Testing Framework Research Articles

Back to Analog. Printing Digital Editions for Data Quality.

This article discusses the challenges of printing TEI XML data sets, and highlights a useful diagnostic value of PDF export for data quality. PDF output, indeed, renders only a part of the encoded information, but it can expose problems pertaining to the semantic accuracy and consistency, syntactic compliance, and completeness of the data. The uncovered problems are presented as possessing three dimensions: internal (the compliance of XML code with data models and schemata), external (the consistency of the print output in rendering the original), and contextual (arising from the interaction of XML with the transformation scenarios). These dimensions help to provide the definition of an error in the context of scholarly digital editions—both retro-digitized and originally digital. The article examines the extended skill set required of quality assurance specialists in social sciences and humanities (SSH) as compared to their industrial counterparts. It also places PDF production within the Agile testing framework, which aims to capture and eliminate defects in the early stages of product development.

A Generalized Wireless Cable Over-the-Air Testing Framework for Arbitrary Field Emulation in Nonanechoic Radio Environment

A Generalized Wireless Cable Over-the-Air Testing Framework for Arbitrary Field Emulation in Nonanechoic Radio Environment

Stress Testing Bank Financial Systems: A Technological Perspective

This study investigates the pivotal role of stress testing in maintaining the resilience and stability of banking financial systems, emphasizing the crucial integration of advanced technology. In an era marked by volatile global financial markets, the capability of banks to endure economic shocks is essential. This research highlights how technological advancements are crucial in developing, implementing, and refining stress testing methodologies to effectively navigate the complexities of contemporary financial systems. Through a comprehensive analysis, including a literature review, methodology overview, and case studies, the paper explores the transformative impact of technology, such as advanced analytics, big data, machine learning, and Blockchain, on stress testing frameworks. It delves into the challenges and opportunities presented by technological integration, underscoring the indispensable role of technology planning associates in aligning stress testing practices with cutting-edge innovations. The paper concludes with insights into future directions for stress testing methodologies and technologies, emphasizing the ongoing need for sophisticated, adaptive models and the crucial collaboration between financial institutions, regulatory bodies, and technology experts to enhance the banking sector’s resilience against financial crises.

ASTER: A Method to Predict Clinically Relevant Synthetic Lethal Genetic Interactions.

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically relevant SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of synthetic lethality. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our computational and functional experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits.

MTR Model-Based Testing Framework MTR Model-Based Testing Framework MTR Model-Based Testing Framework

In this article we propose a novel, free and open- source model-based testing framework for finite state machine specifications. The various model handling and test generation options make the framework suitable for testing complex systems and provide a solid background for investigating different automated test design methodologies. The complexity and fault detection capabilities of the available algorithms are investigated through analytical analyses and simulations applying randomly injected faults.

How Modern Psychometrics Can Change Intelligence Testing of Children and Adolescents in Russia

The assessment of intellectual development in children and adolescents is an essential component of psychodiagnostics, facilitating an understanding of the nature of learning difficulties. Intelligence tests have a history that spans more than a century, and even contemporary versions of widely used intelligence tests (such as the Wechsler or Stanford-Binet tests) are limited by the influence of their traditional paper-based formats. Nevertheless, the advent of computational capabilities and the digitalization of tests have led psychometrics to experience substantial development, opening new possibilities for assessment. The complete application of modern psychometrics holds the promise of revolutionizing the measurement of intelligence. This paper presents the current situation of measuring intelligence in children and adolescents in the world and in Russia. It describes the potentialities presented by modern psychometrics, including the utilization of innovative task formats such as technology-enhanced items, the integration of universal design principles in test development, the adoption of computerized adaptive testing (CAT) and multistage testing (MST) methodologies, the application of multidimensional IRT models to establish both construct and criterion validity, the shift from discrete norms to continuous scaling, and the incorporation of longitudinal measurements. The combination of these features within a computerized intelligence testing framework will enhance respondents' motivation and simplify the test administration process. It will facilitate frequent assessments of a child's progress by presenting varying test versions on each occasion, thereby significantly reducing errors in administration and scoring, while also safeguarding against misuse by non-professionals.

