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International Dermatology Outcome Measures and the American Academy of Dermatology recently agreed on a physician-reported global severity measure to demonstrate quality of care in inflammatory dermatoses.Because patient-reported outcome measures are also important, we aimed to achieve consensus on 1 of these measures for clinical practice.Patients and providers participated in a consensus-building study involving a modified-Delphi technique. Voting was focused on identifying a minimal set of assessments for clinical practice, patient global assessments (PtGAs), Skindex instruments, and final instrument selection for quality improvement.Among 53 stakeholders,70% agreed that identification of patient goals, assessment of treatment harm, and assessment of the adequacy of treatment response were the minimal assessments needed for clinical practice. The most preferred PtGA was a 5-point PtGA (scale 0-4; 0 = clear, 4 = severe) having an optional check-box for worst ever. A new metric to assess change since treatment initiation, which we called a trajectory measure, was proposed. Stakeholders preferred Skindex instruments over PtGAs and a trajectory measure for clinical practice.A small number of patients and caregivers were involved and a consensus was not reached on all items.PtGAs as standalone measures do not adequately capture the patient's assessment of disease severity or the effect of care. The combination of a PtGA with a Skindex instrument or another measure of health-related quality of life might provide a more comprehensive evaluation of patients in clinical practice.

The authors analyze leading management journals of the Academy of Management (the Academy of Management Journal, the Academy of Management Review, the Academy of Management Learning and Education, and the Academy of Management Perspectives, as well as the Journal of Management), collecting and analyzing information on the managerial research assessment tool of impact factor (IF) during two time periods—from 1997 through 2005 and from 2006 through 2019. The authors capture the changing nature of journal strategies, examining the number of references, self-citations, and cross-citations. The study shows a general increase in the number of references used, as well as self-citations and cross-citations, resulting in a corresponding IF gain. The evidence suggests some limitations to adopting performance metrics in academia. Thus, academic managers, editors, and authors who focus on IF may be overestimating its impact, obscuring institutional attempts to measure academic research performance.

Cyber security is guarding computer systems, data, network and other resources from unauthorized access and malicious users. There are no direct methods of measuring strength of cyber security. As they say, “You can’t manage what you can’t measure”. One can easily track the efforts taken for security through cyber security metrics. Being a quantifiable measure, metric can be utilized in tracking the status of a specific process and assess its outcomes along with its strength. This work aims to provide taxonomy of cyber security metrics with five basic metrics, along with the tools under Multi Criteria Decision making approach can be used in evaluation of cyber security strength.

To determine the high risk classes in software system researchers often turn to statistical and computational intelligence models, in preference to more easily performed binary classification through threshold value. In the later case, the knowledge of only threshold values can help the developers and testers to recognize the risk prone classes. The current study aims to identify the dichotomy that proficiently discriminates the faulty and non-faulty classes of the software systems. For the purpose, the study examined seven threshold techniques, i.e., odds ratio, Cohen’s kappa, maximum sum of specificity and sensitivity, Concordance Probability, Alves Rankings, value of an acceptable risk level, and standard deviation plus mean, to identify which ones recognize an optimal threshold for the software metrics. The dichotomous power of any threshold technique depends on the software measures, the optimal values of which are to be identified. This study made use of widely adopted object oriented metrics, Chidamber and Kemerer metric suite. The discrimination results of the techniques were further compared and the observations elicited from the results of experiments revealed that concordance probability and maximum sum of sensitivity and specificity achieved the best performance, whereas the odds ratio performed significantly worse than the best performing methods.

This paper incorporates a new perspective into P2P energy trading coordination schemes for EVs by considering Quality of Service (QoS) management. QoS could be utilized as a control metric to facilitate resilient and reliable transactions according to user preferences. To that end, this paper proposes a novel decentralized QoS-based system for P2P energy trading among EV energy providers and consumers. The system utilizes smart contracts to carry out the matching between EVs and monitor the delivery of a QoS-based P2P contract without the presence of a third party. Two QoS-based mechanisms are proposed to match trading EVs in this system. The proposed mechanisms are designed to match single-consumer to multiple-providers and multiple-consumers to multiple-providers based on consumers' and providers' QoS requirements and offers, respectively. A fuzzy-based approach with minimum and intelligible input is introduced to determine the weight values of each QoS attribute. Further, a penalty mechanism is developed to discourage dishonest requests/offers and ensure that trading parties stick to their contractual obligations. Numerical simulations are conducted to validate the effectiveness of the proposed QoS-based mechanisms.

In this paper, we study the impact of labor unions on product quality failures. We use a product recall as our measure of quality failure because it is an objective metric that is applicable to a broad cross-section of industries. Our analysis employs a union panel setting and close union elections in a regression discontinuity design framework to overcome identification issues. In the panel regressions, we find that firms that are unionized and those that have higher unionization rates experience a greater frequency of quality failures. The results obtain even at a more granular establishment level in a subsample in which we can identify the manufacturing establishment associated with the recalled product. When comparing firms in close elections, we find that firms with close union wins are followed by significantly worse product quality outcomes than those with close union losses. These results are amplified in non–right-to-work states, where unions have a relatively greater influence on the workforce. We find that unionization increases firms’ costs and operating leverage and, consequently, crowds out investments that potentially impact quality. We also find some suggestive evidence that unions may compromise quality by hurting employee morale and by resisting technological upgrades in the firm. Overall, our results suggest that unions have an adverse impact on product recalls, and thus, product quality is an important dimension along which unions impact businesses. This paper was accepted by Gustavo Manso, finance.

This paper presents a survey of image processing techniques proposed in the literature for extracting key cereal crop growth metrics from high spatial resolution, typically proximal images. The descriptive crop growth metrics considered are: crop canopy cover, above ground biomass, leaf area index (including green area index), chlorophyll content, and growth stage. The paper includes an overview of relevant fundamental image processing techniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for ground-truth measurement are described. Image processing methods for metric estimation are described in detail. The performance of the methods is reviewed and compared. The survey reveals limitations in image processing techniques for cereal crop monitoring such as lack of robustness to lighting conditions, camera position, and self-obstruction. Directions for future research to improve performance are identified.

