Search Results (22,391)

Foreign Direct Investment (FDI, hereafter) as a determinant of economic growth has received significant attention in both the theoretical and empirical research literature due to its numerous benefits. However, the FDI–growth relationship is rarely researched for the economy of Saudi Arabia. Amid this backdrop in the literature, this paper focuses on Saudi Arabia to provide fresh, comprehensive evidence about the FDI–growth relationship. Our analysis is based on time series data for the period 1975–2023, which were collected from credible global sources. For estimation, the study adopted ARDL modeling, which is suitable for time series data as it produces both long-run relationships and short-run dynamics simultaneously. Our results show that FDI inflows have a positive and statistically significant influence on economic growth in the long run. Similarly, in the long run, both human capital and trade openness have also improved the long-run growth of Saudi Arabia. Furthermore, a positive and statistically significant influence of natural resources on economic growth is observed in the long run. Moreover, the results show that total factor productivity and domestic investment have not had the desirable influences on economic growth. The short-run results show that the growth performance of Saudi Arabia could be explained by natural resources, domestic investment and human capital. The causality analysis also confirmed a one-way relationship running from FDI inflows towards economic growth. Our results have a significant policy implication for the policymakers of Saudi Arabia. Full article

The aim of this study was to evaluate the effect of adding white mulberry (Morus alba L.) fruit to extruded corn snacks on their polyphenol profile, antioxidant properties, acetylcholinesterase (AChE) inhibitory activity and the preservation of phenolic compounds in an in vitro digestion model. Mixtures of corn grits with 0, 10, 15 and 20% dried mulberry fruit were extruded at temperatures of 100, 120 and 140 °C, and then the total polyphenol content (TPC) and antioxidant activity (IC₅₀ for DPPH) were determined. For selected samples (0%, 140—3E; 15% mulberry, 140—9E; mulberry—13E), further antioxidant tests (FRAP, CUPRAC, Fe²⁺ chelation) were performed, the phenolic compound profile (UHPLC) and AChE inhibition were assessed, and a two-step in vitro digestion was conducted. The addition of mulberry significantly increased TPC- and free-radical-scavenging capacity compared to the control sample, with snacks containing 15% mulberry extruded at 140 °C showing approximately a 3.5-fold higher TPC than the control, while dried mulberry fruit itself exhibited about a five-fold higher TPC than this enriched snack. Among the snacks, the most favorable DPPH-radical-scavenging effect was obtained for the variant with 20% mulberry at 120 °C (IC₅₀ = 0.176 mg/mL), whereas the mulberry fruit extract reached an IC₅₀ of 0.0926 mg/mL. In a two-step in vitro digestion model, the mulberry-enriched snack with 15% fruit retained 69.3% of its initial TPC after the gastric phase and 33.3% after the intestinal phase, compared with 55.0% and 20.0%, respectively, for the control snack, confirming a partial but meaningful preservation of phenolic compounds under simulated gastrointestinal conditions. UHPLC analysis confirmed that mulberry and the enriched snacks are a rich source of chlorogenic acids and their isomers, as well as quercetin and kaempferol glycosides, which largely survived the two-step in vitro digestion, despite an observed decrease in TPC after the gastric stage and a further reduction after the intestinal stage. At the same time, mulberry extract and mulberry-enriched snacks exhibited high antioxidant activity in all tests conducted and in vitro AChE inhibitory activity, suggesting that Morus alba L. fruit has the potential to be used as a natural functional ingredient in the production of gluten-free snacks with antioxidant and potentially neuroprotective properties. Full article

