Robust Performance Measures Research Articles

A Survey of Public Library-Led Digital Literacy Training in Canada: Perceptions of Administrators and Instructors

Objective – This paper presents the results from a survey of administrators and instructors at public libraries across Canada investigating the delivery of digital literacy training led by public libraries. The goal of the survey was to capture a snapshot of the Canadian public library-led digital literacy training landscape and to explore differences in perceptions of training activities between public library administrators and instructors. Methods – An online survey was distributed to administrators and instructors at public libraries across Canada with the help of two national public library associations. The survey instrument was developed based on a theoretical framework from the research team’s prior case study investigations of community-led digital literacy training. The survey included closed- and open-ended questions concerning the availability of adequate/sustained funding, the adequacy of dedicated classroom resources, the competency of teaching staff, the helpfulness of support staff, the amount and frequency of knowledge sharing of best practices, the amount of rigorous and regular performance measurement, the scheduling of the training provided, the skills taught, the pedagogical approaches used, and the marketing carried out. Responses were analyzed using both quantitative and qualitative data analysis techniques. Results – Public library administrators and instructors in Canada are generally satisfied with the delivery of digital literacy training; however, room for improvement exists. Instructors are more positive about the delivery of this training than administrators. Findings support and extend the research team’s conceptual model, specifically in terms of providing more insight and clarity on how the learning environment and program components affect the delivery of digital literacy training led by public libraries. Results highlight how training is situated in context and how libraries need to fine-tune the delivery of this training in ways that are reflective of libraries’ learning environments and program components. Conclusion – Results are of high interest to researchers and library practitioners who wish to leverage evidence-based library and information practice to understand and address the factors affecting the successful delivery of public library-led digital literacy training. Though funding is always an obstacle for any public service organization, libraries can make improvements to the delivery of their training in other ways, such as carrying out more robust performance measurement and using results more transparently, participating in more knowledge-sharing opportunities, and better understanding learner needs and preferences.

Milk Quality and Economic Sustainability in Dairy Farming: A Systematic Review of Performance Indicators

The dairy industry plays a vital role in the global food system, providing a wide range of dairy products that are consumed by millions of people worldwide. Dairy farming provides a daily source of income, creating employment opportunities not only on farms, but also in transportation, milk processing, and the agricultural supply and services sectors. The increasingly pressing challenges and the high competition in the dairy industry, particularly in saturated markets, emphasize the importance for farms to undertake a comprehensive economic sustainability analysis that extends beyond mere yield monitoring. Empirical studies have found a weak adoption of robust performance measurement and control systems in dairy farms. Given the intricate macroeconomic landscape in developed nations and the imperative to address the multifaceted challenges of the industry, this study employs a systematic literature review (SLR) to evaluate whether academic research offers adequate guidance on economic performance indicators. The study finds out that the current repertoire of indicators, while relevant and partially related to quality attributes, fails to encapsulate the intricate interplay of variables and the nature of economic sustainability, highlighting the need to adopt additional indicators into the dimensions of operational efficiency and effectiveness, strategic investments, and financial strength.

Novel deep learning framework for detection of epileptic seizures using EEG signals.

Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process. In this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a single unit. This unit is repeatedly used in the proposed model to extract the features. The features are then passed to the Dense layers to predict the class of the EEG waveform. The performance of the proposed model is verified on the Bonn dataset. To assess the robustness and generalizability of our proposed architecture, we employ five-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity. Our proposed model achieves an accuracy of 99-100% for binary classifications into seizure and normal waveforms, 97.2%-99.2% accuracy for classifications into normal-interictal-seizure waveforms, 96.2%-98.4% accuracy for four class classification and accuracy of 95.81%-98% for five class classification. Our proposed models have achieved significant improvements in the performance metrics for the binary classifications and multiclass classifications. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.

The Influence of Motivation, Training and Compensation on Employee Performance

Purpose: This study aims to analyze the effects of competence, training, and work discipline on the performance of Village Consultative Body (BAMUSKAM) members in Sarmi District. Research Design and Methodology: The research employs a quantitative approach with a survey method, utilizing a questionnaire as the instrument. The sample consists of 56 BAMUSKAM members from Sarmi District. Data analysis was conducted using multiple regression analysis and hypothesis testing with SPSS version 26.0. Findings and Discussion: The results indicate that competence, training, and work discipline each have a positive and significant impact on BAMUSKAM members' performance. Additionally, the combined influence of these variables significantly enhances performance. The findings support the hypothesis that these factors are crucial for improving organizational performance, highlighting the importance of educational and training programs, performance measurement systems, and strict adherence to discipline. Implications: This study underscores the necessity for local governments to invest in comprehensive training and development programs that cater to their specific needs. It also suggests fostering partnerships with educational institutions and implementing robust performance measurement systems that emphasize competence and discipline. The research provides practical insights for local government officials and policymakers to enhance the effectiveness of BAMUSKAM members.

