Development Of Performance Metrics Research Articles

International consensus panel for transurethral resection of bladder tumours metrics: assessment of face and content validity.

To develop performance metrics that objectively define a reference approach to a transurethral resection of bladder tumours (TURBT) procedure, seek consensus on the performance metrics from a group of international experts. The characterisation of a reference approach to a TURBT procedure was performed by identifying phases and explicitly defined procedure events (i.e., steps, errors, and critical errors). An international panel of experienced urologists (i.e., Delphi panel) was then assembled to scrutinise the metrics using a modified Delphi process. Based on the panel's feedback, the proposed metrics could be edited, supplemented, or deleted. A voting process was conducted to establish the consensus level on the metrics. Consensus was defined as the panel majority (i.e., >80%) agreeing that the metric definitions were accurate and acceptable. The number of metric units before and after the Delphi meeting were presented. A core metrics group (i.e., characterisation group) deconstructed the TURBT procedure. The reference case was identified as an elective TURBT on a male patient, diagnosed after full diagnostic evaluation with three or fewer bladder tumours of ≤3 cm. The characterisation group identified six procedure phases, 60 procedure steps, 43 errors, and 40 critical errors. The metrics were presented to the Delphi panel which included 15 experts from six countries. After the Delphi, six procedure phases, 63 procedure steps, 47 errors, and 41 critical errors were identified. The Delphi panel achieved a 100% consensus. Performance metrics to characterise a reference approach to TURBT were developed and an international panel of experts reached 100% consensus on them. This consensus supports their face and content validity. The metrics can now be used for a proficiency-based progression training curriculum for TURBT.

Defining Detection Limits for Continuous Monitoring Systems for Methane Emissions at Oil and Gas Facilities

Networks of fixed-point continuous monitoring systems are becoming widely used in the detection and quantification of methane emissions from oil and gas facilities in the United States. Regulatory agencies and operators are developing performance metrics for these systems, such as minimum detection limits. Performance characteristics, such as minimum detection limits, would ideally be expressed in emission rate units; however, performance parameters such as detection limits for a continuous monitoring system (CMS) will depend on meteorological conditions, the characteristics of emissions at the site where the CMS is deployed, the positioning of CMS devices in relation to the emission sources, and the amount of time allowed for the CMS to detect an emission source. This means that certifying the performance of a CMS will require test protocols with well-defined emission rates and durations; initial protocols are now being used in field tests. Field testing results will vary, however, depending on meteorological conditions and the time allowed for detection. This work demonstrates methods for evaluating CMS performance characteristics using dispersion modeling and defines an approach for normalizing test results to standard meteorological conditions using dispersion modeling.

Relating team atmosphere and group dynamics to student software development teams’ performance

ContextWhile the software engineering community (i.e., those involved with engineering software) is constantly in search of insights into team atmosphere and group dynamics and the way these issues impact team performance, little opportunities typically exist to explore this issue. Student projects offer an opportunity for us to understand these issues, and particularly if these students are on the verge of leaving university for post-study work and using similar practices to those used in industry. ObjectiveWe explore a range of student software development projects’ data and students’ open-ended responses to five group dynamics categories: communication, time management, commitment, problem analysis and solving, and initiative and involvement. MethodWe analyse both quantitative and qualitative data to study the variation in group dynamics across teams developing different software and how these variations correlated with team satisfaction. We also explore the group dynamics themes that evolve from students’ open responses in relation to the five categories. Furthermore, we relate the prevalence of the themes to various software development performance metrics, before exploring the opportunity of predicting an optimum team dynamics. ResultsWe observe variations in the way different teams work, but higher performing teams also committed more to their projects. Various group dynamics themes were evident among functional teams, and specific patterns were more pronounced when teams were productive. Further, while there is no specific group dynamics pattern that predicts project success, successful teams were most organised and reflective. ConclusionCompetence may set the tone for positive group dynamics and team performance. Also, an achievement-driven orientation is as important as the soft skills and interpersonal aspects.

