Metric Measure Research Articles

Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation.

In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.

Reading comics: The effect of expertise on eye movements

The theory of expertise suggests that there should be observable differences in the eye movement patterns between experts and non-experts. Previous studies have investigated how expertise influences eye movement patterns during cognitive tasks like reading. However, the impact of expertise on eye movements in comics, a multimodal form of text, remains unexplored. This article reports on a study that uses eye tracking to examine patterns in the ways that experts and non-experts read comics. Expert participants (14) with experience in reading comics than non-expert participants (17). When controlling for variables such as layout and text quantity, we found significant differences in visual scanning between experts and non-experts. Experts exhibited more frequent saccades and greater amplitude of saccades. Further analysis revealed distinct strategies in processing text and image content between the two groups: the interaction between expertise level and content type in specific AOI showed significant differences across multiple visual measurement metrics, including Average duration of fixations, number of fixations, and number of saccades within AOI. These findings not only support the applicability of the expertise level theory in the field of comic reading but also provide a new perspective for understanding the reading processing of multimodal texts.

Levels of mandatory CSR compliance and the payoff: evidence from India

Purpose This paper aims to examine the diverse levels of corporate social responsibility (CSR) expenditure among Indian companies and its influence on their performance. The study aims to determine whether exceeding the mandatory CSR spending limit provides an edge to companies that outperform in enhancing corporate firm value. Design/methodology/approach A dynamic model using system generalized method of moments (GMM) was used to analyze a balanced panel data set of 191 firms over seven years, spanning from 2016 to 2022. Return on assets was used as a proxy to gauge financial performance. At the same time, the study also examined the robustness of the results by considering return on equity and Tobin’s Q as additional measures. Findings The study results indicate that, in a mandatory CSR setting, all companies are generally perceived as performing and reporting on CSR equally. Hence, it will not make any payoff, although few companies outperform. Therefore, companies should differentiate themselves regarding CSR spending and reporting to claim a competitive advantage in the market. The study also suggests that the payoff of mandatory CSR expenditure for both performing and outperforming companies is reflected more in non-quantifiable firm characteristics than in measurable performance metrics. Research limitations/implications The period of study covers 7 years, i.e. 2015–2016 to 2022–2023. This may limit capturing long-term CSR practices and firm performance trends. Additionally, data from only 191 Indian companies restrict generalizability; future research should include diverse geographic regions with mandated CSR spending to provide a more comprehensive view. In subsequent studies, contextual factors like regulatory changes and macroeconomic conditions could be considered moderating variables. Practical implications The study provides valuable insights to top management, indicating that spending beyond the threshold limit of mandatory CSR spending does not enhance corporate firm value. Instead, this additional investment may yield benefits in the form of goodwill and reputation over the long term. Social implications This study assists corporations in optimizing their CSR strategies to enhance their social and financial performance impact. Moreover, the study suggests ways to improve the CSR payoff and the need for increasing stakeholder satisfaction. Originality/value The study provides original insights into the relationship between mandatory CSR spending and firm performance in the Indian context, revealing that CSR spending does not significantly impact financial metrics, and it highlights the importance of considering a non-quantitative matrix to enhance the firm value in a mandatory CSR setting.

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.

Evaluating Uncertainty Quantification in Medical Image Segmentation: A Multi-Dataset, Multi-Algorithm Study

Deep learning is revolutionizing various scientific fields, with medical applications at the forefront. One key focus is automating image segmentation, a process crucial in many clinical services. However, medical images are often ambiguous and challenging even for experts. To address this, reliable models need to quantify their uncertainty, allowing physicians to understand the model’s confidence in its segmentation. This paper explores how the performance and uncertainty of a model are influenced by the number of annotations per input sample. We examine the effects of both single and multiple manual annotations on various deep learning architectures. To tackle this question, we employ three widely recognized deep learning architectures and evaluate them across four publicly available datasets. Furthermore, we explore the effects of dropout rates on Monte Carlo models by examining uncertainty models with dropout rates of 20%, 40%, 60%, and 80%. Subsequently, we evaluate the models using various measurement metrics. The findings reveal that the influence of multiple annotations varies significantly depending on the datasets. Additionally, we observe that the dropout rate has minimal or no impact on the model’s performance unless there is a substantial loss of training data, primarily evident in the 80% dropout rate scenario.

