Search Results (1,186)

The growing interest in probiotic bacteria within the food industry is driven by their recognized health benefits for consumers. However, preserving their therapeutic viability and stability during gastrointestinal transit remains a formidable challenge. Hence, this research aimed to enhance the viability of Lactobacillus reuteri through microencapsulation using a binary polysaccharide mixture composed of low acyl gellan gum (LAG), high acyl gellan gum (HAG), and calcium for the microencapsulation of L. reuteri. To achieve this, the Box–Behnken design was applied, targeting the optimization of L. reuteri microencapsulated to withstand simulated gastrointestinal conditions. The microcapsules were crafted using the internal ionic gelation method, and optimization was performed using response surface methodology (RSM) based on the Box–Behnken design. The model demonstrated robust predictive power, with R² values exceeding 95% and a lack of fit greater than p > 0.05. Under optimized conditions—0.88% (w/v) LAG, 0.43% (w/v) HAG, and 24.44 mM Ca—L. reuteri reached a viability of 97.43% following the encapsulation process. After 4 h of exposure to simulated gastric fluid (SGF) and intestinal fluid (SIF), the encapsulated cells maintained a viable count of 8.02 log CFU/mL. These promising results underscore the potential of biopolymer-based microcapsules, such as those containing LAG and HAG, as an innovative approach for safeguarding probiotics during gastrointestinal passage, paving the way for new probiotic-enriched food products. Full article

X-ray and EUV solar flare emission cause increases in the Earth’s dayside ionospheric electron density. While the response of the lower ionosphere to X-rays is well studied, the delay between EUV flare emission and the response of the ionospheric F-region has not been investigated. Here, we calculate the delays between incident He II 304 Å emission, and the TEC response for 10 powerful solar flares, all of which exhibit delays under 1 min. We assess these delays in relation to multiple solar and geophysical factors, and find a strong negative correlation (∼−0.85) between delay and He II flux change and a moderate negative correlation (∼−0.55) with rate of increase in He II flux. Additionally, flare magnitude and the X-ray-to-He II flux ratio at peak He II emission show strong negative correlations with delay (∼−0.80 and ∼−0.75, respectively). We also identify longer delays for flares occurring closer to the summer solstice. These results may have applications in upper-ionospheric recombination rate calculations, atmospheric modelling, and other solar–terrestrial studies. We highlight the importance of incident EUV and X-ray flux parameters on the response time of the ionospheric electron content, and these findings may also have implications for mitigating disruptions in communication and navigation systems. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

To address the freezing damage to tunnel lining caused by frost heaving of the surrounding rock in water-rich tunnels in cold regions, a numerical thermo-mechanical coupling model for tunnel-surrounding rock that considers the anisotropy of frost heave deformation was established by examining overall frost heaves in a freeze–thaw cycle. Using a COMSOL Multiphysics 6.0 platform and the sequential coupling method, the temperature field evolution of tunnel-surrounding rock, freezing cycle development, and distribution characteristics of the frost heaving force of a tunnel lining under different minimum temperatures, numbers of negative temperature days, frost heave ratios, and anisotropy coefficients of frost heave deformation were systematically simulated. The results revealed that the response of the temperature field of tunnel-surrounding rock to the external temperature varies spatially with time lags, the shallow surface temperatures and the area around the lining fluctuate with the climate, and the temperature of the deep surrounding rock is dominated by the geothermal gradient. The extent of the freezing cycle and the frost heaving force increase significantly when lowering the minimum temperature. The maximum frost heaving force usually occurs in the region of the side wall and the spring line, and tensile stress is prone to be generated at the spring line; the influence of slight fluctuations in the minimum temperature or the short shift in the coldest day on the frost heaving force is limited. A substantial increase in frost heaving force is observed with higher frost heave ratios; for example, an increase from 0.25% to 2.0% results in a 116% rise at the sidewall. Although the increase in the anisotropy coefficient of frost heave deformation does not change the overall distribution pattern of frost heaving force, it can exacerbate the directional concentration of frost heave strain, which can increase the frost heaving force at the periphery of the top arch of the lining. This study revealed the distribution pattern and key influencing factors of the freezing cycle and frost heaving force for tunnels, providing a theoretical basis and data reference for the frost resistance design of tunnels in cold regions. Full article

