Search Results (5,494)

This study investigated the relationship between digital governance and sustainable development across the European Union (EU-27) during the period 2015–2023. Although digital transformation has become a central policy priority, empirical evidence on how e-government adoption contributes to sustainability performance remains limited. Using panel data from Eurostat and the UN Sustainable Development Solutions Network, the analysis employed advanced econometric techniques, including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Method of Moments Quantile Regression (MMQR), to explore both long-run relationships and heterogeneous effects across countries. The model incorporates key indicators such as the percentage of individuals using e-government services, Gross Domestic Product (GDP) per capita growth, and Research and Development (R&D) expenditure, capturing, respectively, digital governance adoption, innovation potential, and economic capacity, as essential drivers of sustainable development. Results indicate a strong and statistically significant positive association between digital governance adoption and sustainable development outcomes. The quantile regression analysis reveals that this effect is more pronounced in countries with higher innovation intensity and stronger economic capacity, suggesting that digital governance amplifies sustainability benefits in countries with more advanced institutional and technological infrastructures. Robustness checks confirm the stability of the findings across multiple estimation techniques. The results underscore the need for inclusive and innovation-driven digital strategies to ensure that the benefits of digital governance are equitably distributed, ultimately enhancing the EU’s progress towards the Sustainable Development Goals. Full article

Grassland degradation is a critical ecological problem worldwide that threatens ecosystem integrity and functional services. Although previous studies have documented the drivers of climate change, overgrazing, and anthropogenic perturbation, research concerning the impact of invasive alien plants on grassland ecosystems remains limited. The present study, integrating pairwise field investigation of Ageratina adenophora invasion and non-invasion plots across heterogeneous grassland types (tropical grasslands [TG]; tropical shrub-grasslands [TS]; warm-temperate grasslands [WG]; and warm-temperate shrub-grasslands [WS]) and A. adenophora indigenous plants phytotoxicity bioassay, aims to assess the invasibility and resilience of heterogeneous grassland landscapes to A. adenophora invasion. The field investigation demonstrated the greater vulnerability of TG and TS to A. adenophora invasion, whereas WG and WS possessed higher resilience. In addition, regression analysis revealed significant reductions of the Shannon–Wiener index and the Pielou index as the A. adenophora’s important value reached the threshold 0.36. Bioassay showed that A. adenophora aqueous extracts inhibit seed germination and seedling growth of recipient plants, with Saccharum arundinaceum exhibiting the highest tolerance to A. adenophora stress. In summary, our findings not only highlight the flora communities’ dynamics and invasibility of diverse grasslands driven by A. adenophora invasion in subtropical regions but also verify S. arundinaceum’s potential for A. adenophora replacement management. Full article

Hydroxyapatite-based ceramics are widely used in dental bioengineering, yet the reliable assessment of endotoxin activity in solid porous materials remains challenging. This study evaluated the applicability of a whole blood Monocyte Activation Test (MAT) to a hydroxyapatite ceramic relevant to dental applications. Two endotoxin association strategies (immersion and dropwise) were compared, followed by nonlinear modelling of cytokine dose–response curves using four-parameter logistic (4PL) regression and spike-recovery analysis to assess potential material interference. Immersion-based spiking produced reproducible, concentration-dependent cytokine responses, whereas dropwise application resulted in minimal functional recovery. IL-1β, IL-6, and TNF-α displayed sigmoidal dose–response profiles with high goodness-of-fit values (R² ≥ 0.93). Spike recovery remained within the 50–200% acceptance range for most concentrations, with IL-6 showing the most consistent analytical performance. TNF-α exhibited signal saturation at higher endotoxin levels, limiting its dynamic range. Multiplex cytokine profiling confirmed that classical MAT readouts were among the most strongly induced mediators and that hydroxyapatite did not trigger baseline inflammatory activation. These findings demonstrate that whole blood MAT can be reliably applied to hydroxyapatite-based dental ceramics when immersion-based endotoxin association and material-specific methodological optimization are employed. Full article

As the global aging population increases, the early detection and prevention of Alzheimer’s disease (AD) have become important in public health. To solve the problems of subjectivity and low timeliness of traditional assessment methods, this paper proposes a multimodal dementia prevention system that combines physiological sensing, a gamification interface, and a classification model. The system includes an interactive joystick to measure pulse and blood pressure. A Chinese music game app increases the participation of the elderly and reduces their sense of rejection through gamification interaction. After the physiological data were standardized by Z-score, they were input into three small sample classifiers (Gaussian Naïve Bayes, Fisher Linear Discriminant Analysis, and Logistic Regression) for the binary classification of AD. The system performance was evaluated using the Leave-One-Out cross-validation method. Experimental results show that Logistic Regression performed best in situations with extremely small samples and class imbalance, with an F1-score of 0.700, which was higher than the other two. Dynamic features and model fusion technologies need to be integrated to further enhance the clinical application potential of the system in the early prediction of dementia. Full article