Comparative Analysis of Decision Tree and Logistic Regression Models in Employee Recruitment and Selection for Enterprise Success

Enterprise success is determined by competent Human Resources (HR). The recruitment and selection process of employee candidates plays an important role in producing competent human resources as an effective initial selection increases the chances of finding the right candidate for a particular role. This research predicts the likelihood of a candidate being further selected in the interview phase based on behavioral and functional recruitment and selection which are important aspects of a candidate's potential fit and contribution to the enterprise. The research uses a comparison of decision tree analysis models and logistic regression to make predictions with several measurement metrics to see the accuracy and confusion matrix of each model used. Based on evaluation and validation, the decision tree analysis model is superior in prediction even though the results tend to be the same as the logistic regression model. The accuracy value of the classification using the decision tree model was 86.67% with correct prediction results of 78 data from 90 testing data and the accuracy value of the logistic regression model was 85.55% with correct prediction results of 77 data from 90 testing data. The results of the comparison of the two models show that the performance of the decision tree classifier model tends to be better.

Performance reporting practices in state-owned enterprises: A systematic literature review

Background: Performance reporting and measurement in state-owned enterprises (SOEs) have been ambiguous for years subject to the nature of the sector. State-owned enterprises are third sector public entities characterised by hybridity of rationales, heterogeneity of goals and multiple stakeholders’ interest. Besides, the diverse institutional frameworks and ownership structure in SOEs make performance reporting and measurements in the sector more complex than in private profit enterprises.Aim: The systematic review of the literature focusses on performance reporting practices in SOEs. It examines SOE performance report users, performance measurement metrics, and the alignment of objectives and performance information.Method: Critical searches of literature in databases such as Scopus or Science Direct, Emerald Insight and JSTOR were made using ’performance reporting’ and/or ’State-owned Enterprises’ as main keywords. More literature was explored in Google Scholar.Results: The concept of performance reporting in SOEs is embedded in broader subjects such as ‘governance in public sectors’, ‘corporate governance in SOEs’ and ‘accountability in public sector’. Performance reports of SOEs in practice focus on the traditional financial performance measurement.Conclusion: There is no straightforward approach in performance reporting of SOEs in the literature. This shows that performance reporting in SOEs is highly slanted, making it difficult to assess efficiency of SOEs.Contribution: The article contributes in unlocking knowledge in highly blurred practices of performance reporting in SOEs.

Automated sleep apnea detection from snoring and carotid pulse signals using an innovative neck wearable piezoelectric sensor

This study introduces an innovative wearable neck piezoelectric sensor (NPS) that measures snoring vibrations and carotid pulsations, offering a significant advancement in sleep apnea syndrome (SAS) diagnosis. Utilizing advanced algorithms like discrete wavelet transform and dynamic thresholding, the NPS detects snoring events with 83% accuracy, comparable to polysomnography, and calculates key metrics such as the snoring index (SI) and normalized snoring vibration energy (SVE%). Unlike traditional methods, the SVE% from NPS directly correlates with subjective assessments of snoring severity. It also measures carotid pulsation metrics such as pulse rate and the standard deviation of normal-to-normal intervals, achieving 85% accuracy in sleep phase determination against polysomnography. Moreover, NPS surpasses traditional methods in SI and SVE% accuracy, closely aligning with clinical evaluations of SAS severity. This user-friendly technology automates the measurement of critical snoring metrics, transforming SAS diagnosis and treatment by enhancing accessibility and efficiency for healthcare providers and patients.

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

Analysis of Enterprise Internet of Things Maturity Models: A Review

Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity improvements, real-time analysis, and customer satisfaction. Therefore, adopting and implementing this technology in enterprises brings several challenges, such as technological, organizational, security, and maturity issues. However, secondary studies that systematically compile the existing literature on these specific mechanisms for the enterprise domain are still being determined. This article aims to address this knowledge gap by conducting a review to deepen and synthesize the existing knowledge. This research followed established methodologies and protocols to synthesize and analyze the state of the art in the area; 489 documents were retrieved from seven bibliographic databases, and, applying inclusion and exclusion criteria, 36 primary studies were selected. The results indicate that the typical structures of maturity models incorporate technological, organizational, human, performance, and security dimensions through graded levels that denote the sophistication of the Internet of Things. Measurement techniques and metrics vary from model to model. There are few empirical validations or standardized improvement frameworks. The main conclusion is that there is a diversity of models, dimensions, indicators, and methods and a need for more comprehensive, adaptable, and user-friendly tools to help companies assess their Internet of Things maturity and inform future development strategies.

