Search Results (720)

Prolonged drought and soil degradation severely affect soil fertility and limit crop productivity. Superabsorbent hydrogels offer an effective solution for improving water retention in soil and supporting plant growth. In this work, we examined the performance of superabsorbent hydrogels based on sodium alginate, acrylic acid (AA), and poly (ethylene oxide) (PEO) cross-linked with 12.5 kGy using e-beam irradiation. The hydrogels were assessed in various aqueous environments by examining network characteristics, swelling capacity, and swelling kinetics to evaluate the impact of water’s electrical conductivity (which ranges from 0.05 to 321 μS/cm). Morphological and chemical structure changes were evaluated using SEM and FTIR techniques. The results demonstrated that water conductivity significantly affected the physicochemical properties of the hydrogels. Swelling behavior showed notable sensitivity to electrical conductivity variations, with swelling degrees reaching 28,400% at 5 μS/cm and 14,000% at 321 μS/cm, following first-order and second-order kinetics. FTIR analysis confirmed that structural modifications correlated with water conductivity, particularly affecting the O–H, C–H, and COOH groups sensitive to the ionic environment. SEM characterization revealed a porous morphology with an interconnected microporous network that facilitates efficient water diffusion. These hydrogels show exceptional swelling capacity and are promising candidates for sustainable agriculture applications. Full article

The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. Two primary research questions are addressed: whether machine learning models outperform traditional techniques, and whether time series modelling improves prediction accuracy. The analysis includes an evaluation of a range of models, including statistical regressions (OLS, LASSO, ridge), regression trees, neural networks, LSTM, and random forests, which are applied to physical modelling and time series approaches. The results reveal that although machine learning methods can outperform statistical models, particularly with the inclusion of exogenous weather features, they are not universally superior across all forecasting horizons. Furthermore, pure time series approach models yield lower performance. However, a hybrid approach in which physical models are integrated with machine learning demonstrates significantly improved accuracy. These findings highlight the value of hybrid models for photovoltaic forecasting and suggest strategic directions for operational implementation. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments. Full article

Ultraviolet B (UVB) irradiation drives skin photodamage, prompting exploration of natural therapeutics. This study investigated the reparative effects and mechanisms of crude Gastrodia elata polysaccharides (GP) on UVB-induced acute skin damage. GP was extracted from fresh G. elata via water extraction and alcohol precipitation. It is a homogeneous polysaccharide with a weight-average molecular weight of 808.863 kDa, comprising Ara, Glc, Fru, and GalA. Histopathological analysis revealed that topical application of GP on the dorsal skin of mice effectively restored normal physiological structure, suppressing epidermal hyperplasia and collagen degradation. Biochemical assays showed that GP significantly reduced the activities of MPO and MDA following UVB exposure while restoring the enzymatic activities of SOD and GSH, thereby mitigating oxidative stress. Moreover, GP treatment markedly upregulated the anti-inflammatory cytokines TGF-β and IL-10 and downregulated the pro-inflammatory mediators IL-6, IL-1β, and TNF-α, suggesting robust anti-inflammatory effects. Transcriptomics revealed dual-phase mechanisms: Early repair (day 5) involved GP-mediated suppression of hyper inflammation and accelerated necrotic tissue clearance via pathway network modulation. Late phase (day 18) featured enhanced anti-inflammatory, antioxidant, and tissue regeneration processes through energy-sufficient, low-inflammatory pathway networks. Through a synergistic response involving antioxidation, anti-inflammation, promotion of collagen synthesis, and acceleration of skin barrier repair, GP achieves comprehensive repair of UVB-induced acute skin damage. Our findings not only establish GP as a potent natural alternative to synthetic photoprotective agents but also reveal novel pathway network interactions governing polysaccharide-mediated skin regeneration. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

