Search Results (373,105)

Cultivating talent with digital literacy and the capacity for sustainable innovation performance is a critical mission of higher education. Merely enhancing digital skills does not necessarily lead to innovation, and the mechanisms underlying this transformation remain to be clarified. This study adopts human capital theory as its analytical framework and constructs a parallel mediation model, focusing on the dual mediation pathways of proactive social behavior and critical thinking dispositions as “soft skill” mediators. Using a sample of 18,534 undergraduate students from Chinese research universities, the study employed a questionnaire survey and analyzed the data using structural equation modeling and the Bootstrap method to examine the relationship between digital literacy and sustainable innovation performance, as well as the roles of the two mediation pathways. The results indicate a significant positive correlation between Chinese university students’ digital literacy and their sustainable innovation performance. Proactive social behavior and critical thinking dispositions exhibit significant mediating effects between the two variables, forming a dual-path mechanism in which the direct path effect is stronger. The findings deepen our understanding of how digital literacy relates to sustainable innovation performance and provide a reference for higher education institutions in cultivating talent for sustainable innovation. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m² area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially. Full article

Toll-like receptors (TLRs) are essential components of the innate immune system that enable host cells to sense microbial and endogenous danger signals and to initiate inflammatory and antimicrobial responses. Activation of TLRs triggers complex intracellular signaling networks that culminate in the induction of pro-inflammatory cytokines, type I interferons, and co-stimulatory molecules. In addition to the well-characterized nuclear factor κB (NF-κB) and interferon regulatory factor (IRF) pathways, mitogen-activated protein kinases (MAPKs) play a critical modulatory role in TLR signaling. MAPK/ERK kinase (MEK) inhibitors were originally developed for the treatment of cancer and are widely used in clinical oncology. Accumulating evidence indicates that pharmacological inhibition of MEK/extracellular signal regulated kinase (ERK) signaling profoundly affects immune cell function and TLR-driven responses. Depending on timing, dose, and disease context, MEK inhibition can attenuate excessive inflammation but may also interfere with protective host defense mechanisms. This duality highlights the context-dependent role of MEK/ERK signaling in infection and inflammation. In this review, I summarize current knowledge on the integration of MEK/ERK signaling into TLR-mediated innate immune responses and discuss the immunological consequences of MEK inhibition in infectious and inflammatory settings. By synthesizing mechanistic and translational studies, I aim to provide a framework for understanding MEK inhibitors as immune modulators rather than as broadly acting anti-inflammatory agents. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

According to the “Technical Specification for Air Quality Testing in Archives Repositories,” air pollutants in archives can be categorized into exogenous and endogenous pollutants. Common exogenous pollutants include sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and hydrogen sulfide (H₂S), while endogenous pollutants mainly consist of formaldehyde (HCHO) and acetic acid (CH₃COOH). This study combines external electric field technology with density functional theory (DFT) and the B3LYP method to theoretically analyze the spectral characteristics and degradation mechanisms of these six pollutant gases. Molecular models of the six gases were constructed using Gaussian software. The configurations of five pollutant gas molecules (SO₂, NO₂, O₃, H₂S, and HCHO) were optimized using the B3LYP/6-31G(d) basis set, while the configuration of acetic acid was optimized using the B3LYP/3-21G basis set, yielding their stable structures and spectral information. The study found that characteristic peaks in the spectra shifted under the influence of an electric field. Additionally, by scanning the potential energy surfaces of selected molecular bonds under varying electric field strengths along specific directions, the required external electric field strengths for the degradation of the six common pollutant gases in archives were determined as follows: 0.1050 a.u. for SO₂, 0.0975 a.u. for NO₂, 0.0925 a.u. for O₃, 0.1000 a.u. for H₂S, 0.1500 a.u. for HCHO, and 0.0705 a.u. for CH₃COOH. The results clarify the degradation thresholds of these six pollutant gases under an external electric field. The findings indicate that acetic acid (0.0705 a.u.) and ozone (0.0925 a.u.) are highly sensitive to electric fields, while formaldehyde requires the strongest electric field (0.1500 a.u.) for degradation. These results provide a reference and theoretical foundation for electric field-assisted degradation technology targeting pollutant gases in archives. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

