Search Results (18,024)

Wind power output fluctuations, driven by variable wind speeds, create significant challenges for grid stability and the efficient use of wind turbines, particularly in high-wind-penetration areas. This study proposes a combined approach utilizing an ultra-capacitor energy storage system and fuzzy-control-based pitch angle adjustment to address these challenges. The fuzzy control system dynamically responds to wind speed variations, optimizing energy capture while minimizing mechanical stress on turbine components, and the ultra-capacitor provides instantaneous buffering of power surpluses and deficits. Simulations conducted on a 50 kW DFIG wind turbine powering a 23 kW load demonstrated a substantial reduction in power fluctuations by a factor of 3.747, decreasing the power fluctuation reduction scale from 13.04% to 3.48%. These results highlight the effectiveness of the proposed system in improving the stability, reliability, and quality of wind energy, thereby advancing the broader adoption of renewable energy and contributing to sustainable energy solutions. Full article

Karst aquifers in the Mediterranean region are crucial for water supply and agriculture but are increasingly threatened by climate change and overexploitation. The Jebel Zaghouan aquifer, historically significant for supplying Carthage and Tunis, serves as the focus of this study, which aims to evaluate its availability, quality, and vulnerability to ensure its long-term sustainability. To achieve this, various methods were employed, including APLIS and COP for recharge assessment and vulnerability mapping, SPEI and SGI drought indices, and stable and radioactive isotope analysis. The findings revealed severe groundwater depletion, primarily caused by overexploitation linked to urban expansion. Minimal recharge was observed, even during wet periods. APLIS analysis indicated moderate infiltration rates, consistent with prior reservoir models and the MEDKAM map. Isotopic analysis highlighted recharge from the Atlantic and mixed rainfall, while Tritium and Carbon-14 dating showed a mix of ancient and recent water, emphasizing the aquifer’s complex hydrodynamics. COP mapping classified 80% of the area as moderately vulnerable. Monitoring of nitrate levels indicated fluctuations, with peaks during wet years at Sidi Medien Spring, necessitating control measures to safeguard water quality amid agricultural activities. This study provides valuable insights into the aquifer’s dynamics, guiding sustainable management and preservation efforts. Full article

This study examined the impact of the long-term application (25 years) of tea waste (TW), compost (COM), and neem oil cake (NOC) compared to conventional synthetic fertilizers (CONV) on soil thermal and physical properties of a tea-cultivated Ultisol. Soil samples were collected from 0–15 cm and 15–30 cm depths of an experimental site of the Tea Research Institute in Sri Lanka. These samples were analyzed for soil thermal conductivity (k), volumetric heat capacity (C), thermal diffusivity (D), bulk density (BD), aggregate stability, soil organic carbon (SOC), and volumetric water contents at 0 kPa (θ₀) and 10 kPa (θ₁₀). TW and COM significantly (p < 0.05) increased surface SOC, leading to better aggregation, lower BD, and, consequently, a substantial reduction in k and D compared to CONV plots. Further, TW and COM amendments slightly increased C compared to CONV plots due to elevated SOC and water content. However, NOC had no impact on soil thermal and physical properties compared to CONV. The reduced thermal conductivity and thermal diffusivity indicated an improved soil capacity to buffer extreme temperature fluctuations. Moreover, soils treated with TW and COM exhibited greater water retention and improved soil resistance to erosion. The findings suggest that the long-term application of tea waste and compost could be a sustainable soil management strategy for improving soil health and enhancing resilience to climate change in tea-cultivated Ultisols. Full article

Long COVID-19, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), involves symptoms or effects that persist for more than 4 weeks after the initial SARS-CoV-2 infection. One contributing factor to this condition is the disruption in the expression of the antioxidant enzyme Nuclear Factor Erythroid-2 (Nrf2) induced by the COVID-19 infection. Apigenin and related flavonoids, known for their diverse pharmacological activities, including potent antioxidant properties, have emerged as promising candidates for Long COVID-19 therapy. These compounds, particularly apigenin, are recognized for their ability to modulate oxidative stress and inflammation, making them potential activators of the Nrf2 pathway. This study aims to predict the activity of apigenin and its related flavonoids as Nrf2 activators using molecular modeling and molecular dynamics (MD) techniques, providing insights into their therapeutic potential in managing Long COVID-19. The findings from the molecular modeling analysis indicate that apigenin has a favorable affinity, with a free energy value (ΔG) of −6.40 kcal/mol. Additionally, MD simulation results demonstrate the strong stability of the Keap1-apigenin complex, with an average Root Mean Square Deviation (RMSD) value below 0.20 nm and the lowest average Root Mean Square Fluctuation (RMSF) value of 0.86 nm. Using the Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) calculation method, the binding affinity of the Keap1-apigenin complex yields a lower free energy value (ΔG) of −67.039 kJ/mol, consistent with the molecular modeling results. Apigenin also exhibits the ability to inhibit the binding of Nrf2 to the hydrophobic surface of Keap1, with a total energy value of 993.266 kcal/mol and binding affinity value of −1.162 kJ/mol through peptide−receptor docking. In conclusion, the comprehensive results suggest that apigenin has the potential to be a lead compound for developing Nrf2 activators specifically designed for Long COVID-19 therapy. Full article

