Metrics

We present a unifying thesis which posits that the biological resolution of uncertainty is a fundamental adaptation to entropy’s negative impact on the highly ordered molecular structures required to maintain the living state. These molecular biological adaptations are highly conserved and play a critical role in the survival of the earliest multicellular organisms and the vertebrates thereafter. The imperative to reduce cognitive uncertainty is effected through the dopaminergic Medial Forebrain Bundle (MFB) Reward Prediction Error (RPE) mechanism, or its homologous equivalents, to compute a biological valuation of information. This hypothesis is supported by the central role of the MFB seeking system as the neural substrate of exploratory behavior that leads to the reduction of uncertainty when information is apprehended and cognitively assimilated. We define the human experience of intellectual beauty as the subjective emotional reward that is activated by the MFB seeking system. Accordingly, humans experience intellectual beauty when a high-entropy state of confusion is suddenly resolved into a low-entropy state of insight. In humans, the neuroanatomical substrates of inductive reasoning, inquiry, and the intellectual beauty to which they lead are present at birth. What develops postnatally is synaptic plasticity in the connections among these neurons that is activated in the loving didactic relationship that is established between mother and child during infancy. This dynamic is critically dependent on observational learning on the part of the child. It is supported by the joyful engagement and emotional support of the mother. This provides a paradigm of joy in learning that we propose is the developmental origin of intellectual beauty. This is the reinforcement that maintains inquiring behavior in the search for information that is needed to resist the adverse effects of entropy on life. This paper traces the continuous thread of uncertainty resolution from its phylogenetic origins in associative learning to the intuitive science of early childhood, and ultimately to the highest levels of human inquiry in science, as well as literary, musical and visual arts. The intuitive scientific method gives rise to the collective intelligence of groups, an evolved trait that likely contributed to the success of our hominin ancestors. At the societal level, this collective intelligence scales into the institutional working of markets, driving the macroeconomic price discovery of new information to counter entropy. Importantly, we compare the cost of information across the disparate domains of pharmaceutical drug discovery and the contemporary art market to demonstrate that the imperative to reduce uncertainty manifests as a universal, falsifiable mechanism for the “price discovery” of information. Full article

General-purpose bibliometric tools do not address domain-specific needs of occupational safety and health (OSH) evidence mapping, such as quantifying whether the literature connects hazard characterisation with real worker exposure or identifying which articles best bridge science and preventive practice. This paper presents ORISMA (Occupational Risk Integrated Systematic Mapping and Analysis), an open-source R package that introduces five preventive bibliometric indicators: the Worker–Risk Disconnection Index (WRDI), Risk Category Saturation Index (RCS), Material–Gap Profile (MGP), Abstract Sufficiency Score (ASS), and Bridge Article Score (BAS). A complete evidence map is produced through three function calls. To demonstrate the package, we apply it to a corpus of bibliographic records on occupational risks in metal additive manufacturing (metal AM), retrieved from Web of Science, Scopus, ProQuest, and PubMed. After conservative relevance-guard filtering (191 records retained) and three-step deduplication, 76 unique records were analysed. The global WRDI was 0.4474, indicating moderate disconnection between hazard characterisation and worker exposure evidence. Industrial hygiene and emerging technology domains dominated preventive coverage, while safety, ergonomics, psychosocial, and biological risk domains were largely absent. Eighteen strong bridge articles were identified. The material–risk co-occurrence map showed no detected co-occurrence between explosion/fire or asphyxiant-gas risks and any material, reflecting an absence of explicit terminology in the retrieved literature rather than a definitive absence of evidence. ORISMA provides a reproducible, domain-aware framework that requires further validation across independent OSH domains before broad generalization. Full article

