Search Results (44)

This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies. Full article

In the face of escalating cyber threats to critical infrastructures, achieving robust cyber resilience has become paramount. This paper proposes an endogenous security-oriented framework for cyber resilience assessment, specifically tailored for critical infrastructures. Drawing on the principles of endogenous security, our framework integrates dynamic heterogeneous redundancy (DHR) and adaptive defense mechanisms to address both known and unknown threats. We model resilience across four key dimensions—Prevention, Destruction Resistance, Adaptive Recovery, and Evolutionary Learning—using a novel mathematical formulation that captures nonlinear interactions and temporal dynamics. The framework incorporates environmental threat entropy to dynamically adjust resilience scores, ensuring relevance in evolving attack landscapes. Through empirical validation on simulated critical infrastructure scenarios, we demonstrate the framework’s ability to quantify resilience trajectories and trigger timely defensive adaptations. Empiricalvalidation on a real-world critical infrastructure system yielded an overall resilience score of 82.75, revealing a critical imbalance between strong preventive capabilities (90/100) and weak Adaptive Recovery (66/100). Our approach offers a significant advancement over static risk assessment models by providing actionable metrics for strategic resilience investments. This work contributes to the field by bridging endogenous security theory with practical resilience engineering, paving the way for more robust protection of critical systems against sophisticated cyber threats. Full article

This study investigates the development of cognitive, emotional, and social skills in pre-service teachers through extracurricular activities, addressing 21st century challenges in preparing educators for diverse learning environments. It was hypothesized that extracurricular activities would positively influence cognitive skills (self-efficacy, self-regulation), emotional dimensions (professional interest), social competencies (teacher–student relationships), and academic achievement. This study employed predictive correlational methodology based on an integrated theoretical framework combining Social Cognitive Theory, Self-Determination Theory, Self-Regulation Theory, and Interpersonal Relationships Theory within formal–informal learning contexts. A psychometrically robust instrument (“Scale on the Contribution of Extracurricular Activities to Professional Development”) was developed and validated through exploratory and confirmatory factor analyses, yielding a five-factor structure with strong reliability indicators (Cronbach’s α = 0.91–0.93; CR = 0.816–0.912; AVE = 0.521–0.612). Data from 775 pre-service teachers (71.1% female) across multiple disciplines at a Turkish university were analyzed using structural equation modeling (χ²/df = 2.855, RMSEA = 0.049, CFI = 0.93, TLI = 0.92). Results showed that extracurricular participation significantly influenced self-efficacy (β = 0.849), professional interest (β = 0.418), self-regulation (β = 0.191), teacher–student relationships (β = 0.137), and academic achievement (β = 0.167). Notably, an unexpected negative relationship emerged between self-efficacy and academic achievement (β = −0.152). The model demonstrated strong explanatory power for self-efficacy (R² = 72.8%), professional interest (R² = 78.7%), self-regulation (R² = 77.2%), and teacher–student relationships (R² = 63.1%) while explaining only 1.8% of academic achievement variance. This pattern reveals distinct developmental pathways for professional versus academic competencies, leading to a comprehensive practical implications framework supporting multidimensional assessment approaches in teacher education. These findings emphasize the strategic importance of extracurricular activities in teacher education programs and highlight the need for holistic approaches beyond traditional academic metrics, contributing to Sustainable Development Goal 4 by providing empirical evidence for integrating experiential learning opportunities that serve both academic researchers and educational practitioners seeking evidence-based approaches to teacher preparation. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

Call Detail Records (CDRs) from mobile networks offer valuable insights into both network performance and user behavior. With the growing importance of data analytics, analyzing CDRs has become critical for optimizing network resources by forecasting demand across spatial and temporal dimensions. In this study, we examine publicly available CDR data from Telecom Italia to explore the spatiotemporal dynamics of mobile network activity in Milan. This analysis reveals key patterns in traffic distribution highlighting both high- and low-demand regions as well as notable variations in usage over time. To anticipate future network usage, we employ both Automated Machine Learning (AutoML) and the transformer-based TimeGPT model, comparing their performance against traditional forecasting methods such as Long Short-Term Memory (LSTM), ARIMA and SARIMA. Model accuracy is assessed using standard evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R²). Results show that AutoML delivers the most accurate forecasts, with significantly lower RMSE (2.4990 vs. 14.8226) and MAE (1.0284 vs. 7.7789) compared to TimeGPT and a higher R² score (99.96% vs. 98.62%). Our findings underscore the strengths of modern predictive models in capturing complex traffic behaviors and demonstrate their value in resource planning, congestion management and overall network optimization. Importantly, this study is one of the first to Comprehensively assess AutoML and TimeGPT on a high-resolution, real-world CDR dataset from a major European city. By merging machine learning techniques with advanced temporal modeling, this study provides a strong framework for scalable and intelligent mobile traffic prediction. It thus highlights the functionality of AutoML in simplifying model development and the possibility of TimeGPT to extend transformer-based prediction to the telecommunications domain. Full article

