Output Metrics Research Articles

Towards a Hybrid Mass Sensing System by Combining a QCM Mass Sensor With a 3-DOF Mode Localized Coupled Resonator Stiffness Sensor

This paper describes a hybrid mass sensing system comprising a QCM (quartz crystal microbalance) mass sensor operating under atmospheric pressure and a 3-DOF mode localized coupled resonator operating in vacuum. Nanoparticles as consecutive mass perturbations are added onto the QCM, the output signals with respect to the amount of mass change are then being manipulated to generate electrostatic forces. Subsequently, the electrostatic forces act on the 3-DOF mode localized coupled resonator as external stiffness perturbations. The output metrics of the hybrid system were defined as: the resonant frequency shifts, vibration amplitude changes, and the changes in resonance amplitude ratio. Measured data was analyzed for these metrics and compared. This work demonstrated that the proposed hybrid mass sensing system attained a 2.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> N(m · kg)-1 mass to stiffness transduction factor, and has the potential to be employed as a direct liquid contact biochemical transducer.

One size does not fit all: Factors associated with increased frequency of radiation overexposure alerts based on fixed-alert thresholds.

Quantify the expected rate of CT radiation dose alerts for three body regions using accepted radiation dose benchmarks and assess key determinants of alert frequency. This IRB-approved retrospective cohort study evaluated consecutive CT examinations performed between July and December 2013 within an academic medical system. CTDIvol x-ray tube output metrics were compared to the body-region-specific benchmark levels, Achievable Doses (AD), Diagnostic Reference Levels (DRL), and Dose Notification Values (DNV). A logistic regression model for the simulated alerts was fit as a function of the independent predictors: scanner, body region, gender, weight, and age. For 17,000 exams, the proportion of events triggering alerts increased with patient weight. Significant covariates were scanner, body region, patient weight and patient age (all p<0.0001). Odds of alert generation for the AD, DRL, and DNV benchmarks increased by 7.6%, 6.6% and 2.9% per kilogram, respectively, and by 0.8%, 1.1% and -2.7% per year of age (all p<0.0001). Compared to the most highly optimized scanner, odds of alert generation varied by a factor of 595 for AD, 1126 for DRL, and 13 for DNV. Alert frequency was significantly correlated with weight, age, body region and scanner. Controllable factors include scanner functionality and associated protocol optimization. Patient factors driving alert frequency are predominantly weight, and to a lesser degree, age. Size-agnostic fixed dose thresholds can frequently produce false positive alerts in appropriately performed exams of large patients, while missing opportunities to identify outlier scans of higher-than-expected dose in small patients.

Markov Model for Fault Tolerant Machining System Operating under Admission Control Policy

In this study, we analyze Markov model for the performance prediction of failure prone machining device that operates under the fault tolerance measures and admission control policy. The threshold-based admission policy is proposed to examine the functioning of designed fault tolerance system. The stationary queue size distribution of failed machines has been established using a recursive approach. The key output metrics and cost function have been designed to analyze the functional efficiency and cost benefit of the system. The numerical implementation of the performance indices and cost analysis have been done to examine the sensitiveness of the system descriptors. Furthermore, harmony search approach has been used to optimize the total cost associated with different activities.

Performance measurement model for ranking of educational institutes: a fuzzy reasoning approach

Ranking of educational institutes starts gaining a significant attention among all the stakeholders to know the comparative status of the institutes. This paper presents a comprehensive ranking framework based on fuzzy logic using multiple criteria proposed in National institutional ranking framework (NIRF). The proposed model presents a hierarchical structure of metrics proposed by NIRF to measure the performance attributes and then the overall performance of the institute. Mamdani fuzzy reasoning approach has been adopted to address the inexact causal relationship between the leading (input) and the lagging (output) metrics. Finally, the behaviour of the responses produced by the proposed model has been analyzed and also a comparative study has been conducted between National institutional ranking framework (NIRF) and rankings with proposed rankings system for various institutes.

The Paramak: automated parametric geometry construction for fusion reactor designs.