NiTi alloys and thin film/NiTi composites are extensively utilized in frictional environments, particularly those experiencing extreme temperature fluctuations. Current studies mainly focus on preparing wear-resistant films on NiTi alloy surfaces but neglect the potential impact of temperature-induced phase transitions in the NiTi substrate on thin films’ performance. This study examines the effect of NiTi alloy phase transitions, induced by extreme temperature variations, on the tribological properties of TiN thin films on NiTi substrates. TiN films (1 μm thick) were deposited on NiTi alloy surfaces using magnetron sputtering technology. The transition of the main phase in the NiTi substrate between the R phase and the B19′ phase was achieved via liquid nitrogen cooling (−196 °C) and water bath heating (90 °C). XRD, EDS, SEM, and tribological tests analyzed the phase structure, elemental composition, micromorphology, and tribological behavior. Fatigue wear was identified as the predominant wear mechanism for the TiN films, with minor contributions from oxidative and abrasive wear. Phase transition from the R phase to the B19′ phase in the NiTi substrate induced by temperature change couls reduce the wear rate of the TiN film by up to 41.97% and decrease the friction coefficient from about 0.45 to about 0.25. Furthermore, the shape memory effect of the NiTi alloy substrate, caused by B19′ → B2 phase transition, resulted in the recovery of the TiN thin film wear track depth from 920 nm to 550 nm, manifesting a “self-healing” phenomenon. The results in this study are important and necessary for the provision of thin film/NiTi composites in frictional environments. Full article

Museums play a vital role in preserving cultural heritage and for this reason, they require strict indoor environmental controls. Balancing indoor environmental quality with reduced energy consumption poses significant challenges. Over the course of a year (2023), indoor microclimate conditions, atmospheric pollutant concentrations (O₃, TVOC, CO, CO₂, particulate matter), and energy use were monitored at the Archaeological Museum of Kavala. Maximum daily fluctuations in relative humidity were 15% in summertime, while air temperature variations reached 2.0 °C, highlighting unstable microclimatic conditions. Particulate matter was the primary threat to the preservation of artworks, followed by indoor O₃ and NO₂, whose concentrations exceeded recommended limits for cultural conservation. In 2023, the Energy Use Intensity (EUI) was 86.1 kWh m⁻², a value that is significantly correlated with the number of visitors and the outdoor air temperature. Every person visiting the museum was assigned an average of 7.7 kWh of energy. During the hottest days and when the museum was crowded, the maximum amount of energy was consumed. Over the past decade (2013–2023), the lowest EUI was recorded during the COVID-19 pandemic at 53 kWh m⁻². Energy consumption is linked to indoor environmental quality; thus, both must be continuously monitored. Full article

Upwelling is often associated with high productivity, biodiversity, and fishery resource abundance. This study employed a generalized additive model (GAM) to analyze the effects of Ekman pumping and transport on the abundance and distribution of jumbo flying squid (Dosidicus gigas) using wind field data and Chinese commercial fishing catch data off Peru from 2012 to 2020. The results indicate that the spatial distribution of Ekman pumping and transport exhibited significant monthly variation and exerted a considerable impact on the abundance and distribution of D. gigas. Ekman pumping fluctuated between 4.98 × 10^⁻9 to 6.84 × 10^⁻7 m/s, with the strongest upwelling effects observed from February to March and October to December. Ekman transport varied from 0.89 to 2.56 m³/s and peaked in August. The GAM results indicate that the catch per unit effort (CPUE) of D. gigas was significantly affected by Ekman pumping, while the latitudinal gravity centers (LATG) of D. gigas were significantly influenced by Ekman transport and chlorophyll-a concentration (Chl-a). Both hydrodynamic processes had a significant influence on Chl-a. Ekman pumping contributed greatly to upwelling formation, significantly increasing Chl-a concentration in the northern region, while strong Ekman transport pushed high-Chl-a coastal waters offshore in the central and southern regions when Ekman pumping was weaker, resulting in increasing offshore Chl-a concentrations. Furthermore, Chl-a concentration was significantly positively correlated with Ekman pumping after a two-month lag. An El Niño weakened the intensity of Ekman pumping, leading to notable declines in Chl-a concentration and D. gigas CPUE. These findings demonstrate that Ekman pumping and transport significantly influence the distribution of Chl-a, to which D. gigas is sensitive, influencing the abundance and distribution of this species off the coast of Peru. Full article