Defining administrative workflow events as a nonlinear dynamics that assume a random ordered or disordered growth rate of information processing, a method has been proposed for large-scale administrative systems that structures hybrid system variables (continuous or discrete) as iterated and attracted to a fixed-point event at which for all possible metric spaces solutions, the modeling of variables from Lyapunov exponential stability point of view allows the projection of system performance to be oriented, that is, the relationship between the number of agents and the number of administrative services within an administrative workflow environment., 21 pages, 1 table, 17 figures

Introduction Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. Methods We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). Results Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 ( r = −0.91) and PAAM ( r = −0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. Practice Implications Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasksa spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts law), path efficiency (PE), and completion time (CT)). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R2=0.57 and R2=0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R2=0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individuals tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.

An individual's pain experiences vary substantially over time. Though variability in pain may be an important metric which usually predicts health consequences, research on the measurement of pain variability estimates is lacking among older adults. We aimed to examine the reliabilities of both intra-individual mean (IIM) and intra-individual variability (IIV) of pain assessed using ecological momentary assessments (EMA) among racially diverse, systematically recruited community dwelling cohort of older adults. Participants (N = 311, age = 70-91) completed a 14-day EMA protocol which included self-reports of pain intensity, pain interference with activities, and pain interference with concentration multiple times a day. Over a 2-week period, we found excellent reliabilities for both pain IIM (.99), and pain IIV (≥.90). We also found that we need 5 to 6 days to achieve good reliability (.8) for pain IIV, suggesting that a shorter protocol may be used to reduce participants' burden among the current sample, although caution is required when using this result to determine EMA study designs among different samples. Future studies are required to examine the associations of various EMA pain metrics with different health outcomes among older adults to facilitate the detection of underlying mechanisms linking pain to health as a prelude to interventions. PERSPECTIVE: Mean levels and variability in pain intensity, pain interference with activities, and pain interference with concentration can be reliably measured to be linked with various health outcomes in older adults. Future studies including these pain metrics will assess the natural history, the consequences, and effects of intervention of pain.

Hospital readmission is considered a core measure of quality in healthcare. Readmission soon after hospital discharge can result from suboptimal care during the index hospitalization or from inadequate systems for postdischarge care. For many conditions, readmission is associated with a high rate of serious morbidity and potentially avoidable costs. In obstetrics, for postpartum care specifically, hospitals and payers can easily track the rate of maternal readmission after childbirth and may seek to incentivize obstetricians, maternal-fetal medicine specialists, or provider groups to reduce the rate of readmission. However, this practice has not been shown to improve outcomes or reduce harm. There are major concerns with incentivizing providers to reduce postpartum readmissions, including the lack of a standardized metric, a baseline rate of 1% to 2% that is too low to accurately discriminate between random variation and controllable factors, the need for risk adjustment that greatly complicates rate calculations, the potential for bias depending on the duration of the follow-up interval, the potential for the "gaming" of the metric, the lack of evidence that obstetrical providers can influence the rate, and the potential for unintended harm in the vulnerable postpartum population. Until these problems are adequately addressed, maternal readmission rate after a childbirth hospitalization currently has limited utility as a metric for quality or performance improvement or as a factor to adjust provider reimbursement.

In 2022, United States Medical Licensing Examination (USMLE) Step 1 scores will become pass/fail. This may be problematic, as residency programs heavily rely on USMLE Step 1 scores as a metric when determining interview invitations. This study aimed to assess candidate application metrics associated with USMLE Step 1 scores to offer programs new cues for stratifying applicants.Retrospective cohort study analyzing interviewed applicants to one general surgery residency program in 2019 and 2020. Applicant data analyzed included USMLE Step 1 scores, number of publications, clerkship scores, letter of recommendation scores (out of 2, scored by 0.25 interval), interview overall score (out of 5, scored by integer level), and standardized question score (out of 10). Each year, applicant's answers to one standardized behavioral question during their interview were scored by interviewers.Tertiary medical center, academic general surgery residency program.Interviewed applicants at one general surgery residency program whose applications contained complete demographic information (203 out of 247).Multiple Linear Regression revealed that higher surgical clerkship (β = 0.19, p = 0.006) and higher standardized interview question (β = 0.32, p0.001) scores were positively associated with applicant USMLE Step 1 score (F[7, 195] = 6.61, p0.001, R2 = 0.19). Letter of recommendation score, number of peer reviewed publications, gender, race, and applicant type (preliminary/categorical) were not associated with USMLE Step 1 scores.With USMLE Step 1 scores transitioning to pass/fail, surgical residency programs need new selection heuristics. Surgery clerkship scores and standardized behavioral questions answered by applicants prior to the interview could provide a holistic view of applicants and help programs better stratify candidates without USMLE Step 1 scores.

In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.

The loan evaluation is a fundamental task in peer-to-peer (P2P) lending. Effective loan evaluation can help lenders make informed investment decisions. Existing methods do not consider the return of loans in the core learning stage and thus fail to explore the relationship between the return of loans and their final loan payoff outcomes. In this study, we propose a systematic loan evaluation framework called COst Sensitive Loan Evaluation (COSLE). Specifically, we first develop an instance-aware misclassification cost (IMCO) matrix, which specifies personalized cost for each loan. Then, we present a differential labelling algorithm called DILA cost for assigning node labels and assessing the corresponding cost. By integrating these enhancements into the tree-induction process, we construct a node splitting measurement called COG index. It exploits the relationship between the return information and the final payoff outcome. Additionally, we design the LER evaluation metric to measure the ability of a loan evaluation model to increase the lender’s return. Finally, the COSLE is used to improve popular tree models. Extensive experiments based on the Lending Club dataset show that our COSLE can effectively increase the lender’s return.