Examining share repurchase executions: insights and synthesis from the existing literature

This literature review aims to address the critical knowledge gap in the field of share repurchase executions, a financial activity involving companies repurchasing trillions of dollars' worth of their own shares. The significance of understanding these mechanisms and their impact is underscored by their potential influence on the global economy. The paper employs a comprehensive analysis of existing literature, focusing on share repurchase mechanisms and motivations. It scrutinizes both open-market repurchases and Accelerated Share Repurchase contracts. Methodological approaches in current research, such as the use of partial differential equations and tree methods, are also evaluated. The review reveals that the execution phase of share repurchases remains largely unexplored. Unanswered questions persist about trading schedules, implications, costs, broker and corporate performance, and psychological effects of beating a buyback benchmark. Additionally, the review identifies significant limitations in current research methodologies. The paper advocates for the application and development of more advanced tools like machine learning and artificial intelligence to address these gaps. It also suggests potential areas for future research, including the role of technology in share repurchase execution, psychological factors influencing corporate buybacks, and the development of performance metrics for brokers and corporations. The review serves not only to highlight existing gaps in literature but also to suggest avenues for future research that could fundamentally enhance our understanding of share repurchase executions.JEL classificationG1, G12, G14, G02, G4.

A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.

The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.

Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation

Using large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to those metrics. Orbits are defined by a set of Keplerian elements, and the relationship between those elements and the spatial and temporal coverage metrics are examined in order to maximize global and zonal (latitude-dependent) coverage. Additional optimization is performed by dividing a constellation into multiple orbit planes. An iterative method can be applied to this design process to compare the performance of current and previous designs. The main objective of this work is the design of optimized configurations of satellites in low Earth orbiting constellations to maximize the spatial and temporal sampling and coverage provided by its sensors. The key innovations developed are a new cost function which measures the temporal sampling properties of a satellite constellation, and the use of it together with existing cost functions for spatial sampling to design satellite constellations that optimize performance with respect to both performance metrics.

Unified Performance Assessment of Optical Wireless Communication Over Multi-Layer Underwater Channels

In this paper, we model the multi-layer vertical underwater link as a cascaded channel and unify the performance analysis for the underwater optical communication (UWOC) system using generalized Gamma (GG), exponential GG (EGG), exponentiated Weibull (EW), and Gamma-Gamma ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma \Gamma$</tex-math></inline-formula> ) oceanic turbulence models. We derive unified analytical expressions for probability density function (PDF) and cumulative distribution function (CDF) for the signal-to-noise ratios (SNR) considering independent and non-identical (i.ni.d.) turbulent models and zero bore-sight model for pointing errors. We develop performance metrics of the considered UWOC system using outage probability, average bit error rate (BER), and ergodic capacity with asymptotic expressions for outage probability and average BER. We develop the diversity order of the proposed system to provide a better insight into the system performance at a high SNR. We also integrate a terrestrial OWC (TOWC) subjected to the combined effect of generalized Malága atmospheric turbulence, fog-induced random path gain, and pointing errors to communicate with the UWOC link using the fixed-gain amplify-and-forward (AF) relaying. We analyze the performance of the mixed TWOC and multi-layer UWOC system by deriving PDF, CDF, outage probability, and average BER using the bivariate Fox H-function. We use Monte-Carlo simulation results to validate our exact and asymptotic expressions and demonstrate the performance of the considered underwater UWOC system using measurement-based parametric data available for turbulent oceanic channels.

Adaptations in SCOR based performance metrics of airline catering supply chain during COVID-19 pandemic

Purpose: There is the propensity of the Airline catering supply chain to adapt their performance measures in order to meet desired service level due to the challenges of the Covid-19 pandemic. The aim of this paper is to develop a set of metrics for airline catering organization and explore the choices of SCOR based performance metrics during the Covid-19 pandemic.Design/methodology/approach: The SCOR framework is applied in the context of the airline catering supply chain to develop performance metrics. In this case study, the performance metrics model is analysed and validated by experts. Then, metrics are prioritised using MoSCoW method based on the experience of the Covid-19 challenges.Findings: A hierarchical performance measure framework is proposed, and a set of 55 metrics is identified. The validation of these metrics recognises the initial work. With the prioritisation, 13 level-2 &amp; level-3 metrics are considered necessary in addition to 7 level-1 metrics to mitigate Covid-19 pandemic challenges better. Research limitations/implications: This research is based on a single case study. The validation is restricted to a small sample size.Practical implications: With the development of performance metrics and prioritization, airline catering organisation able to monitor their catering logistics performance.Originality/value: The work contributes to the measurement of performance in airline catering logistics, and adapted metrics would help business to be more responsive and flexible as per the market changes to alleviate Covid-19 challenges.