Private Networks or Standalone Non-Public Networks (SNPNs) are essential for Industry 4.0 and enterprise connectivity. However, most existing studies rely on simulations, evaluate only a single radio access technology, or report raw key performance indicators (KPIs) without an interpretable quality assessment framework. In practical deployment, operators require measurement-driven evidence to assess the performance and feasibility of 4G LTE and 5G SNPN solutions. This study presents a controlled experimental comparison of software-defined radio (SDR)-based 4G LTE and 5G SNPNs using the same Universal Software Radio Peripheral (USRP) platform, Open5GS, srsRAN, and commercial off-the-shelf user equipment (COTS-UE). The evaluation was conducted in an indoor environment under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Experimental iPerf3 results show that the SDR-based 5G SNPN achieves higher downlink and uplink throughput than the SDR-based 4G LTE SNPN across all tested scenarios. The 5G deployment reaches up to 55 Mbps downlink and 40.5 Mbps uplink under LOS conditions, while maintaining 42 Mbps downlink and 28 Mbps uplink under NLOS conditions. Furthermore, 5G achieves lower latency than 4G LTE, with average values ranging from 21 ms to 31 ms. To provide interpretable network quality assessment, a Mamdani fuzzy logic-based Network Quality Index (NQI) with 81 inference rules is proposed to map signal-to-interference-plus-noise ratio (SINR), throughput, latency, and jitter into linguistic quality levels. The proposed approach enables nonlinear integration of heterogeneous KPIs and provides a technology-agnostic framework for practical SNPN deployment. Full article

Soil salinity inhibits biological nitrogen fixation (BNF) in legumes, compromising nitrogen nutrition and crop productivity. This study evaluated whether two halotolerant Mesorhizobium ciceri strains (S1, S2) can sustain BNF and alleviate moderate salt stress (100 mM NaCl) in three Tunisian chickpea (Cicer arietinum L.) cultivars (Amdoun, Béja 1, and Nour). Inoculated and non-inoculated plants were grown under controlled conditions. Salinity reduced shoot dry weight by 37.5–42% and severely impaired nodulation (≈60% reduction) in non-inoculated plants. Bacterial inoculation significantly increased germination rate, shoot and root biomass, and nodule number compared to non-inoculated salt-stressed controls. Improved nodulation corresponded to better nitrogen nutrition, reflected by higher leaf chlorophyll content (a proxy for nitrogen status). However, direct measurements of nitrogenase activity (e.g., acetylene reduction assay) are needed to confirm enhanced BNF. Inoculated seedlings also exhibited lower oxidative stress markers (hydrogen peroxide and malondialdehyde) and enhanced antioxidant enzyme activities (superoxide dismutase and glutathione peroxidase), indicating reduced reactive oxygen species damage. Cultivar-specific responses were observed: Amdoun responded best to S1, Béja 1 to S2 for biomass recovery, while Nour showed strong antioxidant induction but limited growth gain. We conclude that halotolerant M. ciceri strains improve chickpea performance under salt stress primarily by sustaining BNF and nodulation, thereby maintaining nitrogen nutrition. Strain–cultivar compatibility is critical for optimizing this bio-inoculant strategy in saline agroecosystems. Our findings identify the combination of cultivar Béja 1 with strain S2 as the most promising for biomass recovery under moderate salinity, providing a practical, strain–cultivar matching framework that can guide the development of effective bio-inoculants for chickpea production in salt-affected areas of Tunisia and similar Mediterranean regions. Full article

We propose a quantitative, spatially explicit framework for assessing local self-sufficiency and resilience within the Water–Energy–Food (WEF) Nexus. The methodology introduces normalized, per capita indicators that quantify the degree of dependence on local versus external resources, explicitly incorporating physical availability, renewability, energy requirements, infrastructure, and land-use constraints. In contrast to conventional composite indices, the proposed framework adopts a non-compensatory structure, whereby deficiencies in one sector cannot be offset by surpluses in another, reflecting the physical constraints of the nexus. Indicator values range from 0 (complete dependence on external resources) to 1 (full local self-sufficiency) and are formulated dynamically, enabling comparison across existing conditions and alternative infrastructural or policy scenarios. The framework is applied as a proof of concept to a small rural settlement in North Euboea, Greece. The results indicate substantial potential for food and renewable energy self-sufficiency under optimized infrastructure configurations, while also revealing critical vulnerabilities associated with groundwater-dependent water supply and seasonal energy imbalances. The analysis further demonstrates how spatial proximity, energy–water coupling, and land-use competition jointly constrain achievable self-sufficiency levels, highlighting trade-offs that are often overlooked in sectoral or purely volumetric assessments. By explicitly linking resource flows with spatial proximity and infrastructural choices, the proposed indicators provide a robust and transparent tool for resilience-oriented planning under conditions of climatic, environmental, and systemic uncertainty. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