Introduction: Deep learning has emerged as a promising approach for automated bone age assessment; however, the scientific development of this field has not been comprehensively characterized. Materials and Methods: A bibliometric analysis of studies published between 2015 and 2025 was conducted using data from Web of Science, Scopus, MEDLINE (PubMed), IEEE Xplore, and Google Scholar. A total of 67 studies were included. Bibliometric mapping, network visualization, and knowledge structure analyses were performed using VOSviewer^® (v. 1.6.20) and CiteSpace^® (v. 6.3.R1). Results: Scientific output increased markedly, from 1 publication in 2015–2016 to 25 in 2025. The period 2019–2020 achieved the highest citation impact (1191 citations across 18 studies). China was the leading contributor, followed by the United States, South Korea, and Turkey. Keyword analysis identified convolutional neural networks, the RSNA dataset, and the Greulich–Pyle method as central research themes. Recent studies have increasingly focused on external validation, multicenter datasets, and transformer-based architectures. Conclusions: Research on deep learning for bone age assessment has grown substantially over the past decade. This bibliometric analysis highlights the field’s major contributors, influential publications, and emerging trends, while emphasizing the need for greater validation, dataset diversity, and standardized reporting. Full article

Background and Aim: Anatomy education has expanded beyond cadaver-centered teaching toward simulation-based, digital, and hybrid approaches. However, it remains unclear how the literature is distributed across these modalities and whether they differ in methodological profile and bibliometric visibility. This study aimed to map Scopus-indexed anatomy education literature across dissection-based, simulation-based, and hybrid approaches. Materials and Methods: We conducted an exploratory scientometric and methodological analysis of Scopus-indexed articles and reviews retrieved on 14 April 2026. The search combined anatomy-related, medical education-related, and modality-related terms referring to dissection or simulation. Records published from 2000 up to the search date were included. Teaching modality, study design, and outcome type were identified through semi-automated lexical coding of title, abstract, author keywords, and index keywords. Logistic regression models were used to examine associations between teaching modality and methodological characteristics, while citation visibility was examined using a multivariable linear regression model of log-transformed citations per year. Results: The final corpus comprised 2116 articles and reviews. After excluding records classified as unclear with respect to teaching modality, 2094 records were included in modality-based analyses. Dissection-based studies were the numerically dominant category, followed by hybrid and simulation-based studies. Simulation-based studies showed higher odds of being experimental than dissection-based studies, whereas both simulation-based and hybrid studies showed higher odds of reporting objective and clinical/procedural outcomes. In the bibliometric model, hybrid studies, reviews, international collaboration, and larger author teams were associated with greater citation visibility. Conclusions: The transition from dissection-centered anatomy teaching to digital and mixed approaches appears not only thematic, but also methodological and bibliometric. Simulation-oriented studies were more frequently framed through experimental and outcome-based designs, whereas hybrid studies showed the strongest independent citation visibility. These findings should be interpreted as exploratory mapping signals rather than evidence of comparative pedagogical superiority. Full article

Large language models and vision–language models already achieve strong results on reasoning tasks, but their reliability under controlled assessment-style conditions remains insufficiently characterized. This paper presents a benchmark study of multimodal multiple-choice mathematical reasoning using 324 Austrian Mathematical Kangaroo competition problems (2022–2024), including both text-only and diagram-dependent items. We evaluate five state-of-the-art models under a controlled protocol that isolates two factors: input modality and prompt format. We compare a strict short-answer condition requiring a single option label (one_liner) with a structured condition eliciting step-by-step reasoning and an explicit final answer (full) while enforcing deterministic decoding and rule-based answer extraction. Performance is assessed using accuracy, abstention rates, and contest-style scoring, supported by paired and unpaired statistical analyses and a structured error taxonomy. The results show that prompt format is the primary driver of performance: structured prompting yields substantial gains across all the models, particularly on text-only items. In contrast, visual-text problems remain consistently harder, with a robust performance gap that persists across prompting conditions, indicating persistent limitations in visual grounding. Model comparisons are additionally influenced by response strategies, especially abstention behavior under strict output constraints. An error analysis reveals systematic failure modes, including constraint violations, inappropriate strategy selection, and diagram misinterpretation, alongside structured biases in multiple-choice selection under constrained prompting. Overall, the findings demonstrate that measured performance is highly sensitive to the interaction between prompt format and input modality. This underscores the importance of treating prompting, decoding, and answer extraction as integral components of evaluation in assessment-oriented settings, where reliability and reproducibility are central. Full article

