Multiple Performance Metrics Research Articles

A multi-measure and hybrid iterative procedure for marine ports (re)design

In view of the high terminal construction and maintenance costs, environmental impacts and risk of shipping accidents, public bodies, port authorities, and terminal operators all around the world are looking for new decision support methodologies, especially in the case of large-scale marine ports (re)design projects. The authors propose a multi-measure, hybrid, and iterative procedure, which benefits from the use of real-time simulation to drive the design engineers through a continuous and gradual improvement of the design solution. After having defined multiple performance metrics (margins for safety, pollutant emissions) as well as having designed and implemented a realistic virtual test environment, the design solutions can be generated according to throughput and traffic requirements, and then tested by key nautical experts. Criticalities as well as potential improvements of the design solution are visually reported according to a set of results statistically collected at the end of each testing session.

An efficient prediction framework for multi-parametric yield analysis under parameter variations

Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multi-parametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net (AEN) method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function (CDF) based on the multiplication theorem and the Markov chain Monte Carlo (MCMC) method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

Impacts of Intelligent Automated Quality Control on a Small Animal APD-Based Digital PET Scanner

Stable system performance is mandatory to warrant the accuracy and reliability of biological results relying on small animal positron emission tomography (PET) imaging studies. This simple requirement sets the ground for imposing routine quality control (QC) procedures to keep PET scanners at a reliable optimal performance level. However, such procedures can become burdensome to implement for scanner operators, especially taking into account the increasing number of data acquisition channels in newer generation PET scanners. In systems using pixel detectors to achieve enhanced spatial resolution and contrast-to-noise ratio (CNR), the QC workload rapidly increases to unmanageable levels due to the number of independent channels involved. An artificial intelligence based QC system, referred to as Scanner Intelligent Diagnosis for Optimal Performance (SIDOP), was proposed to help reducing the QC workload by performing automatic channel fault detection and diagnosis. SIDOP consists of four high-level modules that employ machine learning methods to perform their tasks: Parameter Extraction , Channel Fault Detection , Fault Prioritization , and Fault Diagnosis . Ultimately, SIDOP submits a prioritized faulty channel list to the operator and proposes actions to correct them. To validate that SIDOP can perform QC procedures adequately, it was deployed on a LabPET ™ scanner and multiple performance metrics were extracted. After multiple corrections on sub-optimal scanner settings, a 8.5% (with a 95% confidence interval (CI) of [7.6, 9.3]) improvement in the CNR, a 17.0% (CI: [15.3, 18.7]) decrease of the uniformity percentage standard deviation, and a 6.8% gain in global sensitivity were observed. These results confirm that SIDOP can indeed be of assistance in performing QC procedures and restore performance to optimal figures.

On Secrecy Performance of Multiantenna-Jammer-Aided Secure Communications With Imperfect CSI

In this paper, secure communication aided by a multiantenna cooperative jammer is investigated. Under very practical but adverse assumptions, i.e., no instantaneous jammer–eavesdropper channel state information (CSI) and imperfect instantaneous jammer–receiver CSI, the residual interference from the jammer to the legitimate receiver appears, even with spatial beamforming. Then, this paper analyzes the impact of imperfect CSI on the function of cooperative jamming and derives closed-form expressions of multiple secrecy performance metrics, including ergodic secrecy rate, secrecy outage capacity, and interception probability. Interestingly, it is found that two antennas at the jammer is always optimal. Furthermore, some insights are obtained through asymptotic analysis, e.g., the secrecy performance is saturated as the source transmit power increases, the ergodic secrecy rate is a linear function of CSI feedback bits in the region of high transmit power at the source/jammer, and interception probability is independent of transmit power at the source and is a decreasing function of transmit power at the jammer. Finally, theoretical claims are validated by simulation results.

