Universal Metrics Research Articles

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning-based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans. A dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine-learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere-seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated. Our results found that the highest accuracy was obtained using a light gradient-boosting machine model, which had an MAE of 1.99kg and a strong positive correlation between estimated and actual BW (ρ=0.972). The model demonstrated significant predictive power, with 73% of patients falling within a±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex. The proposed machine-learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

Control-Volume-Based Exergy Method of Truncated Busemann Inlets in Off-Design Conditions

A scramjet engine consisting of several components is a highly coupled system that urgently needs a universal performance metric. Exergy is considered as a potential universal currency to assess the performance of scramjet engines. In this paper, a control-volume-based exergy method for the Reynolds-averaged Navier–Stokes solution of truncated and corrected Busemann inlets was proposed. An exergy postprocessing code was developed to achieve this method. Qualitative and quantitative analyses of exergies in the Busemann inlets were performed. A complete understanding of the evolution process of anergy and the location where anergy occurs in the inlet at various operation conditions was also obtained. The results show that the exergy destroyed in the Busemann inlet can be decomposed into shock wave anergy, viscous anergy and thermal anergy. Shock wave anergy accounts for less than 4% of the total exergy destroyed while thermal anergy and viscous anergy, in a roughly equivalent magnitude, contribute to almost all the remaining. The vast majority of inflow exergy is converted into boundary pressure work and thermal exergy. Some of the thermal exergy excluded by the computation of the total pressure recovery coefficient belongs to the available energy, as this partial energy will be further converted into useful work in combustion chambers.

Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope.

Unmanned aerial vehicle (UAV)-based geological mapping is significant for understanding the geological structure in the high-steep slopes, but the images obtained in these areas are inevitably influenced by the backlit effect because of the undulating terrain and the viewpoint change of the camera mounted on the UAV. To handle this concern, a novel backlit image restoration method is proposed that takes the real-world application into account and addresses the color distortion issue existing in backlit images captured in high-steep slope scenes. Specifically, there are two main steps in the proposed method, which consist of the backlit removal and the color and detail enhancement. The backlit removal first eliminates the backlit effect using the Retinex strategy, and then the color and detail enhancement step improves the image color and sharpness. The author designs extensive comparison experiments from multiple angles and applies the proposed method to different engineering applications. The experimental results show that the proposed method has potential compared to other main-stream methods both in qualitative visual effects and universal quantitative evaluation metrics. The backlit images processed by the proposed method are significantly improved by the process of feature key point matching, which is very conducive to the fine construction of 3D geological models of the high-steep slope.

The Australian Eye and Ear Health Survey (AEEHS): Study protocol for a population-based cross-sectional study.

Vision and hearing impairments are highly prevalent and have a significant impact on physical, psychological and social wellbeing. There is a need for accurate, contemporary national data on the prevalence, risk factors and impacts of vision and hearing loss in Australian adults. The Australian Eye and Ear Health Survey (AEEHS) aims to determine the prevalence, risk factors and impacts of vision and hearing loss in both Aboriginal and Torres Strait Islander and non-Indigenous older adults. The AEEHS is a population-based cross-sectional survey which will include 5,000 participants (3250 non-Indigenous aged 50 years or older and 1750 Aboriginal and Torres Strait Islander people aged 40 years or older) from 30 sites covering urban and rural/regional geographic areas, selected using a multi-stage, random cluster sampling strategy. Questionnaires will be administered to collect data on socio-demographic, medical, ocular and ontological history. The testing battery includes assessment of blood pressure, blood sugar, anthropometry, visual acuity (presenting, unaided, pinhole and best-corrected), refraction, tonometry, slit lamp and dilated eye examination, ocular imaging including optical coherence tomography (OCT), OCT-angiography and retinal photography, and automated visual fields. Audiometry, tympanometry and video otoscopy will also be performed. The primary outcomes are age-standardised prevalence of cause-specific vision and hearing impairment. Secondary outcomes are prevalence of non-blinding eye diseases (including dry eye disease), patterns in health service utilisation, universal health coverage metrics, risk factors for vision and hearing impairment, and impact on quality of life.