Water scarcity is increasingly recognized as a slow-onset ecological crisis with major environmental, socio-economic and governance effects, yet systematic assessments of how research on this topic has evolved remain limited. This study addresses this gap through a bibliometric and thematic analysis of water-scarcity publications from 2000 to 2025, using VOSviewer (version 1.6.20), Biblioshiny™ (Bibliometrix version 4.3.1) and RStudio (version 2024.12.1 + 563) to map research trends, conceptual clusters and leading contributing countries, institutions and authors. The analysis shows that water scarcity research is organized around four dominant themes: adaptive water management and climate resilience, plant physiological responses to drought and water stress, ecosystem resilience and biodiversity under water scarcity, and water-limited agriculture and food security. Early scholarship focused heavily on biophysical processes such as drought tolerance and hydraulic conductivity, while recent studies increasingly incorporate socio-ecological, governance and policy dimensions, reflecting a shift toward holistic, solution-oriented approaches. Overall, the study provides a comprehensive overview of the evolution and global distribution of water scarcity research, highlighting the importance of integrating biophysical knowledge with human-centered strategies to support evidence-based decision-making, strengthen inclusive water governance, and enhance socio-ecological resilience in the face of a changing climate. Full article

The rapid growth of Artificial Intelligence (AI) is fundamentally transforming creative practices across all design disciplines. However, the commitment to addressing the ethical and environmental consequences of this transformation remains critically underexplored. This study aims to quantify the volume, track the evolution, and map the intellectual structure of academic literature at the intersection of AI, Design, and Sustainability. Using a comprehensive bibliometric approach, four distinct datasets were retrieved from the Scopus and Web of Science (WoS) databases on 3 October 2025. The study compares core “AI + Design + Sustainability” papers against an “AI + Design” baseline to assess the relative contribution of the sustainability dimension. The analysis identifies critical research gaps and offers strategic insights for scholars and institutions committed to fostering a more ethically and environmentally responsible design future. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article

The rapid expansion of bibliometric research has generated a large volume of descriptive “snapshot” studies that map publication trends but offer limited strategic or policy-relevant insight. Although improved database access and visualization tools have broadened participation in bibliometric analysis, methodological variability, limited reproducibility, and insufficient evaluative framing constrain its utility for research governance. We argue that bibliometric studies should not be conducted as ends in themselves, but as methods for addressing clearly defined, decision-relevant questions. We define evaluative bibliometrics as decision-oriented analysis grounded in explicit research questions, theoretically aligned indicator selection, temporal sensitivity, robustness assessment, and contextual interpretation. Key methodological considerations are examined, including database selection, search strategy design, attribution bias, normalization approaches, and science mapping parameters. We further synthesize emerging reporting frameworks and propose an evaluative extension framework that integrates decision-context specification with structured transparency requirements. By reframing bibliometrics as a decision-support discipline rather than a descriptive genre, this paper provides a methodological roadmap for researchers, editors, and institutions seeking to enhance the rigor, interpretability, and strategic relevance of bibliometric evidence. Full article

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis. Full article

The educational-Pink Innovation Grade Index (e-PIGI) is a novel metric designed to assess and quantify the level of innovation within educational practices and pedagogical projects. This work introduces the e-PIGI tool, an interactive and survey-based software that enables educators, institutions, and project designers to identify both strengths and limitations in their initiatives across ten key dimensions. These include different categories such as: (i) emerging technologies, (ii) adaptability to diversity, (iii) promotion of 21st-century skills, (iv) collaboration, (v) alignment with regulatory frameworks, (vi) active methodologies, (vii) impact, (viii) sustainability, (ix) evaluation and (x) multidisciplinarity. Each dimension is scored using a pink color-based scale ranging from 0 to 10, with darker tones indicating a higher degree of innovation. The resulting output includes a color-coded innovation score and a circular-shaped pictogram that offers a visual representation of the project’s innovative profile. A threshold value of 50 points is proposed as an operational criterion for identifying projects considered innovative. The tool was validated through expert judgment for content validity and through empirical reliability analysis (Cronbach’s coefficient α = 0.833, n = 126), confirming good internal consistency. To demonstrate its utility, the new tool was applied to a set of educational initiatives, helping to highlight their pedagogical strengths and areas for improvement. As an accessible and scalable instrument, e-PIGI aims to support reflective teaching practices, promote ongoing developments, and facilitate the comparison of innovation across diverse educational contexts. This software highlights the potential of e-PIGI to contribute to a more evidence-based and strategically guided culture of innovation in education. Full article