Optimization of Psychological Characteristics and Education Management Path of Poor College Students in Colleges and Universities Based on Improved PCA

Abstract Aiming at the mental health problems of poor college students, this study proposes an expression recognition method based on the optimization of PCA algorithm and applies it to the mental health test framework. By comparing the differences between poor college students and norms, and using multiple linear regression and typical correlation analysis to explore the impact of educational management paths. The study also addressed the effect of different types of colleges and universities on the level of management of academic support for poor students, and found that 985 colleges and universities significantly outperformed the norm in this area, i.e. P > |t| = 0.031. This provides valuable insights into improving educational and psychological support for poor college students.

Enhancing Digital User Experiences through Personalized Interfaces and Predictive Modelling

This paper explores the development and implementation of a personalization strategy designed to enhance user experiences on digital platforms. By leveraging user behaviour data and machine learning algorithms, we propose a methodology for creating adaptive user interfaces, predictive actions, and personalized user journeys. The paper presents a real-time example application, complete with data analysis, user segmentation, feature engineering, predictive modelling, and a comprehensive A/B testing framework. We aim to demonstrate how personalization can significantly improve user engagement, satisfaction, and platform efficacy

Effects of Peer Teaching Method in Software Testing Course in Comparison with Traditional Teaching Method

This action research project incorporated a peer teaching model for creating an active learning environment for teaching the course “Software Verification, Validation, and Testing”. Topics covered in the course included an overview of software testing, software quality, types of software testing, test case design techniques, test documentation, software test frameworks; model-based testing, software verification, and validation. The research aimed to explore to what extent peer teaching would increase students’ learning motivation, and engagement, develop critical thinking skills, and their performance. The study was conducted for one semester and data were generated using questionnaires, test scores, and student activity records as instruments. The test score data is collected from 10 students taking the course. The data were analyzed using qualitative (textual data obtained from open-ended responses) and quantitative (numerical data from questionnaires, and test scores) methods. The result showed that the peer teaching approach has a positive effect on the attitudes of students toward software testing courses compared to lecture mode instruction.

Determining the Mechanical Properties of Super-Elastic Nitinol Bone Staples Through an Integrated Experimental and Computational Calibration Approach.

Super-elastic bone staples have emerged as a safe and effective alternative for internal fixation. Nevertheless, several biomechanical aspects of super-elastic staples are still unclear and require further exploration. Within this context, this study presents a combined experimental and computational approach to investigate the mechanical characteristics of super-elastic staples. Two commercially available staples with distinct geometry, characterized by two and four legs, respectively, were examined. Experimental four-point bending tests were conducted to evaluate staple performance in terms of generated forces. Subsequently, a finite element-based calibration procedure was developed to capture the unique super-elastic behavior of the staple materials. Finally, a virtual bench testing framework was implemented to separate the effect of geometry from that of the material characteristics on the mechanical properties of the devices, including generated force, strain distribution, and fatigue behavior. The experimental tests indicated differences in the force vs. displacement curves between staples. The material calibration procedure revealed marked differences in the super-elastic properties of the materials employed in staple 1 and staple 2. The results obtained from the virtual bench testing framework have showed that both geometric features and material characteristics had a substantial impact on the mechanical properties of the device, especially on the generated force, whereas their effect on strain distribution and fatigue behavior was comparatively less pronounced. To conclude, this study advances the biomechanical understanding of Nitinol super-elastic staples by separately investigating the impact of geometry and material characteristics on the mechanical properties of two commercially available devices.

A large-scale empirical study on mobile performance: energy, run-time and memory

Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE’s GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues’ priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.

Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery

The evaluation of machine learning (ML) models in identifying novel materials with superior Figure of Merit (FOM) compared to known materials is of utmost importance for exploring their potential applications. A well-designed evaluation should distinguish between good and poor models, avoid bias in the validation and testing set, and offer straightforward interpretable. To fulfill these requirements, a novel metric called Discovery Precision (DP) was proposed to assess the efficiency of ML models for material discovery in terms of probability. To validate the applicability of DP, we have established a comprehensive testing framework for comparing various validation methods and metrics. Through this framework, the DP metric demonstrates excellent performance in material discovery. Additionally, the simulation results indicate that DP effectively assists in model selection and provides an unbiased estimation of model performance.