Physics-Driven Image Dehazing from the Perspective of Unmanned Aerial Vehicles

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively.

Merit-Based Sortition in Decentralized Systems

In decentralized systems, it is often necessary to select an 'active' subset of participants from the total participant pool, with the goal of satisfying computational limitations or optimizing resource efficiency. This selection can sometimes be made at random, mirroring the sortition practice invented in classical antiquity aimed at achieving a high degree of statistical representativeness. However, the recent emergence of specialized decentralized networks that solve concrete coordination problems and are characterized by measurable success metrics often requires prioritizing performance optimization over representativeness. We introduce a simple algorithm for 'merit-based sortition', in which the quality of each participant influences its probability of being drafted into the active set, while simultaneously retaining representativeness by allowing inactive participants an infinite number of chances to be drafted into the active set with non-zero probability. Using a suite of numerical experiments, we demonstrate that our algorithm boosts the quality metric describing the performance of the active set by >2 times the intrinsic stochasticity. This implies that merit-based sortition ensures a statistically significant performance boost to the drafted, 'active' set, while retaining the property of classical, random sortition that it enables upward mobility from a much larger 'inactive' set. This way, merit-based sortition fulfils a key requirement for decentralized systems in need of performance optimization.

Referral, enrollment, and health care use in a comprehensive patient-centered management program for osteoarthritis of the hip and knee

ObjectiveOsteoarthritis management programs (OAMPs) have become a more common way to deliver patient-centered care. However, there is limited information on real-world use of these programs to guide implementation, payment policy, accessibility, and scaling in the United States. This paper describes 5-year use metrics for the Duke Joint Health Program, an OAMP embedded within a US academic health system. MethodThis analysis includes patients referred into the Program between October 2017 and April 2022. We generated descriptive statistics of referral and enrollment totals, demographics and patient-reported measures of enrollees, retention and healthcare use metrics (e.g., office visit frequency), and data capture rates for patient-reported outcomes. ResultsDuring the study period, 6863 patients were referred to the program and 4162 (61 ​%) enrolled. We observed statistically significant differences between those who did and did not enroll by age (mean difference ​± ​SE: 2.49 ​± ​2.8 years), sex (70.0 ​% vs 67.7 ​% female), race (65.1 ​% vs 55.3 ​% Caucasian/White), employment status (50.0 ​% vs 40.2 ​% retired), and insurance type (53.5 ​% vs 47.0 ​% Medicare). The median (Q1, Q3) number of visits was 2 (1, 4) and ranged from 1 to 67. The median (Q1, Q3) number of days from first to last program visit was 23 (0, 84) days. Questionnaire completion rates were 72 ​% at baseline, 46 ​% at 6 weeks, 39 ​% at 3 months, and 40 ​% at 6 and 12 months. ConclusionFindings can guide the planning, development, and implementation of future OAMPs and inform policies to ensure programs are accessible and equitable.

Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.