RSO based Optimization of Random Forest Classifier for Fault Detection and Classification in Photovoltaic Arrays

Fault detection and classification in photovoltaic arrays are critical for increasing grid reliability and reducing the power losses. This paper assesses twelve machine learning classifiers for their effectiveness in detecting and classifying faults in Photovoltaic (PV) systems. Multiple validation methods were used for the algorithm evaluation, including K-fold, stratified K-fold, leave-one-out, and random split cross-validation approaches to ensure robust performance measures. The applied selection criterion of the top performing classifier are the accuracy, precision, recall, and computing efficiency. The utilized dataset, comprising samples with various fault kinds under diverse environmental conditions, received thorough preprocessing to enhance model training and assure generalizability. A large dataset of roughly 10,000 samples was utilized in this research for the model training and to run multiple random tests on new and unseen data. This dataset provides a fair representation of multiple fault types such as the healthy, Line to Line (LL), Line to Ground (LG), Partial Shading (PS), and Complete Shading faults (CS). The data preprocessing comprised normalization, handling of missing values by taking the average, and applying multiple statical analysis approaches to reduce the size of the features matrix and to improve the dependability of the model's predictions across varying operational circumstances. The results illustrate the best performance utilizing the optimized version of the Random Forest classifier, reaching an average fault detection accuracy of 100% and fault classification accuracy of 94.7%, the hyperparameters of the classifier was optimized using Random Search Optimization algorithm(RSO).

Effect of Style on Post-Merger and Acquisition Performance of Commercial Banks in Kenya

Based on the McKinsey 7S Framework, this study was undertaken to assess the effect of Style on post-Merger and Acquisition (M&A) performance of commercial banks in Kenya. The independent variable of the study is Style, operationalized through change communication, post-M&A change management, and post-M&A leadership. Post-M&A integration, the moderating variable, is measured by the degree and rate of integration. This study is anchored on the Three-step change, Resource Based View, and Universalistic Best Practice theories. The Kenyan commercial banking sector has 38 registered commercial banks currently operating in the industry; 29 of which have undergone M&A activities. A sample of 10 banks, involved in M&A activities for a period ranging from six months to five years, was selected. This timeframe recommended by Masoud et al., (2020) is critical for M&As as it allows the assessment of both short-term and medium-term integration outcomes, where initial challenges are addressed and operational synergies start manifesting. Data was collected using both primary and secondary sources. Primary data, analyzed through content and framework analyses provided qualitative insights into integration practices. Secondary data, primarily consisting of financial ratios of bank performance (Return on Assets, Return on Equity, Return on Investment, Operating Profit Margin, and Net Profit Margin) was analyzed using the Independent Sample T-test in SPSS version 28.0. Correlation analysis and inferential statistics were employed to measure the strength of relationships between study variables. The study employed a 95% confidence level with a 5% level of precision, ensuring that the results were robust and could be generalized with minimal error. These levels of precision and confidence enhance the reliability and quality of the research by reducing the likelihood of making incorrect inferences about the broader population. The findings reveal that effective change communication post-M&A significantly enhances employee engagement and reduces resistance, which in turn positively influences overall organizational performance. Change management practices that are structured and inclusive of employee feedback contribute to smoother integration processes, minimizing disruptions to operations and customer service. Leadership style was found to be a critical factor in the success of M&As, with collaborative and change leadership resulting to better post-M&A outcomes. The results further confirmed that the conceptual framework accurately predicted post-M&A performance, with Style having a positive, statistically significant effect. Additionally, post-M&A integration was found to moderate the relationship between Style and post-M&A performance, amplifying the positive outcomes when the integration was well-executed. The study concludes that the style of managing change, communication, and leadership post-M&A is critical in determining the success of commercial banks in Kenya, with a strong emphasis on transparent communication, structured change processes, and adaptive leadership styles. These findings suggest that adopting best practices in these areas can significantly enhance post-M&A performance. To enhance post-M&A performance of commercial banks, this study recommends integration of operations and systems, implementation of robust performance measurement systems, effective communication, leadership, and change management. There is need for future studies on the effects of M&A activities on the risk profile of commercial banks. This could involve examining how changes in the size, diversification, and complexity of the institution following M&A impact its risk exposure and resilience to external shocks.

