Measurement Framework Research Articles

Vision-based displacement measurement method of large-scale bridges using tilt shift camera and fast spatio-temporal context learning

Tracking vibration displacement across multiple points on large-scale bridges in the field poses significant challenges due to perspective distortion by camera tilting, interference from adverse environmental factors and low-resolution images by long distance measurement. To address these issues, this study proposed a novel vision-based displacement measurement framework that combined the Fast Spatio-Temporal Context Learning algorithm (FSTC) with tilt shift cameras to enhance visual tracking accuracy. A procedure was introduced to select suitable tilt shift cameras to eliminate the adverse effects of potential lens distortion. The FSTC algorithm, which integrated the Spatio-Temporal Context Learning algorithm (STC) and FastFlownet, was employed to improve the displacement tracking accuracy of the target object under fog occlusion and during long-distance measurements. Experimental results from slider and shaking table tests demonstrated that the FSTC algorithm outperformed the existing vision-based methods such as STC, Digital Image Correlation (DIC), and Kanade-Lucas-Tomasi (KLT) algorithms in tracking the displacement of the target object, effectively controlling the displacement error (e.g., RMSE and peak error) to below 3 mm. The FSTC algorithm was less sensitive to fog occlusion and exhibited enhanced efficiency in object tracking. Furthermore, the FSTC algorithm, in combination with image resolution reconstruction, enabled subpixel accuracy in tracking the displacement of the target object. A preliminary field application was conducted to track the vibration displacement of the Sanchaji Bridge under wind load, validating the feasibility and applicability of the proposed framework.

Adapting the Primary Care Assessment Tool for sub-Saharan Africa: a validation study.

The World Health Organization's (WHO) measurement framework for primary health care includes the core functions of primary care: first-contact access, comprehensiveness, coordination, continuity, and person-centredness. The Primary Care Assessment Tool (PCAT), originally developed in the USA, was adapted for use by four African countries, and can measure the core functions of primary care. To face and content validate a PCAT for sub-Saharan Africa that measures the core functions of primary care. Nineteen countries within the Primary Care and Family Medicine (PRIMAFAMED) network for sub-Saharan Africa participated in a validation study. Two stages included a PRIMAFAMED workshop to assess face validity and a Delphi study to reach consensus on content validity among an expert panel as well as key stakeholders. Thirteen countries participated in the workshop and suggested rephrasing 39 items, deleting six and adding four new items. Nineteen countries participated in the Delphi study and all 20 panel members reached consensus (>70%) on including the items as written. Seven experts and stakeholders reviewed the PCAT and suggested rephrasing 23 items, deleting one and adding one. The final PCAT for sub-Saharan Africa (SSA-PCAT) consists of 85 items that measure affiliation with the primary care facility, first-contact access and utilisation, comprehensiveness, continuity, coordination, and person-centredness, as well as health, demographic and socioeconomic status. The SSA-PCAT will now be piloted in Benin, Uganda, and South Africa. Further psychometric evaluation will be possible followed by more widespread use by researchers, district managers, and policymakers in the region.

Operational Risk Assessment of Commercial Banks’ Supply Chain Finance

Supply chain finance (SCF) operations require extensive activities and a high level of information transparency, making them vulnerable to operational issues that pose significant risks of financial loss for commercial banks. Accurately assessing operational risks is crucial for ensuring market stability. This research aims to provide a reliable operational risk assessment tool for commercial banks’ SCF businesses and to deeply examine the features of operational risk events. To achieve these goals, the study explores the dependency structure of risk cells and proposes a quantitative measurement framework for operational risk in SCF. The loss distribution analysis (LDA) is improved to align with the marginal loss distribution of segmented operational risks at both high and low frequencies. A tailored copula function is developed to capture the dependency structure between various risk cells, and the Monte Carlo algorithm is utilized to compute operational risk values. An empirical investigation is conducted using SCF loss data from commercial banks, creating a comprehensive database documenting over 400 entries of SCF loss events from 2012 to 2022. This database is analyzed to identify behaviors, trends, frequencies, and the severity of loss events. The results indicate that fraud risk and compliance risk are the primary sources of operational risks in SCF. The proposed approach is validated through backtesting, revealing a value at risk of CNY 179.3 million and an expected shortfall of CNY 204.9 million at the 99.9% significance level. This study pioneers the measurement of SCF operational risk, offering a comprehensive view of operational risks in SCF and providing an effective risk management tool for financial institutions and policymakers.