The Common Vulnerability Scoring System is at the core of vulnerability management for systems of private corporations to highly classified government networks, allowing organizations to prioritize remediation in descending order of risk. With a lack of justification for its underlying formula, inconsistencies in its specification document, and no correlation to exploited vulnerabilities in the wild, it is unable to provide a meaningful metric for describing a vulnerability’s severity, let alone risk. As it stands, this standard compromises the security of America’s most sensitive information systems.

Functioning is an important outcome for the management of rheumatoid arthritis (RA). Heterogeneity of respective patient-reported outcome measures (PROMs) challenges direct comparisons between their results. This study aimed to standardize reporting of such PROMs measuring functioning in RA to facilitate comparability.Common-item nonequivalent group design with the Health Assessment Questionnaire (HAQ) as a common scale across data sets from various countries (including the UK, Turkey, and Germany) to establish a common metric was used. Other PROMs included are the physical function items of the Multidimensional HAQ (MDHAQ), the Disabilities of the Arm, Shoulder, and Hand questionnaire, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the World Health Organization Disability Assessment Schedule II (WHODAS II), the Medical Outcomes Study Short Form 36 (SF-36) health survey, and 4 short forms (20, 10, 6, and 4 physical function items) from the Patient-Reported Outcomes Measurement Information System. As the HAQ includes mobility, self-care, and domestic life items, this study focuses on these 3 domains. PROMs were described using standard error of measurement (SEM) and smallest detectable difference (SDD). A Rasch measurement model was used to create the common metric.The range of the SEM was 0.2 (MDHAQ) to 7.4 (SF-36 health survey physical functioning domain). The SDD revealed a range from 9.7% (WOMAC rating scale) to 33.5% (WHODAS physical functioning domain). PROMs co-calibration revealed fit to the Rasch measurement model. A transformation table was developed to allow exchange between PROM scores.Scores between the daily activity PROMs commonly used in RA can now be compared. Factors such as SEM and SDD help to determine the choice of a PROM in clinical practice and research.

Summary Purpose With the increase in the aging population, the level of frailty has become an important metric for assessing preoperative patient risk. Although medical and surgical specialties continue to adopt and standardize the use of frailty instruments, few studies within the plastic surgery literature have utilized such instruments to predict poor postoperative outcomes. The purpose of this article is two-fold: (1) to provide a comprehensive review of the existing frailty instruments and (2) to summarise the existing evidence regarding the role of pre-operative frailty assessments on peri‑operative morbidity and mortality in plastic surgery. Methods This systematic review was registered a priori on the Open Science Framework ( https://osf.io/vfzw8 ). A computerized database search of Ovid MEDLINE, EMBASE, and Cochrane was performed from database inception to December 13, 2020. All articles that examined the effect of preoperative patient frailty on perioperative morbidity and mortality outcomes following plastic surgery interventions were included for data extraction. Results From the 11 studies included in this review, ten unique frailty instruments were identified. The modified Frailty Index (mFI) and the Fried Frailty Index (FFI) were the most commonly reported frailty measurement tools; however, the FRAIL scale was the only outcome measure identified to be valid, reliable, and responsive to change. Regardless of the frailty measure used, nearly all studies reported that worse surgical outcomes were associated with a higher patient frailty score. Conclusion There is a strong association between higher frailty scores and worse postoperative outcomes in plastic surgery. The FRAIL scale is a clinimetrically sound frailty instrument that should be used in all patients to assess perioperative risk in plastic surgery.

Academic productivity during cardiothoracic surgery residency training is an important program metric, but is highly variable due to multiple factors. This study evaluated the influence of implementing a protocol to increase resident physicians' academic productivity in cardiac surgery.A comprehensive protocol for cardiac surgery was implemented at our institution that included active pairing of residents with academically productive faculty, regular research meetings, centralized data storage and analysis with a core team of biostatisticians, a formal peer-review protocol for analytic requests, and project prioritization and feedback. We compared cardiothoracic surgery residents' academic productivity before implementation (July 2015-June 2017) versus after implementation (July 2017-June 2019). Academic productivity was measured by peer-reviewed articles, abstract presentations (oral or poster) at national cardiothoracic surgery meetings, and textbook chapters.Thirty-four resident physicians (from traditional and integrated programs) trained at our institution during the study. A total of 122 peer-reviewed articles were produced over the course of the study: 74 (60.7%) cardiac- and 48 (39.3%) thoracic-focused. The number of cardiac-focused resident-produced articles increased from 10 preimplementation to 64 postimplementation (0.61 vs 2.03 articles per resident; P .01). Abstract oral or poster presentations also increased, from 11 to 40 (0.61 vs 1.33 abstracts per resident; P = .01). Textbook chapters increased from 4 to 15 following the intervention (0.22 vs 0.5 chapters per resident; P = .01).Implementation of a dedicated protocol to facilitate faculty mentoring of resident research and streamline the data access, analysis, and publication process substantially improved cardiothoracic surgery residents' academic productivity.

