Search Results (1,592)

Predicting the sustainability of national educational systems presents a complex, multifaceted issue due to the intricate connections between education and wider societal, economic, healthcare, and technological sectors. Current educational models tend to be rigid, narrow in focus, and insufficiently responsive to these changing external factors. This research seeks to fill this void by framing education as an ecosystem and creating a methodological framework that merges systems thinking with sophisticated data-driven methods. The study’s aim is to outline, quantify, and analyze the relationships among education-related subsystems to guide the creation of an adaptive, sustainability-focused educational ecosystem. A mixed-methods approach was utilized, incorporating qualitative coding, system mapping, and natural language processing techniques (specifically Word2Vec) to uncover relational patterns within a structured literature set. These findings were integrated with quantitative metrics to assess subsystem efficacy and pinpoint leverage points. The investigation centers on five primary systems in the education ecosystem: Business, Economic, Government, Healthcare, and Sustainability. The Word2Vec analysis identified significant conceptual relationships between these systems, while the quantitative evaluation indicated strong performance across curriculum, policy, and healthcare metrics. Conversely, inclusivity and accreditation displayed weaker outcomes, indicating areas that need focused improvement. The results highlight the benefits of merging systems thinking with NLP-driven relational analysis as a methodological innovation in education research. The study offers evidence-based recommendations for prioritizing factors that can boost system efficacy and create beneficial cross-system ripple effects, aiding in the advancement of adaptive and sustainable educational ecosystems. Full article

The kinematic and dynamic characteristics of the grinding media during the wet grinding process are investigated using a coupled Discrete Element Method (DEM)–Computational Fluid Dynamics (CFD) approach. Firstly, a coupled DEM-CFD model of the vertical spiral agitator mill is established and validated with experimental torque measurements. Subsequently, a velocity analysis model is established using the vector decomposition method. The cylinder is then divided into multiple regions along its radial and axial directions. The effects of spiral agitator rotational speed, diameter, pitch, and media filling level are investigated with respect to the circumferential velocity, axial velocity, collision frequency, effective energy between media, and energy loss of the grinding media. The average effective energy between media is an innovative metric for evaluating the grinding effect. The results indicate that the peripheral region of the spiral agitator demonstrates superior kinematic and dynamic performance. The rotational speed of the spiral agitator exerts a highly significant influence on the kinematic and dynamic characteristics of the media. With a maximum rise of 0.2 m/s in circumferential velocity and a 16.7 J gain in total energy. The media filling level demonstrates a negligible influence on media kinematics, while it profoundly affects dynamic properties, evidenced by a substantial increase of 83.09 J in the total media–media energy. As the diameter increases, the peak media circumferential velocity shifts outward, and the total media–media energy rises by 5.4 J. The spiral agitator pitch has a minimal impact on both the kinematic and dynamic characteristics of the media. Full article

The rapid diffusion of digital technologies, including big data, blockchain, and artificial intelligence, unlocks significant potential for marketing innovation in e-commerce while simultaneously raising fresh governance challenges. Digital platforms, as core infrastructures for online transactions and marketing interactions, have therefore come under increasing regulatory scrutiny amid tensions between technological progress and social stability. This study compiles a comprehensive Chinese Digital Platform Policy dataset consisting of national-level policy documents issued from 2000 through July 2025. We introduce a time-dimension topic clustering approach using density-based LDA algorithm to construct a policy corpus with reduced thematic overlap and develop a document-level policy intensity index by quantifying and aggregating the salience of domain-specific terms across documents. Validation exercises confirm the intensity measure strongly correlates with e-commerce transaction value and with digital innovation, with statistically significant lags consistent with policy implementation and firm adaptation. Beyond offering an empirically grounded metric, our analysis traces the dynamic co-evolution of regulation and technology adoption and identify composition effects—the joint influences of enabling and disciplining policy elements—on market outcomes. We argue that such effects also reconfigure the mix of marketing innovations. Collectively, the corpus and measurement framework provide a foundation for analyzing how regulatory innovation shapes the trajectory of marketing innovation and e-commerce development. Full article

Background: Nearshoring has risen after shocks and policy shifts. We synthesize evidence in a compact loop linking triggers (trade frictions, supply-chain risk, new agreements) to location choices mediated by multidimensional proximity (geographic, institutional, organizational, social, cognitive, functional) to components (manufacturing footprint, Foreign Direct Investment (FDI), employment) and outcomes (spillovers, productivity, innovation) conditioned by absorptive capacity and institutions. Methods: We conducted a literature review using major bibliographic databases. A staged screening pipeline (deduplication, pre-eligibility, and title–abstract screening) preceded full-text coding aligned with the review framework (triggers, proximity, components, outcomes, mediators). Studies were appraised with a five-criterion checklist, and themes were consolidated with basic bibliometric checks. Results: Evidence is North Atlantic and manufacturing-centric. Supply-chain disruptions dominate triggers; non-geographic proximity strongly moderates relocation. FDI anchors ecosystems, while employment effects are lagged and compositional. Strong capability and policy mixes yield broader spillovers; otherwise, benefits remain enclave-like. Sustainability and transformative outcomes are rarely assessed. Conclusions: The loop clarifies feedback from outcomes to future siting. Firms should build proximity beyond geography and pair early FDI with supplier and skills upgrading; policymakers should align instruments to governance, capability formation, and logistics. Research should expand Global South coverage and integrate environmental and inclusion metrics. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