During the conceptual design process of fusion reactors it is useful to rapidly prototype different design concepts and assess their suitability against a range of high level requirements. Rapid prototyping allows the 'fail early' mantra of other fields to be applied to engineering design. Furthermore, the rapid generation of low fidelity analysis allows fast exploration of design space, which enables better decisions to be made during concept selection and the detailed design phase. The Paramak is an open-source tool that aims to provide automated parameter driven 3D CAD models for fusion reactor components and magnetic fusion reactors. The geometry produced is compatible with several analysis workflows and this allows iterative automated model building and analysis to help steer the design concept optimisation process. The Paramak uses CadQuery 2 to create the 3D CAD model. The Paramak framework is used to create a few example reactor configurations including: a spherical reactor, a regular large radius tokamak and a compact submersion tank reactor. Input parameters for the various reactors that the Paramak can generate generally fall into three categories: continuous ranges such as blanket thickness, integer ranges such as number of toroidal field coils and categorical parameters such as type of divertor. The Paramak facilitates parameter studies where users can investigate the impact of input design parameters on the reactor performance. The use of modern software practices allows the geometry to be continuously tested in analysis workflows to ensure it is fit for purpose. The generation of output metrics from input parameters lends itself to the use of data science and machine learning approaches in order to steer the design. The Paramak provides rapid construction of analysis ready CAD in a manner that allows the designer to save time when exploring the design space for design studies and facilitate automated generative design.

Statistical Taylor series expansion: An approach for epistemic uncertainty propagation in Markov reliability models

In this paper we develop a new Taylor series expansion method for computing model output metrics under epistemic uncertainty in the model input parameters. Specifically, we compute the expected value and the variance of the stationary distribution associated with Markov reliability models. In the multi-parameter case, our approach allows to analyze the impact of correlation between the uncertainty on the individual parameters the model output metric. In addition, we also approximate true risk by using the Chebyshev’ inequality. Numerical results are presented and compared to the corresponding Monte Carlo simulations ones.

Ranking Invariant Efficiency Measures of Healthcare Units

Public service spending is an important issue with great economic, social and political ramifications. Consequently, correct measurements with respect to productivity evaluation of such expenditures is of paramount significance. Motivated by evaluating public services from a non-parametric production efficiency perspective and the computational irregularities this may entail, we present refined bounds for the crucial non-Archimedean infinitesimal ({\it aka} epsilon). In non-parametric efficiency models the epsilon plays a key role as a multiplication factor to the sum of input and output slacks in the objective function, equivalently, it is used as a lower bound for the input and output weights in productivity multiplier models. Selecting a value for epsilon is non-trivial since: it has to be sufficiently small to guarantee the envelopment model is bounded (or the multiplier model feasible) yet large enough to provide managerial insight and not cause computational problems; is highly context specific, depending on the input and output metrics; sensitive to underlying assumptions with respect to constant or variable returns-to-scale; and may lead to drastically different relative efficiency rankings. To guarantee the relative ranking of the evaluated units remain consistent we provide two bounds for the epsilon. The first,{\it positive efficiency guarantee}, ensures the obtained efficiency measures are positive and well-defined, and the second, {\it ranking invariance guarantee}, is a refinement such that the relative efficiency rankings are provably consistent. We illustrate our bounds and their implications using data from twelve public healthcare centers.

Mode localized artificial hair sensor with enhanced linearity applied for dual-axis air flow sensing

The weakly coupled resonators based on mode localization are promising in emerging signal measurement fields due to the verified relative mechanical sensitivity enhancement and exceptional ambient robustness. In this work, we report a novel mode localized artificial hair sensor for dual-axis air flow sensing. A new full-scale linear output metric based on differential subtraction of reciprocal amplitude ratios (SRAR) is proposed to suppress the strong inherent non-linearity of amplitude ratio-modulated output metric. The theoretical derivation of mechanical sensitivity demonstrates that the differential SRAR output is strictly linear with the square of input air flow velocity. The fabrication process based on standard deep dry silicon on glass is described and an optimized control and measurement system composed of an analog interface circuit and a digital signal processing circuit is designed for experimental performance evaluation. The tested prototype shows an x-axis mechanical sensitivity of 5.417 × 10−2/(m s)−2 with an x-axis cross-axis coupling coefficient of 0.067 and a y-axis sensitivity of 6.212 × 10−2/(m s)−2 with a y-axis cross-axis coupling coefficient of 0.055. The comparative experiment results confirm that compared to frequency-based output and amplitude ratio-based output, the differential SRAR-based output not only promotes the relative mechanical sensitivity and linearity but also exhibits the superior bias stability, which indicates a better common-mode rejection of ambient fluctuations.