To investigate the non-stationary characteristics of the wind field at the U-shaped canyon bridge site and its impact on fluctuating wind characteristics, a wind observation tower was installed near a cable-stayed bridge. The Augmented Dickey–Fuller (ADF) test was employed to assess the stationarity of wind speed series, while the discrete wavelet transform (DWT) was applied to reconstruct the time-varying mean wind and analyze its effect on fluctuating wind characteristics. Results indicate that wind speeds in this region exhibit bimodal distribution characteristics, with the Weibull-Gamma mixed distribution model providing the best fit. The proportion of non-stationary samples increases with height. Autocorrelation function (ACF), partial autocorrelation function (PACF) tests, and power spectral density (PSD) analysis determined the optimal wavelet decomposition level for wind speed in this region. Analysis of non-stationary samples using db10 wavelet reconstruction reveals that the stationary wind speed model overestimates turbulence intensity but underestimates the turbulence integral scale. The downwind spectrum deviates from the Kaimal spectrum in both low- and high-frequency bands, whereas the vertical spectrum aligns well with the Panofsky spectrum. The findings demonstrate that the wavelet reconstruction method more accurately captures fluctuating wind characteristics under the complex terrain conditions of this canyon area. Full article

Periodic change in reservoir water level will have a significant impact on berthing position, and the impact caused by irregular operation during berthing will cause damage to wharf pile foundations. However, most of the existing monitoring methods adopt irregular methods, so it is difficult to accurately identify and analyze the damage causes. Taking a high-piled wharf in the Three Gorges Reservoir area as an example, the uncertainty of reservoir water level change is quantitatively analyzed. By establishing a simplified parametric wharf calculation model, the data set of an inversion model of pile of a high-piled wharf under ship impact is obtained, and the inversion analysis of pile damage of a high-piled wharf under ship pile is carried out based on the artificial neural network model. The results show that the inversion model can accurately and efficiently identify the intensity of ship impact, and a low water level is better than a high water level in the identification of impact position. In this paper, the behavior of wharf structure before and after damage is analyzed symmetrically under the action of damage inducement. In summary, the inversion analysis method can basically meet the requirements of inversion identification of pile foundation damage of a high-pile wharf in a backwater fluctuation area under ship impact. Full article

The spatial correlation of wall pressure fluctuations is a crucial parameter that affects the structural vibration caused by a turbulent boundary layer (TBL). Although the phase-array technique is commonly used in industry applications to obtain this correlation, it has proven to be effective only for moderate frequencies. In this study, an artificial neural network (ANN) method was developed to calculate the convective speed, indicating the spatial correlation of wall pressure fluctuations and extending the frequency range of the conventional phase-array technique. The developed ANN system, based on a radial basis function (RBF), has been trained using discrete simulated data that follow the physical essence of wall pressure fluctuations. Moreover, a normalization method and a multi-parameter average (MPA) method have been employed to improve the training of the ANN system. The results of the investigation demonstrate that the MPA method can expand the frequency range of the ANN, enabling the maximum analysis frequency of convective velocity for aircraft wall pressure fluctuations to reach over 10 kHz. Furthermore, the results reveal that the ANN technique is not always effective and can only accurately calculate the wavenumber when the standard wavelength is less than four times the width of the sensor array along the flow direction. Full article

Currently, steady-state analysis predominates in combustion chamber design, while dynamic combustion characteristics remain underexplored, and there is a lack of a comprehensive index system to assess dynamic combustion behavior. This study conducts a numerical simulation of the dynamic characteristics of the combustion chamber, employing a method combining large eddy simulation (LES) and Flamelet Generated Manifold (FGM). The inlet air temperature, air flow rate, and fuel flow rate were varied by 1%, 2%, and 3%, respectively, with a pulsation period of 0.008 s. The effects of nine different inlet parameter pulsations on both time-averaged and instantaneous combustion performance were analyzed and compared to benchmark conditions. The results indicate that small pulsations in the inlet parameters have minimal impact on the steady-state time-averaged performance. In the region near the cyclone outlet, which corresponds to the flame root area, pronounced unsteady flame characteristics were observed. Fluctuations in inlet parameters led to an increase in temperature fluctuations near the flame root. Analysis of the outlet temperature results for each operating condition reveals that inlet parameter fluctuations can mitigate the inherent combustion instability of the combustion chamber and reduce temperature fluctuations at the outlet hot spot. Full article