Towards Development of Performance Metrics for Benchmarking SLAM Algorithms

The true autonomy of mobile robots cannot be achieved without Simultaneous Localization and Mapping (SLAM). With this capability, mobile robot could concurrently build a map of the environment and locate itself with respect to the map. Although there are several variants of SLAM algorithms contributed by researchers so far, only a very few works were aimed at comparing their performances with appropriate metrics and providing detailed directions and insights to the user on selection criteria and indicative use cases. In this work, we presented a comparative study of three popular SLAM algorithms and provide some significant quantitative performance measures of the same by using our novel | R | and | S | performance metrics as well as conventional metrics. The comparative study was carried out in ROS (Robot Operating System) using Turtlebot3 robot model on three SLAM packages viz G-mapping, Karto SLAM, and Frontier Exploration SLAM. Furthermore, the results show that the proposed metrics are very efficient and compact in comparing and quantifying the performance of SLAM algorithms.

Digital supply chain performance metrics: a literature review

PurposeIn the previous decade, a substantial amount of research has been undertaken to measure the digitalized supply chain (DSC) performance. This paper aims to present a systematic literature review on DSC performance measurement metrics to apprehend current practices, recognize gaps and advocate future research itineraries.Design/methodology/approachTo guarantee a replicable, rigorous and transparent research process, the authors used a systematic literature review methodology to synthesize the research. A combination of 25 keywords was used to obtain 248 scientific studies in the first step. The balance scorecard (BSC) model was used to categorize 299 gathered performance metrics into 4 BSC perspectives.FindingsThe review highlighted the need for qualitative performance measuring metrics for DSC. During the review, only one study was identified that primarily focused on developing performance metrics for DSC. Additionally, the review identified that metrics related to internal and financial perspectives received the most attention while the “growth and learning” perspective received the least attention. The review also identified that external partners, such as distributors and suppliers, were virtually ignored in previous literature.Originality/valueAlthough numerous literature reviews have been conducted in the past on the performance measuring metrics for supply chain management, no literature review aiming to synthesize the measuring metrics for DSC has yet been undertaken.

Humanities towards a bibliometrics framework

In this article the possibility of using bibliometric measures to assess the study in the sciences is addressed. A review of past attempts to develop bibliometric research methods shows that differences in organizational, epistemological and research practices in the fields of research are all to be taken into consideration. In several areas of humanism, the dependence on collaborators, interdisciplinary and the rural nature of research is emphasized as factors that influence the application of bibliometric methods. Some particularly intriguing approaches as well as the possibility of establishing a Bibliometric for the humanities are addressed. In addition, the intellectual characteristics of some areas should be considered when developing performance metrics and the need of engaging science specialists in the process is underlined. Bibliometrics may be helpful to evaluate the wider impact of scientific research in the future and an assessment of citations can help us understand better how these disciplines are structured and evolved.

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

You Cannot Manage What You Cannot Measure—Developing Central Sterilizing Performance Metrics