A case for information theory-based modelling of human multitasking performance

ABSTRACTTechnological advances have led to increased prevalence of human multitasking. Traditionally, multitasking performance is measured for each individual task, utilising multiple performance metrics. Recently, the human operator informatic model (HOIM) has been developed as a quantitative model of human–machine interaction capable of measuring multitasking performance with a single metric. In this study, we investigated the performance effects of both a different number of tasks and five different task combinations while maintaining a constant level of overall difficulty. Results indicate that, at a constant difficulty level, neither number of tasks nor task combination significantly affects multitasking performance, indicating the reliability of input baud rate as a measure of system complexity. No task interaction effects were found among the different tasks. This study demonstrates the application of information theory for modelling of human multitasking performance and makes a case for the use of the HOIM in future studies of human information processing.

Validation of models predicting modulus of elasticity in Douglas-fir trees, boles, and logs

Acoustic tools have simplified estimation of wood modulus of elasticity (MOE). Strong relationships between acoustic velocity and MOE of logs have encouraged use of acoustics at earlier points in the value chain, culminating in the development of acoustic harvesting systems. With accurate estimates of MOE of individual trees, improvements in efficiency along the value chain and increased value recovery will result. Our aim was to quantify the accuracy of MOE estimates at three distinct points: pre-harvest (standing trees), during harvest (merchantable boles), and post-harvest (5-m logs). We hypothesised that: (1) MOE estimated from acoustic velocity and wood density would provide greatest accuracy; and (2) bole estimates with a resonance tool would be more accurate than tree estimates with a time-of-flight tool. A sample of 168 Douglas-fir (Pseudotsuga menziesii [Mirb. Franco]) trees, representing the variability in acoustic velocity of 700 standing 36–51-year-old trees, was harvested from three sites. Prior to harvest, time-of-flight and breast-height diameter were recorded. After felling, resonance velocities of boles and subsequent 5-m logs were recorded. Discs, cut from log ends, were immersed, and green wood density determined. Half the logs were processed into boards, the other half into veneer sheets, and all products (in excess of 6000) non-destructively tested for MOE. MOE of parent trees, boles, and logs was then calculated from the mean MOE of derived products. Predictive mixed-effects models of tree, bole, and log MOE were developed using data from 139 trees. Fixed effects comprised combinations of velocity squared, wood density, acoustic MOE (derived from the wave equation), diameter, height, taper, and age. Random effects comprised site, plot, and, at the log level, tree. The models were validated using data from the remaining trees and compared using multiple performance metrics. For estimating tree MOE, a model with velocity squared, wood density, and taper as predictors is recommended. For estimating MOE of boles and logs, models with velocity squared and wood density are recommended. The models have an accuracy, as determined by RMSE, of about ± 2 GPa. For accurate MOE estimation, velocity alone is insufficient. Knowledge of wood density is necessary for improved accuracy.

Toward an Algorithm for Estimating Latent Heat Release in Warm Rain Systems

AbstractThis paper outlines an approach for estimating latent heating, surface rainfall rate, and liquid water path in warm rain from downward-viewing W-band radar observations using a Bayesian Monte Carlo algorithm. The algorithm utilizes observed vertical and path-integrated characteristics of precipitating liquid clouds to identify the most appropriate hydrometeor and latent heating structures in a large database of profiles generated using a cloud-resolving model. These characteristics are selected by applying multiple performance metrics to synthetic retrievals. Analysis of the retrievals suggests that a combination of cloud-top, rain-top, and maximum reflectivity heights; vertically integrated reflectivity and attenuation; and a measure of near-surface intensity is sufficient to constrain bulk properties and the vertical structure of warm rain systems. When applied to observations at CloudSat resolution, biases in retrieved liquid water path and surface rainfall rate are small (less than 10%). The algorithm also captures the vertical structure of latent heating, although the magnitudes of integrated heating and cooling exhibit nearly compensating low biases. Random errors are larger owing to the limitations of single-frequency radar observations in constraining drop size distributions. Uncertainties in the altitudes of peak heating and cooling at the pixel scale are typically less than one vertical level, while uncertainties in vertically resolved estimates of heating and cooling rates are on the order of a factor of 2. The utility of the technique is illustrated through application to case studies from airborne radar data from the VAMOS Ocean–Cloud–Atmosphere–Land Study field campaign and satellite observations from CloudSat.

Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance

ABSTRACTRecent studies have compared the performance of machine learning algorithms versus logistic regression models in predicting recidivism. Existing research, however, has not compared their performance to Burgess methodology—a transparent, simplistic and summative classification technique used to develop some of the most widely risk and needs assessment instruments used within corrections today. Using multiple performance metrics and measures of recidivism on 40,740 Minnesota offenders released from prison between 2006 and 2011, we evaluate the performance of prediction models developed with both Burgess methodology and supervised learning algorithms (i.e., statistical and machine learning algorithms). The results show that, compared to the best supervised learning classifiers, use of Burgess methodology yielded inferior performance in terms of predictive discrimination, accuracy, and calibration.

Determinants of corporate environmental reporting: the importance of environmental performance and assurance

Companies are increasingly held accountable for the impact of their decisions and activities on the environment. These developments have been associated with a heightened tendency for companies to publish a variety of information on environmental topics in corporate environmental or sustainability reports. This study explores relationship between the level and nature of voluntary corporate environmental reporting (CER) practices, multiple corporate environmental performance metrics and external assurance. For this reason, measures of greenhouse gas emissions, waste production and water consumption were quantified and a distinction was made between corporate environmental reports with or without external assurance for a sample of Dutch companies during the period 2009–2011. Our results show that greenhouse gas emissions and water consumption, and external assurance play a significant, incremental role in explaining the variation in the level and nature of CER. The results support the view that legitimacy plays an important role in companies' choices concerning environmental disclosure. At the same time, our descriptive statistics indicate that companies systematically disclose an incomplete picture of how their decisions and activities affect the environment. Our findings suggest a need to complement voluntary CER with mandatory requirements for sustainability reporting in combination with strong enforcement mechanisms to urge companies to become more accountable in terms of environmental performance.

Abstract 235: Prehospital STEMI Protocol Administering Aspirin, Ticagrelor, and Heparin Reduces Reperfusion Times

Background: Emergency Medical Service (EMS) agencies serving large rural areas have not traditionally administered Ticagrelor or Heparin IV in the pre-hospital setting for STEMI patients who are ultimately transferred for Primary PCI. A recent international study demonstrated the safety of administering Ticagrelor by EMS. We hypothesized that allowing EMS to administer medications normally given in the hospital setting could be beneficial. A protocol was implemented with the goal being that if the ECG is Positive, give Aspirin, Ticagrelor, and Heparin, also known as (EPATH). Methods: Data was collected on 31 consecutive STEMI activations from a large rural area, from both the regional hospital and by the local EMS agency, prior to implementation of the EPATH Protocol, and 31 consecutive STEMI activations after implementing the EPATH Protocol. Data was stored on a Microsoft Excel spreadsheet, and analyzed using the calculation functions available within the database. Findings: Multiple STEMI performance Metrics improved, including the following: The median first EMS contact to Percutaneous Coronary Intervention (PCI) time was reduced by 28 minutes. The median 911 call to PCI time was reduced by 21 minutes. The rural referral hospital median door to PCI time was reduced by 22 minutes. Conclusion: After implementing the EPATH protocol in a large rural area, which allowed EMS to administer Prehospital Aspirin, Ticagrelor, and Heparin for STEMI, we observed an improvement in multiple STEMI performance metrics, including a 28 minute median reduction in first EMS contact to PCI time. Further research is warranted.