This study presents an approach to quantifying impact-induced damage severity in composites, focusing on synthetic carbon fibre-reinforced polymer (CFRP), natural flax fibre-reinforced polymer (FFRP) and hybrid fibre reinforced polymer (HFRP) composite of carbon and flax. The investigation aims to quantitatively characterise impact damage under energies ranging from 10 to 70 J through acousto–ultrasonics (AU) testing, proposing an efficient technique for evaluating the integrity of various FRP composites under in-service conditions. AU testing was performed at azimuthal angles of 0°, 30°, 45°, 60° and 90°, utilising acousto–ultrasonic waveform indices (AUWIs), such as wave velocity, peak amplitude, energy content, centroid frequency and skewness factor. The damage severity index is correlated with the damage mode. The findings establish that wave velocity is a reliable parameter for quantifying damage severity across all composite material types considered, with high adjusted R² values of 0.92 for CFRP, 0.89 for FFRP and 0.90 for HFRP. Peak amplitude also shows considerable sensitivity. Finally, this research highlights the limitations of traditional non-destructive evaluation (NDE) techniques and demonstrates the potential of combining multi-damage metrics with advanced imaging methods, such as X-ray micro-computed tomography (X-ray µCT) and scanning electron microscopy (SEM), to provide a comprehensive assessment of damage in various composite materials. The proposed methodology offers a promising approach for quantifying the impact damage severity in composite structures, as applicable to wind turbine blades, amongst other structural components. Full article

Populations of European bison (Bison bonasus), Eurasian beaver (Castor fiber), and Eurasian moose (Alces alces) in Poland are currently experiencing significant growth. These species are subject to strict legal protection or specific regulatory frameworks. The purpose of the study is to analyze Polish legislation concerning the protection of selected species and to identify legislative actions that could ensure healthy, sustainable, and well-managed population levels in Poland. The study also explores carefully regulated forms of sustainable use, including the potential consumption of meat from these species. During this research, the methodology of analysis and scientific interpretation of legal acts was used. Case law and relevant socio-economic and environmental factors were also analyzed and highlighted. The results show that the law currently in force and its interpretation may pose challenges to achieving fully effective conservation outcomes. Wildlife protection requires effective, locally adapted population management. Proposals for legal changes that would support diversified and sustainable management approaches, while maintaining a high level of protection, ensuring environmental stability and sustainability, and ensuring the highest standards of public safety, are presented. De lege ferenda postulates indicate that it is essential to balance the legitimate interests of wildlife conservation, public health, and society. Full article

Al-Cu-Fe quasicrystalline coatings were deposited on AISI 321 stainless steel substrates by high-velocity oxy-fuel (HVOF) spraying at oxygen pressures of 3.0, 3.5, and 4.0 bar. The influence of oxygen pressure on the phase composition, microstructure, porosity, corrosion behavior, thermal stability, and microhardness of the coatings was investigated using X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS), ImageJ porosity analysis, electrochemical corrosion testing in 3.5 wt.% NaCl solution, simultaneous thermal analysis (TGA/DSC), and microhardness measurements. XRD analysis revealed the formation of quasicrystalline-related intermetallic phases together with Al, Fe₃Al₁₃, FeAl, Fe₃O₄, CuFe₂O₄, Cu₂O, and CuO phases. The coating deposited at 3.5 bar exhibited the lowest porosity (5.37%), the most homogeneous microstructure, and the largest residual coating thickness after corrosion testing. SEM and EDS analyses indicated that corrosion preferentially initiated at pores, splat boundaries, and phase interfaces, while the coating produced at 3.5 bar demonstrated the most stable surface condition after exposure to a 3.5 wt.% NaCl solution. Thermal analysis showed that all coatings remained stable up to 900 °C. Sample (a) exhibited the lowest mass loss and the highest thermal stability, whereas sample (b) demonstrated the most favorable combination of structural integrity, phase ordering, coating density, corrosion-related performance, and thermal stability. Microhardness values of the coatings ranged from 754 to 778 HV, significantly exceeding that of the AISI 321 substrate. The results demonstrate that oxygen pressure is a critical parameter controlling the microstructure and functional properties of HVOF-sprayed Al-Cu-Fe coatings, with 3.5 bar providing the most balanced set of properties. Full article