A pervasive trend across academia, social cognition, and general communication contexts is the interchangeable use of “information” and “knowledge”, particularly with reference to their forms—explicit knowledge, testimony, and expertise—conveyed by external sources. This raises a fundamental question: is the source perceived, considered, and validated as a reliable knowledge provider or merely as an information carrier? This study investigates seven academic and popular science sources by modelling their constructs of knowledge provision based on epistemological criteria and sociopsychological value, as manifested through the perspectives of university academics. The external sources examined include scientific journal articles, knowledge shared by university lecturers, scholarly monographs, textbooks and handbooks, popular science books and magazines, academic social networks and social media platforms. A quantitative investigation, supplemented by qualitative content analysis, collected assessments from sixty-six university academics in Latvia using the Epistemological Attitude Questionnaire towards Knowledge Sources. Statistical analysis, coupled with an examination and interpretation of academics’ perceptions, comprehension, use, and personal valuation of these sources, elucidated their profiles. The findings provide a holistic picture of these sources, detailing the value, qualities, functionality, and contributions of each type. Interpretations reveal that the designation of a form of “knowledge source” predominantly aligns with scientific and educational sources, whereas “information carriers” or socially functional sources primarily pertain to popular science and social media. Academic social networks, notably, occupy an intermediary position. This study offers critical academic insights into ongoing issues regarding these means of cognition. It prompts a scrutiny of both established traditional sources and contemporary mediums, both academic and popular, encouraging readers to evaluate these compiled images according to the delineated criteria of the theoretical framework. Full article

Despite the significant increase in publications on construction project performance (CPP), there is a deficiency of research that rigorously assesses and synthesizes previous studies to delineate the field’s development, themes, and research gaps. This article employs quantitative and qualitative methodologies to critically evaluate studies on CPP published over the last three decades and indexed in the Scopus database. The quantitative approach includes bibliometric searches and scientometric analyses to assess the extent of research interest and achievements. The qualitative methodology aims to conduct thorough content analysis to classify existing material based on prevalent themes. The results demonstrate an exponential growth of interest in this scientific research topic. Project management, construction industry, construction projects, project performance, and critical success factors are top keywords in pertinent research publications. This research field has been investigated in over 50 nations and published in over 100 scholarly journals. The United States and the Journal of Construction Engineering and Management are the primary contributors. Prior studies were categorized according to their objectives into four main research themes: project performance assessment and prediction, project performance improvement, critical success factors, and key performance indicators. This article uncovers research gaps and suggests avenues for further investigations. Researchers and practitioners may utilize the findings of this study to evaluate and implement CPP principles. Full article

The Internet of Things (IoT) has evolved through the interconnection of edge devices, enabling seamless data exchange across networks. With IoT adoption expanding into various sectors, the massive growth of generated data has raised concerns about the security of edge devices tasked with processing this information. While several metrics exist to assess vulnerability severity and support risk management, many fail to account for the distinct characteristics of IoT environments and lack precision in evaluating hardware-specific vulnerabilities. This paper provides a comprehensive review of current vulnerability metrics and frameworks and introduces a novel method for analyzing memory-related vulnerabilities in IoT edge devices. The proposed approach leverages functional size measurement through COSMIC (ISO 19761), a standardized measurement method for quantifying software functionality. By applying COSMIC, memory-related vulnerabilities can be assessed from a functional perspective. Additionally, a prototype tool is presented that automates the evaluation of memory vulnerabilities on ESP boards using COSMIC-based measurements. Findings highlight the potential of incorporating functional sizing into IoT security assessment practices. Full article

Climate change is the most significant socio-environmental challenges of our time, and education has been recognized as a fundamental strategy to confront it. Yet research efforts have focused more on students than on teachers, despite the latter’s key role in mediating between scientific and curricular knowledge and classroom practice. This study set out to characterize the field of educational research on climate change from the perspective of secondary school teachers. To this end, we conducted a systematic review and bibliometric analysis of 50 peer-reviewed studies from 15 countries (2010–2023). The results show a growing interest over time, with increases associated with international milestones such as the IPCC reports and the Paris Agreement, while declines are observed in connection with political shifts and the COVID-19 pandemic. Consolidated academic reference points were identified, including Eric Plutzer and Maria Ojala, alongside influential international organizations such as the IPCC and UNESCO, suggesting the presence of schools of thought and institutional frameworks that structure the field. Methodologically, descriptive and exploratory studies predominate, with a notable reliance on qualitative and mixed-methods designs using small samples, reinforcing the difficulty of accessing teachers as a research population. Overall, this review highlights significant gaps, particularly the geographical bias toward the Global North, and underscores the urgency of broader, more inclusive, and critically engaged research that positions teachers as essential agents of transformative educational responses to the climate crisis. Full article

