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Radiative cooling is promising in meeting the current global demand for green sustainable development. However, the effective cooling of the existing radiant cooler will be limited due to the serious solar energy absorption and poor thermal insulation performance on the cold side. To addresss these issues, we propose herein a novel composite material of silicon dioxide (SiO 2) aerogel microspheres combined with polyvinylidene fluoride-cohexafluoropropylene (PVDF-HFP) nanofiber membrane, in which SiO 2 aerogel microspheres are firstly synthesized by sol-gel method and then incorporated into PVDF-HFP nanofiber membrane to give radiative cooling performance. As we expected, the molecular vibration characteristics of PVDF-HFP nanofiber membrane and the phonon polarization resonance of Si-O-Si bond in the transparent window can enhance the infrared emissivity of the membrane surface. In addition, the high porosity and the mesoporous structure formed by the interconnection of nano-network skeletons of SiO 2 aerogel microspheres determine its excellent thermal insulation performance. The as-prepared material displays that the average solar reflectance of the composite membrane is 96.07 % and the average infrared emissivity of the atmospheric window is 94.95 %. Notably, when the average solar radiation intensity is 885.56 W•m−2, the passive radiative cooling temperature during the day can reach 11.2 °C. Furthermore, this material has excellent self-cleaning and thermal insulation performance, making it a potential radiant cooling candidate in many fields. [ABSTRACT FROM AUTHOR]

The need for real-time monitoring and alerting systems for space weather hazards has grown significantly in the last two decades. One of the most important challenges for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of an SPE, i.e., they are based on the classification approach. This work is oriented toward the successful implementation of onboard prediction systems, which is essential for the future of space exploration. We present a simple and efficient machine learning regression algorithm that is able to forecast the energetic proton flux up to 1 hr ahead by exploiting features derived from the electron flux only. This approach could be helpful in improving monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance. [ABSTRACT FROM AUTHOR]

Owing to the direct exposure to complex atmospheric environments, the temperature field of composite box girder bridge with corrugated steel webs (CBGB-CSW) is likely non-uniformly distributed. Whereas, the current researches regarding the thermal responses of CBGB-CSW are insufficient, and the thermal responses of CBGB-CSW under solar radiation are still unknown. Therefore, this paper conducted a long-term temperature experiment on a scaled model to explore the temperature distribution characteristics in CBGB-CSW. Meanwhile, a three-dimensional thermal–mechanical coupling Finite Element (FE) model is established to simulate the temperature field in the experiment girder. The accuracy and effectiveness of the developed FE model has been verified by the measured temperature data. Therewith, the thermal responses (i.e., stress and displacement) of a full-scale continuous CBGB-CSW with a span of 150 m are numerically investigated. The results indicate that the maximum stresses always occur at the midspan section (with a depth of 5 m) of the continuous CBGB-CSW, and considerable concentrations of stress are observed in the steel-concrete junction. The maximum longitudinal tensile and compressive normal stresses within 0.4 m of the upper junction can reach 5.55 MPa and −8.46 MPa respectively, and those of the lower junction can reach 6.96 MPa and −7.05 MPa respectively. Besides, owing to the impacts of vertical and horizontal temperature gradients, significant displacements of the whole bridge can also be observed. The maximum vertical displacement (5.33 mm) of the CBGB-CSW is estimated at the top plate in the midspan, while the maximum horizontal displacement (0.74 mm) is estimated at the trough of the southern corrugated steel web in the midspan. Notably, the outcomes of this paper can provide some useful references for engineers and scholars to understand the thermal responses of the CBGB-CSW. [ABSTRACT FROM AUTHOR]

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat–maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat–maize using multi‐source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat–maize was not prominent. Compared with 2001–2005, the maturity date of the two crops in 2016–2020 advanced 1–2 days, while yield increased 659–706 kg ha−1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat–maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Summary: The first step in carbon (C) turnover, where senesced plant biomass is converted through various pathways into compounds that are released to the atmosphere or incorporated into the soil, is termed litter decomposition. This review is focused on recent advances of how solar radiation can affect this important process in terrestrial ecosystems. We explore the photochemical degradation of plant litter and its consequences for biotic decomposition and C cycling. The ubiquitous presence of lignin in plant tissues poses an important challenge for enzymatic litter decomposition due to its biological recalcitrance, creating a substantial bottleneck for decomposer organisms. The recognition that lignin is also photolabile and can be rapidly altered by natural doses of sunlight to increase access to cell wall carbohydrates and even bolster the activity of cell wall degrading enzymes highlights a novel role for lignin in modulating rates of litter decomposition. Lignin represents a key functional connector between photochemistry and biochemistry with important consequences for our understanding of how sunlight exposure may affect litter decomposition in a wide range of terrestrial ecosystems. A mechanistic understanding of how sunlight controls litter decomposition and C turnover can help inform management and other decisions related to mitigating human impact on the planet. [ABSTRACT FROM AUTHOR]

• An orbit-attitude-structure-thermal coupled modelling method is proposed. • A unified-meshes ANCF element for temperature-displacement field is proposed. • Both axial and circumferential heat conduction are considered. • Three quasi-static models are proposed to predict the thermally induced vibration. Large space structures would exhibit orbit-attitude-structure-thermal coupled effects in complicated space environment. Thus, a new thermal-structure coupled modelling method is proposed using gradient-deficient absolute nodal coordinate formulation beam elements. Compared to the previous methods, both axial and circumferential heat conduction are considered. The cubic interpolation in temperature field instead of the linear interpolation is adopted to obtain a unified-mesh temperature-displacement description. The gravitational force and gravity gradient are modelled, and the effects of attitude motion and structural deformation on solar radiation intensity are considered. In addition, three kinds of quasi-static thermal-structure coupled models are proposed based on Euler-Bernoulli beam theory. Based on the quasi-static models, the theoretical formulas are derived to effectively predict the thermally induced vibrations of the beam in space. Four numerical examples are studied to validate the proposed models, including the heat conduction examples with three boundary conditions and a thermal-structure coupled cantilever beam example. Based on the proposed formulation, the orbit-attitude-structure-thermal coupled dynamics of a multibody system consisted of a rigid body and a large flexible appendage is investigated considering the gravity gradient, solar radiation, Earth's shadow, and self-shadow. [ABSTRACT FROM AUTHOR]