Background/Objectives: Current hemoglobin management in neurocritical care relies on static transfusion thresholds, which fail to capture the dynamic nature of hemoglobin changes during ICU care. We aimed to determine whether distinct longitudinal hemoglobin trajectory phenotypes exist among neurosurgical ICU patients and whether specific trajectory patterns independently predict unfavorable neurological outcomes. Methods: In this retrospective observational cohort study, we analyzed 8517 patients admitted to the neurosurgical ICU of a tertiary academic medical center between January 2015 and December 2024. A feature-based Gaussian mixture model was applied to trajectory-derived hemoglobin features over the first 14 ICU days to identify distinct hemoglobin trajectory phenotypes. The association between trajectory class and neurological outcomes was evaluated using propensity score matching. Results: Six distinct hemoglobin trajectory phenotypes were identified. The “Rapid Dropper” phenotype (Class 2; n = 351, 4.1%), characterized by the steepest decline velocity and highest variability, showed dramatically worse outcomes: 36.5% unfavorable neurological outcome (Glasgow Outcome Scale 1–3) versus 3.1% in all other classes combined (odds ratio [OR], 18.10; 95% confidence interval [CI], 14.08–23.27). This association persisted after propensity score matching (OR, 2.40; 95% CI, 1.77–3.26; p < 0.001). Hemorrhagic diagnoses were disproportionately concentrated in this high-risk phenotype. A combined prediction model incorporating trajectory-derived features within 72 h achieved an area under the receiver operating characteristic curve of 0.850 (95% CI, 0.829–0.870). This value reflects retrospective full-trajectory phenotyping; in a 72 h landmark analysis, early-feature prediction achieved an AUC of approximately 0.72. Conclusions: Hemoglobin trajectory phenotyping identified a high-risk “Rapid Dropper” subgroup that was significantly associated with worse short-term neurological outcomes. The rate of hemoglobin decline, rather than any single threshold, was associated with prognostic separation; prospective and external validation is required before these associations can inform transfusion strategy. Full article

Background/Objectives: Curcumin shows broad anticancer activity but limited clinical success as a standalone agent because of poor bioavailability and inconsistent tumor exposure. This review introduces the concept of curcumin as a molecular synergy amplifier and proposes that successful combinations depend on three interdependent determinants: mechanistic complementarity, suppression of adaptive resistance networks, and pharmacokinetic synchronization. Methods: Evidence on combinations with chemotherapeutics, natural bioactives, and nanotechnology-enabled delivery systems was critically evaluated, with emphasis on mechanism, resistance reversal, drug ratio, administration sequence, and tumor exposure. Results: Curcumin enhances therapeutic efficacy by sensitizing cancer cells, suppressing adaptive resistance pathways, targeting cancer stemness, and promoting multiple forms of programmed cell death. Importantly, analysis of current evidence indicates that therapeutic success depends not only on molecular synergy but also on pharmacokinetic synchronization between curcumin and partner agents. Many combinations demonstrating strong in vitro synergy fail to translate in vivo because optimal drug ratios, timing, and tumor exposure cannot be maintained. Nanotechnology-based co-delivery systems partially overcome these limitations through synchronized delivery and controlled release. Conclusions: Curcumin should be viewed as a molecular synergy amplifier whose clinical utility depends on mechanistic complementarity and pharmacokinetic synchronization with co-administered therapies. This framework provides a rationale for the design of next-generation curcumin-based combination therapies and identifies key priorities for clinical translation. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease (CLD) globally and is closely linked to metabolic risk factors and systemic inflammation. Emerging evidence suggests that vitamin D deficiency may influence MASLD severity and outcomes, though limited real-world data often assess long-term clinical outcomes in MASLD patients stratified by vitamin D status. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (2006–2025). Adult patients with MASLD were stratified into two cohorts based on serum 25-hydroxyvitamin D levels: normal (≥30 ng/mL) and deficient (<20 ng/mL). Patients with other CLD, malignancy, decompensated cirrhosis, and relevant confounding conditions were excluded. Primary outcomes included all-cause mortality, hospital readmissions, and ICU admissions at 1-year and 5-year follow-up. Results: After propensity score matching, 6959 patients were included in each cohort. Compared with patients with normal vitamin D levels, those with vitamin D deficiency had significantly higher rates of hospital readmissions, ICU admissions, and all-cause mortality at both 1-year and 5-year follow-up. A 1 year, readmissions occurred in 10% vs. 6%, ICU admissions 2.6% vs. 1.2%, and mortality 1.5% vs. 0.5% of patients (p = 0.01). Similar findings were observed at 5 years, with higher rates of readmissions 15% vs. 10%, ICU admissions 4.4% vs. 2.4% and mortality 3.2% vs. 1.3% in the vitamin D-deficient cohort (p = 0.01). Conclusions: Vitamin D deficiency was associated with significantly increased mortality, hospital readmissions, and ICU admissions among patients with MASLD. Our findings suggest that vitamin D status may represent a valuable prognostic indicator in this population. Although the observational nature of this study precluded establishing causality, our results support the consideration of routine assessment of vitamin D levels in patients with MASLD. Further prospective and mechanistic studies are needed to determine whether vitamin D supplementation can improve outcomes in this population. Full article