Designing an Integrated BSC and IPMS Model Performance Optimization

This study aims to design a performance measurement model by integrating two performance measurement methods, namely the Balanced Scorecard (BSC) and Integrated Performance Measurement Systems (IPMS), to optimize the company's performance. This approach is expected to provide a comprehensive view of the company's performance by combining financial and non-financial indicators and ensuring strategic alignment. The research approach used is descriptive-analytical (literture review), which aims to provide an overview of the performance measurement model design by integrating the Balanced Scorecard (BSC) and Integrated Performance Measurement Systems (IPMS). The study will identify how the integration of these two systems can improve productivity and operational effectiveness. The expected outcome is sustainable performance improvement and productivity enhancement through the implementation of a holistic and integrated performance measurement framework. The novelty of this research lies in the absence of a performance measurement model that combines both methods to measure company performance. The research results in a performance measurement model design that can be implemented to optimize performance in an industry.

Media Literacy in a Digital Age: Taking Stock and Empowering Action

This thematic issue presents 12 articles that explore the transformative potential of media literacy, digital skills, and ICT interventions in diverse contexts and populations, ranging from adolescents and youth with disabilities to university staff and vocational educators. It provides a comprehensive overview of research, including systematic and scoping reviews, empirical studies, and innovative measurement frameworks, highlighting both the opportunities and challenges in fostering media literacy and digital skills. The studies examine positive outcomes, barriers, and enablers of intervention programs, emphasizing the importance of tailoring approaches to specific contexts, addressing systemic inequities, and fostering interdisciplinary and international collaboration. Together, all contributing authors underscore the critical role of media literacy and digital skills in promoting empowerment, equity, and resilience in navigating the complexities of today’s digital age.

Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality

The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements.

Exploring Leadership Practices and Proposed Leadership Development Program: Case Study at Indonesian Aviation Manufacturing Companies

National defense plays a crucial role in maintaining territorial integrity and the security of the nation against various threats. The dynamics of the strategic environment demand updates in human resource development in the defense sector to support sustainable national development. Leadership practices are essential for achieving organizational effectiveness, especially in strategic industries such as aviation manufacturing. This research aims to explore and evaluate leadership development practices in the aviation manufacturing industry using a qualitative approach. Primary and secondary data were collected through interviews with stakeholders involved in leadership development within the company. Data analysis was conducted using NVivo to identify themes and subthemes, as well as to perform coding and data categorization. The research findings reveal several challenges in program implementation, including a lack of measurable evaluation, inconsistencies in procedures, and suboptimal use of learning technologies. These weaknesses limit the organization’s ability to improve performance and long-term sustainability. Based on the analysis of the findings, opportunities for program development include establishing a measurable evaluation framework, developing a standardized framework, and implementing a holistic feedback mechanism. Additionally, upgrading the learning management system to cope with the digital age is a priority to maximize effective and efficient learning.

Dynamic Measures of Team Adaptation.

Dynamic measures of team adaptation based in team cognition theory and the measurement of real-time team cognition are developed. The present study examines the validity and context-specificity of this measurement framework for simulation-based team training. Teams adapt by reorganizing their coordination behavior to overcome challenges in dynamic environments. Theoretically grounded objective metrics for measuring adaptive skill in teams are needed. We developed dynamic measures of team adaptation to help fill this gap. Communication data from critical care air transport team training were analyzed using moving window entropy and recurrence-based determinism metrics of communicative adaptation in response to training event perturbations involving stabilizing deteriorating patient status. The measures were validated across four simulation-based training scenarios using objective and subjective metrics of team performance. We validated performance prediction in all scenarios, demonstrating generalizability. Critically, teams reorganized significantly more during perturbation segments than routine segments, validating the measures as indices of team adaptation. We also observed context-specificity, wherein the relationships between reorganization and successful performance depended on the training scenario. The communicative reorganization measures advanced in this paper present a valid method for assessing adaptive competencies in teams. These analytics generalize in terms of performance prediction across training scenarios, but they are also context-specific, wherein patterns of effective reorganization depend on the type of scenario. We discuss the practical deployment of the measurement framework in a Team Dynamics Measurement System for assessing team adaptation competencies in critical care air transport team training.