Importance: Since the early days of the pandemic, COVID-19 hospitalizations have been used as a measure of pandemic severity. However, case definitions do not include assessments of disease severity, which may be impacted by prior vaccination.Objective: To measure how the severity of respiratory disease changed among inpatients with documented SARS-CoV-2 infection and to measure the impact of vaccination status on these trends, in order to evaluate the accuracy of the metric of “hospitalization plus a positive SARS-CoV-2 test” for tracking pandemic severity.Design: Retrospective cohort of inpatients with laboratory-confirmed SARS-CoV-2. All data were obtained from electronic health records.Setting: Multi-center, nationwide study conducted in the healthcare system of the US Department of Veterans Affairs (VA) from March 1, 2020, through June 30, 2021.Participants: All VA patients admitted to a VA hospital with a laboratory-confirmed SARS-CoV-2 infection within the 14-days prior to admission or during the hospital admission.Main Outcome: Moderate-to-severe COVID-19 disease, defined by use of any supplemental oxygen or documented SpO2 Exposure: SARS-CoV-2 vaccination status at the time of hospitalization. Patients were regarded as fully vaccinated starting 14 days after receiving the second of a 2-dose regimen or 14 days after receipt of a single-dose vaccine.Results: Among 47,742 admissions in 38,508 unique patients with laboratory-confirmed SARS-CoV-2, N=28,731 met the criteria for moderate-to-severe COVID-19. The proportion with moderate-to-severe disease prior to widespread vaccine availability was 64.0% (95% CI, 63.1-64.9%) versus 52.0% in the later period (95% CI, 50.9-53.2%), p-value for non-constant effect, Conclusions and Relevance: The proportion of hospitalizations that are due to severe COVID-19 has changed with vaccine availability, thus, increasing proportions of mild and asymptomatic cases are included in hospitalization reporting metrics. The addition of simple measures of disease severity to the case definition of a SARS-CoV-2 hospitalization is a straightforward and objective change that should improve the value of the metric for tracking SARS-CoV-2 disease burden.

The Emotional Availability Scales (EAS) are the most widely reported observational assessment measure of parent-child relationships and has been of particular interest in understanding differences between samples of depressed and nondepressed mothers and their offspring. Despite its widespread use, psychometric validation of the factor structure in normative samples and the measurement of invariance within clinical samples has not been published. We evaluated the internal structure (dimensionality, reliability, convergent, and discriminant validity) of the EAS fourth edition using a nondepressed sample of 157 Australian women and their infants aged 6 months, including testing the measurement invariance of the EAS between the same nondepressed sample (n = 157), and a depressed group (n = 185) of mother-infant dyads, using MPlus. Participants were recruited from tertiary hospitals, and depression status was established using a diagnostic measure. Higher-order confirmatory factor analyses on the EAS' six dimensions supported a unidimensional factor solution in our data. Full measurement invariance was not demonstrated due to metric noninvariance of the maternal nonintrusiveness and child responsiveness dimensions. Full scalar invariance supported mean comparisons, and a medium effect of .78SD lower mean emotional availability for the depressed group was found; Cohen's d = .63, 95% CI [.41, .85]. While arguments exist for the clinical utility of differentiating between multiple dimensions of emotional availability, the current findings do not support a multidimensional factor structure or full multigroup measurement invariance of the EAS. Similar psychometric investigations of the EAS in clinical and nonclinical samples are needed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This paper provide a qualitative analysis of existing metrics that directly or indirectly quantify energy justice. The main objective of the paper was to determine shortcomings and to suggest improvements in order to enhane existing metrics and create conditions for defining of new energy indicators. The emphasis was placed on the analysis of the readiness of the system for the energy transition. Therefore, elements of the energy trilemma of the observed countries were defined using known parameters related to the transition processes. The use of known economic, political, energy, environmental and other indicators provided the universality of the suggested metric and reduced the impact of subjectivity. Proposed improvements for the metric of energy justice and the defining of new energy indicators served as a help tool for decision-makers in the energy sector. Political solutions should strive to a balancing of the energy trilemma, which was the main precondition for achieving the goals of sustainable development and a just transition. The main results of this paper are the possibility of universal application of metric for the quantification of energy justice and a new composite indicator that indicated the level of energy transition fairness.

Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children.

Measuring teaching quality is challenging to say the least. Each instructor brings his/her training, experience, style, and capabilities to the classroom, making it notoriously difficult to establish a fair teaching quality metric. Commonly used metrics such as student evaluations are one-dimensional and cannot possibly capture all teaching quality elements. A better technique would identify each instructor's strengths on teaching-related activities, as well as places for improvement. While a perfect system may never be found, we present some guidance to broaden our understanding of teaching quality based on an application of service dominant logic and the joint co-creation of value between student and instructor. We also present one attempt to implement a framework, the Danish National Framework for Recognition of Teaching Qualifications, which provides a set of criteria that constitute quality teaching and can be used for assessing teaching qualifications.

Researchers have attempted to improve the project performance using principles like lean, just-in-time, total quality management, etc. However, little research is done to quantitatively assess project performance by using six sigma metrics. In this manuscript, we present weighted Rolled Throughput Yield (RTY), and hence six sigma, based methodology to evaluate the performance of projects, considering multiple criteria. Here, we consider a project decomposed into work breakdown structure of multiple levels. This approach considers seven criteria, classified under five groups namely, quality, cost, time, safety and environmental sustainability for evaluating the performance. Analytical Hierarchy Process (AHP) is used to assign weights to the criteria. RTY obtained is converted into a sigma value. The results are relevant from practical point of view since we consider various elements for performance evaluation, namely, a) complex series-parallel structure of processes in a project; b) hierarchical structure of the project; c) multiple criteria for Sigma Level (SL) computation; d) performance based on “correct first time” concept; e) flexible approach enabling the manager to add/delete the criteria depending on the need. We explain the developed methodology using two practical case studies. This is the first time that the particular six sigma metric is applied to assess the performance of construction and innovation projects. This approach is simple, lucid, yet effective, and we hope it will be useful to researchers and practicing managers alike.