Particle size distribution (PSD) is a key determinant of aerosol optical properties and plays an important role in optical sensing and environmental monitoring. However, estimating PSD from light scattering measurements remains a challenging inverse problem due to its ill-posed nature and sensitivity to noise. To achieve the objective, this study proposed a physics-informed, data-driven inversion framework for PSD reconstruction using multi-angle light scattering signals generated from Mie scattering simulations. Synthetic datasets were generated using Johnson–SB, lognormal, and bimodal lognormal PSDs under various optical conditions, and the resulting scattering intensities were used to train machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). The proposed framework was evaluated using both point-wise error metrics and distribution-based metrics, including Kullback–Leibler divergence and Wasserstein distance. The results showed that RF and XGBoost consistently achieved the highest reconstruction accuracy, with R² values exceeding 0.98 across different PSDs, and significantly outperformed conventional linear baseline methods, including Ridge regression (representing Tikhonov regularization) and Non-negative Least Squares (NNLS). Additional experiments using lognormal and bimodal lognormal PSDs further confirmed the distributional generalization capability of the proposed model. The reconstructed PSDs also showed strong agreement with the reference distributions and remained robust under Gaussian, lognormal, and combined noise perturbations of up to 20%. Therefore, integrating physics-based scattering simulations with machine learning provided an accurate and robust solution for the inverse Mie scattering problem in optical particle characterization. Full article

Accurate determination of endodormancy release is essential for grapevine dormancy management. However, most phenological models are validated only against macroscopic budbreak dates, without physiological verification of predicted release dates. Here, we integrated phenological modeling with biochemical profiling to characterize endodormancy release in two table grape cultivars with contrasting chilling requirements: ‘Muscat Hamburg’ (Vitis vinifera L.) and ‘Shine Muscat’ (Vitis labrusca × V. vinifera). Endodormancy release dates were determined by forced budbreak assays, and chilling and heat requirements were estimated from 5 min temperature records using the Dynamic Model and Growing Degree Hours. ‘Muscat Hamburg’ released endodormancy on December 16 (10.95 Chill Portions), whereas ‘Shine Muscat’ released on January 6 (22.78 CP). Around these dates, coordinated biochemical changes occurred in buds, including starch depletion, hexose accumulation, ABA decline, GA₃ increase, and redox-related changes in H₂O₂ content and CAT activity. These changes were more pronounced in buds than in canes and were not identical across all biochemical indicators. Hydrogen cyanamide treatment induced biochemical changes similar to those observed during natural dormancy release, with cultivar-specific responses consistent across both conditions. These results indicate that experimentally determined endodormancy release dates are associated with population-level physiological changes, supporting the integration of phenological modeling with biochemical characterization in table grape production. Full article

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RG_ratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R² = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated. Full article

Currently, a major challenge for European economies is the volatility of electricity prices, which affects costs borne by households and firms, as well as inflation, economic competitiveness, and energy security. Although the literature has analysed various determinants of electricity prices, there is still limited evidence on the comparative short- and long-term effects of fiscal factors, the natural gas market, and the transition to renewable energy within the Member States of the European Union. This paper analyses the relationship between household electricity prices and a set of economic, climate, and fiscal determinants in EU countries over the period 2017–2025, using panel data econometric methods. The methodology includes pooled OLS models, fixed and random effects estimators, unit root tests, cross-sectional dependence (Pesaran CD) tests, cointegration analysis, and a Panel ARDL-PMG framework, complemented by robustness checks using FMOLS and DOLS-type estimators. The results indicate the existence of a stable long-run equilibrium relationship between the analysed variables, as well as significant cross-sectional dependence among countries, reflecting common shocks and interconnected dynamics in EU energy markets. Fixed effects models are used as the baseline specification, while PMG-ARDL and other dynamic estimators are employed for robustness analysis. The results are consistent across different econometric specifications. The conclusions highlight the dominant role of Household Gas Prices as the main determinant of electricity prices, while energy productivity shows a positive association with electricity price levels. Climate variables exhibit weak and unstable effects, and environmental taxes do not show statistically significant impacts within the sample period. Overall, the findings underline the importance of energy market dynamics, structural factors, and the ongoing energy transition in shaping electricity price developments in the European Union. Full article