Background: Metrics are critical to healthcare, particularly in areas that are highly process oriented. Our hospital’s central sterilizing department functions on standardized processes that have almost no industry specific measurements. Furthermore, the department had no formal internal method for process performance evaluation. A multiphase 6 Sigma DMAIC project was initiated in February 2019 in the central sterilizing department, with ‘phase 1’ being the development of performance metrics to evaluate current state and track future improvement. Methods: A DMAIC team was formed and completed a data inventory that included identifying all existing data sources pertaining to central sterilizing. Sources identified as reliable included count of daily surgical cases, counts of defects based on surgical services communication logs, number of surgical trays processed in the central sterilizing department, and the number of unsterile trays in the department at the start of each 7:00 a.m. shift. Results: A multidisciplinary team including surgeons and senior leadership formed the DMAIC team. Using identified data sources and input from frontline staff and leadership, official metric definitions were created. Weekly defect rates were chosen as a measurement and translated into the number of ready-for-use surgical trays at the time of case setup for every 100 surgical cases. Data from November 2018 through January 20, 2019, demonstrated the mean ready-for-use surgical trays at the time of case setup: for every 100 surgical cases, 96 the surgical trays was ready for use. Further analysis of the data over time demonstrated that variability was out of control. Defects were stratified into 12 categories. Moreover, 6 defects were creating 80% of the issues in trays not being ready: ‘hole in wrapper,’ ‘biohazardous debris,’ ‘nonbiohazardous debris,’ ‘missing instrument,’ ‘incorrect/mislabeled instrument trays,’ and ‘missing indicators.’ Results for unsterile trays at 7:00 a.m. showed that, on average, the central sterilizing department had 100 or more trays at the start of each 7:00 a.m. shift. Conclusions: Without a formal method for data collection, defined metrics, and ongoing analysis, evaluation of performance is based on anecdotal conclusions resulting in missed opportunities for improvement and, subsequently, opportunities for improved patient safety. Regularly published visible dashboards provide a true picture of performance, allowing staff to identify unwanted variation and to put interventions in place so that future variation is better controlled and or prevented. Additionally, having solid metrics, even when no industry ones exist, aid in measuring the effectiveness of improvement efforts, such as the DMAIC project, as they move into subsequent phases.Funding: NoneDisclosures: None

Toward developing performance metrics for library print-based specialized collections using available circulation data and ratio analysis

The purpose of this study is to contribute to the establishment of metrics that can be used by libraries to analyze, anticipate, and plan for resource allocation for specialized print-based collections within an academic library. In the context of transactions associated with the circulation of library materials, that is checkouts, renewals, and holds, the resources this study will propose includes two ratios that can be used for evaluating and justifying continued allocation of library resources to specialized print collections. These ratios proposed are the Proportionate Number (P/N) and Internal P/N (I-P/N) Ratios. Beginning with the conceptual framework of how data driven decision making can contribute to the operation of the library, the study will also provide concrete examples of how to analyze data from an Integrated Library System (ILS). Data will be presented for the case study of Gumberg Library at Duquesne University. The study hopes to address a perceived gap in the literature that percentage of users accessing a collection is often not considered in the analysis of circulation data.

From Defined-Contribution Towards Target-Income Retirement Systems

The current trend towards Defined Contribution (DC) retirement systems around the world has rendered the risk management of pension funds crucial for the financial health of millions of people. Severe market downturns have put in evidence the need for more effective practices to control losses of retirement income for pension funds' investors. Although the move from the static allocation of policy portfolios toward target-date funds (TDFs) encouraged by regulators features significant improvements, the latter strategies completely ignore the variations in the cost of financing future consumption, which lessens the strategies' ability to control losses in retirement income. While optimal long-term portfolio selection literature emphasizes the importance of hedging against changes in the discount rates that determine the cost of financing future consumption, TDFs strategies, regulatory incentives, and reporting to pension funds' investors disregard the variations in long-term discount rates by focusing in absolute returns. We address these shortcomings by (i) developing performance metrics, such as DC funding-ratios, that foster appropriate incentives for pension fund managers and more sensible and simple reporting to their investors, (ii) introducing a series of asset allocation rules designed to secure a minimum level of target-income in retirement regardless of the returns of the risky assets in the portfolio. The strategies are consistent with insights from long-term portfolio theory and have the critical advantage of being free of any model or parameter estimation risks. We illustrate its advantages relative to a standard TDF strategy in terms of retirement security.

Developing clinical performance indicators for pre-hospital blood transfusion: The Thames Valley Air Ambulance approach.