This study presents an optimization approach for sizing photovoltaic (PV) and battery energy storage systems (BESSs) within a DC microgrid, aiming to enhance cost-effectiveness, energy reliability, and environmental sustainability. PV generation is modeled based on environmental parameters such as solar irradiance and ambient temperature, while battery charging and discharging operations are managed according to real-time demand. A simulation framework is developed in MATLAB 2021b to analyze PV output, battery state of charge (SOC), and grid energy exchange. For demand-side management, the Long Short-Term Memory (LSTM) deep learning model is employed to forecast future load profiles using historical consumption data. Moreover, a Multi-Layer Perceptron (MLP) neural network is designed for comparison purposes. The dynamic load prediction, provided by LSTM in particular, improves system responsiveness and efficiency compared to MLP. Simulation results indicate that optimal sizing of PV and storage units significantly reduces energy costs and dependency on the main grid for both forecasting methods; however, the LSTM-based approach consistently achieves higher annual savings, self-sufficiency, and Net Present Value (NPV) than the MLP-based approach. The proposed method supports the design of more resilient and sustainable DC microgrids through data-driven forecasting and system-level optimization, with LSTM-based forecasting offering the greatest benefits. Full article

Photovoltaic panels are pivotal in transforming solar irradiance into electricity, making them a key technology in renewable energy. Despite their potential, the distribution of photovoltaic systems in Romania remains sparse, requiring advanced data analytics for effective management, particularly in addressing the intermittent nature of photovoltaic energy. This study investigates the predictive capabilities of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for forecasting hourly photovoltaic energy production in Romania. The results indicate that CNN models significantly outperform LSTM models, with 77% of CNNs achieving an R² of 0.9 or higher compared to only 13% for LSTMs. The best-performing CNN model reached an R² of 0.9913 with a mean absolute error (MAE) of 9.74, while the top LSTM model achieved an R² of 0.9880 and an MAE of 12.57. The rapid convergence of the CNN model to stable error rates illustrates its superior generalization capabilities. Moreover, the model’s ability to accurately predict photovoltaic production over a two-day timeframe, which is not included in the testing dataset, confirms its robustness. This research highlights the critical role of accurate energy forecasting in optimizing the integration of photovoltaic energy into Romania’s power grid, thereby supporting sustainable energy management strategies in line with the European Union’s climate goals. Through this methodology, we aim to enhance the operational safety and efficiency of photovoltaic systems, facilitating their large-scale adoption and ultimately contributing to the fight against climate change. Full article

A simple and innovative synthesis strategy was established to produce polymer microspheres with a distinctive walnut-like morphology, incorporating Fe₃O₄ nanoparticles within their structure. This was achieved through γ-ray-initiated mini-emulsion polymerization. To ensure high encapsulation efficiency, the surface of the Fe₃O₄ nanoparticles was chemically altered to shift their wettability from hydrophilic to hydrophobic, enabling uniform dispersion within the monomer phase before polymerization. The formation of the walnut-like architecture was found to be significantly influenced by both the polymerization dynamics and phase separation, as well as the shrinkage of the crosslinked polymer network formed between the monomer and the resulting polymer. Divinylbenzene (DVB) was chosen as the monomer due to its ability to generate a mechanically stable polymer framework. The γ-ray irradiation effectively initiated polymerization while preserving structural coherence. A detailed analysis using FTIR, SEM, and TEM confirmed the successful fabrication of the Fe₃O₄-loaded polymer microspheres with their characteristic textured surface. Moreover, magnetic characterization via vibrating sample magnetometry (VSM) indicated pronounced superparamagnetic behavior and strong magnetic responsiveness, highlighting the potential of these microspheres for advanced biomedical applications. Full article