• Dwarf shrubs cooler and moister than the surrounding grassland. • Therefore, dwarf shrubs buffer microclimatic extremities. • Forests and edges had a higher spider richness than grasslands and dwarf shrubs. • We found a different community composition of spiders in the four microhabitats. Climate change negatively affects arthropod biodiversity worldwide. Mitigating the resulting arthropod decline is a great challenge. Dwarf shrubs in open areas might buffer microclimatic extremities by reducing the solar radiation reaching the ground and weakening air circulation near the soil surface. Forest steppes are mosaics of forests and grasslands covering a vast area in Eurasia. This heterogeneous ecosystem offers the opportunity to study the effect of small habitat features, i.e. dwarf shrubs, in dry grasslands and compare the fauna of rosemary-leaved willow (Salix rosmarinifolia) shrubs with forest patch interiors, open grasslands and their edges. We hypothesized that the dwarf shrub microhabitat has a wetter and cooler microclimate than open grassland and a different spider community composition than other forest-steppe microhabitats. We recorded microclimatic parameters with data loggers, measured soil moisture with TDR and collected ground-dwelling spiders with pitfall traps. We detected the highest soil moisture (6.26 ± 1.21%, mean ± 95% confidence interval) and air humidity (80.19 ± 3.19%) in forests and the lowest in grasslands (4.36 ± 0.65%; 66.59 ± 2.53%, respectively). The warmest microhabitats were grasslands (23.23 ± 0.51°C), whereas the coolest microhabitats were forests (18.92 ± 0.41°C). The distinct microclimate of dwarf shrubs was cooler (21.46 ± 0.41°C) and moister (5.43 ± 0.53%) than the surrounding semi-desert like grassland. Furthermore, we found a different spider community composition and trait state composition of spiders in forests, edges, grasslands and dwarf shrub microhabitats. Forests (9.90 ± 0.95) and edges (11.44 ± 1.27) hosted a higher species richness than grasslands (7.08 ± 4.27) and dwarf shrubs (5.09 ± 1.33). We collected larger spiders on the edges than in dwarf shrub microhabitats. The dwarf shrubs hosted a different microclimate and spider community composition from the grassland. Climate change in the forest-steppe region is assumed to be driven by a combination of warming and drying. In the coming decades, drought frequency and severity are predicted to increase. Woody vegetation, even dwarf shrubs, creates a thermal and moisture heterogeneity that might aid arthropods in buffering macroclimatic warming through behavioural thermoregulation. Therefore, their presence on grasslands can benefit the conservation of specialised grassland arthropods. [ABSTRACT FROM AUTHOR]

Due to the popularization of high-speed rail (HSR), there have been increasing concerns of passengers about the thermal comfort of HSR air conditioning. The corrected predicted mean vote (CPMV) model considering the impact of solar radiation on indoor thermal comfort has been previously proposed and validated for accuracy in office buildings. To verify the accuracy of the CPMV model in evaluating the thermal comfort of passengers inside HSR carriages, the field study on HSR in the Yangtze River Delta region of China was conducted in winter and summer. The results indicate thermal sensation vote values obtained from passengers fit well with CPMV values. When the temperature is high in summer and low in winter, the CPMV model ignores people's ability to adapt and tolerate harsh environments. In addition, the thermal preference temperature of passengers in summer is 0.57°C higher than the neutral temperature, indicating that there is an overcooling situation in summer. The study recommends to lower the set temperature of summer air conditioning in HSR. This study contributes to the promotion of the CPMV model on HSR and provides technical support for the design of air-conditioning systems for HSR carriages from the perspective of thermal comfort of passengers. [ABSTRACT FROM AUTHOR]

Introduction Prolonged exposure to ultraviolet (UV) radiation from the sun poses numerous risks to the skin, ranging from premature aging to serious health conditions such as skin cancer. UV radiation can penetrate the skin's surface, causing damage to its cells and DNA, which can lead to the formation of wrinkles, sunspots, and other signs of aging. Therefore, consistent and effective sun protection is essential for maintaining the health and vitality of the skin, as well as reducing the risk of sun-related skin damage and diseases. Aim of the study The objective of the study is to conduct a thorough investigation into different aspects of photoprotection and its effects on the skin. This entails assessing the effectiveness and safety of sunscreens, as well as evaluating the impact of sun-protection clothing on skin health. Materials and methods The purpose of this review is to assess the current literature of the effectiveness of the various sun protection measures. The literature was reviewed in the Pubmed, Google Scholar data base. Results Prolonged UV exposure incites photoaging, carcinogenesis, and immunosuppression, amplifying the risk of skin malignancies. Sun-protective clothing, with adequate UPF ratings, emerges as a pivotal element in mitigating UV-induced skin damage, notably reducing the development of pigmented moles and melanoma. Furthermore, effective sunscreen usage, coupled with broad-spectrum protection, is essential in averting UV-induced skin damage and curbing the incidence of skin cancer. These findings emphasize the imperative of comprehensive photoprotection strategies, integrating sunscreen application, sun-protective clothing, and individual risk assessment, to safeguard against solar-induced skin damage and mitigate the prevalence of skin cancer. [ABSTRACT FROM AUTHOR]

We consider a one-dimensional variant of a recently introduced settlement planning problem in which houses can be built on finite portions of the rectangular integer lattice subject to certain requirements on the amount of insolation they receive. In our model, each house occupies a unit square on a 1 × n strip, with the restriction that at least one of the neighboring squares must be free. We are interested mostly in situations in which no further building is possible, i.e. in maximal configurations of houses in the strip. We reinterpret the problem as a problem of restricted packing of vertices in a path graph and then apply the transfer matrix method in order to compute the bivariate generating functions for the sequences enumerating all maximal configurations of a given length with respect to the number of houses. This allows us to determine the asymptotic behavior of the enumerating sequences and to compute some interesting statistics. Along the way, we establish close connections between our maximal configurations and several other types of combinatorial objects, including restricted permutations and walks on certain small oriented graphs. We then generalize our results in several directions by considering multi-story houses, by varying the insolation restrictions, and, finally, by considering strips of width 2 and 3. At the end we comment on several possible directions of future research. [ABSTRACT FROM AUTHOR]

Multi‐electron involved photoreduction of CO2 to hydrocarbon fuels is a long‐standing challenge, particularly in manipulating the selectivity of the target products. Here, a novel Ag–Pt bimetallic alloy on Zn‐based supports by photo‐deposition is designed for photocatalytic CO2 reduction to CH4 without any additives. This photocatalyst exhibits ≈ 98.9% selectivity for CH4. Experimental and theoretical calculations demonstrated that the excellent performance of the photocatalyst can be attributed to the localized surface plasmon resonance effect of metals, as well as the synergistic effects of the bimetallic sites. These factors are found to be beneficial not only for capturing solar radiation and facilitating electron migration but also for promoting the adsorption and activation of CO2 and the protonation of *CO. As a result, the photocatalyst achieved high selectivity and activity of photoreduction of CO2 to CH4. This work provides insights into the design of photocatalysts with highly selective target products for CO2 reduction. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSIONS Ligusticum chuanxiong Hort., with over 2000 years of medicinal use and cultivation history, is extensively used in clinical settings for treating heart disease, headache, dysmenorrhea, and amenorrhea. Constructing the geographic distribution pattern of L. chuanxiong and identifying the environmental factors limiting its range, as well as clarifying the effects of key environmental factors on the content of major active constituents and transcription regulation, could provide a scientific foundation for the conservation and effective management of this valuable medicinal resource.The results reveal that the predominant environmental factors influencing the distribution were the minimum temperature of the coldest month (Bio6) and solar radiation (Srad), with cumulative account for 87.46% of the importance. Correlation analysis further reveals significant negative correlations between Bio6 and the content of major active constituents in L. chuanxiong, with Srad exhibiting a negative correlation with these constituents. The gene differential expression analysis indicated that the expression levels of some genes associated with growth and active constituent biosynthesis pathways, such as RPT2_13888, UVR8_16871, CLPB3_3155, and 4CLL5_116, varied significantly among locations influenced by differing key environmental factors. Consequently, alterations in the environment were found to influence the gene expression levels within these pathways, resulting in variations in the content of active constituents.These findings contribute to an enhanced understanding of how environmental factors impact the distribution and quality of medicinal plants and offer a theoretical reference for the introduction, cultivation, quality improvement, resource utilization and management of L. chuanxiong. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Continuous heat generation in perovskite solar cells (PSCs), caused by solar radiation, poses a significant challenge to their lifespan. Existing active cooling methods require extra energy input and might not be effective at high temperatures. Most reported passive radiative cooling materials either lack solar transparency or require complex fabrication processes. Here, mesoporous silica nanoparticles are designed, synthesized, and assembled into multilayered stacks with a graded refractive index (GRI) by spray coating them on top of PSCs made from methylammonium lead iodide (MAPbI3) over a large scale (15.6 × 15.6 cm2). This coating offers both high transparency in the visible wavelength and high emissivity in the mid‐infrared region, leading to an average temperature reduction of 6.65 ±  1.48 °C in GRI‐coated MAPbI3 PSCs under outdoor conditions compared to non‐coated references. After 50 d, the GRI‐coated PSCs maintain 80.9 ± 8.7% of their initial photoconversion efficiency, in contrast to 6.1 ± 5.9% for the noncoated ones. The calculated cooling power of the GRI‐coated PSCs is 28.9% higher than that of the reference cells. [ABSTRACT FROM AUTHOR]