In order to improve detection accuracy while minimizing computational overhead, a modified algorithm is proposed based on the YOLO11n baseline. The innovation incorporates a lightweight ADown module into the P4 and P5 layers of the backbone network, strategically reducing computational complexity. Simultaneously, a multi-scale attention mechanism with parallel structure is integrated into the detection head to enhance feature representation, while a micro-detection head is appended to specifically improve the detection of tiny objects. Based on the classic metrics, including parameter count, mAP@50, mAP@50-95, recall, and FPS, the ablation experiments are performed to validate the improvement of the improved algorithm on the CCTSDB2021 dataset. Furthermore, comparative experiments against traditional YOLO variants are conducted on both CCTSDB2021 and TT100K-2021 datasets. Experimental results demonstrate significant improvements across all evaluated metrics for the improved algorithm, highlighting its exceptional capability to balance high accuracy with minimal computational complexity. Full article

The exponential growth in the usage of textual data across industries and data sharing across institutions underscores the critical need for frameworks that effectively balance data utility and privacy. This paper proposes an innovative agentic AI-based framework specifically tailored for textual data, integrating user-driven qualitative inputs, differential privacy, and generative AI methodologies. The framework comprises four interlinked topics: (1) A novel quantitative approach that translates qualitative user inputs, such as textual completeness, relevance, or coherence, into precise, context-aware utility thresholds through semantic embedding and adaptive metric mapping. (2) A differential privacy-driven mechanism optimizing text embedding perturbations, dynamically balancing semantic fidelity against rigorous privacy constraints. (3) An advanced generative AI approach to synthesize and augment textual datasets, preserving semantic coherence while minimizing sensitive information leakage. (4) An adaptable dataset-dependent optimization system that autonomously profiles textual datasets, selects dataset-specific privacy strategies (e.g., anonymization, paraphrasing), and adapts in real-time to evolving privacy and utility requirements. Each topic is operationalized via specialized agentic modules with explicit mathematical formulations and inter-agent coordination, establishing a robust and adaptive solution for modern textual data challenges. Full article

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness. Full article

Amidst the paradigm shift in park city development from quantitative metrics to spatial performance, urban complex parks—a novel green space type developed privately yet fulfilling public functions—present an innovative approach to park provision in high-density urban areas. However, systematic empirical evidence on their social value remains scarce. This study characterizes urban complex parks as a new form of green public space that provides key ecosystem services and proposes a three-dimensional evaluation framework integrating “usage vitality, place attractiveness, and user satisfaction.” Analyzing 19 park-equipped complexes among 75 cases in Shanghai using LBS data and online reviews through controlled linear regression and comparative analysis, our results indicate complexes with parks were associated with significantly outperforming others in place attractiveness and user satisfaction. Key findings include associations with a 413.7 m increase in average OD distance, a 3.4–4.0% higher city-level visitor share, and 5.24 percentage points greater median positive review rate. Crucially, spatial location outweighs green ratio and size in determining social value. Ground-level parks, through superior spatial integration, function as effective “social-ecological interfaces,” significantly outperforming rooftop parks in attracting long-distance visitors, stabilizing foot traffic (≈3% lower fluctuation), and enhancing per-store visitation. This demonstrates that green space quality (experiential quality and spatial configuration) matters more than quantity. Our findings suggest that urban complex parks can create social value through perceivable naturalness and restorative environments, providing an empirical basis for optimizing park city implementation in high-density contexts and highlighting the need to reconcile broad attractiveness with equitable local access. Full article

Temporal knowledge graphs (TKGs) incorporate temporal information into traditional triplets, enhancing the dynamic representation of real-world events. Temporal knowledge graph reasoning aims to infer unknown quadruples at future timestamps through dynamic modeling and learning of nodes and edges in the knowledge graph. Existing TKG reasoning approaches often suffer from two main limitations: neglecting the influence of temporal information during entity embedding and insufficient or unreasonable processing of relational structures. To address these issues, we propose DERP, a relation-aware reasoning model with dynamic evolution mechanisms. The model enhances entity embeddings by jointly encoding time-varying and static features. It processes graph-structured data through relational graph convolutional layers, which effectively capture complex relational patterns between entities. Notably, it introduces an innovative relational-aware attention mechanism (RAGAT) that dynamically adapts the importance weights of relations between entities. This facilitates enhanced information aggregation from neighboring nodes and strengthens the model’s ability to capture local structural features. Subsequently, prediction scores are generated utilizing a convolutional decoder. The proposed model significantly enhances the accuracy of temporal knowledge graph reasoning and effectively handles dynamically evolving entity relationships. Experimental results on four public datasets demonstrate the model’s superior performance, as evidenced by strong results on standard evaluation metrics, including Mean Reciprocal Rank (MRR), Hits@1, Hits@3, and Hits@10. Full article