Neonatal health requires precise lipid quantification in human milk to ensure proper nutritional development. Traditional manual methods, such as the creamatocrit, are limited by human-induced bias and significant measurement uncertainty. This study presents a low-cost Computer Vision System acting as an automated optical sensing modality for estimate the cream fraction (c) using advanced Machine Learning regression, which is subsequently used to derive fat and energy quantification through established analytical equations. The system is optimized for the Gold-LED spectrum, which enhances the dynamic range to 226 a.u. for robust feature extraction. We evaluated 28 distinct ML regression models across three feature spaces (Gray Scale, RGB, and Combined). The results, based on 6400 samples, demonstrate that the Rational Quadratic GPR model achieved the highest predictive stability with a coefficient of determination of $R^2=0.867$. This computational framework achieved a 57.5% reduction in relative error compared to manual benchmarks. SHAP analysis indicates that the model selectively attributes higher importance to Red channel intensities and Blue contrast gradients, which correspond to the optical scattering characteristics of lipid globules. These findings validate the system as a stable sensing modality for non-invasive quantification. The proposed architecture integrates cost-effective hardware with high-precision analytical modeling, offering a reagent-free and operationally feasible alternative for standardized nutritional assessment in neonatal intensive care units and milk banks. Full article

For decades, Saudi Arabia has relied heavily on oil revenues to support its economic growth. While this strategy brought substantial benefits, oil prices and global demand remain volatile, and oil itself is a non-renewable resource. These realities raise important concerns about long-term economic sustainability. In response, the country has pursued economic diversification to reduce risk and build a more resilient growth model. This study examines how the roles of the oil and non-oil sectors in driving GDP growth evolved between 1970 and 2024. To capture differences across economic conditions, the study applies both four and ten quantile regression models. These approaches allow us to observe how sectoral contributions change across low, moderate, and high growth periods. The results show that oil sector growth remains positive and significant across the distribution of GDP growth, with a stronger effect during periods of higher growth. At the same time, the non-oil sector is gaining importance, not only in stronger growth conditions, but is also cushioning the economy in periods of low growth. This signals gradual structural progress toward a more balanced and sustainable economy. The two-state Markov-switching model further identifies two persistent growth regimes: one more oil-dependent and another relatively more diversified. However, oil continues to play a meaningful role in both regimes. Overall, the findings suggest a gradual, steady transition rather than a sharp structural break. For long-term sustainability, Saudi Arabia needs to continue strengthening the productivity, resilience, and competitiveness of its non-oil sectors through its oil revenues accrued during periods of high growth. The implications of this study would be beneficial for all resource-rich economies aiming at economic diversification. Full article

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure–activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm–multiple linear regression (GA–MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure–activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery. Full article

Coastal provinces in eastern China are experiencing rapid urbanization that challenges ecosystem services and low-carbon development. In this study, Zhejiang, Fujian, and Guangdong Provinces were selected, and the influence of land use/land cover change (LUCC) on carbon storage and its spatial heterogeneity was quantified. LUCC datasets for 2000, 2005, 2010, 2015, and 2020 were compiled to describe land-use dynamics over 2000–2020. Carbon storage was estimated with the InVEST model. Land-use patterns for 2035 were simulated using the PLUS model under three scenarios: natural development, ecological protection, and development priority. Spatial autocorrelation analysis and multiscale geographically weighted regression (MGWR) were then used to determine the key drivers of spatial variability in carbon storage. Between 2000 and 2020, farmland, forest, grassland, and unused land showed an overall decline, while water bodies and tt-up land expanded; together, these changes corresponded to a carbon-storage loss of 121.19 Tg. Carbon density exhibited pronounced spatial clustering, with higher values concentrated in mountainous and less urbanized areas; built-up expansion and forest degradation were the primary contributors to carbon loss. By 2035, total carbon storage is projected to decrease by 74.67 Tg under natural development and by 108.54 Tg under development priority, whereas ecological protection is projected to yield the smallest decline (35.71 Tg). These results underscore the importance of sustainable coastal land-use planning and integrated coastal zone management, which balance development and ecosystem services by prioritizing ecological protection, curbing built-up expansion, and promoting forest restoration. Such nature-based solutions can enhance carbon sequestration, strengthen climate resilience, and support China’s low-carbon transition toward its dual-carbon targets. Full article