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

To address critical technical challenges in coalbed methane fracturing, including the uncontrollable release rate of conventional breaker agents and incomplete gel breaking, this study designs and fabricates an intelligent controlled-release breaker system based on paraffin-coated mesoporous silica nanoparticle carriers. Three types of mesoporous silica (MSN) carriers with distinct pore sizes are synthesized via the sol-gel method using CTAB, P123, and F127 as structure-directing agents, respectively. Following hydrophobic modification with octyltriethoxysilane, n-butanol breaker agents are loaded into the carriers, and a temperature-responsive controlled-release system is constructed via paraffin coating technology. The pore size distribution was analyzed by the BJH model, confirming that the average pore diameters of CTAB-MSNs, P123-MSNs, and F127-MSNs were 5.18 nm, 6.36 nm, and 6.40 nm, respectively. The BET specific surface areas were 686.08, 853.17, and 946.89 m²/g, exhibiting an increasing trend with the increase in pore size. Drug-loading performance studies reveal that at the optimal loading concentration of 30 mg/mL, the loading efficiencies of n-butanol on the three carriers reach 28.6%, 35.2%, and 38.9%, respectively. The release behavior study under simulated reservoir temperature conditions (85 °C) reveals that the paraffin-coated system exhibits a distinct three-stage release pattern: a lag phase (0–1 h) caused by paraffin encapsulation, a rapid release phase (1–8 h) induced by high-temperature concentration diffusion, and a sustained release phase (8–30 h) attributed to nano-mesoporous characteristics. This intelligent controlled-release breaker demonstrates excellent temporal compatibility with coalbed methane fracturing processes, providing a novel technical solution for the efficient and clean development of coalbed methane. Full article

The measurement of national well-being has become central to both academic and policy debates, particularly within the framework of sustainable development. In this context, this study investigates the relationship between macroeconomic conditions and subjective well-being in Portugal. Using annual data from 2004 to 2022, we explore the effects of GDP per capita, unemployment, and inflation on the Global Well-Being Index (GWBI). Employing ordinary least squares (OLS) regression, the results indicate a significant positive relationship between GDP per capita and subjective well-being, while inflation is negatively associated. Contrary to expectations, the unemployment rate showed a positive and significant association with the GWBI. This counterintuitive result may reflect institutional buffering effects, such as social safety nets, strong family structures, or lagged responses in perceptions of well-being. Similar patterns were observed in other southern European countries with strong informal social support systems. These findings contribute to a deeper understanding of how economic indicators relate to perceived well-being, particularly in the context of a southern European country. The study offers relevant insights for public policy, including the alignment of macroeconomic management with the Sustainable Development Goals (SDGs), especially SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth). Full article

Multisensory integration is crucial for effective cognitive functioning, especially in complex tasks such as those requiring rapid audiovisual information processing. High-level martial arts routine athletes, trained in integrating visual and auditory cues for performance, may exhibit superior abilities in cross-audiovisual integration. This study aimed to explore whether these athletes demonstrate an expert advantage effect in audiovisual integration, particularly focusing on whether this advantage is due to enhanced automatic auditory processing. A total of 165 participants (81 male, 84 female) were included in three experiments. Experiment 1 (n = 63) used a cross-audiovisual Rapid Serial Visual Presentation (RSVP) paradigm to compare the martial arts routine athlete group (n = 31) with a control group (n = 33) in tasks requiring target stimulus identification under audiovisual congruent and incongruent conditions. Experiment 2 (n = 52) manipulated the synchronicity of auditory stimuli to differentiate between audiovisual integration and auditory alerting effects. Experiment 3 (n = 50) combined surprise and post-surprise tests to investigate the role of automatic auditory processing in this expert advantage. Experiment 1 revealed that martial arts routine athletes outperformed the control group, especially in semantically incongruent conditions, with significantly higher accuracy at both lag3 (p < 0.001, 95% CI = [0.165, 0.275]) and lag8 (p < 0.001, 95% CI = [0.242, 0.435]). Experiment 2 found no significant difference between groups in response to the manipulation of auditory stimulus synchronicity, ruling out an alerting effect. In Experiment 3, martial arts routine athletes demonstrated better performance in reporting unexpected auditory stimuli during the surprise test, indicating enhanced automatic processing capacity. Additionally, a significant improvement in working memory re-selection was observed in the martial arts routine group. The expert advantage effect observed in martial arts routine athletes is attributable to enhanced cross-audiovisual integration, independent of an auditory alerting mechanism. Long-term training improves the efficiency of working memory re-selection and the ability to inhibit conflicting information, suggesting that the expanded capacity for automatic auditory processing underpins their multisensory integration advantage. Full article

