Search Results (1,491)

This study examined a maker-based approach to sustainable digital education in physical education (PE) through a laser-shooting program implemented over a three-year period (2022–2024). While prior studies have largely focused on short-term maker-based PE interventions, less is known about how such practices are refined, stabilized, and diffused across school contexts over time. Using a qualitative case study design, data were collected from lesson plans, instructional artifacts, implementation records, field notes, and semi-structured interviews with five PE teachers, and analyzed using inductive thematic analysis. The findings suggest that, according to teachers’ accounts and classroom documentation, the program was perceived to reduce barriers to participation, diversify student roles, and improve instructional feasibility in indoor PE settings. Over time, the program evolved into a stable and adaptable instructional approach aligned with sustainable digital education, integrating physical computing into embodied learning environments. Diffusion occurred through teacher agency within informal professional networks and institutional training contexts. These findings highlight the potential of maker-based PE as a sustainable digital education approach that may support context-responsive participation, instructional adaptability, and professionally scalable innovation in school PE, with possible relevance for inclusive physical education contexts. Full article

Images of wind turbine blades captured by drones often feature complex backgrounds, and small targets such as minor defects or images have low resolution, leading to reduced recognition rates. To address environments with complex feature backgrounds, this paper proposes the PPS-MSDeim model. Based on the lightweight end-to-end detection framework DEIM-N, it introduces three core innovations to tackle the challenge of detecting small, irregular defects on wind turbine blades against complex backgrounds. First, we design an inverted multi-scale deep separable convolutional module (MDSC). After compressing channels via a bottleneck layer, it concurrently processes 3 × 3, 5 × 5, and 7 × 7 inverted deep separable convolutions. By first fusing channel information and then extracting multi-receiver-field spatial features, this approach enhances the ability to characterize morphologically variable defects while reducing computational overhead. The MDSC is then embedded into the backbone network HGNetv2. Second, we construct a Multi-Scale Feature Aggregation and Diffusion Pyramid Network (MFADPN). Through a Multi-Scale Feature Aggregation Module (MSFAM), it directly fuses features from layers P2 to P5, achieving deep integration of high-level semantics and low-level details. Combining dilated convolutions with expansion ratios of 1, 3, and 5 captures multi-level context, and a Sobel edge branch is introduced to enhance defect contours; subsequently, a feature diffusion operation is performed to distribute the enhanced features back to each level, shortening information paths and preventing signal decay; simultaneously, a high-resolution detection head is added to P2 and the P5 head is removed to improve sensitivity for small object detection. Finally, we propose the PPSformer module to replace the original Transformer encoding layer. It uses patch embedding to convert images into sequences and introduces a multi-head probabilistic sparse self-attention mechanism that focuses only on key-value pairs during attention computation. This design efficiently captures irregularly varying feature information and globally detects data anomalies induced by external defects. This study uses real engineering data sets, and the results show that PPS-MSDeim, based on DEIM, increased mAP@0.5 by 6.7%, reaching 95.1%. mAP@0.5–0.95 increased by 12.0%, reaching 70.1%. This indicates that the proposed method has a significant advantage in detecting defects in wind turbine blades. Full article

This paper presents a probabilistic–statistical approach to the analysis of diffusion processes in randomly nonhomogeneous multilayered bodies under conditions of incomplete experimental information on the boundary. The boundary condition is reconstructed from experimental data using linear regression, while the solution of the corresponding contact initial-boundary value problem is obtained in the form of a Neumann series and averaged over an ensemble of phase configurations. A system of statistical estimates for the solution is developed, including confidence intervals and two-sided critical regions, which provide complementary characteristics of uncertainty. Numerical experiments are performed for six representative samples differing in sample size, variance, and observation interval. It is shown that, despite significant differences in the statistical properties of the input data, the averaged concentration field preserves a qualitatively stable spatio-temporal structure. The results of the article address gaps in existing research by applying a probabilistic-statistical approach that consistently integrates two key elements for the analysis of diffusion processes in multilayer media. The first of these is the reconstruction of boundary conditions using linear regression to recover the conditions at the body boundary based on incomplete experimental data. The second key point is the analysis of uncertainty propagation by combining the regression model with a probabilistic analysis of the corresponding contact initial-boundary value problem, which allows us to quantitatively assess how the errors in the experimental data affect the final solution. From the point of view of mathematical modeling methods, the novelty of the approach lies in the creation of a structural-hierarchical scheme that synthesizes the approaches of mathematical statistics and the theory of random fields. The developed method is a theoretical and computational innovative basis for the analysis of specific physical and technological processes. Full article