This research investigates the integration of the Sustainable Development Goals (SDGs) into the business practices of the Portuguese energy giant EDP Group. We analyse the company’s annual reports, sustainability reports, and public statements to explore the motivations, challenges, and key organisational dimensions involved in this process. Our findings reveal that EDP Group’s strong commitment to sustainability, external pressures, and stakeholder expectations have driven the integration of the SDGs into its strategic and operational plans. The company’s cultural emphasis on environmental and social responsibility and formal management control systems has facilitated this integration. However, challenges such as the lack of standardised metrics to measure social and environmental impacts and the evolving regulatory landscape hinder progress. This study contributes to understanding how large corporations can effectively integrate the SDGs into their business models, providing valuable insights for practitioners and policymakers. Full article

Excessive bait wastage is a major issue in aquaculture, leading to higher farming costs, economic losses, and water pollution caused by bacterial growth from unremoved residual bait. To address this problem, we propose a bait residue detection and counting model named YOLOv8-BaitScan, based on an improved YOLO architecture. The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. The experimental results show that YOLOv8-BaitScan achieves strong performance across key metrics: The recall rate increased from 60.8% to 94.4%, mAP@50 rose from 80.1% to 97.1%, and the model’s number of parameters and computational load were reduced by 55.7% and 54.3%, respectively. The model significantly improves the accuracy and real-time detection capabilities for underwater bait and is more suitable for real-world aquaculture applications, providing technical support to achieve both economic and ecological benefits. Full article

Understanding the structural concordance between taxonomic and functional diversity (FD) metrics is essential for improving the ecological interpretation of community patterns in biomonitoring programs. This study evaluated the concordance between taxonomic and FD metrics of benthic macroinvertebrates along a fluvial habitat quality gradient in the Paute River Basin, Ecuador. Macroinvertebrate communities were sampled over six years at twelve sampling points and assessed using four taxonomic metrics: Shannon diversity (H), the Margalef index (D_Mg), family richness (N), and the Andean Biotic Index (ABI). Functional diversity was evaluated using four metrics: weighted functional dendrogram-based diversity (wFDc), Rao’s quadratic entropy (Rao), functional dispersion (FDis), and functional richness (FRic). The fluvial habitat index (FHI) was used as an environmental reference to evaluate diversity metric responses. K-means clustering was independently applied to each metric, and pairwise concordance was quantified using the Measure of Concordance (MoC) and overlap in sampling points groupings across replicates. Most metrics (except FRic and N) showed clear responsiveness to the FHI gradient, confirming their ecological relevance. Strong structural concordance was observed between H and D_Mg and the FD metrics Rao, FDis, and wFDc, showing that these metrics captured similar yet complementary aspects of community organization. In contrast, ABI showed marked sensitivity to the FHI gradient but low concordance with functional metrics, suggesting distinct dimensions of biological integrity not encompassed by trait-based metrics. These findings highlight the value of combining taxonomic and functional metrics to detect both broad and subtle ecological changes. Integrating metrics with differing structural properties and environmental sensitivities can enhance the robustness of freshwater biomonitoring frameworks, especially in systems undergoing ecological transition or habitat degradation. Full article

This study examines six decades (1964–2023) of changes in motor skills and body dimensions among Japanese 13-year-old students, analyzing grip strength, handball throwing, 50 m dash, endurance running, and composite scores. National Physical Fitness and Motor Skills Survey data were analyzed alongside School Health Examination Survey measurements to identify trends and correlations between performance metrics and anthropometric variables. The results revealed distinct developmental patterns, with motor skills peaking in the 1980s for both genders, followed by decline until 2000, after which boys experienced stagnation while girls showed improvement until 2019. Both genders demonstrated marked decreases following 2020, coinciding with the COVID-19 pandemic. Notably, while height increased significantly over the study period, only boys’ 50 m dash performance showed strong positive correlations with height (r = 0.779) and BMI (r = 0.854). This longitudinal analysis demonstrates how interdisciplinary factors—including educational policy shifts, reduced physical education curriculum hours, changes in urban park design, diminished outdoor play opportunities, and increased sedentary behaviors—collectively impact children’s motor development. These findings hold significant implications for public health initiatives and sports education strategies aimed at reversing concerning trends in youth physical capabilities and addressing the substantial post-pandemic decline in motor performance. Full article

