Output Metrics Research Articles

Comparing Different Output Metrics of High-Resolution MEMS Weakly Coupled Resonant Tilt Sensors

This article presents the theoretical analysis and experimental investigation of a weakly coupled resonant tilt sensor based on different output metrics, including mode frequency, amplitude ratio (AR), amplitude difference, and eigenstate. Especially, we investigate the sensitivity, linear working range, stability, and noise analysis by theoretical analysis and evaluation of experimental results. The tilt sensor sensing element is the weakly coupled resonators, the output metrics of which shift are proportional to the effective stiffness perturbation caused by changes in input inertial force along the sensitive axis when any tilt angular is applied to the tilt sensor. Furthermore, it is demonstrated that there is an optimal operating point for amplitude difference output through theoretical analysis. The experimental results show that the minimum input-referred angle noise densities of the tilt sensor based on the AR and amplitude difference are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.03\times 10^{-{4}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.10\times 10^{-{5}}\,\,^{\circ} /\surd $ </tex-math></inline-formula> Hz, respectively. In addition, the weakly coupled resonant tilt sensor based on the AR and amplitude difference outputs achieve resolutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.79\times 10^{-{5}\circ }$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.53\times 10^{-{5}\circ }$ </tex-math></inline-formula> with an integral time of 15 s, and the mode frequency output achieves a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.141\times 10^{-{5}\circ }$ </tex-math></inline-formula> with an integral time of 0.5 s. The experimental results demonstrate that based on amplitude difference output has the best long-term stability and based on the mode frequency output has better short-term stability. Additionally, it is demonstrated that the output based on the AR balances sensitivity, linear range, and long-term stability.

Using AlphaFold to predict the impact of single mutations on protein stability and function.

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.

Using a single-channel reference with the MBSTOI binaural intelligibility metric

In order to assess the intelligibility of a target signal in a noisy environment, intrusive speech intelligibility metrics are typically used. They require a clean reference signal to be available which can be difficult to obtain especially for binaural metrics like the modified binaural short time objective intelligibility metric (MBSTOI). We here present a hybrid version of MBSTOI that incorporates a deep learning stage that allows the metric to be computed with only a single-channel clean reference signal. The models presented are trained on simulated data containing target speech, localised noise, diffuse noise, and reverberation. The hybrid output metrics are then compared directly to MBSTOI to assess performances. Results show the performance of our single channel reference vs MBSTOI. The outcome of this work offers a fast and flexible way to generate audio data for machine learning (ML) and highlights the potential for low level implementation of ML into existing tools.

Finite Element Analysis and Wear Rate Analysis of Nano Coated High Speed Steel Tools for Industrial Application

Numerous machining process output metrics may be used to evaluate a material's machinability, with tool life unquestionably being the most popular. The rate of tool wear, which is heavily reliant on the current wear mechanisms, determines the tool life in the main. The problem of tool wear needs to be rectified to improve machining quality such as surface roughness and production time. In this study, the thermal barrier coating (TBC) technique on machining tool (HSS) to improve its effectiveness. Utilizing Zirconia and Chromium-based compounds, the HSS tool have been coated. FEA (ANSYS software) was used to examine the material strength and wear of the suggested HSS tool. The tool life for machining is improved by suggesting these nano coated tools. Through this project's efforts, a cutting-edge tool might also be recommended.

Universal design space exploration for building energy design

This paper proposes novel improvements to existing Design Space Exploration (DSE) workflows that enable detailed performance-analysis at the speed of design for all projects at little cost. The authors refer to this methodology as Universal Design Space Exploration (UDSE). Rather than apply DSE to a project-specific challenge, UDSE enables a single pre-simulated design space to be applicable across many projects. The novel scalability of these “universal” design spaces justify investment in ML-powered apps that make pre-simulated analysis instantly accessible, affordable, and impactful across multiple projects. This research showcases the feasibility and potential benefit of UDSE by applying it to the challenge of early conceptual energy modeling. First, a group of experts crafts the input parameters and output metrics of a massive Design Space so that it encompasses the common problem. Then an automated parametric simulation workflow is developed to model and simulate any combination of input parameters. Several hundred thousand iterations are then simulated and analyzed. The result of this analysis guides the design of a prototype app which is powered by an AI surrogate model that allows users to receive instantaneous analysis about any design contained within the design space. This research shows that it is feasible to simulate the massive design spaces required by UDSE using currently available computational resources. We show that the surrogate modeling process is capable of accurately extending relatively limited simulation data to fully map the design space. We also show that these surrogate models can be effectively integrated into custom apps that can automate advanced DSE analysis and deliver insights to design teams in real-time. This paper concludes that UDSE offers a novel and scalable approach to early conceptual performance analysis.

Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control.

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).

Uncertainty Decomposition to Understand the Influence of Water Systems Model Error in Climate Vulnerability Assessments

AbstractClimate vulnerability assessments rely on water infrastructure system models that imperfectly predict performance metrics under ensembles of future scenarios. There is a benefit to reduced complexity system representations to support these assessments, especially when large ensembles are used to better characterize future uncertainties. An important question is whether the total uncertainty in the output metrics is primarily attributable to the climate ensemble or to the systems model itself. Here we develop a method to address this question by combining time series error models of performance metrics with time‐varying Sobol sensitivity analysis. The method is applied to a reduced complexity multi‐reservoir systems model of the Sacramento‐San Joaquin River Basin in California to demonstrate the decomposition of flood risk and water supply uncertainties under an ensemble of climate change scenarios. The results show that the contribution of systems model error to total uncertainty is small (∼5%–15%) relative to climate based uncertainties. This indicates that the reduced complexity systems model is sufficiently accurate for use in the context of the vulnerability assessment. We also observe that climate uncertainty is dominated by the choice of general circulation model and its interactive effects with the representative concentration pathway (RCP), rather than the RCP alone. This observation has implications for how climate vulnerabilities should be interpreted.

Design and Simulation of ETL-Free Perovskite/Si Tandem Cell with 33% Efficiency

Multi-junction (tandem) cell using MAPbI3-xClx and silicon as absorbers has been designed and simulated in this paper. The thickness of the silicon layer in the bottom cell is 2 μm allowing it to absorb the transmitted spectrum from the perovskite subcell as much as possible. The thickness of the MAPbI3-xClx layer is optimized using a proposed algorithm. The output metrics show that the optimum thickness of the MAPbI3-xClx layer was 205 nm. The simulation outputs showed that the proposed tandem cell has an efficiency of 33.09% with an open circuit voltage of 1.9 V and a short circuit current of 19.95 mA/cm2 .

Higher Education, Management Control and the Everyday Academic

This contribution to the Festskrift for Hanne Norreklit examines how the use of management controls by senior management of universities impacts on the everyday academic. This examination is rooted in two interrelated issues, being, first, what should the purpose of a university be and second, what should they look to contribute to society. To keep the analysis that follows manageable, England provides a representative setting due to these issues having been heavily debated there within recent decades. While these debates continue, market-orientated understandings have come to dominate, influencing the focus of successive governments and, in turn, the situated practices of universities. Specifically, this has changed the focus of senior managers, at universities, away from their traditional remit towards revenue generating activities. Key to their efforts is the understanding that external impressions of their university, as provided by, for example, rankings, will affect financial performance. This results in senior management increasingly managing for the rankings and other such external signifiers. In so doing, they have progressively introduced management control regimes that focus the organisation’s activities onto the external signifiers. However, as senior managements’ performance, and thereby rewards, has become increasingly linked to the external signifiers, their focus has narrowed only to the output metrics of the management controls they have put into place. Hence, they become wilfully ignorant of the wider impact these management controls have, and the pressure cooker situation this creates for many everyday academics. While this outcome has been well documented in the extant literature, this article adds, through the analysis of exemplars, how this is resulting in the grimmest of situations, with dire consequences. As part of noting that this could, and should, be different, speculation is raised over whether other management controls may act as release valves to this pressure cooker situation.

Status of the health information system in Ireland and its fitness to support health system performance assessment: a multimethod assessment based on stakeholder involvement

BackgroundBetween 2019 and 2021, the first Irish health system performance assessment (HSPA) framework was developed. As routinely collected health data are necessary to continuously populate indicators of an HSPA framework, a purpose-driven assessment of the health information system (HIS) in Ireland and its fitness to support the implementation of an HSPA framework was conducted. This study reports on the status of the Irish HIS through a multimethod assessment based on continuous broad stakeholder involvement.MethodsBetween May and November 2020, over 50 informants were engaged in individual and group interviews and stakeholder consultation workshops as part of the HIS assessment process. Descriptive themes and high-level data availability heatmaps were derived from interview and workshop data using thematic analysis. Indicator “passports” for the HSPA framework were populated during stakeholder consultation workshops and analysed using univariate descriptive statistics.ResultsThe HIS in Ireland was able to provide administrative, survey and registry-based data for public sector acute care services, focusing on structure, process and output metrics. Significant data availability gaps, most notably from primary care, private hospitals and community care, were reported, with little availability of electronic health record and people-reported data. Data on outcome metrics were mostly missing, as were linkage possibilities across datasets for care pathway monitoring. The COVID-19 pandemic highlighted the national HIS’s shortcomings but also the capacity for rapid development and improvement.ConclusionsA tailor-made assessment of the HIS in Ireland, involving a broad set of relevant stakeholders, revealed strengths, weaknesses and areas for improvement in the Irish health data landscape. It also contributed to the development of a national HSPA framework and momentum to further strengthen data infrastructure and governance, while working towards a more data-driven and person-centred healthcare system. This work demonstrates the utility of an inclusive HIS assessment process and is applicable beyond Ireland, where this case study was conducted.