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification framework on a multivariate time-series data set covering solar cycles 22, 23, and 24. We implement ensemble modeling that merges the results from three proton channels (E ≥ 10 MeV, 50 MeV, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions. Our task is binary classification, such that the aim of the model is to distinguish strong SEP events from nonevents. Here, strong SEP events are those crossing the Space Weather Prediction Center's "S1" threshold of solar radiation storm and proton fluxes below that threshold are weak SEP events. In addition, we consider periods of nonoccurrence of SEPs following a flare with magnitudes ≥C6.0 to maintain a natural imbalance of sample distribution. In our data set, there are 244 strong SEP events comprising the positive class. There are 189 weak events and 2460 "SEP-quiet" periods for the negative class. We experiment with summary statistic, one-nearest neighbor, and supervised time-series forest (STSF) classifiers and compare their performance to validate our methods for prediction windows from 5 minutes up to 60 minutes. We find the STSF model to perform better under all circumstances. For an optimal classification threshold of ≈0.3 and a 60 minutes prediction window, we obtain a true skill statistic TSS = 0.850, Heidke skill score HSS = 0.878, and Gilbert skill score GSS = 0.783. [ABSTRACT FROM AUTHOR]

Solar Radiation Management (SRM) is a climate intervention strategy aimed at mitigating global warming by reducing incoming solar radiation. We investigate the potential influence of SRM on Andean glacier-wide surface mass balance, as glaciers are crucial for downstream ecological functions along the west coast of South America. We numerically simulate the surface mass balance response of thousands of glaciers to SRM and other climate change scenarios throughout the 21st century. Results indicate Pan-Andean negative mass balance, irrespective of the scenario or glacier-climate regimes. However, SRM tends to modify interannual variability and temperature sensitivity in several regions. Our findings also suggest that if SRM had been implemented in the late 1980's, it would have been effective in avoiding the negative trajectory seen today. The Andes feature nearly all mountain hydroclimatic regimes, representing a globally relevant example of SRM impacts. These findings emphasize the urgency of drastic emissions reduction. [ABSTRACT FROM AUTHOR]

Producing and maintaining sexually selected ornaments often hinders survival. Because viability-related traits dictate the survival costs conferred by sexual ornaments, the evolution of viability-related traits can limit and/or compensate for ornament evolution. Here, we examine how the ornamental coloration of male dragonflies co-adapts with thermal physiology--a key suite of viability-related traits that influences nearly all reproductive and ecological interactions. Males of many dragonfly species produce dark color patches on their wings to attract potential mates and intimidate reproductive rivals. However, dark coloration also subjects male dragonflies to heat stress in warm climates by absorbing excess solar radiation. Our phylogenetic comparative analyses revealed that dragonfly species with dark sexual coloration have also evolved increased critical thermal maxima, which may allow them to compensate for ornament-induced heating. This pattern of correlated evolution was especially strong for species that inhabit tropical climates, where the heating costs of dark coloration are most severe. Given that darkened sexual coloration is taxonomically widespread and consistently elevates body temperatures, the pattern of co-adaptation between sexual ornaments and thermal physiology found here could represent a key process driving eco-physiological divergence in the past and influencing how populations respond to the changing climates of the future. [ABSTRACT FROM AUTHOR]

Summary In theory, there is a trade‐off between hydraulic efficiency and safety. However, the strength and direction of this trade‐off at the leaf level are not consistent across studies, and habitat climate may impact this trade‐off. We compiled a leaf hydraulic efficiency and safety dataset for 362 species from 81 sites world‐wide, with 280 paired observations of both traits, and tested whether climate was associated with departure from the proposed trade‐off. The leaf hydraulic efficiency–safety trade‐off was weak (R2 = 0.144) at the global scale. Mean annual precipitation and solar radiation (SR) modified the trade‐off. Species from dry and high SR habitats (e.g. desert and tropical savanna) were generally located above the trade‐off line, indicating that these species tended to have higher leaf hydraulic safety and efficiency than species from wet habitats with low SR (e.g. subtropical monsoon forest and montane rainforest), which were located below the trade‐off line. Leaves with high vein density, dry leaf mass per area, and osmotic regulation enhanced safety without compromising hydraulic efficiency. Variation in the hydraulic efficiency–safety trade‐off at the leaf level likely facilitates plant survival in specific habitats and allows for a more nuanced view of leaf hydraulic adaption strategies at the global scale. [ABSTRACT FROM AUTHOR]

Sustainable school design is becoming increasingly important worldwide, particularly in the UAE, where schools are significant energy consumers. This study explores the impact of courtyard orientation on microclimate and energy consumption in UAE schools, utilizing a standardized template applied across 70 existing schools. By employing advanced simulation tools, ENVI-met and IES-ve software, the research provides a comprehensive analysis of air temperature and energy use related to different courtyard orientations, specifically on key dates of September 21st and March 21st, representing seasonal variations. The results indicate that North-facing courtyards consistently provide cooler microclimates compared to other orientations. Specifically, North-facing courtyards showed temperature reductions of 1.31°C in September and 1.9°C in March compared to the least favorable orientations. This orientation recorded the lowest average mass temperatures of 29.36°C in September and 25.13°C in March, surpassing the West-facing orientation by 0.39°C and 0.45°C, respectively. The primary factor for this improvement is the reduced solar radiation exposure on East-West aligned courtyards, which significantly lowers the heat gain. Additionally, the study assessed Physiologically Equivalent Temperature (PET) readings and cooling demands, both of which were found to be lower in North-facing courtyards. Cooling load reductions varied between 1% and 4%, depending on the day, further emphasizing the efficiency of this orientation. These findings suggest that strategic courtyard orientation is a critical design consideration for enhancing thermal comfort and energy efficiency in school buildings. The implications of this research are significant for sustainable design and construction practices. By highlighting the benefits of optimal courtyard orientation, this study offers practical solutions for reducing energy consumption and improving the indoor and outdoor thermal environments of schools. These insights contribute to the broader goal of developing greener, more sustainable educational facilities, particularly in hot climates like the UAE. This research not only informs architects and urban planners but also supports policymakers in implementing effective sustainability strategies in the educational sector. [ABSTRACT FROM AUTHOR]