The Yellow River Basin, a cradle of Chinese civilization, hosts traditional riverside villages that embody millennia of cultural and ecological heritage. Despite their significance, rapid urbanization and homogeneous rural development have precipitated landscape homogenization and cultural erosion, threatening these villages’ spatial integrity and cultural capital. Current research predominantly focuses on qualitative characterization of architectural heritage, neglecting quantitative assessments of agroecological synergies and systematic resource endowment analysis. This oversight limits the development of proactive conservation strategies tailored to the integrated cultural–ecological value of these villages, hindering their sustainable revitalization within China’s broader Yellow River Basin high-quality development strategy. Here, we develop a comprehensive framework integrating landscape characterization, value assessment, and conservation strategies for traditional villages along Shaanxi’s Yellow River. Using GISs 10.2 multi-criteria analysis, and field surveys, we construct a hierarchical landscape database and evaluate villages across cultural, ecological, and socio-economic dimensions. Our results reveal distinct spatial patterns, with 65% of historical structures clustered in village cores, and identify four landscape zones requiring targeted conservation. High-value villages (e.g., Yangjiagou) exhibit strong cultural preservation and ecological resilience, while lower-scoring villages underscore urgent intervention needs. We propose multi-scale protection strategies, including regional clustering and village-level tailored approaches, to balance conservation with sustainable development. This study fills the critical gap in systematic resource endowment evaluation by demonstrating how integrated cultural–ecological metrics can guide proactive conservation. Our framework not only safeguards tangible and intangible heritage but also aligns with national strategies for rural revitalization and ecological protection. By bridging methodological divides between qualitative and quantitative approaches, this research offers a replicable model for sustainable rural development in ecologically sensitive cultural landscapes globally, advancing the field beyond static preservation paradigms toward dynamic, evidence-based planning. Full article

Nature-based Solutions (NbSs) bridge ecological conservation and human well-being. As the concept gains global traction, its potential for cultural heritage preservation is drawing increasing interest. This study explores the localized application of NbSs in Chinese villages, focusing on two core questions: its compatibility with traditional Chinese construction wisdom and its practical pathways for localization. A literature review reveals strong theoretical alignment between NbS principles and indigenous building practices. This study develops a dual quantitative framework—comprising an NbS evaluation system and a cultural value assessment system—for coupling coordination analysis. Results show a strong interdependence between the integration of NbS principles and village cultural value, with traditional villages (0.7806) achieving a better balance between ecological protection and cultural heritage than non-traditional villages (0.5953), validating the alignment of global NbS principles with local building wisdom. Based on gray relational analysis, disaster risk management and local governance are identified as key NbS dimensions shaping cultural integrity and knowledge continuity. An integrated indicator system combining ecological and cultural metrics is proposed. This study confirms the alignment between global principles and local wisdom, offering an NbS localization framework with insights for heritage conservation. Full article

Background: This study examined the relationship between hearing device price and sound quality. Method: A novel consumer-centric metric of sound quality (“SoundScore”) was used to assess hearing devices’ audio performance. Each hearing device is tested with two fittings. The “Initial Fit” is designed to approximate the most likely fitting for an individual with a mild-to-moderate sloping sensorineural hearing loss. The “Tuned Fit” includes adjusting parameters optimized to hit prescriptive fitting targets (NAL NL2) on an acoustic manikin. Each fitting is evaluated across five dimensions. Both fittings are combined using a weighted average to create a single number from 0 to 5 representative of a device’s overall audio performance. Seventy-one hearing devices were tested. Results: A strong positive correlation was found between hearing device price and SoundScore. The average SoundScore increased dramatically as the price approached USD 1000, with marginal improvements beyond this point. SoundScore was consistently poor for devices under USD 500, highly variable between USD 500–1000, and consistently good over USD 1000. Conclusions: There is a strong but nonlinear relationship between hearing device price and sound quality. This information can aid consumers in making informed decisions while also assisting hearing healthcare professionals in providing comprehensive guidance to their patients. Full article