Problems with universal metrics

Abstract This paper aims to examine the problem of universal metrics developed with English sonnets by analyzing meter in Japanese haiku and Chinese recent-style verse.1 Following Martin (2007), In this study, a haiku is defined as a traditional form of Japanese poetry consisting of three lines, typically with seventeen metrical units per verse arranged in a pattern of five, seven, and five units per line. The data reveals that a system with metrical prominence cannot be applied to Chinese and Japanese metrical verse since linguistic prominence is missing in both languages. The only common feature shared among the metrical systems of the three languages is the existence of a contrast between two types of syllables that belong to opposite classes. However, this contrast only applies in limited situations in the Japanese case. Lastly, instead of a universal metrical structure, the data suggests that the metrical structure in the three languages should be distinguished.

Universal metrics for predicting the activity of a wide range of fuel-cell catalysts

Universal metrics for predicting the activity of a wide range of fuel-cell catalysts

How to measure embodied intelligence?

Embodied intelligence (EI) summarizes design approaches that give robots efficient physical interaction with their surrounding environment. EI has, to date, found an extensive descriptive treatment yet lacks universal metrics. Could we quantify EI? What would be the EI analog to intelligence quotient (IQ) in humans? We first suggest an intelligent unit (intel-unit) as the smallest entity that displays program execution beyond simple stimuli-responsiveness. The emergence of EI by situatedness of encoded physical agents in an environment is explored in three case studies. First, an awn of a wild oat – a non-living structure in nature that satisfies all robot’s descriptions – is analyzed in terms of material-level intelligence. Next, a jocular quantitative scale found with a popular gadget – the ‘fortune teller fish’ – is given treatment in the EI context. Finally, the learned and summarized EI principles are tested on a well-known object with perceived intelligence yet lacking a consensual robotic description – a knitted woollen sweater. We confirm the necessity of treating environment interactions in expressions and definitions of EI, as situatedness is central in defining functionality. Universal metrics would identify non-intuitive representations of EI and lead from empirical to model-based design of unconventional robots. Robots that engage situatedness provide a physical interface to artificial intelligence, similar to our bodies communicating with our brains.

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

Assessment of a Practitioner-Derived Framework for Measuring Girl’s Agency in East Africa

Although soft skills are considered important for quality education, there is little consensus on which skills matter for which populations and few cross-culturally validated measures. We propose a conceptual and methodological remedy to this quandary through an investigation of “agency” as an outcome measure for adolescent girls’ life skills programs. We engage community practitioners in East Africa to derive a four-dimensional structure of agency from theory, practitioner expertise, and empirical evidence. We develop and pilot this regional measure of agency for adolescent girls, utilizing confirmatory factor analysis to confirm the underlying structure of our instrument and to investigate its use as a pre-post measure. Analysis was conducted using data collected from 1,953 girls in four countries. Results indicate a four-latent factor structure representing the construct of agency. We argue that researchers should prioritize investigating the regional limits to validity of contextually specific soft skills measures over establishing universal metrics.

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology.

Tidal emersion effects on universal metrics, elemental contents, and health conditions of Pacific oyster (Crassostrea gigas) on artificial reefs in Yellow River Delta, China

Oysters form natural reefs and are important coastal foundation species that provide a wide range of ecosystem services. Clarification of the mechanisms by which tidal emersion affects oyster reef performance will help guide and direct future oyster reef restoration efforts. In the present study, we constructed artificial Pacific oyster (Crassostrea gigas) reefs in the coastal zones of the Yellow River Delta and quantified the effects of tidal emersion on the growth, density, biomass, element (carbon, nitrogen, phosphorus) content within tissues, and health conditions of oysters. The results showed that temperature change driven by the tidal emersion effect was one of the major forces affecting reef performance. In the experimental field, fully immersed oysters performed better than partially exposed ones in terms of growth and density. The mean biomass of the fully immersed oysters was 2.88-fold higher than that of the partially exposed oysters (p < 0.05). However, we found that tidal emersion had no significant effect on the carbon, nitrogen, and phosphorus content within oyster tissues. Oyster health condition revealed by the biochemical marker and whole-genome gene expression analyses demonstrated that the oysters partially exposed to air presented enhanced oxidative and lipid metabolism and were forced to allocate metabolic energy towards defense against stress rather than growth, which suggested sub-optimal health conditions. Overall, the study indicated that full immersion is optimal for oyster reef restoration in the experimental field. The results of this research will provide future conservationists, aquaculturists, and other specialists with a better understanding of tidal emersion effects on the success of oyster reef restoration to achieve region-specific restoration goals and improve protection of the coastal environment.