Development of an offshore response guidance tool for determining the impact of SSDI on released gas and benzene from artificial subsea oil well blowout simulations

We present an analysis of 2225 simulations of artificial oil well blowouts in nearshore and offshore waters of Newfoundland, Canada. In the simulations, we coupled the VDROP-J and TAMOC models to simulate the fate and transport of oil and gas from the release to the sea surface. Simulations were conducted with and without subsea dispersant injection. We analyzed the simulation database to quantify the mass fraction of oil and gas that surfaces, the mass fraction of released benzene that surfaces, and the horizontal offset to the surfacing zone. These data are also synthesized to yield empirical correlations to predict these output metrics from key input parameters. These correlations are summarized in an excel spreadsheet that allows rapid evaluation of spill dynamics with minimal initial knowledge of spill details. We call this tool an offshore response guidance table, which allows exploration of spill dynamics under diverse spill and response options.

Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.

A Self-Sustained Mass Sensor With Physical Closed Loop Based on Thermal-Piezoresistive Coupled Resonators

This article reports a mode-localized mass sensor based on thermal-actuation piezoresistive-detection self-oscillated weakly coupled resonators. Detailed theoretical models and simulations of the self-oscillation of the coupled resonators were established and were also verified using optical and electrical measurements. The sensor was fabricated and characterized in terms of stability, linearity, sensitivity, and resolution. Supplied with only a constant direct current (DC), the resonant mass sensor could oscillate at its resonant frequency with an ultrahigh quality factor of 95 k in air. By implementing the principle of the mode localization phenomenon, ~200 times parametric sensitivity improvement with amplitude ratio output was implemented compared to the traditional frequency output metric. A real-time mass detector with 84-fg resolution and larger than ~700-pg linear measurement range was obtained.

Development of an Open-Source Autonomous Computational Fluid Dynamics Meta-Modeling Environment for Small-Scale Combustor Optimization

Abstract This work presents an open-source autonomous computational fluid dynamics (CFD) metamodeling environment (OpenACME) for small-scale combustor design optimization in a deterministic and continuous design space. OpenACME couples several object-oriented programing open-source codes for conjugate-heat transfer, steady-state, multiphase incompressible Reynolds averaged Navier-Stokes CFD-assisted engineering design metamodeling. There are fifteen design variables. Nonparametric rank regression (NPRR), global sensitivity analyses (GSA), and single-objective (SOO) optimization strategies are evaluated. The Euclidean distance (single-objective criterion) between a design point and the utopic point is based on the multi-objective criteria: combustion efficiency (η) maximization and pattern factor (PF), critical liner area factor (Acritical ), and total pressure loss (TPL) minimization. The SOO approach conducts Latin hypercube sampling (LHS) for reacting flow CFD for subsequent local constraint optimization by linear interpolation. The local optimization successfully improves the initial design condition. The SOO approach is useful for guiding the design and development of future gas turbine combustors. NPRR and GSA indicate that there are no leading-order design variables controlling η, pattern factor (PF), Acritical , and TPL. Therefore, interactions between design variables control these output metrics because the output design space is inherently nonsmooth and nonlinear. In summary, OpenACME is developed and demonstrated to be a viable tool for combustor design metamodeling and optimization studies.