In many cities, protracted exposure of urban structures to excessive solar radiation and the urban heat island (UHI) phenomenon may cause thermal discomfort for pedestrians in outdoor spaces, particularly in hot-arid regions. This study investigated the impact of urban residential block morphology on microclimate in Isfahan, Iran, using ENVI-met numerical models. The predicted mean vote (PMV) model for residential outdoor spaces was calculated using simulation data for the hottest day of the summer to assess outdoor thermal comfort. Comparison of the alternatives – namely, diagonal blocks, compact linear blocks, continuous curved blocks and dispersed cubic blocks – showed that the microclimate performance of the diagonal type was better than those of the other design options, as the PMV index represented the minimum distance from the ideal thermal comfort range due to a lower sky view factor (SVF) and optimal orientation. The curved blocks performed better in urban open spaces, promoting airflow and reducing UHI, than linear and cubic blocks. The results showed that the PMV index could predict thermal comfort in various urban design patterns and could be affected by the morphology of urban blocks. Variations in building morphology alter the SVF and microclimate parameters, which have an impact on outdoor thermal comfort. [ABSTRACT FROM AUTHOR]

Chlorophyll in phytoplankton absorbs solar radiation (SR) and affects the thermal structure and dynamics within upwelling regions. However, research on this process across global‐scale coastal upwelling systems is still lacking. Here, we use a coupled ocean‐biogeochemical model to investigate differing responses to chlorophyll‐induced solar absorption between Pacific and Atlantic coastal upwelling regions. Chlorophyll‐induced solar absorption leads to colder Pacific coastal upwelling but warmer Atlantic coastal upwelling. In the Pacific, the shading effect of the surface chlorophyll maximum leads to colder subsurface water, which is then upwelled, contributing to cooling. The more stratified upper ocean leads to shallower mixed layer depth, intensifying offshore transport and upwelling. In the Atlantic, the absorption of SR by the subsurface chlorophyll maximum causes warmer and weaker upwelling. The processes described, in turn, trigger positive feedback to ocean biogeochemistry and potentially interact with climate dynamics, underscoring the necessity to incorporate them into Earth system models. Plain Language Summary: Chlorophyll and related pigments in phytoplankton play a key role in absorbing solar radiation and regulating ocean temperatures. In some coastal upwelling regions along the eastern boundaries of oceans, where chlorophyll concentrations are high, studies have suggested that the solar heat absorbed by chlorophyll can influence the temperatures and strength of upwelling. However, there is no study focusing on this process across global coastal upwelling zones. Here, we use computer simulations of ocean and phytoplankton to explore the effects of chlorophyll‐induced solar absorption on upwelling temperatures and strength on a global scale. Our study suggests that this effect varies between Pacific and Atlantic coastal upwelling regions due to their different spatial distributions of chlorophyll: surface chlorophyll in the Pacific warms the water after it has risen to the surface and as it is flowing offshore, while subsurface chlorophyll in the Atlantic warms the water before it rises to the surface. As a result, chlorophyll‐induced solar absorption leads to colder and stronger coastal upwelling in Pacific but warmer and weaker upwelling in Atlantic. Given the limited consideration of this process in previous studies, we emphasize the importance of incorporating it, along with regional differences, into future simulations. Key Points: Chlorophyll‐induced solar absorption leads to colder Pacific coastal upwelling but warmer Atlantic coastal upwellingIn Pacific, chlorophyll‐induced temperature variations intensify ocean stratification and coastal upwelling, in contrast to AtlanticChlorophyll‐induced variations in ocean physics trigger positive feedback, enhancing chlorophyll distributions in coastal upwelling regions [ABSTRACT FROM AUTHOR]

Through applying a 4‐MHz linear frequency modulation waveform, which has high range resolution and signal intensity, we successfully detected for the first time the ionospheric 150‐km echo enhancement at 430–450 MHz of the Ultra‐High‐Frequency (UHF) band using the newly built Sanya Incoherent Scatter Radar (SYISR). The obtained low signal enhancement (less than 0.5 dB) explains why previous UHF experiments did not detect them. We also found that our measured fine structure shows a much wider forbidden region than previous results and covers a much larger altitudinal and local time region. In comparison with recent upper‐hybrid instability theory and simulation, our results confirmed the predicted higher altitude occurrence, wider gaps between enhancements, the turn corner feature around sunrise, and perhaps the weak enhancement, which provide an independent evaluation of the newly proposed mechanism in UHF band. Future UHF experiments could further improve the physical understanding of 150‐km echo phenomenon. Plain Language Summary: In the altitude range of 130–170 km above the Earth's surface, the electrons ionized by the solar radiation effecting on the neutrals can generate enhanced radar echo with specific layered structures if the radar beam points perpendicular to or slightly off perpendicular to the geomagnetic field. Up to date, this phenomenon was only detected by lower frequency radars (30–60 MHz). Its physical mechanism has been a puzzle over several decades. Recently, due primarily to the advancement of numerical simulation technology and the improvement of computing power, this puzzle was resolved well by the newly proposed across scales energy transformation physical mechanism in several recent publications. However, these simulations also concluded that it is hard to be detected by higher frequency radars. In this paper, using our newly built Sanya Incoherent Scatter Radar, we applied a novel experimental setup to gain very high range resolution and signal intensity. We finally successfully detected this phenomenon in 430–450 MHz. By comparing our results with previous measurements and theoretical simulation, we can enhance current physical understanding from the perspective of observations. Key Points: Successful first detection of daytime 150‐km echoes in the Ultra‐High‐Frequency band with Sanya Incoherent Scatter RadarWe detected wider forbidden region and larger altitudinal and local time coverage than previous resultsNarrow layering features not following the zenith angle point to field‐aligned irregularitie echoes [ABSTRACT FROM AUTHOR]

Recent studies have suggested that stratospheric aerosol injection (SAI) of solid particles for climate intervention could reduce stratospheric warming compared to injection of SO2 ${\text{SO}}_{2}$. However, interactions of microphysical processes, such as settling and coagulation of solid particles, with stratospheric dynamics have not been considered. Using a global chemistry‐climate model with interactive solid particle microphysics, we show that agglomeration significantly reduces the backscatter efficiency per unit of injected material compared to mono‐disperse particles, partly due to faster settling of the agglomerates, but mainly due to increased forward‐ over backscattering with increasing agglomerate size. Despite these effects, some materials substantially reduce required injection rates as well as perturbation of stratospheric winds, age of air and stratospheric warming compared to injection of SO2 ${\text{SO}}_{2}$, with the most promising results being shown by 150 nm diamond particles. Uncertainties remain as to whether stratospheric dispersion of solid particles is feasible without formation of agglomerates. Plain Language Summary: [Stratospheric warming is an undesired side effect of climate intervention via SAI. Recent studies have shown that stratospheric warming could be reduced when injecting solid particles instead of gaseous SO2 ${\text{SO}}_{2}$. However, most of these studies looked at the stratospheric particle mass required for a given radiative forcing (RF), without accounting for gravitational settling of particles or the effect of particles sticking together after mutual collision. We show that accounting for these effects significantly reduces the amount of backward reflected radiation per unit of stratospheric particle mass decreasing the radiative efficiency of the injected material. This is due to the combined effect of faster gravitational settling and the larger fraction of forward reflected radiation over backward reflected radiation with increasing agglomerate size. However, we show that injection of diamond particles at a radius of 150 nm instead of SO2 ${\text{SO}}_{2}$ significantly reduces required stratospheric injection rates as well as perturbation of stratospheric winds, age of stratospheric air and stratospheric water vapor concentrations due to small stratospheric warming per unit of RF. However, large uncertainties remain as to whether it will be feasible to inject solid particles into the stratosphere at concentrations low enough to prohibit that the particles stick together.] Key Points: We explore stratospheric injections of six solid particles and gaseous SO2 within a climate model with comprehensive aerosol microphysicsAccounting for settling and agglomeration of solid particles can substantially reduce the radiative forcing (RF) per unit of injected materialInjection of diamond particles (r = 150 nm) instead of SO2 largely reduces stratospheric temperature, circulation and water vapor anomalies [ABSTRACT FROM AUTHOR]