This paper presents a bibliometric and literature review on decarbonization strategies in sustainability-oriented contractor selection within Integrated Project Delivery (IPD) frameworks. The study analyzes 972 journal articles published between 2002 and 2024 from Scopus, complemented by Google Scholar for thematic insights. Bibliometric techniques in R were applied to identify influential publications, research trends, and thematic clusters. The review highlights documented benefits of integrating decarbonization into contractor evaluation, including lifecycle carbon reduction, ESG alignment, and early-stage material optimization. Challenges remain in terms of limited lifecycle data, absence of standard benchmarks, and organizational resistance. Critical success factors identified include policy alignment, availability of assessment data, and collaborative stakeholder engagement. The findings demonstrate that incorporating carbon-related performance indicators into early procurement decisions can reshape prequalification standards and strengthen sustainable project delivery. Citation and co-word analysis reveal emerging research trends, including digital innovations such as artificial intelligence for contractor evaluation and emissions tracking. This study provides both a research foundation and a strategic guide for construction professionals, policymakers, and sustainability advocates aiming to align IPD with global decarbonization targets. Full article

Bibliometric indicators play a key role in assessing research performance at individual, departmental, and institutional levels, influencing both funding allocation, and university rankings. However, despite their widespread use, bibliometrics are often applied indiscriminately and without discrimination, overlooking contextual factors that affect research productivity. This research investigates how gender, academic discipline, institutional location, and academic rank influence bibliometric outcomes within the Greek Higher Education system. A dataset of 2015 faculty profiles from 18 universities and 92 departments was collected and analyzed using data from Google Scholar and Scopus. The findings reveal significant disparities in publication and citation metrics: female researchers, faculty in peripheral institutions, and those in specific disciplines (such as humanities) tend to score lower values across several indicators. These inequalities underscore the risks of applying one-size-fits-all evaluation models in performance-based research funding systems. The paper moves beyond a one-size-fits-all perspective and proposes that bibliometric evaluations should be context-sensitive and grounded in discipline and rank-specific benchmarks. By establishing more refined and realistic expectations for researcher productivity, institutions and policymakers can use bibliometrics as a constructive tool for strategic research planning and fair resource allocation, rather than as a mechanism that reinforces the existing biases. The study also contributes to ongoing international discussions on the responsible use of research metrics in higher education policy. Full article

Artificial intelligence (AI) has rapidly permeated education since 2014, propelled by technological innovation and global investment, yet scholarly discourse on contemporary AI-Education intersections remains largely fragmented. The present study addresses this notable gap through a bibliometric-driven and inductive content analysis to inform future research and practice. A total of 317 articles published between 2014 and October 2024 were retrieved from WOSCC and Scopus following the PRISMA protocol. Keyword co-occurrence and co-citation analyses with VOSviewer (version 1.6.20) were employed to visualize the intellectual structures shaping the field, while qualitative inductive content analysis was conducted to address the limitations of bibliometric methods in revealing deeper thematic insights. This dual-method approach identified four thematic clusters and eleven prevailing research trends. Subsequently, through interpretive synthesis, five interrelated research issues were identified: limited congruence between technological and pedagogical affordances, insufficient bottom-up perspectives in AI literacy frameworks, an ambiguous relationship between computational thinking and AI, a lack of explicit interpretation of AI ethics, and limitations of existing professional development frameworks. To address these gaps pragmatically, thirty issue-specific recommendations were consolidated into five overarching themes, culminating in the Integrated AI-Education Convergence Framework. This framework advocates for pedagogy-centric, ethically grounded, and contextually responsive AI integration within interdisciplinary educational research and practice. Full article

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article