Modeling intellectual capital-based intrapreneurial ability of working professionals through servant leadership and self-efficacy

PurposeIntrapreneurial ability (IA) of employees strengthens an organization's internal as well as external growth. Employees' IA makes innovation a continuous practice and augments organization's intellectual capital (IC). This intellectual capital-based intrapreneurial ability (ICIA) helps professionals to effectively handle changes in the business ecosystem by creating innovative solutions. The onus of assessing and inculcating ICIA is a joint responsibility of both academia and industry. In academia, teacher as a servant leader (TASL) contributes towards building ICIA of working professionals (WP) by enhancing their self-efficacy (SE). The paper aims to strengthen the industry–academia interface by analyzing the role of TASL and SE in influencing the ICIA of WP.Design/methodology/approachUsing a stratified sampling technique, data from 387 WP is analyzed on SmartPLS-4 to study the interrelationship between the stated constructs and the role of SE as a mediator between TASL and ICIA. PLSpredict is used to study the predictive relevance of the proposed model.FindingsHigh R2 = 0.654 shows that 65% of ICIA is determined by SE and TASL; reflecting model's robustness. SE partially mediates the relationship between TASL and ICIA. Results reported a higher ICIA of male WP than their female counterpart. The results indicate the low predictive accuracy of the model.Practical implicationsThe proposed model of industry–academia partnership allows assessment of ICIA for enhancing corporate value in the present gig economy. The study also highlights the relevance of ICIA, particularly, for developing economies. In knowledge-driven economy, exploring the new ICIA will help organizations to draft a more robust performance measurement system.Originality/valueThis unique industry–academia partnership studies the role of TASL towards enhancing SE and ICIA of WP. The novelty of ICIA would enrich and provide a new perspective in IA literature. Additionally, the study also examines the role of gender in the ICIA of WP.

UNVEILING SUCCESS: PERFORMANCE MEASUREMENT SYSTEMS IN QUICK SERVICE RESTAURANT CHAINS

This study examines the importance and effectiveness of performance measurement systems in quick service restaurant (QSR) chains. With the highly competitive nature of the QSR industry, it is crucial for restaurant chains to implement robust performance measurement systems to track and evaluate their operational and financial performance. This research investigates the key metrics and indicators used in performance measurement systems within QSR chains, as well as the impact of these systems on business success. A combination of qualitative and quantitative research methods, including interviews and surveys, is employed to gather insights from industry professionals and managers. The findings shed light on the significance of performance measurement systems in driving operational efficiency, enhancing customer satisfaction, and improving overall business performance in the QSR industry.

Robust reward-risk performance measures with weakly second-order stochastic dominance constraints

Robust reward-risk performance measures with weakly second-order stochastic dominance constraints

A Literature Review on Developing A Project Performance Measurement Strategy for Construction Project

This project aims to develop a project performance measurement strategy for construction projects using Objectives and Key Results (OKRs) methods. Construction projects are complex and involve multiple stakeholders, tasks, and objectives. Therefore, it is crucial to have a robust performance measurement strategy in place to ensure that the project is completed on time, within budget, and with the desired quality. This literature review aims to analyses the existing research on developing a project performance measurement strategy for construction projects. The review will cover topics such as the objectives of project performance measurement, the challenges in measuring project performance, the methods for developing a performance measurement strategy, and the key performance indicators (KPIs) used in the construction industry. Additionally, the review will examine the application of the Objectives and Key Results (OKR) method for project performance measurement and its effectiveness in the construction industry. The literature review will provide valuable insights into the best practices for developing a project performance measurement strategy for construction projects, and will be useful for project managers, researchers, and practitioners in the construction industry.

Bug detection in Java code: An extensive evaluation of static analysis tools using Juliet Test Suites

AbstractPrevious studies have demonstrated the usefulness of employing automated static analysis tools (ASAT) and techniques to detect security bugs in software systems. However, these studies are usually focused on analyzing the effectiveness of the tools using open‐source tools based on C/C++ source code. The choice for making an appropriate decision on the most suitable tool for bug detection in Java code software remains a relatively unexplored domain. To address this deficiency, this study empirically evaluates eight widely used ASATs, namely, Findbug, PMD, YASCA, LAPSE+, JLint, Bandera, ESC/Java, and Java Pathfinder using the Juliet Test Suite (Test Suite v1.2). Additionally, we assessed the performance of the detection capabilities for the aforementioned bug detection tools using robust performance measures such as precision, recall, Youden index, and the OWASP web benchmark evaluation (WBE). The experimental results show that the tools obtain precision values ranging from 83% to 90.7% based on the studied datasets. Specifically, the Java Pathfinder achieves the best precision score of 90.7%, followed by YASCA and Bandera with a precision score of 88.7% and 83%, respectively. Similarly, Bandera, ESC/Java, and Java Pathfinder obtain a Youden index of 0.8, which indicates the effectiveness of the tools in detecting security bugs in Java source code.

