Current Metrics Research Articles

IE-IQA: Intelligibility Enriched Generalizable No-Reference Image Quality Assessment.

Image quality assessment (IQA) for authentic distortions in the wild is challenging. Though current IQA metrics have achieved decent performance for synthetic distortions, they still cannot be satisfactorily applied to realistic distortions because of the generalization problem. Improving generalization ability is an urgent task to make IQA algorithms serviceable in real-world applications, while relevant research is still rare. Fundamentally, image quality is determined by both distortion degree and intelligibility. However, current IQA metrics mostly focus on the distortion aspect and do not fully investigate the intelligibility, which is crucial for achieving robust quality estimation. Motivated by this, this paper presents a new framework for building highly generalizable image quality model by integrating the intelligibility. We first analyze the relation between intelligibility and image quality. Then we propose a bilateral network to integrate the above two aspects of image quality. During the fusion process, feature selection strategy is further devised to avoid negative transfer. The framework not only catches the conventional distortion features but also integrates intelligibility features properly, based on which a highly generalizable no-reference image quality model is achieved. Extensive experiments are conducted based on five intelligibility tasks, and the results demonstrate that the proposed approach outperforms the state-of-the-art metrics, and the intelligibility task consistently improves metric performance and generalization ability.

Kidney Allocation: The Path Forward.

Kidney Allocation: The Path Forward.

From extra to actor: Facilitating automated conditioning in animal-robot interaction

Innovations related to animal care in livestock production increasingly rely on the automation of management procedures utilizing machines and robots. However, current solutions incorporate animals mainly passively, e.g. by measuring their health status and predicting their behavior. This approach neglects that animals are able to signal their needs actively and can thus provide information about their environment and cooperate in management tasks. The key to unlock this potential is to establish bidirectional communication between animals and machines. This can be achieved by automatically condition animals to show stimulus-controlled operant behavior. However, this automation of animal learning requires metrics for the learning progress that are applicable in a variety of contexts. We show that the current learning metrics are not transferable in this regard and propose a normalized precision metric as an alternative. This metric was utilized to analyze the learning performance of adult sows in an exemplary signal discrimination task, set in three fundamentally different training conditions. The results demonstrate that the normalized precision metric is unaffected by differences in the distribution of learned events. Moreover, it fulfills basic plausibility criteria for the assessment of animal learning that are not satisfied by other metrics, such as comparable start and end values under different conditions. This provides the precondition for a broader utilization of cooperative livestock farming - a new form of integrating animals in management tasks.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework.

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments’ ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline’s task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline’s advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method’s predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences.

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

A climate goal–based, multicriteria method for system evaluation in life cycle assessment

Our ability to restrict global warming to the established objectives in the Paris Agreement depends on the metrics used to evaluate the climate change impact. Stemmed on the criticism of the current GWP metrics used with life cycle assessment (LCA), this study proposes new indicators and an interpretation grid for climate change impact in LCA, which are adaptive to present, short-term, and long-term climate goals. The global mean temperature change (GMTC) is used in the indicators’ definition and time parameters are introduced for a multicriteria evaluation of climate change impact. We adopt calendar-related time targets instead of a fixed time horizon. The systems are analyzed on a real-time scale and with respect to a climate-target point in time (for example, 2050 as objective for climate neutrality), in contrast with conventional LCA. The objective is to provide flexibility in system evaluation, adaptable to current and future targets. Four indicators are introduced: (1) the amplitude of the temperature change (GMTCmax), representative for climate extreme events; (2) the time at which GMTC starts to definitely decrease and its distance with respect to the goal (tlast_peak); (3) the time climate neutrality is reached and its distance with respect to the goal (tneutral); and (4) the accumulated warming until a targeted time, representative for sea-level rise and ice melting (integrated GMTC at a given time target, for example at the end of century iGMTC2100). An analysis grid is proposed based on these indicators and illustrated on 26 emission profiles involving long- and short-lived greenhouse gases with various temporalities, as well as on two dynamic LCA case studies. In the group of neutral systems, temporality is responsible for variations in GMTCmax and iGMTC2100. Increasing the frequency of emission/capture events flattens both indicators and provides the best performance. Equal CO2 emission systems are discriminated primarily by tlast_peak, while in the case of methane, more relief is observed for all indicators. The method allows for the design of tailored mitigation solutions in LCA application examples. The indicators are able to discriminate and rank systems that are considered to be non-impacting, equivalently impacting, or neutral in LCA-GWP metrics. Such metrics is necessary to correctly (avoid strong simplifications) and unambiguously (with unaltered physical parameters, closer to climate physics) evaluate the effect on climate, mitigation solutions, neutrality, and support decision-making.

