Search Results (748)

Diffractive sail components may be used in part or whole for in-space propulsion and attitude control. A sun-facing hybrid diffractive solar sail having reflective front facets and transmissive side facets is described. This hybrid design seeks to minimize the undesirable scattering from side facets. Predictions of radiation pressure are compared for analytical geometrical optics and numerical finite difference time domain approaches. Our calculations across a spectral irradiance band from 0.5 to 3 μm suggest the transverse force in a sun facing configuration reaches 48% when the refractive index of the sail material is 1.5. Diffraction measurements at a representative optical wavelength of 633 nm support our predictions. Full article

Accurate rice yield prediction remains a major challenge due to the complex and nonlinear interactions among cultivar, environment, and phenology. Existing approaches often focus on analyzing individual components while ignoring their interdependencies, which results in limited predictive accuracy and generalizability. To overcome these problems, this study proposes a novel interpretable random forest model that integrates cultivar, environmental, and phenological dimensions. Different from conventional approaches, the proposed method incorporates a factor-combination optimization strategy to identify the most effective information for yield estimation. For analysis, 24 key determinants were screened, including the geographical location, meteorological conditions, phenological events, and cultivar traits. The RF models were also evaluated when built with seven factor combinations. The results reveal the following: (1) Meteorological conditions play a dominant role during the vegetative growth period, including net solar radiation (r = 0.42), daylength (r = 0.38), and thermal summation (r = 0.29). On the other hand, thermal summation (r = 0.28), mean minimum temperature (r = −0.23), and mean temperature (r = −0.20) are most relevant during the reproductive growth period. (2) The full-factor model achieves optimal performance (RMSE = 601.45 kg/ha and MAE = 454.98 kg/ha, R² = 0.77). (3) Importance analysis reveals that meteorological factors provide the greatest contribution (53.59%), followed by phenological factors (20.39%), geographical factors (17.20%), and cultivar (8.82%), respectively. The results also reveal that threshold effects of key determinants on yield, and identify mid-April to early May as the optimal sowing window. These findings demonstrate that integrating cultivar, environment, and phenology factors creates a powerful predictive model for rice yields. Full article

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

This review provides a comprehensive synthesis of the coupled effect of temperature and solar radiation on photovoltaic (PV) module performance and lifespan. Although numerous investigations have examined these stressors in themselves, this research addresses their interrelationship and evaluates the way climatic conditions affect short-term performance fluctuation and long-term degradation mechanisms. The assessment consolidates outcomes from model strategies, laboratory tests, and field monitoring studies. Through the presentation of these findings in a narrative form, the paper identifies recurring difficulties in terms of the absence of shared assessment metrics and the low level of standardisation of long-term test regimes. Second, it underlines the importance of predictive modelling and live monitoring as important management tools for coupled stressors. Finally, the review points out research gaps and underscores future research avenues, including ongoing work towards the development of a coupling index, a composite measure being piloted in individual studies, and advancements in materials technology, predictive methodology, and durability testing. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article

Investigating the factors determining the co-existence of closely related species is key to understanding how biodiversity is structured and maintained. To this end, we seek to comprehend the geographical distribution of species, their range overlap, and the evolutionary and ecological mechanisms that promote co-existence in ecological communities. In the Anthropocene, climate change dramatically shapes ecosystems along with species distributions. Here, we focus on six co-occurring species of Swertia (Gentianaceae). For instance, all of them grow within an area of 2 km² in the north of Kunming city, occupying different microhabitats. We employed the maximum entropy model (MaxEnt) and a geographic information system (ArcGIS) to predict how future climate change will impact their distribution. We also tested the relationship between ecological niche overlap and phylogenetic distance among these species. We found that these six species co-occur in the mountains of Yunnan, Sichuan, and Guizhou provinces. Precipitation in the warmest quarter, mean temperature of driest quarter, solar radiation, annual temperature range, and elevation influence their distribution. In the predicted future climate, four outcrossing species, S. bimaculata, S. kouitchensis, S. punicea, and S. cincta, will expand their distribution range. The other two self-pollinating species, S. macrosperma and S. nervosa, will experience range contractions. We found no significant correlation between ecological niches and the phylogenetic distances of these species. Under the future climate scenarios, the six species of Swertia plants will tend to grow in clusters, suggesting a higher likelihood of co-occurrence in the future, and creating a potentially high level of habitat and resource competition among them. These findings hold significant implications for the conservation of Swertia. Full article