The Efficacy of Culturally Responsive Pedagogy for Low-Proficiency International Students in Online Teaching and Learning

International students with low academic English proficiency face challenges with reading their course materials and writing assignments. Their challenges are exacerbated during remote learning, as they remain in their home countries, immersed in their home languages, which may be quite distant from academic English. To investigate the effects of culturally responsive pedagogy for international students online, quantitative and qualitative data were collected from a learner-driven, instructor-facilitated (LeD-InF) support program at a large university in southern Ontario. This fully online delivery of the Reading and Writing Excellence (RWE) program was re-envisioned from a long-running co-curricular program that addressed students’ academic English reading, writing, and critical thinking needs. Among eight groups (with the total enrolment of 154) in the Fall 2020 academic term cycle and nine groups (with the total enrolment of 226) in the Winter 2021 academic term cycle of the online RWE program, the intervention groups that were additionally supported with culturally responsive pedagogy had the highest volume of writing output and engagement metrics among all groups. The text data (of student voices and experiences) also reinforces the efficacy of culturally responsive pedagogy in facilitating student experience, constructing identities, promoting learner agency, increasing satisfaction, improving students’ perceptions of learning, and realizing transformative inclusivity.

Regional Public Health Training Centers: An Essential Partner in Workforce Development.

Regional Public Health Training Centers: An Essential Partner in Workforce Development.

Automatic Text Summarization of Biomedical Text Data: A Systematic Review

In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.

Studying first passage problems using neural networks: A case study in the slit-well microfluidic device.

This study presents deep neural network solutions to a time-integrated Smoluchowski equationmodeling the mean first passage time of nanoparticles traversing the slit-well microfluidic device. This physical scenario is representative of a broader class of parametrized first passage problems in which key output metrics are dictated by a complicated interplay of problem parameters and system geometry. Specifically, whereas these types of problems are commonly studied using particle simulations of stochastic differential equationmodels, here the corresponding partial differential equationmodel is solved using a method based on deep neural networks. The results illustrate that the neural network method is synergistic with the time-integrated Smoluchowski model: together, these are used to construct continuous mappings from key physical inputs (applied voltage and particle diameter) to key output metrics (mean first passage time and effective mobility). In particular, this capability is a unique advantage of the time-integrated Smoluchowski model over the corresponding stochastic differential equationmodels. Furthermore, the neural network method is demonstrated to easily and reliably handle geometry-modifying parameters, which is generally difficult to accomplish using other methods.

Performance analysis of solar-powered integrated desalination and air conditioning system

ABSTRACT During the operation of the vapor compression refrigeration (VCR) cycle, heat is absorbed in the evaporator and rejected from the condenser to the environment. The heat rejected by the condenser is often wasted, and it raises the ambient air temperature, resulting in thermal pollution. Apart from that, freshwater scarcity is a severe concern in many areas. An integrated system of humidification-dehumidification (HDH) desalination and air conditioning using waste heat from the VCR system is proposed to address these difficulties. The system provides freshwater, cooled air, and hot water while preventing thermal pollution. Although the combination of desalination and cooling systems has piqued the attention of a few researchers, the duct architecture used in this work is a first. The study determines an appropriate humidifier spray water temperature based on human comfort conditions. It investigates the effects of ambient temperature and relative humidity, saline water temperature, and humidifier efficiency on output and performance metrics. The experimental studies are used to validate the thermodynamic model established in this paper. From one cubic meter of air, 31.50 kJ of heat and 40.53 mL of freshwater are extracted using 0.66 kW of power. The cooling performance (CP), gained output ratio (GOR), coefficient of energy performance (COEP), and exergy efficiency for the given operating parameters are 1.34, 4.29, 1.72, and 13.99%, respectively. The cost of freshwater production is $ 0.010076 per liter.

How more sophisticated leaf biomass simulations can increase the realism of modelled animal populations

Animal biodiversity, and its key roles in ecosystem state and functioning, is facing critical challenges in the wake of anthropogenic activities. It is urgently necessary to improve understanding of the interconnections between animals and the vegetation within ecosystems. Process-based modelling has shown to be a mighty tool in making assessments on ecological processes. We assess the effect of different vegetation models on simulated animal biodiversity by replacing the vegetation module within Madingley, a multi-trophic model of functional diversity with LPJ-GUESS, a dynamic global vegetation model. We compare the output metrics of the model system to Madingley’s default version for four ecosystem types around the globe and analyse whether the realism of the simulation results increased as a result of the coupling between Madingley and LPJ-GUESS. Simulated animal populations react to the coupling by shifting towards smaller individuals with a higher abundance. General shifts in body mass and animal distributions can be traced back to ecological processes, allowing in-depth analysis of heterotrophic responses to changes in leaf biomass. We also derive power-law relationships for herbivory to NPP and herbivore biomass to NPP and conclude that the coupled model system simulates animal populations that follow reasonable power-laws which are similar to power-laws derived from empirical data. Our results indicate that developing process-based model systems is a viable way to assess multi-trophic interconnections between animal populations and the ecosystems vegetation.