A hybrid approach to the discrepancy in financial performance’s robustness

Performance measurement is a crucial ingredient in the industry of investment funds. Mainly grounded on indices of risk-adjusted returns, it requires historical data to estimate the relevant statistics such as the Sharpe ratio. Therefore the measurement process is sensitive to outliers in the time series underlying historical data. Since alternative measures are available for performance evaluation, we propose an iterative methodology for a set of eleven indices (including the Sharpe ratio) in order to: (a) quantify their intrinsic degree of statistical robustness; (b) find different sensitivity to alternative outliers configuration. This methodology is a combination of a reasonable definition of breakdown point and the definition of discrepancy of a finite point set. A suitable Monte Carlo simulation provides numerical evidence of changing sensitivity among all considered performance measures, instead the classical definition of breakdown point only shows lack of robustness among all indices without further specification. Our approach may be useful in choosing the most robust performance measure to be employed in investment management, especially when robust portfolio optimization has to be used.

The Effect of IFRS Adoption on Corporate Performance of Listed Firms in Nigeria

International Financial Reporting Standards (IFRS) are standards and framework adopted globally to harmonize divergent national standards and improve financial reporting quality. This study empirically investigated the effect of IFRS adoption on the performance of listed firms in Nigeria using data from all the 21 banks listed on the Nigerian Stock Exchange as at 31st December 2016. Using two robust performance measures – profitability and growth, a multiple regression model was used to test the hypothesis of this study. The research also carried out a pre and post study to investigate if there is any significant difference in the growth of listed banks in Nigeria following the adoption of IFRS. The study reveals that the adoption of IFRS has no significant effect on performance vis – a – vis the profitability and growth of listed banks in Nigeria. Findings from this study also reveals that IFRS adoption has no significant effect on the Pre and Post growth of listed banks in Nigeria.

Optimal decision making in post-hazard bridge recovery strategies for transportation networks after seismic events

In this study, optimal post-hazard bridge recovery strategies were proposed for transportation networks under seismic conditions. To predict the performance of the transportation network, a robust performance measure, total system travel time (TSTT), was employed, and an artificial neural network (ANN)-based surrogate model was developed to enable an accelerated Monte Carlo analysis. In addition, a sensitivity analysis based on the benefit–cost ratio was proposed to support optimal decision making immediately after an earthquake. To demonstrate the proposed methodology, an actual transportation network in South Korea was adopted, and a network map was reconstructed based on geographic information system (GIS) data. A surrogate model for network performance evaluation was constructed using training data generated based on historical earthquake epicenters. In addition, the damage ratio and required recovery days according to the damage states of bridges were employed to perform network recovery analysis. For the numerical analysis, a limited budget was set for each scenario, and the recovery and damage curve were compared with existing priority strategy. The numerical results showed that the priority strategy of bridge restoration determined through the benefit–cost analysis generated a faster recovery curve and significantly reduced the damage, as compared to existing strategy. Therefore, it is concluded that the proposed methodology enables optimal decision making and also helps risk management that can minimize the economic damage.

Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania

Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 and June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This paper concludes by recommending that the techniques described in this paper be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.

A pseudo-spectrum based characterization of the robust strong H-infinity norm of time-delay systems with real-valued and structured uncertainties

Abstract This paper provides a mathematical characterization of the robust (strong) H-infinity norm of an uncertain linear time-invariant system with discrete delays in terms of the robust distance to instability of an associated characteristic matrix. The considered class of uncertainties consists of real-valued, structured, Frobenius norm-bounded matrix uncertainties that act on the coefficient matrices. The robust H-infinity norm, defined as the worst-case value of the H-infinity norm over all admissible uncertainty values, is an important measure of robust performance to quantify the worst-case disturbance rejection of an uncertain dynamical system. For the considered system class, this robust H-infinity norm is however a fragile measure, as for a particular instance of the uncertainties, the H-infinity norm might be sensitive to arbitrarily small perturbations on the delays. Therefore, we introduce the robust strong H-infinity norm, inspired by the notion of strong stability of delay differential equations of neutral type, which takes into account both the uncertainties on the system matrices and infinitesimal delay perturbations. This quantity is a continuous function of both the elements of the system matrices and the delays. The main contribution of this work is the introduction of a relation between this robust strong H-infinity norm and the robust distance to instability of an associated characteristic matrix. This relation is subsequently employed in a novel algorithm for computing the robust strong H-infinity norm of uncertain time-delay systems.