A Jack of All Trades But a Master of None? Evaluating the Performance of the Level of Service Inventory–Revised (LSI-R) in the Assessment of Risk and Need

ABSTRACTWe evaluate the performance of the LSI-R in assessing risk and need among 26,001 offenders released from Minnesota prisons between 2003 and 2011. Using multiple performance metrics, the results reveal the LSI-R incorrectly classified 41 percent of the offenders, and its area under the curve (AUC) was 0.628. Drawing on the extensive program evaluation research conducted on Minnesota prisoners over the last decade, we evaluated the LSI-R’s assessment of need by examining the relationship between LSI-R domain scores, participation in effective interventions, and recidivism outcomes. The findings provide, at best, moderate support for the LSI-R’s performance in assessing need. Interventions targeting the education/employment, alcohol/drug, and emotional/personal domains generally yielded better recidivism outcomes among offenders with higher scores in these domains.

Measuring performance of sustainable manufacturing with recyclable wastes: A case from China’s iron and steel industry

In recent years, performance evaluation of sustainable operations has attracted a significant amount of research. The current paper seeks to evaluate sustainable manufacturing performance when wastes are recycled and re-used. For example, a typical sustainable process in iron and steel making involves in the first stage forging iron and steel products and in the second stage disposing the wastes. The sustainable manufacturing process then re-uses the recycled waste water in the first stage production. It is important that performance metrics include the operations related to recycling and re-use of the wastes. In a simplified view, such a sustainable operation is a two-stage process. Due to the ability of incorporating multiple performance metrics, data envelopment analysis (DEA) is used to develop our evaluation approach. While traditional DEA treats such two-stage manufacturing process as a black box by ignoring the internal conflicts and cooperation among multiple manufacturing stages, this paper proposes a modified two-stage network DEA model that deals with good and undesirable outputs from the sustainable manufacturing process. A Nash bargaining game between efficiencies of two-stages is proposed to produce unique and fair efficiency decomposition for the two-stage sustainable manufacturing process. The modified two-stage network DEA model is applied to wastewater recycling and reusing in a set of iron and steel makers in China. The results show that the proposed model is more effective than the black box DEA model in calculating efficiency of both two-stages and in identifying the sources of the inefficiency of overall sustainable manufacturing processes.

Optimal Modality Selection for Cooperative Human-Robot Task Completion.

Human-robot cooperation in complex environments must be fast, accurate, and resilient. This requires efficient communication channels where robots need to assimilate information using a plethora of verbal and nonverbal modalities such as hand gestures, speech, and gaze. However, even though hybrid human-robot communication frameworks and multimodal communication have been studied, a systematic methodology for designing multimodal interfaces does not exist. This paper addresses the gap by proposing a novel methodology to generate multimodal lexicons which maximizes multiple performance metrics over a wide range of communication modalities (i.e., lexicons). The metrics are obtained through a mixture of simulation and real-world experiments. The methodology is tested in a surgical setting where a robot cooperates with a surgeon to complete a mock abdominal incision and closure task by delivering surgical instruments. Experimental results show that predicted optimal lexicons significantly outperform predicted suboptimal lexicons (p <; 0.05) in all metrics validating the predictability of the methodology. The methodology is validated in two scenarios (with and without modeling the risk of a human-robot collision) and the differences in the lexicons are analyzed.

How animals glide: from trajectory to morphology

Animals that glide produce aerodynamic forces that enable transit through the air in both arboreal and aquatic environments. The relative ease of gliding compared with flapping flight has led to a large diversity of taxa that have evolved some degree of flight capability. Glide paths are curved, reflecting the changing forces on the animal as it progresses through its aerial trajectory. These changing forces can be under control of the glider, which uses specific aspects of anatomy to modulate lift, drag, and rotational moments on the body. However, gliders share no single anatomical or behavioral feature, and some species are unspecialized for gliding, producing aerodynamic forces using posture and orientation alone. Animals use gliding in a broad range of ecological roles, suggesting that multiple performance metrics are relevant for consideration, but we are only beginning to understand how gliders produce and control their flight from takeoff to landing. In this review, we focus on the physical aspects of how glide trajectories are produced, and additionally discuss the range of morphologies and postures that are used to control aerial movements across the broad diversity of animal gliders.