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article

Background: This study examines how national innovation performance shapes the diffusion of advanced diagnostic technologies across European healthcare systems. Strengthening technological capacity through innovation is increasingly essential for resilient and efficient health services. The analysis quantifies the influence of innovation capacity on the availability of medical imaging technologies in 26 EU Member States between 2018 and 2024. Methods: A balanced panel dataset was assembled from Eurostat, the European Innovation Scoreboard, and World Bank indicators. Dynamic relationships between innovation performance and the adoption of CT, MRI, gamma cameras, and PET scanners were estimated using a two-step approach combining General-to-Specific (GETS) outlier detection with Robust Least Squares regression to address heterogeneity and specification uncertainty. Results: Higher innovation scores significantly increase the diffusion of R&D-intensive technologies such as MRI and PET, while CT availability shows limited responsiveness due to market maturity. Public health expenditure supports frontier technologies when strategically targeted, whereas GDP growth has no significant effect. Population size consistently enhances technological capacity through scale and system-integration effects. Conclusions: The findings show that innovation ecosystems, rather than economic growth alone, drive the modernization of diagnostic infrastructure in the EU. Integrating innovation metrics into health-technology assessments offers a more accurate basis for designing innovation-oriented investment policies in European healthcare. Full article

Urban Green Spaces (UGSs) are essential for ecological sustainability and public health, offering benefits such as air pollution reduction, urban cooling, and recreational opportunities. However, existing evaluation tools remain inconsistent, often assessing isolated dimensions like accessibility or aesthetics without fully integrating health considerations. A systematic approach is needed to understand how these tools measure UGS quality and their relevance to health outcomes. This study employs a literature review (PRISMA framework) to analyze UGS evaluation tools with a focus on quality and health implications. A search in Scopus and Web of Science identified 14 relevant studies. Data extraction examined tool structure, assessed dimensions, data collection methods, geographic applications, and integration of health indicators. The review identified 13 distinct tools varying in complexity and methodology, from standardized checklists to GIS-based analyses. While key dimensions included accessibility, safety, aesthetics, and biodiversity, health-related factors were inconsistently integrated. Few tools explicitly assessed physical, mental, or social health outcomes. Technological innovations, such as Google Street View and AI-based analysis, emerged as enhancements for UGS evaluation. Despite methodological advances, gaps remain in linking UGS quality assessments to health outcomes. The lack of standardized health metrics limits applicability in urban planning. Future research should focus on interdisciplinary frameworks integrating environmental and health indicators to support the creation of sustainable and health-promoting UGS. Full article

The advent of digital transformation, social learning, and the increasing use of artificial intelligence is driving requisite changes in the development of data centres, which are buildings designed to process and store data. Green innovation is an integral component of the sustainable development of data centre units. Solutions utilising green and blue infrastructure in data centres are being currently introduced with the objective of optimising energy consumption and reducing energy demand. The primary aim of the research is to analyse the utilisation of biomass production and blue–green infrastructure in data centres. The article provides a consolidated set of key performance indicators (KPIs): energy efficiency, water use, waste heat utilisation, renewable energy integration, hourly carbon-free matching, embodied carbon, and land use impacts, that can be used to compare different data centre designs. Traditional PUE-centric evaluations are broadened by added metrics such as biodiversity/green area, intensity, and 24/7 CFE, reflecting the broader, multi-dimensional sustainability challenges highlighted in the current literature. Twelve international case studies described in the literature were compared and the feasibility of the Polish pilot project in Michalowo was assessed to illustrate specific cases related to energy-saving solutions and the use of renewable energy sources in data centres. Full article

The agricultural economic policy in Mexico has inadequately addressed the integrated sustainability needs of the rural sector. This study adopts a sustainability perspective to examine economic policy distortions and market failures in the export-oriented rose cultivation sector, and evaluates their effects on the economic and social sustainability of producers in Tenancingo and Villa Guerrero, Mexico. A Policy Analysis Matrix (PAM) and CONEVAL poverty line metrics were used to evaluate private and social profitability as indicators of financial viability and resource use efficiency. Findings indicate that, despite being supported by distortionary policies, the rose export sector remains competitive and financially viable, constituting a key pillar of economic sustainability. Moreover, the social profitability of rose production exceeded its private profitability, suggesting a net positive socioeconomic benefit and a sustainable allocation of resources from a societal perspective. Furthermore, per capita income in the rose production unit (RPU) exceeded the poverty line established by CONEVAL, directly supporting social sustainability and strengthening livelihood resilience. The study concludes that current resource allocation mechanisms are inefficient for sustainability over the long term. It emphasizes the need for policy shifts toward greater innovation, more effective technology transfer, improved market access, and stronger human capital to strengthen the sustainability of the sector as a whole. Rose cultivation exhibited a significant positive multiplier effect on the regional economy, reinforcing its contribution to sustainable rural development. Full article