Enhancing Performance of Software Testing Automation using Selenium Web Grid

Web application testing is crucial to ensuring software quality and dependability. Selenium, a popular open-source testing framework, is widely used for automating web browsers to perform various testing tasks. One of the key components of Selenium is Selenium WebGrid, a distributed testing infrastructure that allows running tests in parallel across multiple nodes. This paper presents a comprehensive study on measuring the performance of Selenium WebGrid. The objective is to evaluate its efficiency, scalability, and reliability in executing test scripts across different browsers and operating systems. The study compares the performance of Selenium WebGrid with other testing approaches, such as traditional Selenium Grid and single-node Selenium execution. The methodology employed in this study involves designing a set of representative test cases that simulate real-world scenarios. These test cases cover a range of typical web application functionalities, including form submissions, page navigation, and data validation. The performance metrics considered for evaluation include execution time, resource utilization, and test throughput. To measure the performance of Selenium WebGrid, a testbed environment is set up consisting of multiple nodes with different configurations. The test scenarios are executed on the WebGrid infrastructure, and the performance metrics are collected and analyzed. The results are then compared with those obtained from traditional Selenium Grid and single-node Selenium execution to identify the performance advantages and limitations of Selenium WebGrid. It demonstrates the improved scalability and efficiency of WebGrid in executing tests across multiple nodes simultaneously. The results also provide insights into the resource requirements and trade-offs associated with using Selenium WebGrid compared to other testing approaches. This work adds to the body of knowledge by presenting empirical data on Selenium WebGrid's performance characteristics. It serves as a valuable resource for software testing professionals and researchers interested in leveraging Selenium WebGrid for distributed web application testing.

The effects of the EBA's stress testing framework on banks' lending

This paper investigates the impact of the European Banking Authority (EBA)'s supervisory stress tests on bank lending. Using a sample of 282 European banks over the period 2006–2018, we find that stress-tested banks experience higher credit risk and reduce lending for specific loan types. In particular, due to country heterogeneities, we find that the contraction in lending is more pronounced for stress-tested banks in the GIIPS region. Our results also suggest that the elevated credit risk of highly-exposed stress-tested banks can be a driving factor of a reduction in bank lending. Consequently, prudential measures requiring banks to hold higher capital buffers are justified to contain credit risk shocks.

Enhancing Performance of Software Testing Automation using Selenium web grid

Web application testing is crucial to ensuring software quality and dependability. Selenium, a popular open-source testing framework, is widely used for automating web browsers to perform various testing tasks. One of the key components of Selenium is Selenium WebGrid, a distributed testing infrastructure that allows running tests in parallel across multiple nodes. This paper presents a comprehensive study on measuring the performance of Selenium WebGrid. The objective is to evaluate its efficiency, scalability, and reliability in executing test scripts across different browsers and operating systems. The study compares the performance of Selenium WebGrid with other testing approaches, such as traditional Selenium Grid and single-node Selenium execution. The methodology employed in this study involves designing a set of representative test cases that simulate real-world scenarios. These test cases cover a range of typical web application functionalities, including form submissions, page navigation, and data validation. The performance metrics considered for evaluation include execution time, resource utilization, and test throughput. To measure the performance of Selenium WebGrid, a testbed environment is set up consisting of multiple nodes with different configurations. The test scenarios are executed on the WebGrid infrastructure, and the performance metrics are collected and analyzed. The results are then compared with those obtained from traditional Selenium Grid and single-node Selenium execution to identify the performance advantages and limitations of Selenium WebGrid. It demonstrates the improved scalability and efficiency of WebGrid in executing tests across multiple nodes simultaneously. The results also provide insights into the resource requirements and trade-offs associated with using Selenium WebGrid compared to other testing approaches. This work adds to the body of knowledge by presenting empirical data on Selenium WebGrid's performance characteristics. It serves as a valuable resource for software testing professionals and researchers interested in leveraging Selenium WebGrid for distributed web application testing.