On comeager sets of metrics whose ranges are disconnected

For a metrizable space X, we denote by Met(X) the space of all metric that generate the same topology of X. The space Met(X) is equipped with the supremum distance. In this paper, for every strongly zero-dimensional metrizable space X, we prove that the set of all metrics whose ranges are closed totally disconnected subsets of the line is a dense Gδ subspace in Met(X). As its application, we show that some sets of universal metrics are meager in spaces of metrics.

Making energy justice work for women in rural sub-Saharan Africa: A qualitative diagnostic from Benin, Senegal, and Togo

A carbon neutral world by 2050. Few would oppose the idea of embedding justice in the energy systems painted in the emerging net zero scenario. However, how does the lofty ideal of justice translate for communities distant from elite centers of knowledge and power? Focusing on women from rural communities in Benin, Senegal, and Togo, this paper applies the double lens of gender and capability to energy justice principles and addresses the following questions: 1) How do these rural women currently experience energy justice principles? And 2) What energy interventions could enhance these women's capabilities in the future? The findings from our field interviews offer a diagnostic of energy justice for rural women, connecting the limited availability and affordability of energy sources to sustainability concerns in a vicious cycle of resource scarcity and impaired capabilities. We then propose interventions to apply justice principles in the local context and argue that operationalizing energy justice requires making gender equity a cross-cutting dimension in the energy justice framework. We contend that building just energy systems by 2050 would be best achieved by focusing on human capabilities, i.e., promoting people's opportunities to live the life they value over allegedly universal energy metrics.

A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space

Rice blast (RB, caused by the fungus Magnaporthe oryzae) is the most devastating disease in global rice production, and can cause significant yield losses and increasingly threaten global food security. Accurate detection of RB occurrence with a universal metric is crucial to facilitate early disease prevention and curtailing the disease expansion but has not been addressed to date. This study aimed to design a rice blast index (RIBI) for quantifying the disease index (DI) and tracking the smallholder rice blast dispersal over multiple spatial scales. To achieve this goal, a large dataset including leaf- and canopy-scale reflectance spectra and satellite imagery was acquired within the framework of seven independent campaigns over four years (2018–2021). Specifically, an extensive collection of Magnaporthe oryzae infected samples were analyzed to examine the specific spectral response to pathogen infection in paddy rice from leaf to near-ground canopy scales. Two variants of the RIBI were developed, which were RIBInir = (R753-R1102)/(R665 + R1102) and RIBIred = (R753-R1102)/(R665 + R1102) based on the single-band separability and exhaustive search of band combinations. They were subsequently evaluated for quantifying the RB occurrence from ground to space. Spatial cluster analysis was then integrated with the superior RIBI adjusted for Sentinel-2 imagery to explore the spatio-temporal dynamics of pathogen infection, and to reveal the within-field hotspots of potential rice blast dispersal in smallholder farms.The results demonstrated that both RIBInir and RIBIred exhibited high overall accuracies for the classification of infected and healthy samples at the leaf scale under greenhouse in 2018 (RIBInir: 81.41%; RIBIred: 84.62%) and 2019 (RIBInir: 81.30%; RIBIred: 90.37%) and field conditions in 2020 (RIBInir: 86.36%; RIBIred: 89.39%). Compared with traditional VIs (Near-ground: R2 < 0.47, satellite: R2 < 0.54), the RIBInir yielded improved R2 in quantifying the DI from in situ spectra to satellite imagery (Near-ground: R2 = 0.73, satellite: R2 = 0.78). The strongest DI-RIBInir relationship was attributed to the use of two near-infrared (NIR) bands that helped enhance the unique spectral responses in the NIR region induced by pathogen infection, in contrast to the extensively studied visible region. Multi-temporal analysis of Sentinel-2A derived RIBInir successfully captured the temporal dynamics of RB infection and recovery and yielded compelling maps showing the spatial propagation and attenuation of disease over time. This research opens new opportunities towards quantifying field disease occurrence and detecting the within-field hotspots of potential disease dispersal in a timely manner from publicly available satellite imagery.