Global potential distribution of Anastrepha grandis (Diptera, Tephritidae) under climate change scenarios

The current distribution of Anastrepha grandis, South American cucurbit fruit fly, under current climate conditions is restricted to South and Central America. However, this species is considered a quarantine pest and its presence in fruits is highly unwanted in exports, mainly to territories that offer conditions for their establishment. According to recent studies, changes in climate conditions may contribute to a range expansion due to the reduction of cold-induced stress, which still prevents the colonization of many areas. By predictive modeling, this study aimed to answer the question regarding the potential distribution of the South American cucurbit fruit fly under climate change, using two global circulation models – HadGEM2-ES and Miroc5 – in four change scenarios representing simulations of radiative force at 2.6 W.m-2, 4.5 W.m-2, 6 W.m-2, and 8.5 W.m-2 for the years of 2050 and 2070. All tested scenarios supply environmental suitability conditions for the establishment of the fly in other continents, especially in regions of central Africa, Southeast Asia, and Australasian territories. Furthermore, areas of medium and low suitability predicted in all future scenarios are larger than current metrics and the fly will benefit from climate change, as environmental suitability areas move forward towards the poles.

Generative Models for De Novo Drug Design.

Artificial intelligence (AI) is booming. Among various AI approaches, generative models have received much attention in recent years. Inspired by these successes, researchers are now applying generative model techniques to de novo drug design, which has been considered as the "holy grail" of drug discovery. In this Perspective, we first focus on describing models such as recurrent neural network, autoencoder, generative adversarial network, transformer, and hybrid models with reinforcement learning. Next, we summarize the applications of generative models to drug design, including generating various compounds to expand the compound library and designing compounds with specific properties, and we also list a few publicly available molecular design tools based on generative models which can be used directly to generate molecules. In addition, we also introduce current benchmarks and metrics frequently used for generative models. Finally, we discuss the challenges and prospects of using generative models to aid drug design.

Competing risks in quality and safety research: a framework to guide choice of analysis and improve reporting

When comparing hospitals on their readmission rates as currently done in the Hospital Readmission and Reduction Program (HRRP) in the USA, should we include the competing risk of mortality after...

Comparing performance of discomfort glare metrics in high and low adaptation levels

Current discomfort glare prediction metrics usually account for at least one of the two categories of effects that induce discomfort glare – the saturation and contrast effects. Saturation-driven metrics (overall illuminance on the eye) are suited for brightly lit scenes in general. On the contrary, contrast-driven metrics (luminance ratio in the field of view) usually perform better in high contrast conditions such as with small-sized bright glare sources. Only a few existing metrics consider both effects, such as the Daylight Glare Probability (DGP). However, even these “hybrid” metrics may underperform in conditions other than those considered when they were developed, such as in dim scenes with high contrast glare. This paper investigates the ability of current glare indicators to predict perceived discomfort glare in user-evaluated scenes depending on two different adaptation levels. Towards this end, we used a composite dataset of six laboratory studies performed previously and separately in various parts of the world. According to Receiver Operator Characteristics (ROC) findings and complementary statistical research, the hybrid metrics DGP and Eccologit perform best in both investigated ranges (dimmer and brighter scenes). For the single-effect metrics, the contrast-driven metrics appear to perform better than saturation-driven metrics in lower adaptation levels (dimmer scenes), while the reverse is seen in higher adaptation levels (brighter scenes). As a result, metrics that only consider one effect should be used with caution. Although hybrid metrics continue to perform well in the investigated scenes, further research is needed to extend their applicability to a larger variety of lighting conditions that may be observed in work environments.

CASH CONVERSION CYCLE SENSITIVITY BY MODERATING ROLE OF EXCHANGE RATES VOLATILITY ON FIRM’S FINANCIAL PERFORMANCE

The cycle of cash conversion relates to the time spread between the value of cash paid for purchases and the cash receipt from turnover. Using the State Bank of Pakistan data, this study introduces the direct and moderating role of the exchange rate, effective through the efficient execution of the cash conversion cycle between Pakistani 302 manufacturing companies from 1999–2015. Using the fixed effect as the static panel model and system GMM as a dynamic panel, it is observed that the exchange rate plays an authoritative moderating role between the cash conversion cycle and the financial performance. Results of the investigation have shown that in static panel analysis with the cash conversion period, the exchange rate has a positive and substantial moderating effect on return on assets and return on equity whereas that ER has a major negative impact on return on assets and return on equity using dynamic panel data analysis GMM. The issue of endogeneity in the static panel is addressed using the advanced approach of the standard error of the panel correction standard error method that changed the position of the significance of the moderator variable. Observers, therefore, intend to evaluate the fluctuations in the exchange rate as one of the variables of the financial output moderator in the context of current metrics such as asset’s returns, equity’s returns and gain more practical expression within their investigated results.

We know how to act.