The integration of nanofluids into solar collectors has gained increasing attention due to their potential to enhance heat transfer and support the transition toward low-carbon energy systems. However, a systematic understanding of their photothermal performance under the direct absorption mode remains lacking. This review addresses this gap by critically analyzing the role of nanofluids in solar energy harvesting, with a particular focus on the direct absorption mechanisms. Nanofluids enhance solar radiation absorption through improved light absorption by nanoparticles, surface plasmon resonance in metals, and enhanced heat conduction and scattering effects. The novelty of this work lies in its comparative evaluation of advanced nanofluids, including magnetic nanofluids, plasma nanofluids, and nanophase change slurries, highlighting their unique capabilities in flow manipulation, thermal storage, and optical energy capture. Future research directions are identified, such as the life cycle assessment (LCA) of nanofluids in solar systems, applications of hybrid nanofluids, development of predictive models for nanofluid properties, optimization of nanofluid performance, and integration of Direct Absorption Solar Collectors (DASCs). In addition, challenges related to the stability, production cost, and toxicity of nanofluids are critically analyzed and discussed for practical applications. This paper offers guidance for the design and application of high-performance nanofluids in next-generation solar energy systems. Full article

Hydroponic systems can drain nutrient-rich waste into the environment. Increasing irrigation efficiency would decrease effluent and improve cost efficiency for growers. However, current methods accessible to small- and mid-sized growers to determine moisture content in growth media are often imprecise. Simplified transpiration models could inform irrigation needs. This study aimed to improve transpiration estimates using vapor pressure deficit (VPD) and solar radiation. We compared our model to an existing transpiration model. Three years of transpiration and environmental data from tomato production were used to calibrate (year 2) and validate (years 1 and 3) the model. Randomly chosen subsets from all years of data were also used. The new model (TVPD) predicted the observed values more closely than the previous model (PG) in year 1 (TVPD: RMSE = 0.1570 mm, r² = 0.95; PG: RMSE = 0.5594 to 0.6875 mm, r² = 0.27 to 0.78) but not in year 3 (TVPD: RMSE = 0.5430 mm, r² = 0.44; PG: RMSE = 0.1873 to 0.2065 mm, r² = 0.95). TVPD calibrated using random subsets of the combined data improved consistency and predictive capacity (RMSE = 0.2387 to 0.2419 mm, r² = 0.87 to 0.91). TVPD is a simpler alternative to complex models and to those focusing on solar radiation alone. TVPD is less reliable under low solar radiation (year 3); however, reliability could be improved by calibration across a broader environmental range. TVPD also allows for exploration of the relative influences of low VPD and high solar radiation on evapotranspiration found in greenhouse settings. Full article

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m⁻²) and lowest in deciduous needleleaf forests (148.31 gC m⁻²). NEP in China’s forests increased by a rate of 1.67 gC m⁻² annually, with most regions exhibiting a 275.32 gC m⁻² annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies. Full article

Achaea lienardi is a polyphagous moth occurring in sub-Saharan Africa. It is a fruit-sucking moth, causing secondary damage to fruit such as citrus and peaches, while the larval stage can cause significant tree defoliation, including in several indigenous trees, wattle, Eucalyptus, and castor oil plants, amongst others. In February and March of 2025, a massive outbreak of the caterpillars was observed in the Hluhluwe-iMfolozi Park in South Africa, feeding primarily on Tamboti trees (Spirostachys africana). Satellite imagery from the previous five years was examined, but no similar large defoliation events were observed during this period. Climate data for the last five years (September 2019–March 2025) were collated and examined to determine the conditions supporting the outbreak. Above average winter rainfall, early spring rains, sustained rains, and high humidity in January and February, with warm nighttime temperatures, likely acted in concert to create favourable conditions for the caterpillar outbreak. This outbreak coincided with historic outbreaks of the African armyworm (Spodoptera exempta) in the summer rainfall areas of South Africa where precipitation, temperature, solar radiation, and humidity were found to be critical factors affecting armyworm outbreaks. Further research is required to determine specific criteria to enable predictions of future outbreaks. Full article

Under the context of global climate change, the frequent occurrence of extreme low-temperature events poses a severe challenge to plant distribution and ecosystem stability. The arid and semi-arid regions of Northwestern China, as a sensitive response area to global change, have proven to possess significant development potential with their unique desert vegetation systems. This study focuses on the ecological adaptability mechanisms of Nitraria tangutorum Bobr., a key species of the desert ecosystem in Northwestern China, and systematically analyzes the evolution patterns of its geographical distribution under the coupled effects of climate change and human activities through a MaxEnt model. The research conclusions are as follows: (i) This study constructs a Human Footprint-MaxEnt (HF-MaxEnt) coupling model. After incorporating human footprint variables, the AUC value of the model increases to 0.914 (from 0.888), demonstrating higher accuracy and reliability. (ii) After incorporating human footprint variables, the predicted area of the model decreases from 2,248,000 km² to 1,976,000 km², with the High Suitability experiencing a particularly sharp decline of up to 79.4%, highlighting the significant negative impact of human disturbance on Nitraria tangutorum. (iii) Under the current climate baseline period, solar radiation, precipitation during the wettest season, and mean temperature of the coldest month are the core driving factors for suitable areas of Nitraria tangutorum. (iv) Under future climate scenarios, the potential distribution area of Nitraria tangutorum is significantly positively correlated with carbon emission levels. Under the SSP370 and SSP585 emission pathways, the area of potential distribution reaches 172.24% and 161.3% of that in the current climate baseline period. (v) Under future climate scenarios, the distribution center of potential suitable areas for Nitraria tangutorum shows a dual migration characteristic of “west–south” and “high altitude”, and the mean temperature of the hottest month will become the core constraint factor in the future. This study provides theoretical support and data backing for the delineation of habitat protection areas, population restoration, resource management, and future development prospects for Nitraria tangutorum. Full article