Recent studies have shown that it is possible to characterize subject bias and variance in subjective assessment tests. Apparent differences among subjects can, for the most part, be explained by random factors. Building on that theory, we propose a subjective test design where three to four team members each rate the stimuli multiple times. The results are comparable to a high performing objective metric. This provides a quick and simple way to analyze new technologies and perform pre-tests for subjective assessment., Comment: IEEE Transactions on Multimedia

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

With digital gaming's increasing popularity, Educational Digital Games (EDG) are being more commonly used to complement children's early education. Controlled EDG provides educators a way to observe progress. Many existing applications fail to generate automatic data collection to provide reliable information for feedback on academic aspects needed. This paper describes the usefulness of MIDI-AM, a series of EDG, to link a dashboard, including informative outcomes about use and Playability. It explains how rules of fuzzy logic and Natural Language (NL) can provide consumable feedback. The research objective provides a new component in a longitudinal study to identify a more efficient process for developing and implementing a module to refine the dashboard metrics and outcomes of MIDI-AM EDG. The initial platform was redundant and created inconsistent results. Using Artificial Intelligence (AI) generates valuable information to refine the process of generating feedback reports using detailed data interpretations in NL.

Chapter 8 presents some techniques for combining basic user experience (UX) metrics, such as task success, time on task, and subjective ratings, to form new metrics or an overall “usability score.” An easy way to combine different usability metrics is to determine the percentage of users who achieve a combination of goals. This tells you the overall percentage of users who had a good experience with your product (based on the target goals). This method can be used with any set of metrics and is easily understood by management. One way of combining different metrics into an overall “usability score” is to convert each of the metrics to a percentage and then average them together. This requires being able to specify, for each metric, an appropriate minimum and maximum value. Another way to combine different metrics is to convert each metric to a z score and then average them together. Using z scores, each metric gets equal weight when they are combined, but the overall average of the z scores will always be 0. This metric is useful in comparing different subsets of the data to each other, such as data from different iterations, different groups, or different conditions. The Single Usability Metric (SUM) technique is another method for combining different metrics, specifically task completion, task time, errors, and task-level satisfaction rating. The method requires entry of individual task and participant data for the four metrics. The calculations yield a SUM score, as a percentage, for each task and across all tasks, including confidence intervals. Various types of graphs and charts can be useful for summarizing the results of a usability test in a “usability scorecard.” A combination line and column chart is useful for summarizing the results of two metrics for the tasks in a test. Radar charts are useful for summarizing the results of three or more metrics overall. A comparison chart using Harvey Balls to represent different levels of the metrics can summarize the results effectively for three or more metrics at the task level. Perhaps the best way to determine the success of a usability test is to compare the results to a set of predefined usability goals. Typically these goals address task completion, time, accuracy, and satisfaction. The percentage of users whose data met the stated goals can be a very effective summary. A reasonable alternative to comparing to predefined goals, especially for time data, is to compare actual performance data to the data for experts. The closer the actual performance is to expert performance, the better.

The purposes of clinical decision support include improvement of quality, safety, and cost-effectiveness of care processes. This chapter includes under the term quality measurement (QM) measures of appropriateness of care from all three perspectives of quality: that is, safety and cost-effectiveness are also included. The chapter discusses how measurement and reporting are by themselves a passive form of clinical decision support (CDS). As a first line of using electronic health records (EHRs) to improve care, therefore, there is widespread adoption of efforts to use collected patient data to create quality measures and provide feedback and reporting of them to recipients. Moreover, ratings on quality measures are increasingly used as benchmarks for rating individual providers, practices, or hospitals that are available to the public, or as a metric for value-based reimbursement in emerging accountable care organization (ACO) type models of health care finance reform. Thus, they provide a very strong motivation for the adoption of CDS prospectively to maximize opportunity for meeting benchmark goals. In this chapter, QM (and reporting) are both discussed with respect to being a form of CDS, also showing how they differ from CDS in the traditional sense.

BACKGROUND High-risk therapies (HRTs), including medications and medical devices, are an important driver of preventable harm in children’s hospitals. To facilitate shared situation awareness (SA) and thus targeted harm prevention, we aimed to increase the percentage of electronic health record (EHR) alerts with the correct descriptor of an HRT from 11% to 100% on a high-acuity hospital unit over a 6-month period. METHODS The interdisciplinary team defined an HRT as a medication or device with a significant risk for harm that required heightened awareness. Our aim for interventions was to (1) educate staff on a new HRT algorithm; (2) develop a comprehensive table of HRTs, risks, and mitigation plans; (3) develop bedside signs for patients receiving HRTs; and (4) restructure unit huddles. Qualitative interviews with families, nurses, and medical teams were used to assess shared SA and inform the development and adaptation of interventions. The primary outcome metric was the percentage of EHR alerts for an HRT that contained a correct descriptor of the therapy for use by the care team and institutional safety leaders. RESULTS The percentage of EHR alerts with a correct HRT descriptor increased from an average of 11% to 96%, with special cause variation noted on a statistical process control chart. Using qualitative interview data, we identified critical awareness gaps, including establishing a shared mental model between nursing staff and the medical team as well as engagement of families at the bedside to monitor for complications. CONCLUSIONS Explicit, structured processes and huddles can increase HRT SA among the care team, patient, and family.

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results: Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion: The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

The well-known clinical axiom declaring that 'common things are common' attests to the pivotal role of probability in diagnosis. Despite the popularity of this and related axioms, there is no operationalized definition of a common disease, and no practicable way of incorporating actual disease frequencies into differential diagnosis. In this essay, we aim to disambiguate the definition of a common (or rare) disease and show that incidence-not prevalence-is the proper metric of disease frequency for differential diagnosis. We explore how numerical estimates of disease frequencies based on incidence can be incorporated into differential diagnosis as well as the inherent limitations of this method. These concepts have important implications for diagnostic decision making and medical education, and hold promise as a method to improve diagnostic accuracy.