Pulmonary bioengineering holds significant promise for the development of functional lungs suitable for transplantation in patients with terminal lung diseases; however, it encounters considerable challenges. The inherent structural complexity, diverse cellular composition, and the intricate process of re-endothelialization the pulmonary vasculature complicate efforts to reconstruct viable lungs for transplantation. This study aimed to establish an innovative re-endothelialization technique utilizing decellularized scaffolds, integrating canine yolk sac-derived endothelial precursor cells with mechanical respiratory stimuli within a bioreactor framework. Wistar rat lungs were subjected to a decellularization protocol employing SDS + Triton X-100 0.5% and subsequently assessed for cytocompatibility with murine fibroblasts (3T3) and yolk sac (YS) cells in fragments. Following this, the recellularization of the whole-lung scaffold was evaluated under constant mechanical respiratory stimulation with YS cells. Each stage of the process was rigorously analyzed using histological staining, DAPI, scanning electron microscopy (SEM), and genomic DNA quantification. The findings reveal that the implemented alternating decellularization protocol resulted in a structured scaffold conducive to the culture of various cell types in fragments. When subjected to the complete scaffold recellularization model, the results indicated that YS cells are advantageous for the re-endothelialization process. Moreover, when employed in conjunction with the bioreactor model incorporating respiratory stimulation, these cells demonstrated enhanced cellular diffusion capacity and facilitated more homogeneous recellularization of the entire organ. These results signify a notable advancement in the reconstruction of new tissues for pulmonary transplantation. Full article

Light-responsive hydrogel-based oscillators typically exhibit small oscillation amplitudes because solvent diffusion is intrinsically slow, and their dependence on external periodic light modulation further results in limited amplitude, poor stability, and insufficient autonomy. Inspired by the trigger and sliding mechanism of the ancient crossbow, this study introduces an innovative system that integrates a sliding-block mechanism with time-delay feedback, breaking from conventional approaches that rely on hydrogel inertia or external modulation, within a purely theoretical and simulation-based framework. By establishing a nonlinear dynamic model coupling solvent diffusion, photoisomerization, and optical attenuation, this research shows through numerical simulations that the system can exhibit two distinct modes under constant illumination: a stable state and a self-sustained oscillatory state. The model predicts that the oscillation frequency can be flexibly tuned by varying key parameters, including the crosslinking density, Flory–Huggins interaction parameters of the spiropyran and hydrophilic polymer, ring-opening reaction rate, light intensity, fraction of light-sensitive molecules, and sliding displacement, whereas the initial absorption coefficient has only a minor influence. The slider displacement is also identified as an effective means to regulate the oscillation amplitude. Furthermore, the expansion force at the container bottom is predicted to oscillate synchronously with the hydrogel’s volume change. This theoretical framework represents a paradigm shift from “static small deformation” to “dynamic large-amplitude oscillation”, significantly enhancing the mechanical responsiveness of the material. This work provides a novel and controllable strategy for the conceptual design of autonomous light-driven micromechanical systems. Full article

Microorganisms represent the Earth’s most abundant biomass and a vast reservoir of genetic diversity. However, traditional agar plate methods fail to recover the vast majority of these species, leaving a “microbial dark matter” that holds immense potential for the discovery of novel antibiotics and bioactive compounds. While conventional techniques such as selective media and enrichment culture remain foundational, they are inherently limited by community biases and the inability to support low-abundance, oligotrophic species. To address these bottlenecks, a diverse array of innovative isolation strategies has emerged. This review systematically categorizes and evaluates these methodologies, ranging from in situ cultivation to high-resolution single-cell manipulation. We first examine membrane diffusion-based cultivation (e.g., iChip), which mimics natural microenvironments to resuscitate recalcitrant microbes. Subsequently, we explore high-throughput single-cell technologies, including microfluidics for physicochemical separation, optical tweezers for precise manipulation, and fluorescence-activated cell sorting (FACS). Special attention is given to Raman-activated cell sorting (RACS) as a label-free functional screening tool and reverse genomics for targeted capture. By synthesizing the strengths and limitations of these approaches, we propose integrated workflows designed to accelerate the mining of untapped microbial resources. Full article