Soil-Powered Computing

Human-caused climate degradation and the explosion of electronic waste have pushed the computing community to explore fundamental alternatives to the current battery-powered, over-provisioned ubiquitous computing devices that need constant replacement and recharging. Soil Microbial Fuel Cells (SMFCs) offer promise as a renewable energy source that is biocompatible and viable in difficult environments where traditional batteries and solar panels fall short. However, SMFC development is in its infancy, and challenges like robustness to environmental factors and low power output stymie efforts to implement real-world applications in terrestrial environments. This work details a 2-year iterative process that uncovers barriers to practical SMFC design for powering electronics, which we address through a mechanistic understanding of SMFC theory from the literature. We present nine months of deployment data gathered from four SMFC experiments exploring cell geometries, resulting in an improved SMFC that generates power across a wider soil moisture range. From these experiments, we extracted key lessons and a testing framework, assessed SMFC's field performance, contextualized improvements with emerging and existing computing systems, and demonstrated the improved SMFC powering a wireless sensor for soil moisture and touch sensing. We contribute our data, methodology, and designs to establish the foundation for a sustainable, soil-powered future.

Mobile Phone Technology for Preventing HIV and Related Youth Health Problems, Sexual Health, Mental Health, and Substance Use Problems in Southwest Uganda (Youth Health SMS): Protocol for a Pilot Randomized Controlled Trial.

East and Southern Africa have the highest HIV incidence and prevalence in the world, with adolescents and young adults being at the greatest risk. Despite effective combination prevention tools, including the recently available pre-exposure prophylaxis (PrEP), HIV incidence among adolescents and young adults in Uganda remains high, and PrEP use remains low. Mental health and substance use (behavioral health) play a role in sexual behavior and decision-making, contributing to an increase in the risk for acquiring HIV. Interventions that target multiple HIV risk factors, including sexual and mental health and problematic substance use, are crucial to ending the HIV epidemic. Yet few interventions addressing HIV related health disparities and comorbidities among adolescents and young adults in East and Southern Africa currently exist. This study aims to evaluate the acceptability and feasibility of Kirabo, an SMS text message intervention informed by the information, motivation, and behavior model and to be disseminated through secondary schools. The study will gather preliminary estimates of Kirabo's effectiveness in increasing HIV testing and linking users to mental health counselors. We identified Mobile 4 Reproductive Health for adaptation using the assessment, decision, administration, production, topical experts, integration, training, testing (ADAPT-ITT) framework. Mobile 4 Reproductive Health is an evidence-based automated 2-way SMS text messaging and interactive voice response platform that offers sexual and reproductive health information and links users to HIV clinics in East Africa. Through ADAPT-ITT we refined our approach and created Kirabo, an SMS text message-based intervention for linking adolescents and young adults to health services, including HIV testing and mental health counseling. We will conduct a 2-arm randomized controlled trial in Masaka, Uganda. Adolescents (N=200) will be recruited from local schools. Baseline sociodemographic characteristics, HIV test history, and behavioral health symptoms will be assessed. We will evaluate acceptability and feasibility using surveys, interviews, and mobile phone data. The preliminary efficacy of Kirabo in increasing HIV testing and linking users to mental health counselors will be evaluated immediately after the intervention and at the 3-month follow-up. We will also assess the intervention's impact on self-efficacy in testing for HIV, adopting PrEP, and contacting a mental health counselor. Intervention adaptation began in 2019. A pretest was conducted in 2021. The randomized controlled trial, including usability and feasibility assessments and effectiveness measurements, commenced in August 2023. Kirabo is a tool that assists in the efforts to end the HIV epidemic by targeting the health disparities and comorbidities among adolescents in Uganda. The intervention includes local HIV clinic information, PrEP information, and behavioral health screening, with referrals as needed. Increasing access to prevention strategies and mitigating factors that make adolescents and young adults susceptible to HIV acquisition can contribute to global efforts to end the HIV epidemic. ClinicalTrials.gov NCT05130151; https://clinicaltrials.gov/study/NCT05130151. DERR1-10.2196/49352.