Solar eclipses present a valuable opportunity for controlled in situ ionosphere studies. This work explores the response of the upper atmosphere’s F-layer during the total eclipse of April 8, 2024, which was primarily visible across North and South America. Employing a multi-instrument approach, we analyze the impact on the ionosphere’s Total Electron Content (TEC) and Very Low Frequency (VLF) signals over a three-day period encompassing the eclipse (April 7 to 9, 2024). Ground-based observations leverage data from ten International GNSS Service (IGS)/Global Positioning System (GPS) stations and four VLF stations situated along the eclipse path. We compute vertical TEC ($VTEC$) alongside temporal variations in the VLF signal amplitude and phase to elucidate the ionosphere’s response. Notably, the IGS station data reveal a decrease in $VTEC$ during the partial and total solar eclipse phases, signifying a reduction in ionization. While VLF data also exhibit a general decrease, they display more prominent fluctuations. Space-based observations incorporate data from Swarm and COSMIC-2 satellites as they traverse the eclipse path. Additionally, a spatiotemporal analysis utilizes data from the Global Ionospheric Map (GIM) database and the DLR’s (The German Aerospace Center’s) database. All space-based observations consistently demonstrate a significant depletion in $VTEC$ during the eclipse. We further investigate the correlation between the percentage change in $VTEC$ and the degree of solar obscuration, revealing a positive relationship. The consistent findings obtained from this comprehensive observational campaign bolster our understanding of the physical mechanisms governing ionospheric variability during solar eclipses. The observed depletion in $VTEC$ aligns with the established principle that reduced solar radiation leads to decreased ionization within the ionosphere. Finally, geomagnetic data analysis confirms that external disturbances do not significantly influence our observations. Full article

Fasting–feeding timing is a crucial pattern implicated in the regulation of daily circadian rhythms. The interplay between sleep and meal timing underscores the importance of maintaining circadian alignment in order to avoid creating a metabolic environment conducive to carcinogenesis following the molecular and systemic disruption of metabolic performance and immune function. The chronicity of such a condition may support the initiation and progression of cancer through a variety of mechanisms, including increased oxidative stress, immune suppression, and the activation of proliferative signaling pathways. This review aims to summarize current evidence from human studies and provide an overview of the potential mechanisms underscoring the role of chrononutrition (including time-restricted eating) on cancer risk. Current evidence shows that the morning chronotype, suggesting an alignment between physiological circadian rhythms and eating timing, is associated with a lower risk of cancer. Also, early time-restricted eating and prolonged nighttime fasting were also associated with a lower risk of cancer. The current evidence suggests that the chronotype influences cancer risk through cell cycle regulation, the modulation of metabolic pathways and inflammation, and gut microbiota fluctuations. In conclusion, although there are no clear guidelines on this matter, emerging evidence supports the hypothesis that the role of time-related eating (i.e., time/calorie-restricted feeding and intermittent/periodic fasting) could potentially lead to a reduced risk of cancer. Full article

Electricity price forecasting is of significant practical importance, and improving prediction accuracy has become a key area of focus. Although substantial progress has been made in electricity price forecasting research, the unique characteristics of the electricity market make prices highly sensitive to even minor market changes. This results in prices exhibiting long-term trends while also experiencing sharp fluctuations due to sudden events, often leading to extreme values. Furthermore, most current models are “black-box” models, lacking transparency and interpretability. These unique features make electricity price forecasting particularly complex and challenging. This paper introduces a forecasting framework that incorporates the Seasonal Trend decomposition using Loess (STL), Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), and Shapley Additive Explanations (SHAPs) and applies it to forecasting in the electricity markets of the United States and Australia. The proposed forecasting framework significantly improves prediction accuracy compared to nine other baseline models, especially in terms of RMSE and R² metrics, while also providing clear insights into the factors influencing the forecasts. On the U.S. dataset, the RMSE of this framework is 12.7% lower than that of the second-best model, while, on the Australian dataset, the RMSE of the SLGSEF is 2.58% lower than that of the second-best model. Full article