The current–voltage (I-V) characteristic provides essential performance parameters of a solar cell, influenced by temperature and solar radiation. The efficiency of a solar cell is sensitive to variations in these conditions. This study electrically characterized a polycrystalline silicon solar cell in a solar simulator chamber at temperatures of 25–55 °C and irradiance levels of 600–1000 W/m². The acquired data were used to train and evaluate neural network models to predict the I-V characteristics of a polycrystalline silicon solar cell. Two recurrent neural network architectures were tested: LSTM and the GRU model. The performance of the model was assessed using MAE, RMSE, and R². The GRU model achieved the results, with MAE = $2.813\times {10}^{-3}$, RMSE = $5.790\times {10}^{-3}$, and R² = 0.9844, similar to LSTM (MAE = $2.6613\times {10}^{-3}$, RMSE = $5.858\times {10}^{-3}$, R² = 0.9840). These findings highlight the GRU network as the most efficient approach for modeling solar cell behavior under varying environmental conditions. Full article

Accurate computation of external heat flux is critical for spacecraft thermal analysis and thermal control system design. The traditional method, which adopted the uniform planetary infrared radiation model (UPIRM), is inadequate for lunar orbital missions due to the extreme planetary surface temperature variations. This study proposes an external heat flux calculation method for lunar orbits by integrating a non-uniform lunar surface temperature model derived from Lunar Reconnaissance Orbiter (LRO) Diviner radiometric data. Specifically, the lunar surface temperature data were first fitted as functions of latitude ($\psi$) and position angles ($\zeta$) through data regression analysis. Then, a comprehensive mathematical framework is established to analyze solar radiation, lunar albedo, and lunar infrared radiation components, incorporating orbital parameters such as beta angle ($\beta$), orbital inclination ($i$) and so on. Coordinate transformations and numerical integration techniques are employed to evaluate heat flux distributions across cuboidal orbiter surfaces. It is found that the lunar infrared radiation heat flux manifests pronounced fluctuation, peaking at 1023 W/m² near the lunar noon region while plummeting to 20 W/m² near the midnight region under the orbital parameters investigated in this study. This study demonstrates the essential role of the non-uniform planetary infrared radiation model (NUPIRM) in enhancing prediction accuracy by contrast, offering foundational references for thermal management in future lunar and deep-space exploration spacecraft. Full article

Monitoring water quality in narrow tributary bays of large river-type reservoirs is hindered by sparse sampling and cloud-limited imagery. This study develops a Trophic State Index (TSI) inversion for Xiangxi Bay, a major tributary bay of the Three Gorges Reservoir, using Landsat data and a backpropagation (BP) neural network, with hyperparameters tuned via a grid search algorithm (GSA). Environmental drivers such as water temperature, solar radiation, and photosynthetically active radiation were combined with Landsat spectral bands. Eleven sites measured monthly in 2009 yielded 98 samples after preprocessing, and training achieved R² = 0.94. Predictions for 2009 show clear spatiotemporal heterogeneity: those for April and September (TSI = 48–59) exceeded those for July and October (46–56), with mid–lower reaches (52–59) being higher than mid–upper reaches (47–54). Out-of-period predictions for April/June 2019 and August/November 2020 were consistent with seasonal expectations, with higher spring–summer TSIs (2019: 50–57; 2020 August: 45–55) than in November 2020 (37–47). Key limitations include the small sample size, cloud-related data gaps, and sensitivity to evolving reservoir operations. This framework demonstrates a practical route to the satellite-based monitoring and mapping of trophic status in narrow reservoir tributaries. Full article

Solar gasification is a thermochemical process that relies on concentrated solar radiation to heat steam and biomass to produce syngas. This study uses Machine Learning to model solar gasification using steam as an oxidizer, incorporating both thermodynamic simulations and predictive algorithms, developed using Python (version 3.11.13) scripting, to understand the relationship between the input and output variables. Three models—Artificial Neural Networks, Support Vector Machines, and Random Forests—were trained using datasets including biomass composition, solar irradiance (considering a solar furnace), and steam-to-biomass ratios in a downdraft or fluidized bed gasifier. Among the models, Random Forests provided the highest accuracy (average R² = 0.942, Mean Absolute Error = 0.086, and Root Mean Square Error = 0.951) and were used for feature importance analysis. Results indicate that radiative heat transfer and steam-to-biomass ratio are the parameters that result in the largest increase in the syngas heating value and decrease in the tar contents. In terms of composition, the hydrogen contents have a direct relationship with the H₂ and tar formed, while the carbon content affects the carbon conversion efficiency. This work highlights the of feature importance analysis to improve the design and operation of solar-driven gasification systems. Full article