Expanding PyProcar for new features, maintainability, and reliability

This paper presents a comprehensive update to PyProcar, a versatile Python package for analyzing and visualizing density functional theory (DFT) calculations in materials science. The latest version introduces a modularized codebase, a centralized example data repository, and a robust testing framework, offering a more reliable, maintainable, and scalable platform. Expanded support for various DFT codes broadens its applicability across research environments. Enhanced documentation and an example gallery make the package more accessible to new and experienced users. Incorporating advanced features such as band unfolding, noncollinear calculations, and derivative calculations of band energies enriches its analytic capabilities, providing deeper insights into electronic and structural properties. The package also incorporates PyPoscar, a specialized toolkit for manipulating POSCAR files, broadening its utility in computational materials science. These advancements solidify PyProcar's position as a comprehensive and highly adaptable tool, effectively serving the evolving needs of the materials science community. New version program summaryProgram title: PyProcarCPC Library link to program files:https://doi.org/10.17632/d4rrfy3dy4.2Developer's repository link:https://github.com/romerogroup/pyprocarLicensing provisions: GPLv3Programming language: PythonSupplementary material: Pyprocar-Supplementary InformationJournal reference of previous version: Comput. Phys. Commun. 251 (2020) 107080, https://doi.org/10.1016/j.cpc.2019.107080Does the new version supersede the previous version?: YesReasons for the new version: Changes in the directory structure, the addition of new features, enhancement of the manual and user documentation, and generation of interfaces with other electronic structure packages.Summary of revisions: These updates enhance its capabilities and ensure developers' and users' maintainability, reliability, and ease of use.Nature of problem: To automate, simplify, and serialize the analysis of band structure and Fermi surface, especially for high throughput calculations.Solution method: Implement a Python library able to handle, combine, parse, extract, plot, and even repair data from density functional calculations from diverse electronic structure packages. PyProcar uses color maps on the band structures or Fermi surfaces to give a simple representation of the relevant characteristics of the electronic structure.Additional comments including restrictions and unusual features: PyProcar can produce high-quality figures of band structures and Fermi surfaces (2D and 3D), projection of atomic orbitals, atoms, and/or spin components.

Evaluation Performance of Three Standardization Models to Estimate Catch-per-Unit-Effort: A Case Study on Pacific Sardine (Sardinops sagax) in the Northwest Pacific Ocean

Catch-per-unit-effort (CPUE) standardization in fisheries is a critical foundation for conducting stock assessment and fishery conservation. The Pacific sardine (Sardinops sagax) is one of the economically important fish species in the Northwest Pacific Ocean (NPO). Hence, the importance of choosing an appropriate CPUE standardization model cannot be overstated when it comes to achieving a precise relative abundance index for the efficient management of Pacific sardine fishery. This study’s main aim was to assess and compare the efficacy of three models, specifically the General Linear Model (GLM), the Generalized Linear Mixed Model (GLMM), and the spatio-temporal GLMM (VAST), in the CPUE standardization for Pacific sardine fishery in the NPO, with the ultimate goal of identifying the most appropriate model. An influence analysis was applied to analyze the impact of individual variables on the disparity among standardized and nominal CPUE, and the main explanatory variables influencing standardized CPUE were identified. A coefficient–distribution–influence (CDI) plot was generated to analyze the impact of the different models on the annual standardized CPUE. Additionally, a simulation testing framework was developed to evaluate the estimated accuracy of the three models. The results indicated that the standardized CPUE and the nominal CPUE exhibited similar trends between 2014 and 2021 for the three models. Compared to the GLM and the GLMM, the VAST demonstrates larger conditional R2 and smaller conditional AIC, indicating a better performance in standardizing the CPUE for Pacific sardines due to its consideration of spatial and temporal variations. The interaction terms within the three models exert significant influences on the annual standardized CPUE, necessitating their inclusion in the model construction. CDI plots indicate that the spatio-temporal influence of the VAST model exhibits a smaller variation trend, suggesting that the VAST is more robust when standardizing the CPUE for Pacific sardines. Simulation testing additionally demonstrated that the VAST model displays smaller model root mean squared error (RMSE) and bias, establishing it as the superior performer for standardizing CPUE. Our results provide a theoretical basis for the scientific management of Pacific sardines in the NPO and can be extended to CPUE standardization for other small pelagic fish species worldwide.