Objective The role of ideal cardiovascular health (CVH) metrics in developing hearing loss remains uncertain. Thus, our objective was to analyze the connection between hearing loss and ideal CVH metrics in a 10-year retrospective cohort. Study design Retrospective cohort study. Setting A health management center in Taiwan. Participants Participants who underwent the first annual health check-up between 2000 and 2006 and with a follow-up check-up more than ten years later. Main outcome measures Hearing thresholds were measured at 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz. Individuals with a best ear pure-tone audiometry four-frequency average of >25 dB HL were defined as having hearing loss. The ideal CVH metrics were classified into 7 categories based on the American Heart Association's definition. The associations of hearing loss with the sum of the ideal CVH metrics and each ideal CVH metric were examined by multiple logistic regression analysis. Results The present study consisted of 6,974 participants. The 10-year follow-up showed that the odds ratio (OR) of hearing loss was 0.74 for participants with 5-7 ideal CVH metrics (95% CI, 0.59-0.93, p=0.01) compared to those with 0-2 ideal CVH metrics. Among the CVH metrics, participants with an ideal smoking status might have reduced odds of developing hearing loss; the OR was 0.72 (95% CI, 0.58-0.89, p=0.003). Conclusions Participants with an increased number of ideal CVH metrics and better performance on the smoking metric had a significantly protective effect regarding hearing loss development.

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator’s attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator’s learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

Objective:This large international cohort study aimed to investigate the associations of examined lymph node (ELN) number with accurate staging and long-term survival in pancreatic adenocarcinoma (PaC) and to robustly determine the minimal and optimal ELN thresholds.Summary Background Data:ELN number is an important quality metric in cancer care. The recommended minimal ELN number in PaC to accurately stage cancer varies greatly across guidelines, and the optimal number especially to adequately stratify patient survival has not yet been established.Methods:Population-based data on patients with stage I to II PaC resected in 2003 to 2015 from the US Surveillance, Epidemiology, and End Results (SEER)-18 Program and Netherlands National Cancer Registry (NCR) were analyzed. Associations of ELN number with stage migration and survival were evaluated using multivariable-adjusted logistic and Cox regression models, respectively. The series of odds ratios (ORs) for negative-to-positive node stage migration and hazard ratios (HRs) for survival with more ELNs were fitted using a LOWESS smoother, and structural breakpoints were determined by Chow test.Results:Overall 16,241 patients were analyzed. With increasing ELN number, both cohorts exhibited significant proportional increases from nodenegative to node-positive disease [ORSEER-18= 1.05, 95% confidence interval (CI) = 1.04-1.05; ORNCR= 1.10, 95% CI = 1.08-1.12] and serial improvements in survival (HRSEER-18= 0.98, 95% CI = 0.98-0.99; HRNCR= 0.98, 95% CI = 0.97-0.99) per additional ELN after controlling for confounders. Associations for stage migration and survival remained significant in most stratifications by patient, tumor, and treatment factors. Cut-point analyses suggested a minimal threshold ELN number of 11 and an optimal number of 19, which were validated both internally in the derivative US cohort and externally in the Dutch cohort with the ability to well discriminate different probabilities of both survival and stage migration.Conclusions:In stage I to II PaC, more ELNs are associated with more precise nodal staging, which might largely explain the survival association. Our observational study does not suggest causality, and does not encourage more extended lymphadenectomy before further randomized evidence is obtained. Our results robustly conclude 11 ELNs as the minimal and suggest 19 ELNs as the optimal cut-points, for evaluating quality of lymph node examination and possibly for stratifying postoperative prognosis.

The product return rate (RR) is an important metric for retailers; even small RR changes can significantly impact retailers’ profit. Companies and researchers typically favor and employ one of three methods to calculate the RR: based on the number of returned items, these items’ revenue, or their profit contribution. Interviews with 24 managers and industry experts reveal that two methods, item-based and revenue-based, are often used. However, little is known about how much the interpretation of RRs depends on the calculation method. In this article, the authors rely on extensive datasets to investigate these potential differences empirically. Analyzing more than 8 million transactions at sixteen different retailers, the authors find that RRs calculated via the three methods differ on average by 24.3%. The size of these differences makes cross-sectional comparisons of RRs calculated via different methods difficult. In contrast, the authors find that the developments of the RRs over time are similar, which allows for a meaningful time-series comparison of RR developments, regardless of the method. Finally, this research shows that all three calculation methods result in RRs that are equally insightful (leading) indicators of relevant retailer performance metrics.

Purpose Evidenced-based decision-making is rooted in comparative clinical studies; however, a small number of outcome event reversals have the potential to change study significance. The purpose of this study was to determine the utility of applying fragility analysis to comparative studies in the published orthopaedic shoulder literature. Methods Comparative clinical shoulder research studies reporting 1:1 dichotomous categorical data were analyzed in 6 leading orthopaedic journals between 2006 and 2016. Statistical significance was defined as a P value of less than .05. The fragility index (FI) for each study outcome was determined by the number of event reversals required to change the P value to either greater or less than 0.05, thus changing the study conclusions. The associated fragility quotient (FQ) was determined by dividing the FI by the total population comprising a particular outcome. Results Of the 23,897 studies screened, 3,591 met search criteria, with 198 comparative studies ultimately included for analysis, 67 of which were randomized controlled trials. There were 357 total outcome events with 74 reported as significant and 283 as not significant. The FI was 4 (IQR 2-6) with an associated FQ of 0.066 (interquartile range [IQR] 0.038-0.102). There was no difference in statistical fragility between randomized and nonrandomized trials with both revealing a FI of 4 and FQ of 0.068 (IQR 0.044-0.107) and 0.065 (IQR 0.031-0.101), respectively. Conclusions This current analysis reveals that comparative shoulder studies published in six leading orthopaedic journals are at risk of statistical fragility. As such, contemporary clinical shoulder literature may not be as robust as traditionally perceived with the reversal of only a few outcome events required to change study significance. Therefore, we advocate the reporting of both FI and FQ in addition to the P value as statistical complements to all comparative investigations to provide a more comprehensive understanding of trial stability and significance in the published shoulder literature. Clinical Relevance Comparative study designs are commonly employed in shoulder research. Several studies in both the general medical and orthopaedic literature have identified a lack of statistical robustness through comprehensive fragility analysis. Our findings demonstrate the P value may be an inadequate independent statistical metric requiring the complement of a FI and FQ to aid in the interpretation and understanding of study significance for clinical decision-making.