Against the backdrop of China’s “dual carbon” goals and rising global uncertainties, new energy technology innovation plays a critical role in advancing low-carbon transitions and ensuring energy security. However, existing studies mainly focus on single policy instruments, with limited attention to the causal effects of comprehensive, location-based policies. This study treats the establishment of National Green Industrial Parks (GIPs) as a quasi-natural experiment and employs a multi-period difference-in-differences (DID) approach based on panel data from 289 Chinese cities over 2008–2023. The results show that GIPs significantly increase local new energy innovation by approximately 19.1%, and this effect remains robust across multiple tests. Mechanism analysis indicates that fiscal support, green innovation, and industrial agglomeration are the main driving channels. Heterogeneity analysis further reveals stronger effects in the biomass (ρ = 0.243, p < 0.01) and wind energy (ρ = 0.179, p < 0.01) sectors, as well as in cities located southeast of the Hu Huanyong Line, with higher fiscal expenditure, and in non-resource-based cities. These findings provide empirical evidence for optimizing industrial park policies and promoting energy transition through localized policy diffusion. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

With the shortage of natural aggregates and the massive accumulation of iron tailings (ITs) solid waste restricting the sustainable development of asphalt pavement engineering, replacing natural aggregates with ITs has become a promising low-carbon solution with prominent economic and social benefits. However, the poor interfacial adhesion between ITs and asphalt severely restricts the engineering application of tailings, and the micro-interaction mechanism at their interface still lacks systematic clarification, which is the key research gap addressed in this work. Different from conventional macro road performance tests, this study innovatively combined molecular dynamics (MD) simulation with microscopic characterization, including Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM), to comprehensively reveal the interfacial interaction mechanism between ITs and asphalt at the molecular and microscales. The results indicate that asphalt molecules exhibit higher aggregation concentration and diffusivity on Al₂O₃ and Fe₂O₃ surfaces than on SiO₂ surfaces, proving stronger interfacial interaction between asphalt and iron-rich oxide minerals. Moderate temperature optimizes the adhesion performance of asphalt with Al₂O₃ and Fe₂O₃, while the interfacial bonding of asphalt on CaCO₃ and SiO₂ weakens as temperature rises. The silane coupling agent KH-550 can effectively react with acidic minerals, SiO₂ minerals in ITs, which significantly increases the concentration, diffusion coefficient, and distribution uniformity of asphalt molecules at the interface. FT-IR results verify that the combination of ITs and asphalt mainly relies on physical adsorption without generating new chemical bonds. AFM tests further confirm that alkaline minerals improve the surface roughness of asphalt mastic, and KH-550 greatly enhances the micro-adhesion force of the interface. The novelty of this work lies in clarifying the mechanism of typical mineral components in ITs and revealing the modification enhancement law of silane coupling agent and alkali minerals at the micro level. This study provides a scientific theoretical support for the high-value engineering utilization of ITs in asphalt pavement, and offers a reference for optimizing the interfacial modification design of solid waste aggregate. Full article

Unmanned Aerial Vehicles (UAVs/drones) have emerged as transformative tools for coastal environmental monitoring, yet the field’s intellectual evolution and operational maturity remain incompletely characterized. This study employs citation network analysis via Litmaps to map the structure, consolidation, and knowledge diffusion patterns of coastal drone research from 2013 to 2024. A corpus of 47 influential articles was identified through systematic citation connectivity criteria, revealing three distinct phases: Seminal (≤2016), Consolidation (2017–2022), and Innovation (≥2023). Results demonstrate that foundational RGB photogrammetry protocols established in 2013–2016 remain standard references in 2024, indicating methodological maturity rather than obsolescence. However, substantial geographic concentration exists (Mediterranean institutions dominate early development), with application imbalances: temporal monitoring (46.8%) dominates while policy-relevant erosion/risk assessment comprises only 8.5%. Despite documented technical adequacy (sub-centimeter accuracy, 70–80% cost reduction vs. alternatives), the transition to operational coastal programs faces institutional rather than technological barriers. The analysis concludes that realizing UAV operational potential requires coordinated institutional development across management agencies, research institutions, capacity-building programs, and equitable data governance frameworks. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