Cone beam computed tomography (CBCT) can be used to evaluate the inter-fraction anatomical changes during the entire course for image-guided radiotherapy (IGRT). However, CBCT artifacts from various sources restrict the full application of CBCT-guided adaptive radiation therapy (ART). Inter-fraction anatomical changes during ART, including variations in tumor size and normal tissue anatomy, can affect radiation therapy (RT) efficacy. Acquiring high-quality CBCT images that accurately capture patient- and fraction-specific (PFS) anatomical changes is crucial for successful IGRT. To enhance CBCT image quality, we proposed PFS lung diffusion models (PFS-LDMs). The proposed PFS models use a pre-trained general lung diffusion model (GLDM) as a baseline, which is trained on historical deformed CBCT (dCBCT)-planning CT (pCT) paired data. For a given patient, a new PFS model is fine-tuned on a CBCT-deformed pCT (dpCT) pair after each fraction to learn the PFS knowledge for generating personalized synthetic CT (sCT) with quality comparable to pCT or dpCT. The learned PFS knowledge is the specific mapping relationships, including personal inter-fraction anatomical changes between personalized CBCT-dpCT pairs. The PFS-LDMs were evaluated on an institutional lung cancer dataset, quantified by mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM) metrics. We also compared our PFS-LDMs with a mainstream GAN-based model, demonstrating that our PFS fine-tuning strategy could be applied to existing generative models. Our models showed remarkable improvements across all four evaluation metrics. The proposed PFS-LDMs outperformed the GLDM, demonstrating the effectiveness of our proposed fine-tuning strategy. The PFS model fine-tuned with CBCT images from four prior fractions, reduced the MAE from 103.95 to 15.96 Hounsfield units (HU), and increased the mean PSNR, NCC, and SSIM from 25.36dB to 33.57dB, 0.77 to 0.98, and 0.75 to 0.97, respectively. Applying our PFS fine-tuning strategy to a Cycle GAN model also showed improvements, with all four fine-tuned PFS Cycle GAN (PFS-CG) models outperforming the general Cycle GAN model. Overall, our proposed PFS fine-tuning strategy improved CBCT image quality compared to both the pre-correction and non-fine-tuned general models, with our proposed PFS-LDMs yielding better performance than the GAN-based model across all metrics. Our proposed PFS-LDMs significantly improve CBCT image quality with increased HU accuracy and fewer artifacts, thus better capturing inter-fraction anatomical changes. This lays the groundwork for enabling CBCT-based ART, which could enhance clinical efficiency and achieve personalized high-precision treatment by accounting for inter-fraction anatomical changes.

Denoising astronomical images using an enhanced Pix2Pix model

Astronomical images are frequently affected by sensor noise, which can negatively impact the accuracy of subsequent data analysis. To address this issue, this study proposes an enhanced Pix2Pix generative adversarial network model that incorporates Residual Blocks and Self-Attention mechanisms to improve denoising performance. The effectiveness of the proposed model is evaluated by comparing it with traditional denoising methods, standard Pix2Pix, Pix2Pix with Residual Blocks, and Pix2Pix with Self-Attention using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. The findings demonstrate that the Pix2Pix model, when combined with both Residual Blocks and Self-Attention, significantly outperforms other models in noise reduction and detail preservation. This improved approach offers a robust solution for high-quality processing of astronomical images, providing clearer and more reliable data for scientific analysis. The results highlight the potential of advanced deep learning techniques in overcoming the challenges posed by sensor noise in astronomical imaging.

Organizational performance as dependent variable in strategic human resource management literature – a journey so far

PurposeOrganizational performance (OP) is one of the most important constructs in management research and all functions are evaluated by their contribution toward OP. This paper evaluates the current state of the research on OP in strategic human resource management (SHRM) literature. The study intends to generate new ideas for ongoing research in the field, facilitate the strategic alignment of HR operations and provide practical guidance on adopting OP measurement metrics for evidence-based decision-making at both organizational and individual levels. Consequently, a dual-method systematic review methodology was employed to achieve the stated objectives. This research underpins its theoretical argument on a resource-based view.Design/methodology/approachThis research paper follows a systematic review of 127 empirical studies conducted in the last three decades, in which, the selection of OP as a dependent variable is evaluated. This systematic review followed the integrated and systemic review of literature combining Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis. The tools used for bibliometric analysis are Biblioshiny package from R software and VOSviewer software.FindingsOut of selected 127 empirical studies, none of the studies provide a comprehensive measuring framework. As a result, the current literature review provides an expanded list of OP indicators and a measurement framework with 10 different performance perspectives based on Business Process Measurement literature.Research limitations/implicationsPerformance is a complex concept that requires understanding its relationships and the impact of context and measures. Objective measures alone may not capture this, so research is needed to determine the best combinations of subjective and objective measures. This systematic literature review identifies gaps in existing literature on performance measurement indicators in management and provides an exhaustive list of 161 indicators. These indicators can be used by practitioners and researchers to choose appropriate ones based on their needs. Future research should focus on case studies to validate results and enhance performance measurement systems in SHRM research.Practical implicationsThe study emphasizes the importance of examining the multidimensionality of OP, which is linked to stakeholders affected by performance measures, the assessment environment, and the time frame when gauging performance.Originality/valueThis review analyzed the intellectual structure of SHRM and OP research field and identified various research fronts. This study added to the literature a measurement framework with 10 perspectives in which 161 performance indicators were categorized.