BACKGROUND CONTEXT There is growing interest among payers in profiling hospital value and quality-of-care, including both the cost and safety of common surgeries, such as lumbar fusion. Nonetheless, there is sparse evidence describing the statistical reliability of such measures when applied to lumbar fusion for spondylolisthesis. PURPOSE To evaluate the reliability of 90-day inpatient hospital costs, overall complications, and rates of serious complications for profiling hospital performance in lumbar fusion surgery for spondylolisthesis. STUDY DESIGN/SETTING Data for this analysis came from State Inpatient Databases from nine states made available through the Healthcare Cost and Utilization Project. PATIENT SAMPLE Patients undergoing elective lumbar spine fusion for spondylolisthesis from 2010 to 2017 in participating states. OUTCOME MEASURES Statistical reliability, defined as the ability to distinguish true performance differences across hospitals relative to statistical noise. Reliability was assessed separately for 90-day inpatient costs (standardized across years to 2019 dollars), overall complications, and serious complication rates. METHODS Statistical reliability was measured as the amount of variation between hospitals relative to the total amount of variation for each measure. Total variation includes both between-hospital variation (“signal”) and within-hospital variation (“statistical noise”). Thus, reliability equals signal over (signal plus noise) and ranges from 0 to 1. To adjust for differences in patient-level risk and procedural characteristics, hierarchical linear and logistic regression models were created for the cost and complication outcomes. Random hospital intercepts were used to assess between-hospital variation. We evaluated the reliability of each measure by study year and examined the number of hospitals meeting different thresholds of reliability by year. RESULTS We included a total of 66,571 elective lumbar fusion surgeries for spondylolisthesis performed at 244 hospitals during the study period. The mean 90-day hospital cost was $30,827 (2019 dollars). 12.0% of patients experienced a complication within 90 days of surgery, including 7.8% who had a serious complication. The median reliability of 90-day cost ranged from 0.97to 0.99 across study years, and there was a narrow distribution of reliability values. By comparison, the median reliability for the overall complication metric ranged from 0.22 to 0.44, and the reliability of the serious complication measure ranged from 0.30 to 0.49 across the study years. At least 96% of hospitals had high (> 0.7) reliability for cost in any year, whereas only 0-9% and 0-11% of hospitals reached this cutoff for the overall and serious complication rate in any year, respectively. By comparison, 10%–69% of hospitals per year achieved a more moderate threshold of 0.4 reliability for overall complications, compared to 21%–80% of hospitals who achieved this lower reliability threshold for serious complications. CONCLUSIONS 90-day inpatient costs are highly reliable for assessing variation across hospitals, whereas overall and serious complications are only moderately reliable for profiling performance. These results support the viability of emerging bundled payment programs that assume true differences in costs of care exist across hospitals.

The accurate and reliable estimation of irrigation water requirements is essential for efficient water resource management. Evapotranspiration (ET) is a significant factor in irrigation water consumption. It is the combination of evaporation from the soil and transpiration from the leaf surface. Proper estimation of ET is required to understand water resource management, crop water requirement analysis, drought monitoring, etc. ET depends on several factors such as weather conditions, land type, soil type, crops, etc., and its accurate estimation on a large spatial scale is a challenge. Many methods have been developed to estimate ET over the past few decades, and satellite-based ET estimation is one among them. This article reviews remote sensing-based ET estimations such as SEBI, SEBS, SSEB-op, TSM, SEBAL, METRIC, and Modified Priestley-Taylor. Their merits, demerits, and suggestions are also discussed. This study gives more insight into the importance of remote sensing-based ET estimation.

Fixed site monitoring is the primary method of measuring surface ozone pollution for health advisories and pollutant reduction, but the spatial scale may not reflect the current population distribution or its future growth. Moreover, formal methods for the placement of ozone monitoring sites within populations regions omit important spatial criteria producing monitoring locations that could unintentionally bias the exposure burden. Because ozone pollution endangers human health, triggering shortness of breath, causing asthma attacks and mortality, a need exists for assessing the spatial representativeness and data gaps of existing pollution monitors and to evaluate future placement strategies of additional monitors. A new metric, the potency index, for assessing the placements of monitors in relation to population distribution was developed. This index was used to evaluate the configuration of the ozone pollution monitoring network in relation to the population distribution in Dallas-Fort Worth (DFW). Location-allocation models were used to optimize future sensor quantity and placement. Finally, we propose the development of a decision support system that uses evaluation and optimization methods to improve air pollution monitoring objectives.

This research presents a retail analytics application which uses machine learning (ML) to identify and predict under- and overperforming consumer packaged goods (CPGs) using retail scanner data. Essential to measuring market performance at the SKU level is the relationship between distribution and market share (the velocity curve). We validate that ML can reproduce the velocity curve, and ML is further used to predict underperforming, in-line performing, and overperforming SKUs relative to the velocity curve, based on a range of variables (SKU features) at a point in time. Our ML approach can correctly predict 83% of SKUs as under-, in-line-, or overperforming based on their characteristics. The research analyzes 9,321 SKUs of 2,565 brands across seven product categories of CPGs which were sold in 8,117 stores from 49 different retail chains of five different retail channels located in the US states of California, New York, Texas, and Wisconsin. The retail stores comprise convenience stores, drug stores, food stores, liquor stores, and mass merchandise retail stores. The data is Nielsen retail store scanner data for the calendar year 2014. The relationship between distribution and market share is a market-wide proxy for the ratio of relative sales in a category to, for example, aggregate shelf space, a key retail productivity metric. We further find indications that the distribution of SKUs across different store sizes, the stores’ category specialization, the line length of the brands, the overall performance of the parent brand, and sales consistency are the most important characteristics for the prediction of market share performance beyond the velocity curve. The methods and results presented will help CPG marketers (suppliers and retailers) understand which SKUs are under-, in-line-, or overperforming and the potential factors contributing to that performance. Optimizing assortments and portfolios is essential to decrease failure rates of individual SKUs. ML approaches can evolve to complementary support tools for such management problems.