This paper examines online learner engagement, a complex concept encompassing several dimensions (behavioral, emotional, and cognitive) and recognized as a key indicator of learning effectiveness. Engagement involves participation, motivation, persistence, and reflection, facilitating content understanding. Predicting engagement, particularly behavioral engagement, encourages interaction and aids teachers in adjusting their methods. The aim is to develop a predictive model to classify learners based on their engagement, using indicators such as academic outcomes to identify signs of difficulty. This study demonstrates that engagement in quizzes and exams predicts engagement in lessons, promoting personalized learning. We utilized supervised machine learning algorithms to forecast engagement at three levels: quizzes, exams, and lessons, drawing from a Kaggle database. Quiz and exam scores were employed to create predictive models for lessons. The performance of the models was evaluated using classic metrics such as precision, recall, and F1-score. The Decision Tree model emerged as the best performer among those evaluated, achieving 97% and 98.49% accuracy in predicting quiz and exam engagement, respectively. The K-Nearest Neighbors (KNN) and Gradient Boosting models also showed commendable performance, albeit slightly less effective than the Decision Tree. The results indicate a strong correlation between engagement predictions across the three levels. This suggests that engagement in quizzes and exams, known as assessments, is a pertinent indicator of overall engagement. Active learners tend to perform better in these assessments. Early identification of at-risk learners allows for targeted interventions, optimizing their engagement. Full article

Background: The Glasgow Coma Scale (GCS) is a cornerstone in neurological assessment, providing critical insights into consciousness levels in patients with traumatic brain injuries and other neurological conditions. Despite its clinical importance, traditional methods for predicting GCS scores often fail to capture the complex, multi-dimensional nature of patient data. This study aims to address this gap by leveraging machine learning (ML) techniques to develop accurate, interpretable models for GCS prediction, enhancing decision making in critical care. Methods: A comprehensive dataset of 759 patients, encompassing 25 features spanning pre-, intra-, and post-operative stages, was used to develop predictive models. The dataset included key variables such as cognitive impairments, Hunt and Hess scores, and aneurysm dimensions. Six ML algorithms, including random forest (RF), XGBoost, and artificial neural networks (ANN), were trained and rigorously evaluated. Data preprocessing involved numerical encoding, standardization, and stratified splitting into training and validation subsets. Model performance was assessed using accuracy and receiver operating characteristic area under the curve (ROC AUC) metrics. Results: The RF model achieved the highest accuracy (86.4%) and mean ROC AUC (0.9592 ± 0.0386, standard deviation), highlighting its robustness and reliability in handling heterogeneous clinical datasets. XGBoost and SVM models also demonstrated strong performance (ROC AUC = 0.9502 and 0.9462, respectively). Key predictors identified included the Hunt and Hess score, aneurysm dimensions, and post-operative factors such as prolonged intubation. Ensemble methods outperformed simpler models, such as K-nearest neighbors (KNN), which struggled with high-dimensional data. Conclusions: This study demonstrates the transformative potential of ML in GCS prediction, offering accurate and interpretable tools that go beyond traditional methods. By integrating advanced algorithms with clinically relevant features, this work provides a dynamic, data-driven framework for critical care decision making. The findings lay the groundwork for future advancements, including multi-modal data integration and broader validation, positioning ML as a vital tool in personalized neurological care. Full article

Although food seems abundant in the European Union, challenges related to specific aspects of food security continue to exist and require ongoing attention. A country’s food security depends on various economic, social, environmental, and institutional factors, which are studied using several scientific research methodologies. The role of institutions in determining national success and failure has been increasingly emphasized in recent academic discourse. Our research makes a novel contribution to the literature on institutions and food security by integrating New Institutional Economics with food security metrics. It aims to examine the relationships between food security dimensions and country-specific institutional matrices in the twenty EU member states from 2012 to 2019. How strong were those relationships, and how did they differ between the new and old member states? Food security is proxied by the Global Food Security Index and its three pillars (economic accessibility, physical availability, and quality and safety). The institutional quality of a country is represented by the Worldwide Governance Indicators (regulatory quality, rule of law, and control of corruption). Using the food security indices as the dependent variables, we apply multiple regression models to identify which institutions determined national food security over time. The study revealed that between 2012 and 2019, there was no evidence of sigma convergence or reduction in the dispersion of institutional quality (except for control of corruption) and overall food security within the EU20. The domestic institutions were generally statistically significantly positively related to the GFSI and its elements. The weakest correlations for the EU20 were those linking institutional variables with food quality and safety. The rule of law, incorporating such formal institutions as the quality of contract enforcement and property rights, positively affected food security within the EU20, with the mostgreatest impact on food quality, safety, and availability. The dependence of food security on national institutional factors was stronger in new member states from Central and Eastern Europe. The exploratory results shed some light on the role of institutions in shaping food security. However, further research is required to gain a more detailed understanding of this phenomenon. The research findings suggest that policymakers in the EU countries could enhance national institutions to promote food security and, consequently, achieve the Sustainable Development Goals more effectively. Full article