The current tools and metrics we use limit our understanding of the food systems we need to change. True cost accounting can help address complexity and achieve greater sustainability, says Ruth Richardson.

Diagnostic Capability of a New Objective Method to Assess Meibomian Gland Visibility.

The diagnosis of dry eye disease and meibomian gland dysfunction (MGD) is challenging. Measuring meibomian gland visibility may provide an additional objective method to diagnose MGD. This study aimed to evaluate the ability of new metrics to better diagnose MGD, based on measuring meibomian gland visibility. One hundred twelve healthy volunteers (age, 48.3 ± 27.5 years) were enrolled in this study. Ocular surface parameters were measured using the Oculus Keratograph 5M (Oculus GmbH, Wetzlar). Subjects were classified according to the presence or absence of MGD. New metrics based on the visibility of the meibomian glands were calculated and later compared between groups. The diagnostic ability of ocular surface parameters and gland visibility metrics was studied through receiver operating characteristic curves. Logistic regression was used to obtain the combined receiver operating characteristic curve of the metrics with the best diagnostic ability. Statistically significant differences were found between groups for all ocular surface parameters and new gland visibility metrics, except for the first noninvasive keratograph breakup time and gland expressibility. New gland visibility metrics showed higher sensitivity and specificity than did current single metrics when their diagnostic ability was assessed without any combination. The diagnostic capability increased when gland visibility metrics were incorporated into the logistic regression analysis together with gland dropout percentage, tear meniscus height, dry eye symptoms, and lid margin abnormality score (P < .001). The combination of median pixel intensity of meibography gray values and the aforementioned ocular surface metrics achieved the highest area under the curve (0.99), along with excellent sensitivity (1.00) and specificity (0.93). New meibomian gland visibility metrics are more powerful to diagnose MGD than current single metrics and can serve as a complementary tool for supporting the diagnosis of MGD.

The Dark Side of Chasing the Perfect Donor. Is it Time for Us to Change Cardiac Transplant Program Performance Metrics?

The Dark Side of Chasing the Perfect Donor. Is it Time for Us to Change Cardiac Transplant Program Performance Metrics?

Evaluating power system network inertia using spectral clustering to define local area stability

The transitioning from synchronous machine generation to large-scale integration of inverter-based renewable generation introduces new challenges in power system frequency stability. This paper proposes a new approach for evaluating power system frequency stability based on network clusters. First, the most common metrics for power system frequency stability are presented, with their advantages and disadvantages. Then, we establish the link between frequency response and network topology. Based on the pros and cons of current frequency stability metrics and the influence of network topology, a proposal is made for a new approach to evaluate frequency stability using Spectral Graph Theory to partition a power system network into clusters for analysis. Consider the displacement of synchronous generator inertia, together with the spatial–temporal fluctuations/noise fed in from spatially distributed variable renewable energy sources. Then the proposed clustering approach has two main advantages over traditional methods for evaluating power system frequency robustness. The first is a spatial awareness of a power system network in terms of frequency-stable areas. The second advantage is the ease of interpretation of the metric result for robust network planning.

Centile Charts for Monitoring of Weight Loss Trajectories After Bariatric Surgery in Asian Patients.

Following bariatric surgery, accurate charting of weight loss and regain is crucial. Various preoperative factors affect postoperative weight loss, including age, sex, ethnicity, and surgical type. These are not considered by current weight loss metrics, limiting comparison of weight loss outcomes between patients or centers and across time. Patients (n=1022) who underwent sleeve gastrectomy (n=809) and gastric bypass (n=213) from 2008 to 2020 in a single center were reviewed. Weight loss outcomes (% total weight loss) were measured for 60 months postoperatively. Longitudinal centile lines were plotted using the post-estimation predictions of quantile regression models, adjusted for type of procedure, sex, ethnicity, and baseline BMI. Median regression showed that %TWL was 1.0% greater among males than females (β = +1.1, 95% CI: +0.6 to +1.7, P = <0.0001). Participants who underwent SG had less %TWL compared to GB (β = -1.3, 95% CI: -1.9 to -0.8, P < 0.0001). There was a trend towards less %TWL among the Indian and Malay participants compared to Chinese. Age and diabetes were not significant predictors. Reference centile charts were produced for the overall cohort, as well as specific charts adjusted for type of bariatric procedure, sex, ethnicity, and baseline BMI. Centile charts provide a clinically relevant method for monitoring of weight trajectories postoperatively and aid in realistic and personalised goal setting, and the early identification of "poor responders". This is the first study to present post-bariatric surgery centile charts for an Asian cohort.