An Automated ALARA Method for Ultrasound: An Obstetric Ultrasound Feasibility Study.

Ultrasound users are advised to observe the ALARA (as low as reasonably achievable) principle, but studies have shown that most do not monitor acoustic output metrics. We developed an adaptive ultrasound method that could suggest acoustic output levels based on real-time image quality feedback using lag-one coherence (LOC). Lag-one coherence as a function of the mechanical index (MI) was assessed in 35 healthy volunteers in their second trimester of pregnancy. While imaging the placenta or the fetal abdomen, the system swept through 16 MI values ranging from 0.15 to 1.20. The LOC-versus-MI data were fit with a sigmoid curve, and the ALARA MI was selected as the point at which the fit reached 98% of its maximum. In this study, the ALARA MI values were between 0.35 and 1.03, depending on the acoustic window. Compared to a default MI of 0.8, the pilot acquisitions suggested a lower ALARA MI 80% of the time. The contrast, contrast-to-noise ratio, generalized contrast-to-noise ratio, and LOC all followed sigmoidal trends with an increasing MI. The R2 of the fit was statistically significantly greater for LOC than the other metrics (P < .017). These results suggest that maximum image quality can be achieved with acoustic output levels lower than the US Food and Drug Administration limits in many cases, and an automated tool could be used in real time to find the ALARA MI for specific imaging conditions. Our results support the feasibility of an automated, LOC-based implementation of the ALARA principle for obstetric ultrasound.

Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

The goal of this paper was to conduct a systematic literature analysis on the application of different types of artificial intelligence models in surface water quality monitoring. The analysis focused on the methods used, the location of the experiments, the input parameters, and the output metrics applied to categorise the results presented. Furthermore, the main contribution of this paper is to synthesise the existing body of knowledge in the state of the art (the last decade) and identified common threads and gaps that would open up new challenging, exciting and significant research directions. From the study, it was observed that Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilised artificial intelligence models for water quality monitoring and assessment in the last decade. Most of the studies using Neural Networks in surface water quality monitoring and assessment are originated in Iran and south-east Asia. ANFIS, Wavelet-ANN (W-ANN) and Wavelet-ANFIS (W-ANFIS) were most accurate for the prediction of surface water quality. There was no clear relationship between data size and R2 value (at the testing stage). Biochemical oxygen demand (BOD) was the most investigated parameter in surface water quality monitoring and assessment. An appraisal of recent literature was also presented and knowledge gaps and future perspective in the research area were proposed.

A Mode-Localized Resonant Accelerometer With Self-Temperature Drift Suppression

This paper theoretically and experimentally demonstrates the temperature drift suppression capability of a mode-localized accelerometer based on three degree of freedom weakly coupled resonators. The theoretical model of the temperature characteristics of the amplitude ratio and frequency of the mode-localized accelerometer under the influence of Young's modulus were established. The effect of the temperature on the input conversion mechanism and weakly coupled resonators are both included in the model. The simulation results show that thermal stress is the main factor affecting the output metric of the mode-localized accelerometer with mechanical coupling beam and, consequently, a stress relief structure is proposed to suppress the effect of stress on the output metric. Compared with the mode-localized accelerometer without stress relief structure, the temperature coefficients of sensitivity and two output metrics of the accelerometer with the stress relief structure are both suppressed by one order of magnitude via the simulation and experiment verification from 30°C to 60°C. And the temperature suppression coefficient of amplitude ratio is improved by 43.9 times than that of the frequency which shows that the mode-localized accelerometer based on amplitude ratio has an excellent temperature drift suppression capability.

