the effects of key controlling parameters (the external voltage and pressure) on the device output metrics (current, maximum values of electric field, shear stress and flow velocity) were evaluated. A large portion of current flowing through the whole system goes through the nanochannels, rather than the middle part of the microchannel, with its value increasing linearly with the increase of voltage. Due to the local ion depletion near micro-nanofluidic junction, significantly enhanced electric field (as much as 15 fold at V=1 V and P0=0) as well as strong shear stress (leading to electrokinetic flow) is generated. With increasing external pressure, both electric field and shear stress can be increased initially (due to shortening of depletion region length), but are suppressed eventually at higher pressure due to the destruction of ion depletion layer. Insights gained from this study could be useful for designing nonlinear electrokinetic pumps and other systems.

Comparison of Currents derived from X-band Radar and collected In-situ Data in Ameland Inlet

Velocities derived from X-band radar were compared to depth averaged ADCP measurements in a complex tidal inlet system at Ameland, the Netherlands. Inclusion of depth assimilation and ensemble averaging in radar calculations led to smaller differences between ADCP and radar. The observed differences were clustered and related to water level elevations, wind velocities, wave periods, wave heights, spatial coherence in radar output and error metrics of the radar fitting procedure. Larger waves and higher wind velocities were observed to benefit radar agreement with ADCP results. Rising water levels benefitted agreement in east west direction. Falling water levels benefitted agreement in north south direction. Confidence intervals of the fitting procedure were observed to coincide with differences between ADCP and radar and potential for filtering based on them was shown. Nevertheless, an unclarified tendency towards northwestern bias, which may be specific to the comparison locations, remains. The radar at Ameland monitors the whole inlet system and provides current velocities everywhere in its range. This study shows that its currents are in good agreement with ADCP depth averaged currents throughout most of the tidal cycle. Furthermore, it stresses radar’s potential for better monitoring of the coast and for cost effective coastal field measurements to obtain large datasets, even in hydrodynamically very complex regions.

Benchmarking CME Arrival Time and Impact: Progress on Metadata, Metrics, and Events

AbstractAccurate forecasting of the arrival time and subsequent geomagnetic impacts of coronal mass ejections (CMEs) at Earth is an important objective for space weather forecasting agencies. Recently, the CME Arrival and Impact working team has made significant progress toward defining community‐agreed metrics and validation methods to assess the current state of CME modeling capabilities. This will allow the community to quantify our current capabilities and track progress in models over time. First, it is crucial that the community focuses on the collection of the necessary metadata for transparency and reproducibility of results. Concerning CME arrival and impact we have identified six different metadata types: 3‐D CME measurement, model description, model input, CME (non)arrival observation, model output data, and metrics and validation methods. Second, the working team has also identified a validation time period, where all events within the following two periods will be considered: 1 January 2011 to 31 December 2012 and January 2015 to 31 December 2015. Those two periods amount to a total of about 100 hit events at Earth and a large amount of misses. Considering a time period will remove any bias in selecting events and the event set will represent a sample set that will not be biased by user selection. Lastly, we have defined the basic metrics and skill scores that the CME Arrival and Impact working team will focus on.

A principled approach to conductivity uncertainty analysis in electric field calculations

Uncertainty surrounding ohmic tissue conductivity impedes accurate calculation of the electric fields generated by non-invasive brain stimulation. We present an efficient and generic technique for uncertainty and sensitivity analyses, which quantifies the reliability of field estimates and identifies the most influential parameters. For this purpose, we employ a non-intrusive generalized polynomial chaos expansion to compactly approximate the multidimensional dependency of the field on the conductivities. We demonstrate that the proposed pipeline yields detailed insight into the uncertainty of field estimates for transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), identifies the most relevant tissue conductivities, and highlights characteristic differences between stimulation methods. Specifically, we test the influence of conductivity variations on (i) the magnitude of the electric field generated at each gray matter location, (ii) its normal component relative to the cortical sheet, (iii) its overall magnitude (indexed by the 98th percentile), and (iv) its overall spatial distribution. We show that TMS fields are generally less affected by conductivity variations than tDCS fields. For both TMS and tDCS, conductivity uncertainty causes much higher uncertainty in the magnitude as compared to the direction and overall spatial distribution of the electric field. Whereas the TMS fields were predominantly influenced by gray and white matter conductivity, the tDCS fields were additionally dependent on skull and scalp conductivities. Comprehensive uncertainty analyses of complex systems achieved by the proposed technique are not possible with classical methods, such as Monte Carlo sampling, without extreme computational effort. In addition, our method has the advantages of directly yielding interpretable and intuitive output metrics and of being easily adaptable to new problems.

Navigating the rising metrics tide in the 21st century: which way for academic librarians in support of researchers in sub-Saharan Africa?

Academic librarians in sub-Saharan Africa, just like their counterparts in the rest of the world, are under pressure to provide relevant information services to satisfy varied client needs. Research administrators, grant offices, researchers, students and university managers, among others, are increasingly turning to academic librarians for information to meet their myriad research-related information and metrics needs. Sub-Saharan African countries’ emphasis on strengthening science, technology and innovation has exerted further pressure on information professionals and other stakeholders to develop and appropriately apply bibliometrics and altmetrics to inform decision-making and policy-formulation processes. Research output and impact metrics are not only quickly evolving but their volume has been unprecedented. This paper’s focus is the application of bibliometrics and altmetrics by academic librarians to support researchers in their respective universities. The paper argues that there is need for up-skilling or re-skilling the 21 st -century sub-Saharan African academic librarian to select and apply appropriate metrics, tools and strategies to support researchers as they execute their responsibilities. The paper offers possible areas of training for academic librarians.