Adhesive joints GFRP/Steel bonded with epoxy adhesive were exposed to the external environment for one year, a tropical environment with temperature variations, humidity, radiation, and salt spray, due to its proximity to the sea. The shear strength of the joint was measured over the exposure time, as well as their chemical and physical changes. Tg of the adhesive was evaluated through DMA, and changes in the composite matrix were studied using FTIR. A significant reduction in joint strength was observed within the first 30 days, but this reduction quickly stabilized. High corrosion of the metal adherend was evident, which was also observed in the bonded region, significantly impacting the adhesive strength of the joints. Changes in the color of the composite were also identified due to exposure to solar radiation, which also affected the chemical bonds, as indicated by FTIR. The Tg of the adhesive decreased in the first 90 days and then gradually increased. These changes may have been related to the humidity, initially decreasing the Tg, and the exposure to higher ambient temperatures, which causes post cure effect. Therefore, the proposed external exposure conditions contain factors capable of causing significant changes in the joint and its components. [ABSTRACT FROM AUTHOR]

The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135–210 min and winter drying took 145–225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10− 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945–15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly. [ABSTRACT FROM AUTHOR]

Solar energy is one of the renewable and clean energy sources. Accurate solar radiation (SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were developed in this study. Next, a data pre-processing technique named multivariate variational mode decomposition (MVMD) was used to construct the MVMD-GRU and MVMD-LSTM hybrid models. To better test the performance of proposed simple and hybrid models, four stations located in the Illinois State of the USA (i.e., Dixon Springs, Fairfield, Rend Lake, and Carbondale) were considered as the study sites. Whole the simple and hybrid models were established under two different strategies, i.e., local and external. In the local strategy, SR of each location was estimated using the minimum and maximum air temperatures from the same station. While, minimum and maximum air temperatures as well as SR data from the nearby station were utilized in external strategy to estimate SR time series of any target site. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics were used when evaluating the models performances. The overall results revealed that the proposed MVMD-GRU and MVMD-LSTM hybrid models illustrated better SR estimates compared to the simple GRU and LSTM in both the local and external strategies. The values of error metrics obtained for the superior hybrid models (i.e., MVMD-LSTM) during the testing period were as: RMSE = 2.532 MJ/m2.day, MAE = 1.921 MJ/m2.day, R2 = 0.916 at Dixon Springs; RMSE = 2.476 MJ/m2.day, MAE = 1.878 MJ/m2.day, R2 = 0.921 at Fairfield; RMSE = 2.359 MJ/m2.day, MAE = 1.780 MJ/m2.day, R2 = 0.924 at Rend Lake; RMSE = 2.576 MJ/m2.day, MAE = 1.941 MJ/m2.day, R2 = 0.914 at Carbondale. Therefore, the coupled models proposed in this study can be possibly recommended as suitable alternatives to the simple deep learning models with a reliable precision in estimating SR time series. [ABSTRACT FROM AUTHOR]

The Expected Solar Performance and Ramp Rate tool (ESPRR) is an open-source interactive web-based application that reliably calculates ramp rate (RR) statistics and an expected power generation time series for prospective photovoltaic (PV) systems. Users create PV systems by defining site parameters. ESPRR uses those parameters with irradiance data from the National Solar Radiation Database (NSRDB) to create a time series of power output from which RR statistics are calculated. This study rigorously evaluates ESPRR's performance using 5 years of measured power output from a fleet of utility-scale systems and finds that ESPRR calculates stress-case RRs within an error of 0.05 MW/min and 0.42 MW/min for the worst-case RRs. We evaluate the expected AC power output in clear-sky conditions and find an NRMSE of less than 10% and an NMBE of less than 6% for the fleet's largest system. The NRMSE is 10%-15% of system capacity for nonclear-sky conditions, and the NMBE is about zero. The evaluation shows that ESPRR can estimate PV output and RRs that are representative of operational systems, meaning users can use the results from ESPRR in the decision-making process for designing new systems or when adding systems to an existing fleet. Since only system parameters are required to site a proposed system anywhere on a map, users can site and reposition a fleet of PV systems in a way that reduces significant RRs. As the grid-tied PV capacity continues to increase, the mitigation of significant RRs grows in importance. ESPRR can help developers and utilities create geographically diverse fleets of PV systems that will promote grid reliability and avoid significant RRs. ESPRR source code is available at https://github.com/UARENForecasting/ESPRR. [ABSTRACT FROM AUTHOR]

Covering approximately 71 % of Earth's surface and absorbing almost 70 % of the solar radiation energy, water presents a tremendous opportunity for hydropower generation, revealing considerable promise for future applications. Benefited from the low cost, negligible pollution, and the characteristic of solely utilizing ambient thermal energy, hydrovoltaic (HV) technology has garnered significant attention in recent years for its substantial contributions to energy harvesting and conversion. While traditional hydrovoltaic generators (HVGs) have predominantly utilized two‐dimensional (2D) structures, the emergence of three‐dimensional (3D) HV materials signifies a pivotal shift due to superior specific surface areas, intricate porous architectures and enhanced mechanical strength. Herein, we summarized the development of 3D HVGs, categorizing them into flow‐induced, moisture‐induced, and evaporation‐induced types. We explored their working mechanisms, evolutions, strategies for electricity output enhancement and the limitations they face. Moreover, we discussed the integration of HVGs with other energy conversion technologies and the development of comprehensive HVG systems that exploit various water sources for energy generation. At last, we highlighted the challenges confronting 3D HVGs and anticipated future directions for this burgeoning field. [ABSTRACT FROM AUTHOR]

The kinetic tendencies in the destruction of cephalosporin antibiotics (ceftriaxone (CEF)) in photoinitiated oxidative systems were studied using a xenon lamp as a source of quasisolar radiation (UV–Vis). It was established that the oxidative systems under study can be arranged in the following series according to the efficiency and rate of antibiotic destruction: {UV–Vis/Fe2+/S2O } > {Fe2+/S2O } {UV–Vis/S2O } > {UV–Vis}. The optimum conditions for the oxidative destruction of CEF in the {UV–Vis/Fe2+/S2O } system are reached at [S2O ] : [CEF] = 30 : 1 and [S2O ] : [Fe2+] = 1 : 0.1. As the temperature increases to 40°C, the initial CEF oxidation rate and efficiency increase. The apparent activation energy of the CEF oxidation in the {UV–Vis/Fe2+/S2O } system was 45 kJ mol–1, which is comparable to the values obtained for cephalosporin antibiotics. It was proved, using inhibitors of radical reactions, that the oxidative destruction of CEF in combined {UV–Vis/Fe2+/S2O } proceeds by a multiradical mechanism involving reactive oxygen species (ROS): hydroxyl radicals and sulfate and superoxide radical anions. The obtained tendencies are in good agreement with the results of open-air studies with natural solar radiation; in the {Solar/Fe2+/S2O } system, the oxidation of CEF is significantly intensified due to combined activation of persulfate by the iron ions, the UV-C (<300 nm) component of natural solar radiation, and the thermal exposure. The results indicate that using the combined {Solar/Fe2+/S2O } oxidation system is promising for the destruction of antibiotics in order to reduce their release into the environment. [ABSTRACT FROM AUTHOR]