Is low-density a viable territorial category for enhancing EU cohesion policy implementation?

ABSTRACT The European Union's (EU) Cohesion Policy (CP) promotes balanced territorial development between and within countries and calls for mechanisms to enhance the current approaches to territorial, economic and social cohesion. Low-density (LD) areas emerged as a territorial category to define the difference between rural and urban areas. Although the scientific literature discusses various topics and activities in low-density areas, there is hardly any holistic definition. Furthermore, their role in improving CP implementation remains unclear. This paper is an exploratory study and aims to examine the specific characteristics of LD areas through a systematic literature review and see if and how it supports the implementation of CP. The research shows that LD is a holistic category and points beyond low population density. It deserves special attention from the perspective of the EU CP implementation, especially in the Mediterranean region with its specific geographic and socio-cultural features. Our results also show that identifying LD rural areas and addressing their challenges could contribute to a more targeted implementation of CP. Creating adjusted measurement frameworks and tools and from there, spatial identification and mapping are vital for the place-based implementation of EU CP 2021–2027 and fair allocation of EU and state funds.

Through Their Eyes: Defining 'good life' in dementia for health economics and outcomes research.

Despite progress in involving people living with dementia in health research, disparities remain, particularly in the field of health economics and outcomes research, where excessive reliance on proxy reports from care partners or healthcare professionals persists even when people are capable of self-reporting, leading to inequitable inclusion. This study aimed to understand the concept of a 'good life' from the perspectives of people living with dementia and examine how well current preference-based outcome measures used in health economics and outcomes research capture these perspectives. Twenty-three community-dwelling people with mild to moderate dementia in Ireland participated in in-depth interviews (September 2022-February 2023). Data were analysed using an adapted Colaizzi's seven-step phenomenological method, followed by mapping identified themes against ten commonly used preference-based outcome measures in dementia research. Results show that participants perceive a 'good life' through independence, social connections, engagement with life, adaptation to changing abilities. As dementia progresses, perceptions shift from striving for independence and active life engagement to accepting assistance and increased social support. This transition reflects a journey of adaptation, where participants redefine their sense of a 'good life' with evolving challenges and capabilities. The mapping analysis reveal that current preference-based outcome measures vary considerably in their coverage of these themes (33-83%), with none fully capturing the dynamic and evolving nature of a 'good life' as described by participants. These findings expose disparities between lived experiences and current measurement frameworks in health economics and outcomes research in dementia, calling for developing flexible and adaptive approaches to quality of life assessment in progressive conditions-approaches that not only reflect quality of life as defined by those living with the condition but also accommodate its dynamic and evolving nature throughout individual disease trajectories.

Sustainable Consumer Behavior in the Social Exclusion Context: Impact on Upcycled Product Adoption and Environmental Sustainability Metrics

This study investigates how social exclusion influences sustainable consumption patterns, specifically examining its impact on upcycled product adoption as a key metric of circular economy implementation, while also examining the moderating effects of self-efficacy and consumer innovativeness. As social isolation becomes increasingly prevalent in modern society, understanding its effects on sustainable consumption behaviors has become crucial. A survey involving 857 participants was executed in October 2024, and the data were analyzed using structural equation modeling with EQS 6.4. The results revealed that social exclusion positively influences both purchase intention for upcycled products and the perception of ethical consumption. Furthermore, an enhanced perception of ethical consumption significantly enhances purchase intention for upcycled products. Self-efficacy demonstrated significant moderating effects throughout all relationships in the model, notably pronounced in the relationship between social exclusion and ethical consumption awareness. Consumer innovativeness exhibited a significant moderating effect solely on the association between social exclusion and ethical consumption perception, but not on purchase intentions. This research advances sustainability measurement frameworks by establishing quantifiable links between psychological factors and sustainable consumption outcomes, offering practical metrics for assessing the social dimensions of sustainability implementation.