Sampling strategies for approximating patient variability in population-based finite element studies of total hip replacement.

Total hip replacements must be robust to patient variability for long-term success in the population. The challenge during the design process is evaluating an implant in a diverse population but the computational cost of simulating a population of subject-specific finite element (FE) models is not practical. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models to approximate the variability in output metrics. Comparing with the median and distribution of the 95th percentile composite peak micromotion (CPM) and polar gap in the full cohort (CPM median: 136μm, interquartile range [IQR]: 74-230μm) (Polar Gap median: 467μm, IQR: 434-548μm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169μm, IQR: 78-236μm) (Polar Gap median: 469μm, IQR: 448-537μm). Convex hull sampling (41 subjects) more closely captured the output metrics (CPM median: 99μm, IQR: 70-191μm) (Polar Gap median: 456μm, IQR: 418-533μm) but required over three times the number of subjects. Volume reduction of the convex hull captured the extremes of variability with subsets of 5 to 20 subjects, while the largest minimum-distance strategy captured the variability toward the middle of the cohort. These strategies can estimate the level of variability in FE model output metrics with a low computational cost when evaluating implants during the design process.

A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios

Abstract. To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land-use and climate change on biodiversity and ecosystem services (i.e., nature's contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) – SSP1xRCP2.6, SSP3xRCP6.0, SSP5xRCP8.6 – to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenario selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem services models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modeling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modeling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.

Point-of-care colorimetric analysis through smartphone video

Point-of-care (POC) tests often rely on smartphone image methods for colorimetric analysis, but the results of such methods are frequently difficult to reproduce or standardize. The problem is aggravated by unpredictable image capture conditions, which pose a significant challenge when low limits of detection (LOD) are needed. Application-specific smartphone attachments are often used to standardize imaging conditions, but there has recently been an interest in equipment-free POC colorimetric analysis. Improved output metrics and preprocessing methods have been developed, but equipment-free imaging often has a high LOD and is inappropriate for quantitative tasks. Additional work is necessary to replace external smartphone attachments with algorithms. Towards this end, we have developed a video processing method that synthesizes many images into a single output metric. We use image features to select the best inputs from a large set of video frames and demonstrate that the resulting output values have a stronger correlation with laboratory methods and a lower standard error. The developed algorithm only requires 20 s of video and can easily be integrated with existing processing methods. We apply our algorithm to the NS1-based sandwich ELISA for Zika detection and show that the LOD is two times lower when our video-based method is used.

Weighting Factor Design in Model Predictive Control of Power Electronic Converters: An Artificial Neural Network Approach

This paper proposes the use of an artificial neural network (ANN) for solving one of the ongoing research challenges in finite set-model predictive control (FS-MPC) of power electronics converters, i.e., the automated selection of weighting factors in cost function. The first step in this approach is to simulate a detailed converter circuit model or run experiments numerous times using different weighting factor combinations. The key performance metrics [e.g., average switching frequency ( $f_{{\rm sw}}$ ) of the converter, total harmonic distortion, etc.] are extracted from each simulation. This data is then used to train the ANN, which serves as a surrogate model of the converter that can provide fast and accurate estimates of the performance metrics for any weighting factor combination. Consequently, any arbitrary user-defined fitness function that combines the output metrics can be defined and the weighting factor combinations that optimize the given function can be explicitly found. The proposed methodology was verified on a practical weighting factor design problem in FS-MPC regulated voltage source converter for uninterruptible power supply system. Designed weighting factors for two exemplary fitness functions turned out to be robust to load variations and to yield close to expected performance when applied both to detailed simulation model (less than 3% error) and to experimental test bed (less than 10% error).

Suppression on Nonlinearity of Mode-Localized Sensors Using Algebraic Summation of Amplitude Ratios as the Output Metric

Compared with the classic frequency-modulated resonant sensors, the recently emerging sensing scheme based on the mode localization phenomenon of the weakly coupled resonators provides the improved sensitivity by more than 2 orders of magnitude using amplitude ratio as the output metric. However, the inherent loci veeringphenomenon of the amplitude ratio over stiffness perturbation curvegenerates strong or weak nonlinearities in different zones, severelylimiting the linear sensing ranges. In this paper, we propose to use the algebraic summation of the amplitude ratios of two vibrational modes for eliminating the nonlinear errors caused by loci veering, extending the linear sensing range to the fullscale. This new output metric was verified as being very effective to suppress the nonlinear errors using two mode-localized stiffness sensors with 2 and 3 degrees of freedom coupled resonators. This is the first theoretically and experimentally demonstrated full-scale linear output metric for mode-localized sensors, which will push forward more applications of the mode-localized sensing scheme.

Ultimate Limits of Differential Resonant MEMS Sensors Based on Two Coupled Linear Resonators.

In this paper, we investigate how additive noise, e.g., thermomechanical noise, impacts the resolution of mode-localized resonant sensing architectures based on two passively coupled linear resonators. Existing work suggests that the ultimate resolution of these sensors can be improved by decreasing the coupling coefficient of the resonators. The present work gives an analytical proof that this result does not hold, and that the resolution of such sensors is actually independent of the coupling strength. These results are established for different output metrics and operating points, in closed loop and in open loop, and compared to those obtained with other approaches based on actively coupled resonators.

Transmission on empirical dynamic contact networks is influenced by data processing decisions

Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R0, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.