Multivariate time series anomaly detection holds critical application value in key domains such as industrial system monitoring, financial risk management, and medical surveillance. However, existing approaches face two major challenges: reconstruction-based or prediction-based models tend to adapt to anomalous patterns during training, thereby weakening the distinction between normal and abnormal samples; furthermore, the non-stationary nature of time series leads to distribution shifts between training and testing data, impairing model generalization. To address these issues, this paper proposes the TFCID model. The model innovatively leverages diffusion principles to effectively impute missing time series data while capturing significant frequency-domain features. In the temporal processing stream, an unconditional diffusion model combined with imputation masking is employed to achieve high-precision imputation of randomly missing values, effectively preventing anomalies from interfering with model training. In the frequency-domain processing stream, an amplitude-aware frequency-domain masked autoencoder is introduced to specifically capture periodic or trend-based pattern anomalies. The model mitigates distribution shift by constraining the discrepancy between temporal and frequency-domain representations via adversarial contrastive learning, and uses this discrepancy as a robust anomaly scoring metric. Experimental results on five public benchmark datasets show that TFCID significantly outperforms state-of-the-art methods in detection accuracy (F1-Score), validating its effectiveness in anomaly detection tasks. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

Since 1978, Chinese–foreign cooperation in running schools (CFCRS) has evolved from fragmented pilot initiatives into a policy-coordinated system of higher education internationalization. This study employs an exploratory sequential mixed-methods design to examine how national policy shifts reshaped the structure of CFCRS collaboration networks between 1978 and 2025. Integrating longitudinal policy analysis with Social Network Analysis (SNA), the research identifies five policy-driven stages: exploratory opening, legal institutionalization, regulated development, quality enhancement, and strategic repositioning. Network analysis shows that increasing density, expanding degree centrality of leading institutions, and greater diversification of international partners reflect growing integration into global transnational higher education networks. At the same time, persistent structural concentration in key institutional hubs and regulated entry into partnerships indicate strong path dependence shaped by state-steered governance. The network also exhibits a disciplinary shift toward engineering and STEM collaborations aligned with national innovation strategies, alongside gradual spatial diffusion from coastal regions toward central and western provinces. Conceptually, the findings demonstrate that state-coordinated internationalization can generate dense and diversified collaboration networks without fully liberalizing governance structures. The CFCRS case thus illustrates a model of hybrid governance, where centralized policy coordination coexists with expanding network-based international partnerships. Full article

Multi-source environmental data are increasingly used for measurement, reporting and verification, and for coordinating low-carbon innovation across interorganizational networks. However, voluntary data sharing remains limited because participants face asymmetric costs, leakage and compliance risks, and uncertainty in value capture. This study develops a network evolutionary game model to examine how cooperative data sharing emerges and stabilizes in green innovation networks. We specify a two-strategy game in which heterogeneous agents choose between sharing and withholding. The payoff structure integrates private innovation gains from their own data, cross-partner synergy, external incentives, fixed governance costs, and stock-scaled sharing and risk burdens. Agents interact on a Barabási–Albert scale-free network and update strategies via local imitation under a Fermi rule. Simulations show that cooperation can diffuse from low initial participation and converge to a high-sharing regime when benefit allocation and incentive intensity jointly offset cost and risk frictions. Several governance levers exhibit threshold-type effects, including the allocation share, the opportunity loss of non-sharing, and the marginal cost–risk burden. Multi-source synergy and subsidies further raise the attainable cooperation level, but with diminishing marginal returns. Degree heterogeneity accelerates diffusion once hub organizations adopt sharing, while also raising fairness concerns when benefits concentrate on central nodes. Overall, the findings provide green-innovation-specific governance conditions that translate threshold regions into implementable design targets for sustainable environmental data sharing. Full article