Best Practices in Cybersecurity Training: Enhancing Employee Awareness to Mitigate Risks

This technical article explores the critical role of cybersecurity training programs in addressing the growing challenges of cyber threats in modern organizations. The article examines how human factors contribute to security vulnerabilities while also presenting opportunities for strengthening organizational defense through comprehensive training initiatives. Through this detailed article of current research and industry reports, this article investigates the implementation of structured training approaches, measurement frameworks, and cultural transformation strategies. The article encompasses key aspects including baseline assessments, multi-tiered training architectures, performance metrics, security-conscious culture development, technical integration through learning management systems, compliance documentation, and future-proofing methodologies. The article demonstrates that organizations implementing comprehensive security awareness programs experience significant improvements in threat detection, incident response, and overall security posture while highlighting the importance of leadership engagement and continuous adaptation in maintaining effective cybersecurity training programs.

Direct detection method for cusp thickness in wavy thin-film flow using an optical waveguide film

The wave crest (cusp) of the disturbance wave in thin liquid film flow is an important factor contributing to heat/mass transfer, e.g., fuel rods in boiling water reactors, stator/rotor blades in steam turbines, and cleaning/drying wafer processes in semiconductor manufacturing. We developed a new technique for directly detecting the thickness of wave cusps using array-based sensing with an optical waveguide film (OWF). This technique, based on geometrical optics assumptions, simultaneously obtains information on liquid films’ thickness and their interfacial shape, i.e., whether or not the local interface is convex upward. We first performed a pseudo-film flow measurement using a metal specimen to confirm the basic principle. According to the results, a meaningful signal indicating the wave-cusp passage, along with a thickness signal, was detected simultaneously. The OWF signal processing for cusp thickness detection was newly established based on this fact. We then applied this technique to a wavy liquid film flow formed on a flat plate in the entry region. A series of experiments were performed over a wide range of air speeds (jG = 20–70 m/s). As a result, the cusp thicknesses of relatively large waves on the wavy interface were successfully extracted from the OWF output signal. Further, the major thickness variables (i.e., base film thickness, median film thickness, and cusp thickness) were compared with those of conventional thickness estimation methods, which showed reasonable agreement. This paper provides a framework for wavy thin-film flow measurements via OWF that is specialized for directly detecting local thickness profiles.Graphical abstract

Performance Measurement Framework Using Human Resource Analytics for Private Higher Education Institutions

Performance Measurement Framework Using Human Resource Analytics for Private Higher Education Institutions

Effective engagement in computerized cognitive training for older adults.

Computerized cognitive training (CCT) is a frontline therapy to prevent or slow age-related cognitive decline. A prerequisite for CCT research to provide clinically relevant improvements in cognition is to understand effective engagement, i.e., the pattern of energy investment that ensures CCT effectiveness. Even though previous studies have assessed whether particular variables (e.g., gamification) predict engagement and/or CCT effectiveness, the field lacks a systematic approach to understanding effective engagement. Here, by comprehensively reviewing and evaluating engagement and adjacent literature, we propose a standardized measurement and operational framework to promote effective engagement with CCT targeting cognitive decline in older adults. We suggest that promoting effective engagement with CCT has two key steps: 1) comprehensively measuring engagement with CCT and 2) identifying which aspects of engagement are essential to achieve the pre-specified outcome of clinically relevant improvements in cognition. The proposed measurement and operational framework of effective engagement will allow future research to maximize older adults' engagement with CCT to slow/prevent age-related cognitive decline.