Pesticide application coverage training (PACT) tool: development and evaluation of a sprayer performance diagnostic tool

Current operator feedback from in-field pesticide application operations conveys limited information and often does not allow the operator to visualize a true representation of their performance. Farm management information systems (FMIS) typically do not account for overlap, varying application rates across the width of the spray boom during turns, or off-rate errors due to controller response. The pesticide application coverage training (PACT) tool was developed to deploy data analytics methodologies to sprayer operational data collected during field applications. The goal was to compare enhanced feedback via the PACT tool versus data generated from commercially available FMIS software today. Data were collected for multiple Nebraska fields and processed by the PACT program which consisted of a novel MATLAB program developed for this project. The PACT program successfully generated high-resolution as-applied maps and the automated application report further quantified the contributions of these errors to the total error to illustrate how overlap, turning errors or controller response issues may have individually affected application accuracy. PACT program output metrics were compared with current data provided by FMIS software. Field-average metrics were not found to be significantly different when comparing the PACT program to the FMIS output; however, when examining how in-field errors were distributed amongst various application rate ranges, significant differences were noted in comparison to the FMIS output. Thus, the PACT program was able to quantify and illustrate application rate variation due to boom section overlap and turning movements unaccounted for in traditional FMIS software.

Data-Driven Derivation of an Analytic Model for Parallel Servers With Job Replication

The job replication problem has been studied recently as a mechanism to improve performance and availability of systems with $n$ n parallel servers, each with its own queue. A dispatcher using some policy sends $d ~(1 \leq d \leq n)$ d ( 1 ≤ d ≤ n ) copies of a job to $d$ d of the servers. Copies are eliminated from the system as soon as the first copy completes from any of the $d$ d servers. This article introduces a data-driven method to derive closed-form expressions for the average response time and other metrics of jobs as a function of the degree of replication $d$ d . This method consists of developing a simulator for the system in order to generate a very large number of datasets for a wide range of input parameters. A statistical and visualization analysis of the data provides the analytical models. It is important to emphasize the difference between using simulation methods to obtain the value of metrics (e.g., average response time) of a computer system given values of input parameters and using our data-driven method to obtain closed-form expressions that relate output metrics to input parameters. The latter is the focus of our approach. The analysis presented here covers results for homogeneous and heterogeneous servers with exponentially distributed service times and for homogeneous servers with hypo-exponentially and hyper-exponentially distributed service times. This article also presents a closed-form equation for the optimal replication degree for the case of homogeneous servers with hypo-exponentially distributed service times.

Bottleneck Detection in Cloud Computing Performance and Dependability: Sensitivity Rankings for Hierarchical Models

Cloud computing became widespread on IT industry, saving costs of acquisition and maintenance for companies of all sizes, and enabling fair management of resources according to the demand. Stochastic models can enable performance and dependability evaluation of cloud computing systems efficiently, what is needed for proper capacity planning. Distinct models may be combined in a hierarchy to address the huge number of components and levels of interaction among the system parts. Identification of bottlenecks in such composite models might be hard yet, due to the huge amount of input factors and variables which may interfere with the results. This paper proposes a method for bottleneck detection of computational systems represented with hierarchical models, that is remarkably applied in cloud computing systems. This is achieved through the composition of indices computed from lower level models in equations and solution methods of the top level model, for computing the sensitivity indices of all parameters with respect to a global system measure. A unified sensitivity ranking, comprising the composite indices, indicates the parameters with highest impact on output metrics. A case study supports the demonstration of accuracy and utility of our methodology. The study addresses a web service running on a private cloud with auto scaling mechanisms. The methods and algorithms presented here are helpful for decision-making when designing and managing cloud computing infrastructures, regarding incremental and architectural improvements.

Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.

Functional Morphology of Gliding Flight I: Modeling Reveals Distinct Performance Landscapes Based on Soaring Strategies.