Concurrent bandwidth scheduling for big data transfer over a dedicated channel

Many applications in various domains are producing colossal amounts of data, now frequently termed as 'big data', which must be transferred over long distances for remote operations. Such data transfer requires dedicated channels with high and stable bandwidth provisioned by high-performance networks HPNs through bandwidth reservation. For a data transfer task, from an application user's perspective, a common goal is to achieve the earliest completion time ECT, while from a network operator's perspective, a common goal is to achieve the shortest duration SD. In this paper, we investigate the problem of scheduling as many concurrent bandwidth reservation requests BRRs as possible over one dedicated channel in an HPN while achieving the average ECT and the average SD of scheduled BRRs. We show that both problems are NP-hard, and propose a heuristic algorithm for each. The performance superiority of the proposed algorithms is illustrated by extensive simulations in comparison with three other algorithms in terms of multiple performance metrics.

A genetic programming framework to schedule webpage updates

The quality of a Web search engine is influenced by several factors, including coverage and the freshness of the content gathered by the web crawler. Focusing particularly on freshness, one key challenge is to estimate the likelihood of a previously crawled webpage being modified. Such estimates are used to define the order in which those pages should be visited, and thus, can be exploited to reduce the cost of monitoring crawled webpages for keeping updated versions. We here present a Genetic Programming framework, called $$ GP4C $$GP4C--Genetic Programming for Crawling, to generate score functions that produce accurate rankings of pages regarding their probabilities of having been modified. We compare $$ GP4C $$GP4C with state-of-the-art methods using a large dataset of webpages crawled from the Brazilian Web. Our evaluation includes multiple performance metrics and several variations of our framework, built from exploring different sets of terminals and fitness functions. In particular, we evaluate $$ GP4C $$GP4C using the ChangeRate and Normalized Discounted Cumulative Gain (NDCG) metrics as both objective function and evaluation metric. We show that, in comparison with ChangeRate, NDCG has the ability of better evaluating the effectiveness of scheduling strategies, since it is able to take the ranking produced by the scheduling into account.

OFDMA-Based Channel-Width Adaptation in Wireless Mesh Networks

Channel-width adaptation can optimize multiple performance metrics of a wireless communication link, including transmission rate, communication range, resilience to delay spread, and power consumption. Supporting variable channel width has been considered one of the most critical features of a communication radio. How to leverage a channel-width adaptive radio to improve throughput of a wireless network is a challenging problem in the medium access control (MAC) layer. So far, there exist research results on either theoretical analysis or protocol implementation for a point-to-multipoint (PMP) infrastructure network. However, the impact of channel width to a multihop wireless network has not been fully investigated yet. More specifically, how to exploit variable channel width to enhance throughput of a multihop wireless network remains an unresolved research issue. This paper addresses this issue in wireless mesh networks (WMNs) considering orthogonal frequency-division multiple-access (OFDMA)-based channel-width adaptation. Theoretical analysis is first carried out to identify appropriate algorithms for channel width adaptation. To this end, resource allocation with OFDMA-based channel-width adaptation is formulated as an optimization problem, which is proved to be NP-complete. To reduce the computational complexity, a greedy algorithm is derived to obtain a suboptimal solution. Based on such a greedy algorithm, a distributed MAC protocol is designed for channel-width adaptation for OFDMA-based WMNs. It takes advantage of variable channel width in different time slots to achieve highly efficient resource allocation. Simulation results illustrate that the distributed MAC protocol significantly outperforms MAC protocols based on traditional channel-width adaptation.