Embracing Sustainability: Unveiling a Fresh Impact Measurement Framework for Development Financial Institutions in Malaysia

Embracing Sustainability: Unveiling a Fresh Impact Measurement Framework for Development Financial Institutions in Malaysia

Ellis의 비합리적 신념의 개념적 타당화 분석 및 대안적 측정도구 틀의 탐색

This study aimed to conduct a conceptual validation of Ellis's irrational beliefs, analyze the limitations of existing measurement tools, and propose a framework for a new measurement tool. First, an examination of the irrationality of 11 Ellis' irrational beliefs revealed that each belief violates "logical validity" by accepting propositions that cannot stand alone, "empirical verification" in that many incompatible facts actually occur, "flexibility" by keeping unidirectionality and rigidity, "practicality" by producing negative outcomes and "adaptive emotion" by triggering maladaptive emotions and behaviors. Additionally, a content analysis of three domestic irrational beliefs scales revealed several limitations: They fail to differentiate extreme responses for each item, do not conceptually focus on the relevant factor, reflect beliefs related to personal traits and situations rather than general objects, measure emotional and behavioral states instead of thoughts, and exhibit conceptual expressional overlapping. Finally, a framework for a new measurement tool was proposed to address these issues. This tool presents third-person scenarios including the core content of each Ellis's irrational belief and asks respondents to rate the similarity between their and the protagonist's thoughts. This self-reflective measurement framework is expected to reduce defensive responses commonly associated with self-report scales of irrational beliefs.

CARE TRANSITION EXPERIENCES OF HISPANIC PATIENTS WITH DEMENTIA FOLLOWING EMERGENCY DEPARTMENT DISCHARGE

Abstract Older patients report fragmented emergency department (ED) care, including insufficient communication with clinicians and inadequate referrals to outpatient care and social services. These challenges are likely even greater for those with cognitive impairment (CI) and their care partners (CPs) and especially for non-English speaking populations, where language differences bring additional challenges. This study seeks to fill a gap in the literature by exploring ED discharge care transition experiences for Hispanic patients with dementia. Ten dyads - consisting of a patient with CI or dementia diagnosis and their CP - were enrolled in the ED and completed semi-structured interviews (via video or telephone) 1 week later addressing their ED and post-discharge experiences. Interviews investigated participants’ experiences in the ED and subsequent care transitions back to the community. Interviews were recorded, transcribed and coded by three research team members for thematic content, adopting a combined deductive-inductive approach until thematic saturation was reach across all dyads. Prespecified codes were adapted using the National Quality Forum’s Quality Measurement Framework on ED Transitions of Care and included eight outcomes for ED transitions of care: accessibility of services, shared decision-making, connection and alignment, care coordination, information sharing/communication, safety, healthcare utilization/costs, and experience of care. Key study findings emphasize patient and care partner priorities of shared decision-making with the ED treating team, how patients and care partners manage language barriers and cognitive impairment challenges and identifying factors that either support or hinder the quality and safety of ED discharge care and transitions back home.

Experimental Test of Nonlocality Limits from Relativistic Independence

Quantum correlations, like entanglement, represent the characteristic trait of quantum mechanics and pose essential issues and challenges to the interpretation of this pillar of modern physics. Although quantum correlations are largely acknowledged as a major resource to achieve quantum advantage in many tasks of quantum technologies, their full quantitative description and the axiomatic basis underlying them are still under investigation. Previous works have suggested that the origin of nonlocal correlations is grounded in principles capturing (from outside the quantum formalism) the essence of quantum uncertainty. In particular, the recently introduced principle of relativistic independence has given rise to a new bound intertwining local and nonlocal correlations. Here, we test such a bound by realizing together sequential and joint weak measurements on entangled photon pairs, allowing us to simultaneously quantify both local and nonlocal correlations by measuring incompatible observables on the same quantum system without collapsing its state, a task typically forbidden in the traditional (projective) quantum measurement framework. Our results demonstrate the existence of a fundamental limit on the extent of quantum correlations, shedding light on the profound role of uncertainty in both enabling and balancing them. Published by the American Physical Society 2024

Predictive analytics of selections of russet potatoes

AbstractWe explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high‐yield, disease‐resistant, and climate‐resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state‐of‐the‐art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1‐score, and Matthews correlation coefficient (MCC) for model evaluation. The top‐performing models, namely a feedforward neural network classifier (Neural Net), a histogram‐based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data‐generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data‐generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC‐ROC) and MCC. The simulation results highlight that non‐linear models like SVM and HGBC consistently show higher AUC‐ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision‐making in breeding programs.