AbstractThis paper addresses the global issue of water scarcity and the need for sustainable development by exploring eco-friendly desalination technology – solar still. Although solar still has lower productivity compared to conventional fossil-fueled desalination technologies, much research is being conducted to enhance its productivity. Most studies focus solely on optimizing water mass in solar still, neglecting other correlated factors. Hence, this paper presents mathematical modeling and experimental studies on the impact of water mass, initial temperature, and solar insolation on solar still performance in Polish weather conditions. The findings reveal that solar still productivity ranges from 215 mL/m2/day to 5079 mL/m2/day between June and October in Poland. It is observed that solar still productivity decreases with increasing water mass beyond the critical threshold of solar insolation on the glass cover plane, which is 2.676 kWh/m2/day. However, below that threshold, the effect of water mass on the solar still productivity strongly depends on its initial temperature. [ABSTRACT FROM AUTHOR]

Solar collector systems efficiently transform sunlight into energy that may be used to meet various needs. This research aimed to use the Taguchi method to determine the ideal operating parameters for a solar thermal collector with a rectangular spiral absorber. Controllable parameters including mass flow rate, solar radiation, and absorber design were manipulated during the energy recovery process, and features like PV temperature and outlet water temperature were used to assess the system's effectiveness. The findings indicate that certain criteria significantly affect response indicators. The observed percentage contribution of absorber design, solar radiation, and the mass flow rate was 69.19%, 27.99%, and 2.83% in PV surface temperature. In comparison, the individual percentage contributions were 73.63%, 13.51%, and 10.57% for absorber design, solar radiation and mass flow rate for water output temperatures. The present model's R2 values for PV and outlet water temperatures are 97.24% and 99.67%, respectively. The Predictive regression model was found in fine harmony and the maximum percentage error is limited to 0.68%. The maximum analytical electrical efficiency was observed with a spiral rectangular absorber of 14.57% at the lowest mass flow rate of 0.04 kg/s at the lowest radiation level of 600 W/m2. In comparison, maximum analytical thermal efficiency was observed with a spiral rectangular thermal absorber of 63.56% at the highest flow rate of 0.06 kg/s and the highest solar radiation level of 1000 W/m2. The analytical and experiment findings were in better agreement in this study, with the highest relative error of 7.52%. According to the study's findings, the rectangular absorber-based PVT system is at its best at a higher mass flow rate to lower PV temperature and boost thermal energy recovery via water. The present research work can be extended for exergy, environmental, and economic feasibility analysis. [ABSTRACT FROM AUTHOR]

During the period from 2019 to 2021, a series of experiments were carried out to study the uptake of tritium by crops in an area heavily contaminated with atmospheric tritium oxide (HTO), at the former Semipalatinsk test site in Kazakhstan. A quantitative assessment is given of the tritium uptake by typical crops (lettuce, tomatoes, peppers and beans) cultivated all over Kazakhstan in the case of a short-term tritium oxide vapor exposure. The plant samples were collected during and after exposure and analyzed for the tritium concentration in two chemical forms: tissue-free water tritium (TFWT) and organically bound tritium (OBT). During the entire series of experiments, the tritium concentration in free water from leaves and ambient air was of the same order of magnitude. The tissue water tritium concentrations of stems and edible parts was 1 to 2 orders of magnitude lower than in the surrounding air. The average value of the TFWT/HTOatm ratio in the leaves and the edible part was (0.73±0.2) and (0.04±0.002), respectively. The organically-bound tritium concentration is 1–2 orders of magnitude lower than the tissue water tritium and ambient air concentrations. Under aerial tritium oxide uptake, the distribution of tritium in non-leafy crops was as follows: leaf–stem–fruit (in decreasing order). After exposure, a non-significant amount of tritium is firmly retained in plants for a long time. The tissue water tritium concentrations correlate closely with atmospheric tritium oxid (r = 0.76), correlate weakly with temperature (r = 0.43) and relative humidity (r = -0.43), and correlate moderately with solar radiation intensity (r = 0.56). There was no reliable correlation between the concentration of tritium in organic matter and in ambient air. The concentration of tritium in the free water of leaves is closely correlated with the concentration in the free water of the stems (r = 0.95) and fruits (r = 0.78). The organically-bound tritium concentration in leaves is closely correlated with the organically-bound tritium concentration in stems (r = 0.99) and fruits (r = 98). The results of the study should be considered when evaluating the impact of tritium oxide emissions on the population living near nuclear power. [ABSTRACT FROM AUTHOR]

Energy generation and economic development are closely linked, with energy playing a pivotal role in wealth creation. However, the finite nature of fossil fuel resources and associated environmental challenges has emphasized the need for sustainable energy alternatives based on renewable sources. Among these alternatives, photovoltaic (PV) systems hold significant potential for fostering a sustainable energy system. Despite ongoing material research, achieving a major breakthrough in enhancing the conversion efficiency of commercial PV modules remains a challenge. To optimize PV system yield, installation must consider geographic location and design factors. PV modules often yield different results compared to manufacturer specifications due to factors such as high temperatures, dust, and arid to semiarid climates. Therefore, it is crucial to conduct outdoor testing and characterization of PV modules tailored to specific locations, such as Jordan, to maximize system performance. This research aims to examine the impact of ambient temperature, dust accumulation, and solar irradiance intensity on PV system performance in Jordan, providing valuable insights through monitoring power output variations. The findings will contribute to improving PV system efficiency in Jordan's unique climate and aid manufacturers in developing innovative PV applications. The collected data includes solar radiation, temperature, voltage, current, and output power. The results confirm that the Azraq site is a better location when compared to Mafraq based on the possibility of generated more output power using a PV system. [ABSTRACT FROM AUTHOR]

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms. [ABSTRACT FROM AUTHOR]

Typhoons are one of the most important weather systems that can cause severe ozone (O3) pollution in eastern China. While the effects of individual typhoons on O3 concentrations have been extensively studied, the effects of consecutive northward typhoons and the underlying mechanisms remain unclear, partly due to the complex processes involved. Here, Typhoons Maysak and Haishen, two consecutive northward typhoons in 2020, are selected to investigate their impact on the O3 pollution in eastern China. The results show that consecutive northward typhoons not only produced and maintained meteorological conditions conducive to O3 generation (e.g., elevated temperatures and intensified solar radiation), but also facilitated local accumulation and cross-regional transport of O3. These factors jointly led to a 30% increase in O3 concentration in eastern China with a prolonged period of O3 pollution. Our work underscores the significance of complex meteorological conditions in O3 pollution occurrences during extreme weather events, advancing our understanding of how consecutive northward typhoons affect air quality. [ABSTRACT FROM AUTHOR]