The endothermic nature of methanol steam reforming (MSR) for hydrogen production induces varying thermal effects along the flow direction, resulting in a non-uniform temperature distribution within the catalytic bed. Optimizing temperature uniformity has been demonstrated to enhance hydrogen production efficiency. In this study, a novel Fresnel lens-driven non-evacuated tapered cavity solar reactor was proposed for methanol steam reforming, which can provide a reference for optimizing hydrogen production using Fresnel lens solar concentrators. The thermal flux distribution on the reactor’s inner walls was determined by Monte Carlo ray-tracing simulations. A three-dimensional CFD model integrating fluid flow, heat and mass transfer, and methanol steam reforming reaction kinetics was developed to investigate the effects of key operational parameters on this novel reactor performance. Multi-objective optimization using response surface methodology revealed that high reactant inlet temperature (T_in > 550 K) and low flow velocity (u_in < 0.2 m/s) conditions significantly improve reactor methanol conversion (99.99%) and hydrogen yield (91.48%), but at the cost of increased CO selectivity (S_CO > 28%). Conversely, low temperature (T_in < 500 K) and high flow velocity (u_in > 0.4 m/s) conditions suppress CO formation (S_CO < 0.03%), although with reduced hydrogen production efficiency. Full article

The rapid evolution of Padel in Saudi Arabia (SA) has positioned the sport as a popular recreational and social activity, mainly among young adults. However, limited research has examined how different forms of sport motivation are associated with mental health outcomes in this emerging context. Drawing on Self-Determination Theory (SDT), this study investigated the associations between intrinsic and extrinsic motivation and depression, stress, and anxiety among Padel players in SA. A quantitative, cross-sectional online survey was conducted with a sample of 475 players, the majority of whom were aged 17–35 and held at least a bachelor’s degree. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to evaluate the relationships between multidimensional motivation factors and mental health symptoms. The findings revealed a nuanced, at times paradoxical, pattern of relationships. Intrinsic motivation to experience stimulation (engaging in an activity because of the positive sensations, excitement, enjoyment, or stimulation that the activity itself provides, rather than for external rewards or pressures) was consistently associated with lower levels of depression, stress, and anxiety, suggesting that enjoyment-driven involvement is associated with better mental health outcomes. In contrast, intrinsic motivation to accomplish was positively correlated with all three mental health indicators, indicating that achievement-oriented engagement might intensify emotional pressure. Among extrinsic motivations, external regulation was significantly associated with poorer mental health outcomes. In contrast, introjected regulation unexpectedly displayed a negative association with psychological distress, demonstrating a potentially adaptive role in this setting. Identified regulation, however, was not significantly associated with any mental health symptoms. These results underscore the “double-edged” nature of sport motivation, showing that not all internal or external motives yield uniformly positive consequences. The study contributed to the growing literature by providing a context-specific understanding of how motivational dynamics function within a rapidly growing sport in Saudi Arabia. In practice, the findings suggested that enjoyment-based involvement was associated with more favourable mental health outcomes, whereas performance-related pressures might be associated with less favourable outcomes. Full article

Predicting pain in physical rehabilitation is challenging due to subjectivity, patient variability, and self-report bias, especially in telerehabilitation. This study aims to determine whether machine-learning models based on heart rate (HR), heart rate variability (HRV), and peripheral oxygen saturation (SpO₂) can reliably detect clinically meaningful pain during real rehabilitation sessions, including home-based settings where self-report is least reliable; we hypothesized that these low-cost, non-invasive markers carry sufficient information to flag low-to-moderate pain episodes without relying on self-report. We combined these markers with machine-learning models. These markers were selected for their association with autonomic pain responses and ease of measurement with only two low-cost, non-invasive sensors (a wearable band providing HR and HRV, and a fingertip oximeter providing SpO₂) suitable for clinical and home-based rehabilitation. We evaluated linear regression (LR), random forest (RF), and artificial neural networks (ANNs) using data from 25 participants (aged 20–50) undergoing lower-limb rehabilitation. Signals acquired at 1 Hz were processed via temporal filtering, quality screening, and three missing-value strategies (interpolation, zero imputation, deletion) before normalization and training. LR showed limited predictive power. RF achieved 97.77% accuracy in detecting low-pain episodes, and balanced per-class performance under deletion (76.64%). ANN models contributed a more balanced three-class profile on interpolated data but remained sensitive to class imbalance. Given high-pain scarcity in supervised therapy and underreporting at home, reliable detection of low-to-moderate pain enables timely therapy adjustments. Unlike prior studies using experimentally induced pain, this work captured naturally occurring pain during real rehabilitation, making findings applicable to clinical and telerehabilitation contexts. Physiology-based models with low-cost sensors show promise for personalized rehabilitation, improving adherence and enabling proactive adjustments without added complexity. Full article