Abstract 302: Novel implementation of cloud computing and mobile technology to transform data collection and management in cancer epidemiology

Abstract Cancer epidemiology cohorts (CECs) make vital contributions to molecular epidemiology and translational research. Large, well-annotated CECs require many years and substantial resources to build, mature, and maintain, but many CECs use inefficient legacy methods to collect and manage data. CECs must become more cost-efficient and find better ways to gather, store, and share data, and declining response rates suggest CEC participants increasingly require more personal and engaging methods. The California Teachers Study (CTS) is a CEC that has followed 133,479 female public school professionals since 1995-1996. We implemented a cloud-based customer relationship management (CRM) solution from Salesforce.com for a 4-year project (2012-2016), funded by the NCI and NIEHS, that is collecting 21,722 blood or saliva samples from CTS participants for use in future biomarker discovery and validation research. Phlebotomists at 3 sites (COH, CPIC, &amp; UCI) visit participants in-person, conduct interviews, collect blood, and ship samples overnight to Fisher BioServices in Maryland for processing and storage. Our cloud-based system uses the web, smartphones, and tablets to recruit participants; schedule appointments; aggregate questionnaire data; and automate specimen collection, shipping, and tracking. Phlebotomists use a custom-built mobile app to scan consent forms, blood tubes, and FedEx shipping labels. The app includes a smart e-questionnaire that is customized for each person based on her answers to previous CTS questionnaires. Each night, the system automatically assembles and transmits a detailed shipping manifest directly to Fisher BioServices. All data are available for immediate access via a secure, CTS-specific Salesforce.com instance, in which we can track multiple progress and performance metrics on demand. Extensive security and validations protect data privacy and confidentiality. By using mobile technology to automate the study protocol across all sites, eliminating manual data entry during collection appointments, and removing data silos, our innovative approach increases productivity, ensures protocol compliance across study sites, and reduces data management costs. The Salesforce.com system is easily scalable for additional types of data collection and is an excellent tool for improving CEC infrastructure. Citation Format: Jennifer L. Benbow, Christina A. Clarke, Hannah L. Park, Christine N. Duffy, Nadia T. Chung, Amy E. Brankin, Karen Angell-Mendez, Argyrios Ziogas, Rich Pinder, Dennis Deapen, Hoda Anton-Culver, Susan L. Neuhausen, Leslie Bernstein, Sophia S. Wang, James V. Lacey. Novel implementation of cloud computing and mobile technology to transform data collection and management in cancer epidemiology. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 302. doi:10.1158/1538-7445.AM2014-302

MCDM APPROACH TO EVALUATING BANK LOAN DEFAULT MODELS

Banks and financial institutions rely on loan default prediction models in credit risk management. An important yet challenging task in developing and applying default classification models is model evaluation and selection. This study proposes an evaluation approach for bank loan default classification models based on multiple criteria decision making (MCDM) methods. A large real-life Chinese bank loan dataset is used to validate the proposed approach. Specifically, a set of performance metrics is utilized to measure a selection of statistical and machine-learning default models. The technique for order preference by similarity to ideal solution (TOPSIS), a MCDM method, takes the performances of default classification models on multiple performance metrics as inputs to generate a ranking of default risk models. In addition, feature selection and sampling techniques are applied to the data pre-processing step to handle high dimensionality and class unbalancedness of bank loan default data. The results show that K-Nearest Neighbor algorithm has a good potential in bank loan default prediction.

Responsive envelope tessellation and stochastic rotation of 4-fold penttiles

This paper reports on the design and control of a responsive envelope based on the rotation of tessellated components. The study investigates responsive and dynamic approaches for building facades and envelopes to regulate solar shading, light control, views and thermal gain within the building. It is well known that near real-time visual output from computational simulation can significantly impact the prediction of dynamic building-environment interactions and lead to the development of smart, adaptable, net zero energy buildings. To address these motivations, this paper reports the development of an experimental simulation of a responsive envelope based on using a 4-fold penttile scheme. The simulation is developed using a novel pentagonal approach involving component (tile) design, tessellation and control methods. The paper further elaborates on the geometry and control aspects of the facade subdivision and presents the results of applying this tessellation geometry to a building envelope shading study based on facade components with rotation. Finally, it tests the “responsiveness” to multiple performance metrics by applying a non-deterministic exploration method for the stochastic rotation of individual panels. The sophisticated evaluation of alternative envelope configurations for a set of performance criteria require a tighter computational coupling between modelling and control of dynamic geometry.