Using observation data and numerical simulations, we have demonstrated that the stratospheric Quasi-Biennial Oscillation (QBO) can predict extreme high temperatures (EHTs) in South Asia in June. The vertical structure of the QBO plays a crucial role in this prediction. When the QBO in June shows easterlies (westerlies) at 50 hPa and westerlies (easterlies) at 70 hPa, more (fewer) EHT events occur. This likely results from the QBO's vertical structure causing positive (negative) temperature anomalies in the lower stratosphere and negative (positive) static stability anomalies near the tropical tropopause. These anomalies enhance (weaken) convective activity over the equatorial Indian Ocean, leading to anomalous circulation with ascending (descending) air over the equatorial Indian Ocean and descending (ascending) air over northern and central South Asia. This suppresses (promotes) convection over northern and central South Asia, affecting cloud formation and precipitation. Consequently, more (less) solar radiation reaches the region, along with weaker (stronger) evaporative cooling effects, warming (cooling) the surface and creating a background state conducive to (against) EHT events. Additionally, the opposite zonal winds at 30 hPa and 50 hPa in April may serve as a reference factor for predicting the probability of EHT events in northern and central South Asia. This study provides a potential approach for forecasting tropospheric extreme weather events based on stratospheric signals. [ABSTRACT FROM AUTHOR]

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, "PV" specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model's efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model. Highlights: Introduction of a novel approach: This study presents a pioneering methodology focusing exclusively on supervised machine learning techniques for short-term uncertainty management within 100% PV microgrids, aimed at optimizing energy management efficiency. Comprehensive comparative analysis: Through meticulous comparative investigation, various regression techniques for predicting solar radiation within PV microgrids are scrutinized, providing valuable insights into their efficacy across diverse environmental conditions. Validation across diverse locations: The validation of findings across four distinct geographical locations, encompassing both PV and concentrated PV (CPV) systems, substantially enhances the generalizability of the results, advancing the understanding of solar radiation prediction dynamics for renewable energy integration strategies. [ABSTRACT FROM AUTHOR]

Adequate fertilizer concentration and use of shade nets can favor the development and yield of agricultural crops. The objective of this investigation was to evaluate the growth of roselle plants with nutrient solutions of different electrical conductivities (ECns) (1.0, 2.0, 3.0, 4.0 and 5.0 dS m−1) and under different colored shade nets (red, blue, black) compared with full sun. The experiments were conducted in a controlled greenhouse environment and in full sun in the Plant Production Department of ESALQ-USP, Piracicaba, SP, Brazil. The experiments were organized using a 4 × 5 randomized block design. The results of analysis of variance and regression showed a significant impact of EC and colored shade nets on plant height, stem diameter, number of leaves, number of flowers, fresh and dry mass of shoots and fresh and dry calyxes. The data were subjected to analysis of variance and regression, which showed a quadratic effect for the variables studied, with increasing values up to 3.0 dS m−1; after this value, there was a decrease. Increasing ECns up to approximately 3.0 dS m−1 promoted increments of 2.34% in plant height, 7.21% in number of leaves, 19.76% in shoot fresh mass and 12.38% in shoot dry mass. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand a physical mechanism to determine the surface temperature of clothes in calm and fine conditions of outdoors. We observed surface temperatures of polo shirts of the same material and design but different colors. The shirts were placed in unshaded and well-ventilated outdoor, open spaces on sunny summer days. The maximum difference between dark green or black and white was more than 15 °C during calm, fine weather and was greatest when the solar radiation was strong. If the transmission of solar radiation energy through a shirt is ignored to calculate the absorption by the shirt, the difference in solar radiation absorption due to different colors is as much as 24% in the maximum, and if considered, we concluded that an absorption difference of 34% led to a temperature difference of 15℃. When we compared the brightness of the colors, we found that the albedo of both the visible and NIR bands explained why the red and green colors were so different with respect to the surface temperatures we observed. The reflection in the NIR bands was also an important determinant of the surface temperature. An additional experiment using masks showed that the temperature difference between white and black was almost eliminated at a wind speed of ~ 3 m/s. The color of clothing is therefore a target for small-scale adaptation to climate change. [ABSTRACT FROM AUTHOR]

Solar geoengineering, also called sunlight reflection or solar radiation modification (SRM), is a potential climate response that would cool the Earth's surface and reduce many other climate changes by scattering on order 1% of incoming sunlight back to space. SRM can only imperfectly correct for elevated greenhouse gases, but it might complement other climate responses to reduce risks, while also bringing new risks and new challenges to global governance. As climate alarm and calls for effective near-term action mount, SRM is attracting sharply increased attention and controversy, with many calls for expanded research and governance consultations along with ongoing concerns about risks, misuse, or overreliance. We review SRM's history, methods, potential uses and impacts, and governance needs, prioritizing the approach that is most prominent and promising, stratospheric aerosol injection. We identify several policy-relevant characteristics of SRM interventions and identify four narratives that capture current arguments over how SRM might be developed or used in sociopolitical context to either beneficial or destructive effect, with implications for near-term research, assessment, and governance activity. [ABSTRACT FROM AUTHOR]

Purpose:Demodex infestation is a risk factor for several ocular surface diseases. However, the prevalence of ocular Demodex infection in the ultra-high altitude population is not clear. This study aimed to compare the prevalence and factors associated with Demodex in populations residing in ultra-high altitude region and sea level areas. Methods: Consecutive patients who visited Shigatse People's Hospital (> 4,000 m) and Shanghai Tongren Hospital (sea level) for eye complaints between January 2023 and January 2024 were included. Subjects were divided into ultra-high altitude and sea level groups. All subjects underwent eyelash epilation for ocular Demodex identification and counting. Demographic and lifestyle information was also collected. Results: A total of 517 subjects were eligible, including 255 subjects in the ultra-high-altitude group and 262 subjects in the sea level group. In the overall analysis, the prevalence of ocular Demodex infection was significantly different between the ultra-high-altitude and sea level groups (15.7% vs. 33.2%, P < 0.001). Multiple logistic regression showed that age, time spent outdoors, and makeup were associated with ocular Demodex infection in both groups. In addition, in the ultra-high-altitude group, people who wear sun hats outdoors were more likely to be infected with Demodex. Conclusion: The infection rate of ocular Demodex in the residents of ultra-high altitude area was significantly lower than that in the residents of sea level area, which may be related to lower ambient temperature, lower humidity, and higher solar radiation. Additionally, age, time spent outdoors, and makeup may be associated with ocular Demodex infection. [ABSTRACT FROM AUTHOR]

Albedo plays a key role in regulating the absorption of solar radiation within ice surfaces and hence strongly regulates the production of meltwater. A combination of Landsat and Sentinel 2 data provides the longest continuous medium resolution (10–30 m) earth surface observatory records. An albedo product (harmonized satellite albedo, hereafter HSA) has already been developed and validated for the Greenland Ice Sheet (GrIS), using harmonized Landsat 4–8 and Sentinel 2 datasets. In this paper, the HSA was validated for various Arctic and alpine glaciers and ice caps using in situ measurements. We determine the optimal spatial window size in point-to-pixel analysis, the best practices in evaluating remote sensing algorithms with groundtruth data, and cross sensor comparison of the Landsat 9 (L9) and Landsat 8 (L8) data. The impact of the spatial window size on measured ice surface homogeneity and albedo validation was analysed at both local and regional scales. Homogeneity statistics calculated from the grey-level co-occurrence matrix (GLCM) suggest that the ice surface becomes more homogeneous as the image resolution becomes coarser. The optimal spatial window size was found to be 90 m, based on maximizing the statistical and graphical measures while minimizing the root mean square error and bias. HSAs generally agree closely with in situ albedo measurements (e.g. Pearson's R ranges from 0.68 to 0.92) across various Arctic and alpine glaciers and ice caps. Cross sensor differences between L9 and L8 are minor, and we suggest that no harmonization is necessary to add L9 to our HSA product. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model's performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter's efficiency compared to the inverter's power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere–atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caxiuanã-PA forest (2005–2008), climatic data, CO2 exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site's energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (−569.7 ± 444.9 gC m−2 year−1), with intensity varying according to the site's annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas. [ABSTRACT FROM AUTHOR]