Contracting treatment services in Australia: Do measures adhere to best practice?

BackgroundContracting non-government services to provide alcohol and other drug (AOD) treatment requires the specification of performance measures to ensure accountability for public funds. There is currently no standardised approach by funders to the measurement of performance of AOD treatment in Australia. Funding to non-government organisations (NGOs) is complex, with NGOs managing funding agreements from a range of sources that contain a large number of differing performance measures. This study aimed to assess performance measures used in contracts for NGO AOD treatment providers and how they align with best practice. MethodsPerformance measures contained in funding agreements for treatment providers were collected from a diverse sample of both funders (n=8) and treatment providers (n=20) resulting in more than 1,100 measures. The list of measures was synthesized to a finite, non-duplicative list (n=537). Measures were assessed by three raters against 11 criteria (for example, measurable and timely) documented as best practice elements of performance measures (Council of Australian Governments, 2011a). Measures were also coded into different measurement types (such as outcome and process). ResultsNone of the 537 unique performance measures used in funding agreements for treatment providers fully met the criteria for best practice in performance measurement. Whilst the literature and government policy is being directed towards outcomes-based funding and reporting, only 7.6% of measures were classified as outcome measures. The majority of measures were classified at output (41.3%) and process (23.6%) measures. ConclusionCurrent measures in contracts applied by funders to treatment services do not adhere to best practice. The development and implementation of new performance measures is required to inform AOD policy and accountability of public funds. Further, identifying more robust performance measures has the potential to lead to reduced reporting burden on service providers and better monitoring of service quality and outcomes.

Implementation of Evidence-Based Recommendations for Preventive Services in the Veterans Health Administration.

The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. To date, there has been scant research on how VHA adopts clinical preventive services guidelines and how U.S. Preventive Services Task Force recommendations factor into the process. Researchers conducted semistructured interviews with eight VHA leaders to examine how they adopt, disseminate, and measure adherence to recommendations. Interviews were recorded, transcribed, and aggregated into a database to enable sorting and synthesis. Themes were identified across the key informant interviews. The development of VHA clinical prevention guidelines is coordinated by the National Center for Health Promotion and Disease Prevention. A VHA Advisory Committee discusses and votes to approve or disapprove each guideline. Several factors can impact the ability of a veterans affairs medical center to implement a guideline, such as local system capacity and priorities for quality improvement. Methods to promote implementation include electronic reminders, educational events, and a robust performance measurement system. Provision of evidence-based clinical preventive services is an important part of VHA's effort to provide high-quality care for Veterans. Recent achievements in lung cancer, colorectal cancer, and Hepatitis C screening highlight VHA's successful approach to implementation of preventive services guidance.

Constructing a Performance Measure of Future Time Orientation

Three studies present the development of the Scrambled Sentences Task for Future Time Orientation (SST-FTO), a performance measure of future time orientation. In Study 1, undergraduate research assistants rated a pool of 30 experimental items as delineating between future and present time orientations demonstrating the content validity of SST-FTO items. Study 2 established the convergent validity of the SST-FTO administered online showing performance related to a self-report measure of time orientation and a performance measure of delay discounting but not to a measure of temporal focus. Study 3 further established the validity of a shortened SST-FTO administered online following an episodic future thinking intervention known to affect delay discounting. The intervention influenced delay discounting but not SST-FTO performance. SST-FTO measures correlated with pre-intervention self-reported future orientation and predicted delay discounting even after accounting for intervention effects and self-reported future orientation. Internal consistency of the measure was demonstrated in all three studies through item-total correlations and Cronbach’s alphas. Results suggested the SST-FTO is a robust performance measure of future time orientation, predictive of decision-making on performance measures of delay discounting.

A financial fraud detection indicator for investors: an IDeA

Fraud detection is a key issue for investors and financial authorities. The Ponzi schemeorganized by Bernard Madoff is a magnified example of a financial fraud, always possiblewhen well-orchestrated. Traditional methods to detect fraud require costly and lengthyinvestigations that involve complex financial and legal knowledge, as well as highly skilledanalysts. Based on the motto “too good to be true” that should be adopted by any rationalinvestor, we propose herein the use of a robust performance measure (named GUN*) toconstruct an Index for detection of anomalies (called IDeA). This index is based on thebasic intuition that it is not possible to properly evaluate a fund as “good” regardless thecharacteristics and risk aversion of investors. After defining the intuition behind such anindex and its economic theoretical background, we illustrate our innovative operationsresearch methodology for fraud detection and demonstrate its usefulness studying theemblematic case of the fraud by Madoff.