In this article, we describe how we developed and validated key performance indicators (KPIs) for pre-hospital blood transfusion and offer suggestions for other organisations wishing to develop performance metrics. KPIs are metrics that compare actual care against an ideal structure, process or outcome standard. An increasing number of UK-based pre-hospital critical care services now carry blood components to enable pre-hospital blood transfusion. A working group of pre-hospital physicians and paramedics was formed to create and validate performance indicators that reflected a high-quality pre-hospital transfusion. This was performed by literature searching and reviewing consensus documents that guide the best practice and then adjusting the indicators as the process evolved. Throughout the year, the performance against the domains was monitored monthly and outputs communicated within the clinical staff of the organisation; at the end of the year, the domains were amended. The final list of performance indicators was as follows: (a) rationale for transfusion documented in the notes; (b) rationale for transfusion in line with Thames Valley Air Ambulance blood transfusion guideline; (c) aggressive management of hypothermia; (d) tranexamic acid administered within an hour of injury; (e) evidence of bleeding in hospital; (f) monitoring of adverse effects of blood transfusion; (g) overall-was the use of blood justified; and (h) no units wasted this month. This study has shown that it is feasible to devise and implement clinical performance indicators for pre-hospital blood transfusion and that their use has increased the focus on this important area.

Theory-Based Development of Performance Metrics for Comparing Multireactant Enzymes

The kcat/KM ratio holds substantial significance as a comparative figure of merit in the evaluation of enzymes with uni-molecular mechanisms. However, its applicability in the evaluation of multi-reactant systems is frequently not justified. Here, we derive figures of merit for multi-reactant enzyme mechanisms based on the stabilization of the transition states and the kinetic Haldane equilibrium relationships. For most bi-molecular reactions, the ratio of kcat/KiaKb (where Kia is the dissociation constant of A and Kb is the Michaelis constant of B) is best suited for comparing enzyme performance, especially at non-saturating reactant concentrations. The value of this parameter for assessing the performance of a series of oxidoreductase mutants is demonstrated. Figures of merit for other common enzymatic mechanisms are derived, which enable a theory-based approach for comparing steady-state catalytic performances of enzymes with multiple reactants.

Strategic planning and performance measurement: engaging the community to develop performance metrics

ABSTRACT Fairfax County, Virginia is the Commonwealth’s largest county, and one of the largest and wealthiest local jurisdictions in the United States. Its economy is large and diverse, as is its population, so measuring the success of the economy extends beyond traditional measures of economic development. In this paper, we describe a multi-year effort to conceive of performance management from a strategic planning perspective and develop a set of metrics that move beyond the traditional instrumentally focused metrics and views performance management as a governance process that builds relationships between stakeholders and fosters mutual learning. The metrics that emerged from this process were developed by engaging the public early and working with key county stakeholders to arrive at a set of metrics that encompassed not just county leaders’ views of what makes a county economy work, but those of citizens, businesses, schools, nonprofits and other members of the public as well.

Deliverology in Community Economic Development: Enhancing Results Frameworks and Performance Measurement

&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;span&gt; &lt;/span&gt;This paper will assess the viability of implementing an alternative delivery model and performance measurement framework – commonly known as ‘deliverology’ – at the level of community economic development. First, a review of relevant literature on traditional economic development delivery models and performance metrics is conducted to determine strengths and weaknesses. Next, a deliverology approach is defined and analyzed to determine whether such a model can address the weaknesses of more traditional approaches. The results indicate that a deliverology approach has many potential advantages for economic development service delivery and addresses many of the weaknesses of current models and frameworks. Since deliverology remains rather new compared to more traditional approaches, further research in terms of a case study in a large urban municipality is recommended as a way to test the applicability of deliverology to community economic development. &lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;deliverology, performance measurement, results and delivery framework, community economic development, service delivery model&lt;/p&gt;

Organization development components, process and performance

Nowadays, any organization faces external challenges and changes and the organizational development has been seen as an important tool in managing and planning organization growth. Organizational development works to improve organizations and balance the strengths and weaknesses of a business that is extremely imperative and should not be overlooked. The organizational development has been defined as an effort that is planned at organization level and starts from the top of the organizational hierarchy in order to enhance the organization’s effectiveness through planned interventions in organizational processes. The paper emphasizes the organizational climate, organizational culture, organizational structure and organizational strategy as the key components of organizational development. The situational assessment, intervention planning, implementing the intervention, data gathering, results analysis and feedback have been determined as the main steps in the organizational development process. The human process interventions, techno-structural interventions, human resource management interventions and strategic interventions have been identified as the main categories which lead to organizational improvement. The impact of organizational development interventions on company performance has been studied and the organizational development performance metrics have been proposed in the article.