Body-centered cubic (BCC) refractory high-entropy alloys (RHEAs) demonstrate significant potential as nuclear structural materials due to their exceptional mechanical properties and radiation tolerance. While Zr-containing RHEAs often develop multiphase structures through Zr-rich phase precipitation to enhance high-temperature mechanical performance, their irradiation response mechanisms remain poorly understood. This study investigated the microstructure evolution and radiation damage behavior in equiatomic MoNbTiVZr RHEA under Au-ion irradiation at fluences of 2 × 10¹⁵, 4 × 10¹⁵, and 1 × 10¹⁶ ions/cm². Microstructural characterization revealed that the annealed alloy primarily consisted of near-equiatomic BCC1 phase, Zr-rich BCC2 phase, (Mo,V)Zr Laves phase, and ordered Zr₂C phase. Post-irradiation analysis showed distinct defect evolution patterns: the BCC1 phase developed fine dislocation loops, while the Zr-rich BCC2 and Zr₂C phases exhibited dislocation clusters and dense dislocation networks, respectively. BCC1 phase exhibited the most pronounced irradiation hardening corresponding to its fine, dispersed dislocation loop characteristics. Phase separation induced by Zr precipitation reduced chemical complexity, accelerating irradiation defect evolution. These findings demonstrated that Zr-rich phase precipitation detrimentally impacted the radiation resistance of BCC-structured RHEAs, suggesting that single-phase stability should be prioritized in nuclear material design. Full article

In this study, we present bi-layered hydrogel systems that incorporate different sizes and shapes of gold nanoparticles (nanospheres and nanorods) for potential use in areas such as photoactuators, soft robotics, artificial muscles, drug delivery and tissue engineering. The synthesized nano Au-PNiPAAm/PVA bi-layered hydrogel nanocomposites provide the unique ability to exhibit controlled motion upon light exposure, indicating that the above systems possess the capability of photo–thermal energy conversion. The chosen synthesis approach is a combination of chemical production of gold nanoparticles (AuNPs) followed by gamma radiation formation of crosslinked polymer networks around them, as the final step, which also allows for sterilization in a single technological step. According to the TEM analysis, the gold nanospheres (AuNSs) with mean diameters of around 17 and 30 nm, as well as nanorods (AuNRs) with an aspect ratio of around 4.5, were synthesized and used as nanofillers in the formation of nanocomposites. Their stability within the polymer matrix was confirmed by UV–Vis spectral studies, by the presence of local surface plasmon resonance (LSPR) bands, typical for nanoparticles of various shapes and sizes. Morphological studies (FE-SEM) of hydrogels revealed the formation of a porous structure with PNiPAAm hydrogel as an active layer and PVA hydrogel as a passive layer, as well as a stable interfacial layer with a thickness of around 80 μm. The synthesized bi-layered photoactuators showed a photo–thermal response upon exposure to irradiation of green lasers and lamps that simulate sunlight, resulting in bending motion. This bending response reveals the huge potential of the obtained materials as soft actuators, which are more flexible than rigid systems, making them effective for specific applications where controlled movement and flexibility are essential. Full article

A cheap, environmentally friendly, easily scalable post-treatment of Na-ZSM-5 (Si/Al molar ratio = 20 or 30) via electron-beam irradiation to produce hierarchical H-ZSM-5 as a propylene-increasing fluid catalytic cracking additive was performed. Higher specific surface areas and highly accessible porous systems were obtained among the irradiated samples. A combination of ²⁷Al, ¹H magic angle spinning nuclear magnetic resonance and NH₃-temperature-programmed desorption methods showed that upon irradiation, some of the framework’s tetrahedral Al atoms were removed as non-framework Al atoms via flexible coordination with Si-OH groups (either framework or non-framework defects), thus increasing the H-ZSM-5 acidity and stability during hydrothermal dealumination. The enhanced selectivity and stability toward propylene production over the irradiated H-ZSM-5 samples were attributed to the integration of the reserved population of medium acid sites into the highly accessible hierarchical network. N₂ adsorption–desorption isotherm data showed that the Si-rich H-ZSM-5 samples possessed an obvious ink-bottle-shaped micro-mesopore network and a greater degree of disordered orientation of the straight pore systems toward the exterior surfaces. Micro-activity test data suggested that with an increasing Si/Al ratio, the H-ZSM-5 additives lost some extent of their cracking activity due to the constricted hierarchical pore network toward the exterior surface but gained more stability and selectivity for propylene due to the reserved medium acid sites. Full article

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article