Context: The coat is the first defense layer protecting animals from direct sunlight, influencing radiant-heat absorption and loss. Dark coat colour may increase animal heat impact because of higher absorption of solar radiation. Aims: We investigated the impact of pigmentation intensity on the heads of animals and their thermal responses to solar radiation. Furthermore, we compared the thermal responses between dark and light brown-coloured animals, to determine whether the duration of sun and shade exposure (3 h each) is sufficient to differentiate the heat gain and losses between the two groups. Methods: The effect of coat colour on rectal and surface temperatures in Boer goats, classified according to their head pigmentation pattern as dark (DB, N = 11) or light brown (LB, N = 11), exposed to sun and shade, was evaluated. The animals' body and head surface temperatures were measured the same day before, during, and after sun exposure by using infrared thermography. Individual thermographic images were analysed by body regions (rump, leg, neck) and head areas (eye, ear, upper and lower part of the head, and the muzzle). Key results: During solar radiation, all measured variables increased. In the shade, the rump, leg, and neck surfaces remained elevated, whereas the head area cooled down faster, with the highest temperature changes measured for the ear of about 10%. Most surface cooling occurred within 30 min after returning to the shade. Coat colour groups differed only in higher overall muzzle temperature of DB than that of LB goats (P = 0.048). Correlations between rectal and surface temperatures were low to medium, ranging from −0.02 (eye) to 0.31 (muzzle). Conclusions: The head area is suggested as the important thermal window for heat dissipation and selective brain cooling. Light brown-headed animals were more efficient in eliminating the heat gained from solar radiation when returned to shade. Implications: The colouration of the animal's head may play an important role for its heat tolerance. As an easy-to-recognise characteristic, coat colour can serve as a selection trait for local goat populations. In view of global warming, selection for lighter coat colour in domestic ruminants may also become important even under temperate climates. Coat colour plays an important role in heat tolerance of livestock, because dark coat colour may increase animal heat stress as a result of higher absorption of solar radiation. We focused on the influence of head pigmentation of Boer goats and found light brown-headed animals to be more efficient in eliminating the solar heat gained during outdoor grazing when returned to the shade. In view of global warming, breeding for light coat colour in domestic ruminants could alleviate heat stress. [ABSTRACT FROM AUTHOR]

Purpose: Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods: The objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results: Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion: As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace. [ABSTRACT FROM AUTHOR]

The modeling and forecasting of Total Electron Content (TEC) play a major role in influencing signals from satellite-based navigation systems and impact the performance of diverse satellite-dependent technologies. The intensity of solar ionizing radiation and the state of geomagnetic field activity influence the Global Navigation Satellite System (GNSS)-TEC. This paper uses a Linear TEC Function (LTF) climatology model to understand ionospheric behavior under solar and geomagnetic activities that cause variations in the electron distribution of the ionosphere medium. The LTF model integrates representations of solar EUV photon (MgII) and geomagnetic (SYMH) activities, incorporating solar-modulated oscillations (periodic variations) at four seasonal cycles and a linear trend. The LTF model examined the time series of GPS-TEC at a location (geographic 34.95° N, 134.05° E) with a time resolution of 1 h, from 1997 to 2016, covering solar cycles 23 and 24. The Root Mean Square Deviation (RMSD) and correlation coefficient between the GNSS-TEC and model TEC (LTF) was 5.30 TECU and 95 %. The results indicate that solar components, as well as annual and semi-annual variations, have a significant impact on the daily average TEC. Solar activity appears to be the predominant determining factor of TEC during the solar phases of cycles 23 and 24. In contrast, periodic influences primarily outline TEC during periods characterized by minimal solar activity. The geomagnetic component presents an increased influence, particularly during storm periods. The model demonstrates superior performance in Total TEC modeling compared to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study employs a machine learning approach to investigate the impact of climate change on fig production in Turkey. The eXtreme Gradient Boosting (XGBoost) algorithm is used to analyze production performance and climate variable data from 1988 to 2023. Fig production is a significant component of Turkey's agricultural economy. Therefore, understanding how climate change affects fig production is essential for the development of sustainable agricultural practices. RESULTS: Despite an observed increase in fig production between 2005 and 2020, potential yield may be negatively impacted by climate variables. Identifying the specific climatic factors affecting fig production efficiency remains a challenge. In the study, two different machine learning models are created: one for fig production yield per decare and another for fig production yield per bearing fig sapling. Eight climate variables (16 variables considering day and night values) serve as independent variables in the models. The models reveal that temperature change has the highest impact, with a percentage contribution of 41.30% in the first model and 43.90% in the second model. Thermal radiation (day and night) and 2 m temperature also significantly affect individually fig production. Wind speed, precipitation and humidity contribute to a lesser extent. CONCLUSION: This study illuminates the intricate interrelationship between climate change and fig production in Turkey. The utilization of machine learning as a predictive tool for future production trends and an instrument for informing agricultural practices is a valuable contribution to the field. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A numerical approach to solve the perturbed Lambert's problem is presented. The proposed technique uses the theory of functional connections, which allows the derivation of a constrained functional that analytically satisfies the boundary values of Lambert's problem. The propagation model is devised in terms of three new variables to mainly avoid the orbital frequency oscillation of Cartesian coordinates. Examples are provided to quantify robustness, efficiency, and accuracy on Earth- and Sun-centered orbits with various shapes and orientations. Differential corrections and a robust Lambert solver are used to validate the proposed approach in various scenarios and to compare it in terms of speed and robustness. Perturbations due to Earth's oblateness, third body, and solar radiation pressure are introduced, showing the algorithm's flexibility. Multi-revolution solutions are obtained. Finally, a polynomial analysis is conducted to show the dependence of convergence time on polynomial type and degree. [ABSTRACT FROM AUTHOR]

The exploration of small bodies in our solar system is of great interest for the planetary science community due to their high scientific value. However, their generally weak and irregular gravity fields increase the difficulty associated with close proximity operations. Moreover, solar radiation pressure (SRP) can significantly perturb the motion of objects in their vicinity, particularly for bodies with high area-to-mass ratios. In this work, we adopt the polyhedral gravity model and identify natural dynamical structures that can be used for mission operations. Further, we study forced periodic motion in the body fixed frame while accounting for the effect of SRP with eclipses. Overall, our work seeks to identify suitable orbits and locations in the vicinity of small bodies that can be exploited for the design of science orbits. To obtain periodic orbits in the model accounting for SRP perturbations, we use a Melnikov function to find orbits that satisfy resonances with the asteroid spin and show no net change in energy over the orbit. We then use a differential correction scheme to find numerical solutions in the time-periodic model. Our test cases are potentially hazardous asteroid 101955 Bennu and main belt asteroid 16 Psyche. [ABSTRACT FROM AUTHOR]