The physics of flight influences the morphology of bird wings through natural selection on flight performance. The connection between wing morphology and performance is unclear due to the complex relationships between various parameters of flight. In order to better understand this connection, we present a holistic analysis of gliding flight that preserves complex relationships between parameters. We use a computational model of gliding flight, along with analysis by uncertainty quantification, to (1) create performance landscapes of gliding based on output metrics (maximum lift-to-drag ratio, minimum gliding angle, minimum sinking speed, and lift coefficient at minimum sinking speed) and (2) predict what parameters of flight (chordwise camber, wing aspect ratio [AR], and Reynolds number) would differ between gliding and nongliding species of birds. We also examine performance based on the soaring strategy for possible differences in morphology within gliding birds. Gliding birds likely have greater ARs than non-gliding birds, due to the high sensitivity of AR on most metrics of gliding performance. Furthermore, gliding birds can use two distinct soaring strategies based on performance landscapes. First, maximizing distance traveled (maximizing lift-to-drag ratio and minimizing gliding angle) should result in wings with high ARs and middling-to-low wing chordwise camber. Second, maximizing lift extracted from updrafts should result in wings with middling ARs and high wing chordwise camber. Following studies can test these hypotheses using morphological measurements.

A genetic-based data mining model for customer relationship management in B2B ecommerce

Customer Relationship Management (CRM) improves the responsiveness and understanding of business strategies among employees and helps to achieve better customer service. However, in Business-to-Business (B2B) eCommerce, CRM data are rarely analyzed across market segments or customer categories, and customer-to-business relationship also poses issues. Thus, making appropriate decisions in the CRM model is difficult. This study presents a modified B2B CRM using the Genetic algorithm and Data Mining Techniques to improve decision making. The model classifies consumers into consumers of Repeat and Shop-and-Go. Modified data mining C5.0 and the Genetic algorithm was employed to optimize rules generated by the decision tree algorithm. The findings showed that the proposed model allocates resources effectively to the most profitable customers’ decisions. The output metrics are machine time, calibration graph, and ROC curve. In comparison with the conventional C5.0, k-NN, and Support Vector Machine, the proposed model has greater accuracy of 89.3 percent.&#x0D; Keywords: Customer Relationship Management, B2B eCommerce, Genetic Algorithm, Data Mining

Improving Academic Biobank Value and Sustainability Through an Outputs Focus.

Although it is generally accepted that human tissue biobanks are important to facilitate progress in health and medical research, many academic biobanks face sustainability challenges. We propose that biobank sustainability is challenged by a lack of available data describing the outputs and benefits that are produced by biobanks, as reflected by a dearth of publications that enumerate biobank outputs. We further propose that boosting the available information on biobank outputs and using a broader range of output metrics will permit economic analyses such as cost-consequence analyses of biobank activity. Output metrics and cost-consequence analyses can allow biobanks to achieve efficiencies, and improve the quality and/or quantity of their outputs. In turn, biobank output measures provide all stakeholders with explicit and accountable data on biobank value, which could contribute to the evolution of biobank operations to best match research needs, and mitigate some threats to biobank sustainability.

A new way to visualize and report structural and data uncertainty in stock assessments

We present a visual and tabular representation of fisheries stock assessment model outputs to rapidly examine and effectively communicate sensitivity analysis results from numerous alternative model comparisons. This approach uses multiple output metrics to identify which alternative stock assessment model configurations relative to the reference model deserve further attention when quantifying intermodel uncertainty. An accompanying table of likelihood components, parameters, and model-derived quantities highlights where major changes exist compared with the reference model. The general method is applicable to any stock assessment and should aid in model behavior diagnosis and communicating uncertainty to managers. Specific examples and code are provided for the Stock Synthesis modelling framework.

Monitoring indoor exposure to combustion-derived particles using plants

Indoor plants can be used to monitor atmospheric particulates. Here, we report the label-free detection of combustion-derived particles (CDPs) on plants as a monitoring tool for indoor pollution. First, we measured the indoor CDP deposition on Atlantic ivy leaves (Hedera hibernica) using two-photon femtosecond microscopy. Subsequently, to prove its effectiveness for using it as a monitoring tool, ivy plants were placed near five different indoor sources. CDP particle area and number were used as output metrics. CDP values ranged between a median particle area of 0.45 × 102 to 1.35 × 104 μm2, and a median particle number of 0.10 × 102 to 1.42 × 10³ particles for the indoor sources: control (greenhouse) < milling machine < indoor smokers < wood stove < gas stove < laser printer. Our findings demonstrate that Atlantic ivy, combined with label-free detection, can be effectively used in indoor atmospheric monitoring studies.