Background/Objectives: mRNA vaccines introduced during the COVID-19 pandemic were a significant step forward in the rapid development and deployment of vaccines in a global pandemic context. These vaccines showed good protective efficacy, but—due to limited breadth of the immune response—they required frequent boosters with manufactured spike sequences that often lagged behind the circulating strains. In order to enhance the breadth, durability, and magnitude of immune responses, we studied the effect of combining priming with an RNA vaccine technology with boosting with protein/adjuvant using a TLR4-agonist based adjuvant. Methods: Specifically, four proprietary adjuvants (EmT4^TM, LiT4Q^TM, MiT4^TM, and AlT4^TM) were investigated in combination with multiple modes of SARS-CoV-2 vaccination (protein, peptide, RNA) for their effectiveness in boosting antibody responses to SARS-CoV-2 spike protein in murine models. Results: Results showed significant improvement in immune response strength and breadth—especially against more distant SARS-CoV-2 variants such as Omicron—when adjuvants were used in combination with boosters following an RNA vaccine prime. Conclusions: The use of novel TLR4 adjuvants in combination with protein or RNA vaccinations presents a promising strategy for improving the efficacy of vaccines in the event of future pandemics, by leveraging rapid response using an RNA vaccine prime and following up with protein/adjuvant-based vaccines to enhance the breadth of immunity. Full article

This study addresses real-world scenarios such as impacts from hailstones, gravel, or external debris, where multiple simultaneous impacts occur on composite structures. The effects of multiple impacts are analyzed in terms of time lag between impacts, energy levels, and the relative positions of the impacts. Quasi-simultaneous impacts with varying energies are performed, and their results are compared in terms of energy dissipation, delaminated area, and material response. It is observed that, beyond a certain distance between impacts, two simultaneous impacts become equivalent to two mono-impacts. Additionally, a specific energy threshold per projectile exists beyond which the effect of multiple impacts becomes negligible. The findings highlight that the parameter time interval between impacts is the most significant factor in multi-impact studies, unlike in mono-impact scenarios where this parameter does not exist. The study underscores the importance of impact energy, the number of simultaneous impacts and their positions in the evolution of delamination and damage in composite materials. This article provides experimental data on simultaneous multi-impacts that researchers could use to validate their models using a high-complexity problem. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

The relationship between trade openness and economic performance has been widely debated and researched during the last several decades. However, the specific influence of trade openness from the perspective of controlling the inflation rate is rarely researched specifically for the Kingdom of Saudi Arabia (KSA). Accordingly, this research paper attempts to test the influence of trade openness on inflation, focusing on KSA. The paper utilizes historical data from 1975 to 2023 and employs the “Autoregressive Distributed Lag (ARDL)” and “Nonlinear Autoregressive Distributed Lag (NARDL)” cointegration techniques to assess the responsiveness of the inflation rate to increased trade openness. The results of the ARDL demonstrated the positive influence that trade openness has on inflation, which is a rejection of Romer’s hypothesis. The findings of the NARDL also rejected Romer’s hypothesis by demonstrating a positive relationship between the positive shocks in trade openness and the inflation rate. Similarly, our results illustrated a significant negative impact of domestic industrialization and government expenditure on inflation. Moreover, we found that the inflation rate in KSA is significantly dependent on economic performance. Finally, our findings demonstrated that the natural resource sector is unable to explain the inflationary pressure in KSA significantly. Full article

This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and long-term responses of condition indicators to shocks in oil temperature, offering a robust framework for a counterfactual analysis. To complement the time-domain perspective, we applied a wavelet coherence analysis (WCA) to explore time–frequency co-movements and phase relationships between the condition indicators under varying operational regimes. The DARDL results revealed that the ball energy, cage energy, and inner and outer race indicators significantly increased in response to the oil temperature in the long run. The WCA results further confirmed the positive association between oil temperature and the condition indicators under examination, aligning with the DARDL estimations. The DARDL model revealed that the ball energy and the inner race energy have statistically significant long-term effects on the oil temperature, with p-values < 0.01. The adjusted R² of 0.785 and the root mean square error (MSE) of 0.008 confirm the model’s robustness. The wavelet coherence analysis showed strong time–frequency correlations, especially in the 8–16 scale range, while the frequency-domain causality (FDC) tests confirmed a bidirectional influence between the oil temperature and several condition indicators. The FDC analysis showed that the oil temperature significantly affected the BGCIs, with evidence of feedback effects, suggesting a mutual dependency. These findings contribute to the advancement of predictive maintenance frameworks in HUMSs by providing practical insights for enhancing system reliability and optimizing maintenance schedules. The integration of dynamic econometric approaches demonstrates a robust methodology for monitoring critical mechanical components and encourages further research in broader aerospace and industrial contexts. Full article