Fault Diagnosis of Induction Motors under Limited Data for Across Loading by Residual VGG-Based Siamese Network

This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models.

Efficiency metrics for performance measurement: a review in higher education of main methods and determinants

PurposeThis study aims to provide an overview of the state-of-the-art in efficiency measurement within higher education (HE). Specifically, it seeks to gather all relevant articles on the topic and subsequently categorize these studies using a flowchart based on two core aspects of the topic.Design/methodology/approachThis study employs bibliometric and content analyses to conduct a systematic literature review. The Preferred Reporting Items for Systematic Review (PRISMA) framework is used to identify the search protocol, followed by analyses to classify and categorize articles.FindingsThe bibliometric analysis identifies prominent themes, methodologies and literature gaps. The content findings highlight key insights on higher educational institution (HEI) efficiency, including organizational structures, services and operational activities.Originality/valueThis research contributes to the existing knowledge by synthesizing global literature on HEI’s efficiency. Utilizing the flowchart developed by the authors, the study captures the state-of-the-art based on two critical aspects: methodologies and content. Insights from the analysis and subsequent classification of previous literature provide valuable directions for future research.

How to get closer to actual forest stand height using GEDI? A case study in central European Scots pine stands

ABSTRACT Global Ecosystem Dynamics Investigation (GEDI) Satellite Laser Scanning mission plays a key role in forest monitoring, conducting global measurements of forest height and structure. However, the use of various stand height metrics and definitions applied to describe forest height complicates study comparisons and the selection of most suitable GEDI metric for stand height determination. Our study was focused on finding the optimal GEDI relative height (rhG) metric to describe the height of Scots pine stands. We compared GEDI rhG parameters with several metrics commonly used as reference forest stand height (a series of Airborne Laser Scanning point cloud percentiles and relative height metrics of simulated waveforms; rhSW). Results showed that GEDI rhG metrics in the 0.95–0.99 rank range can accurately describe forest stand height (ALS reference: RMSE ≤ 2.79 m; %RMSE ≤12.57%; rhSW reference: RMSE ≤ 2.80 m; %RMSE ≤12.21%). Moreover, our analyses suggested that different rhG metrics can be used to describe forest stand height depending on its height and canopy cover to increase measurements accuracy. We recommended a set of GEDI rhG metrics for adequate height measurements of Scots pine stands that can be used by forestry practitioners.

A systematic review of the Metaverse in formal education

PurposeIn light of the burgeoning interest in the Metaverse within educational contexts, this study provides a comprehensive review to address the knowledge gap prevalent among K-12 and higher education teachers and educators. The increasing integration of the Metaverse into classroom settings necessitates a systematic exploration of its impact on subject-specific pedagogy, assessment methods, research methodologies and overall learning outcomes.Design/methodology/approachThe research design involves a systematic review of 34 selected studies published between 2009 and 2023. The inclusion criteria prioritize investigations into Metaverse applications in classroom teaching, with a focus on subject disciplines, pedagogical approaches, measurement metrics and research methodologies. The selected studies undergo a detailed analysis and synthesis to extract meaningful patterns and trends.FindingsThe analysis reveals a predominant concentration on higher education, encompassing both science, technology, engineering, and mathematics (STEM) and non-STEM disciplines. However, few studies adopt comprehensive experimental designs. This suggests a gap in the methodological rigor of current research on Metaverse integration in education. While the Metaverse's impact on higher education is evident, the study underscores the need for more robust experimental designs and comprehensive research methodologies.Research limitations/implicationsThe limited exploration of students' experiences with the Metaverse in educational settings highlights a nascent research area that warrants further investigation. Future research should prioritize measuring students' perceptions and performance through indicators such as test grades to enhance our understanding of the Metaverse's efficacy in educational contexts.Originality/valueThe study aims to offer insights into the current state of Metaverse integration in education and identify areas for further research and development.