User Engagement on Wikipedia, A Review of Studies of Readers and Editors

Is it an encyclopedia or a social network? Without considering both aspects it would not be possible to understand how a worldwide army of editors created the largest online knowledge repository. Wikipedia has a consistent set of rules and it responds to many of the User Engagement Framework attributes, and this is why it works. In this paper, we identify these confirmed attributes as well as those presenting problems. We explain that although having a strong editor base Wikipedia is finding it challenging to maintain this base or increase its size. In order to understand this, scholars have analyzed Wikipedia using current metrics like user session and activity. We conclude there exist opportunities to analyze engagement in new aspects in order to understand its success, as well as to redesign mechanisms to improve the system and help the transition between reader and editor.

UK paediatric surgical academic output (2005–2020): A cause for concern?

BackgroundThe publication record can be regarded as a key metric of the academic output of a craft surgical speciality with an almost exponential increase in the number of such publications worldwide over the past 20 years (Ashfaq et al. J Surg Res 2018;229:10–11). We aimed to examine and explore if this was the experience within UK paediatric surgery centres. MethodsThe academic search engine Scopus™ (Elsevier) was used to track every paediatric surgeon's (NHS or University) publication history between Jan. 2005 – Dec. 2020. This was validated by an algorithmic search of PubMed™. The h-index (citations/publication), considered a validated metric of career academic output, was also calculated for each individual surgeon. A Field-Weighted Citation Index (Scopus™) (FWCI) was used to assess impact of individual publications. Textbooks, book chapters, abstracts, duplications (“double dipping“) and output attributed to UK BAPS-CASS national studies were excluded. Some output(s), not considered as relevant to “paediatric surgery”, was edited. Data are quoted as median(range). ResultsDuring this 16-year period, there were 3838 publications identified from 26 centres with a “top ten” listing of those paediatric surgical units contributing over half the output (n = 2189, 57%). To look for evidence of trend(s) we analysed the output from these surgical centres in two 5-year periods (2005–9 and 2015–19) and showed an overall fall in output(s) - [730 (53.4%) to 645 (46.4%)] with 6/10 (60%) ‘ top ten ‘ centres here recording a reduction in publications. The median h-index of the 232 contributing paediatric surgical consultants was 12 (range 1–56). The best performing publication from the “top ten” centres had 96.5(51–442) citations with the FWCI being 4.5 (2.2 – 30.2). ConclusionsThis study highlights current paediatric surgery publication output metrics in UK centres. There is evidence of a relative reduction in outputs overall which is a cause for concern for the future, although individual publications from the 10 most active units in the UK remain highly cited. These findings may serve purpose in several ways: (i) UK paediatric surgical centre rankings may be helpful for guiding residency / trainee application; (ii) surgical research funding for the top performing units may be better facilitated and finally (iii) UK centres showing a ‘ fertile ground ‘ for nurturing and training paediatric surgeons with academic aspirations could be useful for future workforce planning.

Ultrawide Field Imaging in Diabetic Retinopathy: Exploring the Role of Quantitative Metrics.

Ultrawide field imaging (UWF) has allowed the visualization of a significantly greater area of the retina than previous standard approaches. In diabetic retinopathy (DR), significantly more lesions are seen on UWF imaging compared to the seven-standard ETDRS fields. In addition, some eyes have lesions that are located predominantly in the peripheral retina that are associated with an increased risk of DR progression. The current DR severity scales are still largely based on clinically visible retinal microvascular lesions and do not incorporate retinal periphery, neuroretinal, or pathophysiologic changes. Thus, current scales are not well suited for documenting progression or regression in eyes with very early or advanced DR, nor in the setting of vascular endothelial growth factor inhibitors (antiVEGF). In addition, the categorical system is highly subjective, and grading is variable between different graders based on experience level and training background. Recently, there have been efforts to quantify DR lesions on UWF imaging in an attempt to generate objective metrics for classification, disease prognostication and prediction of treatment response. The purpose of this review is to examine current quantitative metrics derived from UWF fluorescein angiograms and UWF color imaging to determine their feasibility in any potential future DR classification.

Some Memories Never Fade: Inferring Multi-Scale Memory Effects on Habitat Selection of a Migratory Ungulate Using Step-Selection Functions

Understanding how animals use information about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store information about experienced landscapes, however, remains an understudied topic in movement ecology. By studying how species select for familiar locations, visited recently and in the past, we can gain insight to how they store and use local information in multiple memory types. In this study, we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado, United States to investigate the influence of spatial experience over different time scales on seasonal range habitat selection. We inferred the influence of short and long-term memory from the contribution to habitat selection of previous space use within the same season and during the prior year, respectively. We fit step-selection functions to GPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection, inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals, models incorporating covariates representing both recent and past experience and environmental covariates performed best. In the top model, locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-term memory in driving seasonal range habitat selection. Further, the influence of previous space uses was stronger in the summer relative to winter, which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-term information about seasonal ranges, which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly, these findings provide novel insight into how species store and use information over different time scales.