Background: Mitochondrial dysfunction is central to the pathogenesis of acute myocardial infarction (AMI), but mitochondria-related molecular biomarkers and mechanisms remain incompletely defined. This study aimed to identify mitochondria-associated biomarkers in AMI and elucidate their functional roles in mitochondrial dynamics, extracellular matrix (ECM) remodeling, and cardiac protection. Methods: Two GEO datasets (GSE19322, GSE71906) were analyzed to identify mitochondria-related differentially expressed genes (DE-MRGs) by intersecting DEGs with MitoCarta3.0 genes. Functional enrichment (GO/KEGG), LASSO regression, ROC curves, and nomogram modeling were employed to screen biomarkers. Immune infiltration profiling, GeneMANIA, GSEA, TF-mRNA and ceRNA network construction, and drug prediction analyses were performed. Expression validation was conducted via RT-qPCR, Western blot (WB), and immunohistochemistry (IHC) in murine AMI models and hypoxia-induced cardiomyocytes. Functional assays assessed cardiac performance (echocardiography), infarct size (TTC staining), fibrosis (Masson/Sirius red), oxidative stress (ROS), and ECM remodeling (MMP9/TIMP1 axis). Results: We identified 295 DE-MRGs, enriched in oxidative phosphorylation and mitochondrial metabolic pathways. Machine learning and validation analyses pinpointed MTFP1 and DNAJC28 as AMI biomarkers with strong diagnostic accuracy. In vivo and in vitro studies confirmed marked downregulation of MTFP1 post-AMI and under hypoxia. AAV9-mediated MTFP1 overexpression improved cardiac function, reduced infarct size, attenuated fibrosis, and decreased ROS levels. Mechanistically, MTFP1 upregulated phosphorylated DRP1 (Ser616) without altering total DRP1, balanced MMP9/TIMP1 activity, and suppressed fibrosis markers (COL1A1, α-SMA). Gelatin zymography indicated that MMP9 activation remained restrained despite elevated pro-MMP9, consistent with TIMP1-mediated regulation. Hypoxia-induced cardiomyocytes showed similar antifibrotic and antioxidative responses following MTFP1 overexpression. Conclusions: Our study identified MTFP1 as a novel mitochondria-related biomarker and therapeutic modulator in AMI. MTFP1 exerts cardioprotective effects by restoring mitochondrial fission balance and ECM remodeling through the p-DRP1/MMP9/TIMP1 signaling axis, attenuating fibrosis and oxidative stress. These findings provide mechanistic insight into mitochondria-targeted cardioprotection and highlight MTFP1 as a promising diagnostic and therapeutic target for AMI. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

This study examines the wage effects of educational mismatch in China by jointly addressing sample selection, endogeneity, and nonlinear career-stage heterogeneity within a unified econometric framework. Although educational mismatch has been widely studied, existing evidence largely relies on linear models that overlook experience-dependent wage dynamics and potential selection and endogeneity biases. Using data from the 2020 wave of the China Family Panel Studies (CFPS), this study extends the Duncan–Hoffman model by integrating a sample-selection-corrected threshold regression estimated via instrumental variables. This approach allows the identification of experience thresholds at which the wage effects of overeducation and undereducation differ across regimes. The results reveal pronounced nonlinearities in mismatch-related wage differentials. Overeducation is associated with wage penalties at early career stages, but these penalties weaken and, in some cases, disappear once workers surpass the estimated experience threshold. In contrast, undereducation yields modest wage premiums early in the career but becomes increasingly penalized at higher experience levels. Substantial gender heterogeneity is also observed: male workers are better able to use accumulated experience to offset educational shortfalls, whereas female workers face more persistent penalties, particularly at later career stages. Full article

Biocrusts are critical yet threatened components of dryland ecosystems, and predicting their functional trait dynamics under future scenarios is essential for conservation planning. Using 129 occurrence localities and 84 trait sampling sites across three precipitation zones in China’s Mu Us Sandland, we combined MaxEnt habitat modeling with Random Forest regression to predict biocrust functional traits—including coverage, thickness, and total volume for both moss and cyanobacterial crusts—under current conditions and 12 future climate–land use scenarios (four SSPs × three time periods: 2050s–2090s). Soil nitrogen, annual precipitation, and soil potassium emerged as key environmental drivers of biocrust habitat distribution. Currently, moss crusts cover 7.63% of the study area (thickness: 10.56 mm) and cyanobacterial crusts cover 5.88% (thickness: 4.88 mm), with a total biocrust volume of 4629 × 10⁴ m³. Across the emission and policy gradient, functional traits exhibited contrasting responses: coverage showed scenario-dependent declines, while thickness remained relatively stable. Under SSP126, moss coverage declined by 3.32% and cyanobacterial coverage by 2.80% by the 2070s, with total volume decreasing by 2064.76 × 10⁴ m³; by the 2090s, moss coverage partially recovered (+0.26%). In contrast, SSP370 and SSP585 projected sustained losses without recovery. A striking divergence emerged: cyanobacterial thickness increased consistently (+0.02 to +0.23 mm) even as coverage declined, while moss thickness fluctuated within ±0.13 mm. Notably, high-precipitation transitional zones (362–434 mm) exhibited the greatest vulnerability, with moss coverage declining 3× more under SSP126 than SSP585 by the 2070s and volume losses persisting through the 2090s. These findings provide spatially explicit predictions of biocrust traits and quantitative baselines for prioritizing conservation in transitional zones facing accelerating environmental pressures. Full article