Planning resource allocation for husbandry management by portfolio optimization

The husbandry management is assessed in general by comparison of current and past economical results, which are used as a universal business metric. Sustainable management in general targets minimization of risk and maximization of the return for managing business activities. The minimization of the economic risk allows for decreasing the potential losses for the husbandry management and they are leading criteria for planning future resource allocations. The new point added in this research concerns simultaneously inclusion in the portfolio problem the risk formalization both as a standard deviation of return and the probability for losses as value-at-risk. Several portfolio problems are defined, considering the probability of losses as a goal function or constraint in the portfolio problems. The inclusion of these two formalizations allows the portfolio risk to decrease additionally in comparison with the classical portfolio problems, where the risk is quantified as a standard deviation of the portfolio return. The peculiarities of these problems and the corresponding optimal solutions are analyzed, which allows quantifying the resource allocation per different business activities. Numerical experiments are made with real data on animal husbandry, available from the Bulgarian National Statistics and the results are illustrated in a graphical way. The empirical comparison with these data gives benefits in decreasing the risk when both risk formalizations are applied in the portfolio problem.

Bayesian Classification of Nonmeteorological Targets in Polarimetric Doppler Radar Measurements

Abstract The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and nonmeteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, nonmeteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 nonmeteorological classes, belonging to five top-level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of nonmeteorological targets also enables a large array of emerging applications, utilizing nonprecipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and the specificity of the customers’ requirements. Significance Statement In addition to meteorological echoes, weather radars observe a wide variety of nonmeteorological phenomena including birds, insects, and human-made objects like ships and aircraft. Conventionally, these data have been rejected as undesirable disturbance, but lately their value for applications like aeroecological monitoring of bird and insect migration has been understood. The utilization of these data, however, has been hampered by a lack of comprehensive classification of nonmeteorological echoes. In this paper we present a comprehensive, fine-grained, probabilistic classifier for all common types of nonmeteorological echoes which enables the implementation of a wide range of novel weather radar applications.

Using an Annual Diversity, Equity and Inclusion Dashboard to Accelerate Change in Academic Radiology Departments

Despite widespread interest in creating a more equitable and inclusive culture, a lack of workforce diversity persists in Radiology, in part due to a lack of universal and longitudinal metrics across institutions. In an attempt to establish benchmarks, a subset of the Society of Chairs of Academic Radiology Departments (SCARD) Diversity, Equity and Inclusion (DEI) Committee volunteered to design a DEI dashboard as a potential tool for academic radiology programs to use to document and track their progress. This freely-available, modular dashboard includes suggested (plus optional department-defined) DEI activities/parameters and suggested assessment criteria across three domains: faculty, residents & fellows, and medical students; it can be completed, in whole or in part, by departmental leaders annually. The suggested metrics and their associated rubrics were derived from the collective experiences of the five working group members, all of whom are chairs of academic radiology departments. The resulting dashboard was unanimously approved by the remaining 14 DEI committee members and endorsed by the SCARD board of directors.

A contrastive consistency semi-supervised left atrium segmentation model.

Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes much time and labor. Although supervised deep learning methods can somewhat solve this problem, a high-efficient deep learning model requires abundant labeled data that is hard to acquire. Therefore, the research on automatic LA segmentation of leveraging unlabeled data is highly required. In this paper, we propose a semi-supervised LA segmentation framework including a segmentation model and a classification model. The segmentation model takes volumes from both labeled and unlabeled data as input and generates predictions of LAs. And then, a classification model maps these predictions to class-vectors for each input. Afterward, to leverage the class information, we construct a contrastive consistency loss function based on these class-vectors, so that the model can enlarge the discrepancy of the inter-class and compact the similarity of the intra-class for learning more distinguishable representation. Moreover, we set the class-vectors from the labeled data as references to the class-vectors from the unlabeled data to relieve the influence of the unreliable prediction for the unlabeled data. At last, we evaluate our semi-supervised LA segmentation framework on a public LA dataset using four universal metrics and compare it with recent state-of-the-art models. The proposed model achieves the best performance on all metrics with a Dice Score of 89.81%, Jaccard of 81.64%, 95% Hausdorff distance of 7.15mm, and Average Surface Distance of 1.82mm. The outstanding performance of the proposed framework shows that it may have a significant contribution to assisting the therapy of patients with atrial fibrillation. Code is available at: https://github.com/PerceptionComputingLab/SCC.

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. This research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

Uncertainty‐guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium‐enhanced MRI

Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentationresults. In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on thesurface. We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparableperformance. We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on thesurface.