It is commonly assumed that photovoltaic (PV) modules have a 25-year lifetime. Warranties last this long, and techno-economic projections use this figure when calculating system performance. Yet, PV modules do not stop functioning after this time. Many modules still perform at above 80% of their original power rating after 25 years, and there is neither an economic nor an ecological reason to retire them. In this study, we explore the implications of transitioning from a mindset with finite module-life to a long-term perspective for PV-modules. We introduce an economic model to reflect such a long-term view. Using this model, we show that degradation gains in importance compared to module price. We calculate a cost-entitlement of up to 12ct/W for utility installation when improving annual degradation rates from 0.5% to 0.2%. We show that maintenance becomes more valuable – about 33% compared to a system with 30-year lifetime - and that time-constraints for maintenance disappear. We define a new lifetime metric - the minimum economically useful lifetime of a PV module as the time the module needs to be operated before replacement becomes economically preferable. We find that modules currently installed should be operated for at least 25 years and ideally for 50 years. Our metric links module life with degradation rate and, we calculate that reducing annual degradation rates from 0.5% to 0.2% increases the useful lifetime by a factor of 1.69. Such an increase would defer module end of life by decades and reduce resources required for maintenance by 40%.

Subtle radiographic findings can increase the challenge of diagnosing external iliac artery endofibrosis. We evaluated a new metric, the bicycle exercise ankle brachial index recovery time (BART), in a cohort of cyclists with symptomatic external iliac artery endofibrosis. BART was defined as the time required in minutes for the ankle brachial index to return to 0.9 after a period of exercise. Surgical correction resulted in an improvement in BART postoperatively (4.5 ± 4.1 vs 9.1 ± 4.3 minutes; P < .001), with improved values correlating with better patient satisfaction. Documentation of the BARTs before and after surgical treatment provides an additional measure of postoperative hemodynamic improvement.

The actual dose received during a computed tomography (CT) examination depends on both the patient size and the radiation output of the scanner. To represent the actual patient morphometry, a new radiation dose metric named size-specific dose estimates (SSDEs) was developed by the American Association of Physicists in Medicine in 2011. The purpose of this article is to review the SSDE concept and the factors influencing it. Moreover, the appropriate methodology of SSDE determination and the application of SSDE as a diagnostic reference-level quantity is critically analyzed based on the data available in the literature. It is expected that this review could potentially increase awareness among CT users of the effective utilization of SSDE as a tool to aid in the optimization of radiation dose in CT.

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behavior through wide use of smartphones, effective data collection and Big Data analysis, resulting in assessment and improvement of driver behavior and safety. The objective of the present study is to investigate the impact of detailed trip characteristics on the frequency of harsh acceleration and harsh braking events through an innovative smartphone application developed within the framework of BeSmart project. A 200-driver naturalistic experiment spanning 12 months is carried out since July 2019. During the first two months, participants were asked to drive in the way they usually did, without receiving any feedback on their driving behavior from the application. Over the subsequent two months, participants were provided with personalized feedback, a trip list and a scorecard regarding their driving behavior, allowing them to identify their critical deficits or unsafe behaviors. Some of the most important risk factors, such as speed and driving above the speed limit, usage of mobile phone while driving and harsh events (acceleration and braking) are recorded through the application and subsequently analyzed. Generalized Linear Mixed-Effects Models were fitted to the trips of car drivers who made frequent trips for both experiment phases in order to model the frequencies of harsh events. Results indicate that maximum speed, the percentage of speeding duration and total trip duration are positively correlated with both harsh acceleration and harsh braking frequencies. On the other hand, the exposure metric of total trip distance was found to be negatively correlated with both harsh event types. A small positive correlation of the percentage of mobile use duration with harsh accelerations was also detected.

Genuine engagement by academic staff and students in reflective practice in a time of great institutional pressure and a neo-liberal agenda for more metrics driven practice has become increasingly difficult to set time aside for. While more and more feedback is being requested on teaching practice, the quality, validity or reliability of this feedback is not always apparent. This case study explores a project developed at a small-medium sized institution, which aimed to provide an alternative rationale for lecturers to gain richer feedback about their teaching and student experience. The UK Engagement Survey was used in an original way as a reflective tool to increase the engagement with the survey itself and thus enhance the quality of the data. This article will outline the interactive, workshop-based nature of our approach and the effects this has had on the nature of the survey data and response rate.

Availability of accelerometer data has made it possible to objectively and continuously monitor sedentary behavior. Various summaries of the extensive accelerometer data have been used to understand the relationship between sedentary behavior and health. However, the widely used summary measures on sedentary bouts, average bout length or its derivatives, fail to reveal patterns of accumulated sedentary behavior over time. Studies have suggested that prolonged uninterrupted sedentary behavior can be an important metric that is related to health states. Yet existing measures to capture the prolonged sedentary patterns either rely on parametric assumptions on the underlying distribution of sedentary bout length or have to categorize sedentary bout length into somewhat arbitrary categories. Gini index was also used; however, it only measures the variability in bout lengths but not the actual length. To overcome these limitations, we proposed a non-parametric weighted survival function to characterize uninterrupted sedentary behavior over time in a continuous fashion and used the area under the survival curve as a new summary measure to quantify sedentary behavior. We showed that this measure is a weighted average of bout length and contains the information on both the mean and variability of bout lengths. We demonstrated in the simulation studies that the proposed measure could better identify prolonged uninterrupted sedentary behavior and predict health outcomes. We applied this new measure and existing sedentary measures to data from the Hispanic Community Health Study/Study of Latinos to examine the association between sedentary behavior and overweight/obesity.