Structural health monitoring (SHM) is essential for assessing the safety and serviceability of bridge structures. Identifying progressive and concurrent damage remains challenging due to the complex and continuous nature of structural deterioration. This study proposes a hybrid one-dimensional convolutional neural network and long short-term memory (1D-CNN–LSTM) framework for vibration-based damage localisation and severity estimation in reinforced concrete bridges. Operational modal analysis is applied to field-measured vibration data from an in-service bridge. A finite element model is updated using particle swarm optimisation, reducing frequency discrepancies from 7–17% to within ±3%. Progressive single-, double-, and triple-element damage scenarios are simulated through systematic stiffness degradation. The resulting modal frequency data are used to train 1D-CNN–LSTM models using Pareto front optimisation. The proposed framework achieves coefficients of determination above 0.80 with low prediction errors (MSE and MAE < 2) for single- and double-element damage scenarios. The results support the use of the proposed framework for screening-level assessment of bridge damage under controlled simulated conditions. Full article

In soils, mercury is found bound to organic matter, clays, and iron/manganese oxides, which are also major constituents of archaeological pottery. Although pottery is the most researched cultural material with archaeometric techniques, its mercury content remains largely unexplored. To address this gap, we studied Late Neolithic Penha-type pottery from NW Spain. The Late Neolithic was a period of widespread exploitation and circulation of mercury-bearing resources. A total of 92 samples from five archaeological sites were analysed to determine their mercury, carbon, sulfur, and iron content, as well as their spectroscopic properties (FTIR-ATR). Mercury was detected in all samples, with concentrations ranging from 6 to 1086 ng g⁻¹. Neither organic matter (C and S) nor iron compounds (Fe) were found to explain Hg concentrations, suggesting that diagenetic mercury incorporation was unlikely. Mercury was found to be related to kaolinite structural transformations, with concentrations decreasing with increasing degree of transformation. Kaolinite transformation depended on firing conditions (temperature and time), pointing to thermal desorption of the mercury present in the clay. The large observed variability most probably resulted from poorly controlled firing conditions. Nevertheless, whether mercury content reflects unintentional incorporation from naturally mercury-rich raw materials or a deliberate selection or addition (e.g., of cinnabar) during pottery manufacture remains to be further explored. Full article

Hepatitis E virus (HEV) is an emerging zoonotic pathogen in Europe, mainly transmitted via consumption of naturally contaminated food or contact with infected animals. People living in the Balkans have diverse dietary habits, with high pork consumption in some countries, making this region a relevant setting for investigating HEV seroprevalence and its possible determinants. The current study aimed to estimate pooled HEV seroprevalence among adults in the general population and blood donors and to assess factors associated with regional variation. Twenty-eight eligible studies were identified from PubMed, Scopus, and Web of Science following the PRISMA guidelines. Pooled prevalence estimates were calculated using a random-effects meta-analysis of proportions implemented via a generalized linear mixed model (GLMM) with logit transformation. Potential factors associated with HEV seroprevalence, including national pork consumption, serological assay type, population group, year of publication, sex, and country, were evaluated. The pooled anti-HEV seroprevalence was estimated to be 5.68% (95% CI: 3.48–9.12%), with substantial heterogeneity. Country-specific estimates ranged from 1.01% in Greece to 26.66% in Bulgaria. Subgroup analyses showed significant variation according to national pork consumption category, serological assay type, year of publication, and country. However, meta-regression indicated that methodological and temporal factors, particularly serological assay type and year of publication, were the main significant moderators, whereas national pork consumption was not independently associated with seropositivity. Therefore, pork consumption should be interpreted as an exploratory ecological indicator rather than as evidence of a direct association. The methodological differences contribute substantially to the variability in HEV seroprevalence across the Balkans, emphasizing the need for standardized diagnostic approaches within a One Health framework. Full article