The glioma tumor microenvironment (TME) exhibits profound heterogeneity that drives tumor progression and therapy resistance. By integrating single-cell RNA sequencing (eleven samples) and spatial transcriptomics (two samples), the cellular components of the glioma microenvironment were deconvoluted, revealing tumor-associated foam cells (TAFCs) as the most abundant and centrally connected subtype. The high expression of two prognostic candidate genes, growth differentiation factor 8 (GDF-8, also known as myostatin, MSTN) and transcription factor 12 (TCF12), in TAFCs was found to be correlated with poor overall survival. These two genes were associated with M2 macrophage infiltration, altered cholesterol homeostasis, and immunosuppressive signaling. Regulatory network and pathway analyses, based on computational motif enrichment and co-expression analysis, linked them to ribosome, Notch signaling, DNA repair, and cell-cycle pathways. Pseudotime trajectories revealed dynamic expression during differentiation. Additionally, drug sensitivity prediction analysis demonstrated that MSTN expression was significantly associated with sensitivity to paclitaxel and VE-822, while TCF12 expression showed potential associations with sensitivity to cytarabine, olaparib, Wee1 inhibitor, paclitaxel, and VE-822. Logistic regression analysis combining clinical parameters with MSTN and TCF12 expression effectively achieved risk stratification for glioma, with higher composite scores predicting worse 2- and 3-year survival outcomes. Calibration curves demonstrated high consistency between nomogram-predicted overall survival probabilities and actual observed outcomes. Immunofluorescence confirmed upregulated expression of MSTN and TCF12 in glioma tissues and their co-localization with macrophages. In conclusion, this study identified TAFCs as the central cells in the glioma microenvironment, with their signature genes MSTN and TCF12 representing candidate immunometabolic signatures associated with macrophage-mediated immunosuppression and metabolic reprogramming in glioma, suggesting their potential as biomarkers for patient stratification and as targets for immunometabolic therapies. Full article

The goal of this paper was to develop an approach to managing the investment mechanism in an innovative financial economy, which would fit the modern era of global uncertainty. To achieve this, we conducted trend, correlation, and regression analyses of risk management in venture investing in BRICS+ based on statistics for the period of global uncertainty (2014–2025). The compiled econometric model of the effectiveness of risk management in venture investing in the innovative financial economy of BRICS+ amid global uncertainty highlighted differences in approaches to managing the investment mechanism in this economy, depending on the level of risk it entails. In the age of free trade, the approach involved the use of the two tools of risk management of venture investing within the state management of an innovative economy: acceleration of economic growth and energy transition. In the current age of global uncertainty, there is a need for a new approach. It is developed in this paper and involves the use of market management tools: high-tech exports and the export of intellectual property objects. The perspectives of accelerating the development of an innovative financial economy of BRICS+ in the age of global uncertainty include the revision of the approach to the management of the investment mechanism in an innovative financial economy. For this, it is recommended to increase revenues from selling rights for intellectual property objects at a higher rate compared to recent years and to make a transition to an increase in the share of high-tech exports in the structure of industrial exports. The advantages of the proprietary model include the disclosure of the poorly studied experience of developing countries, accounting for global uncertainty (in the world economy), and a larger period of empirical research of the economies of the countries of BRICS+, which encompasses 2014–2025 and ensures a fuller and more precise and reliable interpretation of the dynamics of risks of venture investing and return on the measures of risk management in these countries. Full article

Dynamic Message Signs (DMSs) play a critical role in conveying real-time traffic information to drivers; however, their effectiveness heavily relies on how messages are structured and displayed, particularly regarding phasing duration and content length. This study examines the influence of these two factors on driver readability, comprehension, and gaze behavior using an advanced virtual reality (VR) driving simulator. Controlled experiments simulated four DMS scenarios, combining two phasing intervals (2.5 and 4 s) with short and long message formats, adhering to Michigan Department of Transportation (MDOT) guidelines. The experiment integrated eye-tracking technology to measure fixation duration and frequency, while statistical methods, including survival analysis and LASSO regression, were employed to identify significant predictors of message readability. Results revealed that shorter messages with shorter phasing intervals led to the highest comprehension rates and reduced cognitive strain. Furthermore, individual characteristics such as gender, driving speed, and highway driving experience significantly affected how drivers engaged with DMS messages. These findings contribute to the development of more effective DMS deployment strategies and provide practical design recommendations to enhance traffic safety and information delivery on high-speed roadways. Full article