Search Results (2,046)

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

Solar-Driven Photothermal Membrane Distillation (SPMD) integrates solar energy using photothermal coatings on the hydrophobic membranes, such as carbon black nanoparticles coated on PVDF membranes, to achieve localized heating at the liquid–vapor interface. This approach enhances energy efficiency by mitigating temperature polarization and reducing thermal energy demands compared to conventional membrane distillation (MD). However, the challenges of fouling and scaling, which can significantly impair membrane performance, continue to be a serious concern, similar to other MD configurations. This comprehensive review establishes a unified framework connecting core transmembrane mass and heat transfer mechanisms with the thermodynamic pathways of surface fouling and scaling. We critically evaluate various strategies for mitigating scaling and fouling, including the development of omniphobic membranes, the introduction of nano/micro bubbles, the addition of anti-scalants and surfactants, and the implementation of chemical and mechanical pretreatments. Subsequently, the impact of photothermal coatings, applied to the feed–membrane interface in SPMD to absorb solar radiation, on scaling and fouling resistance is also discussed. Finally, we provide a comprehensive review of advanced computational paradigms, for both coupled radiative-thermal and dynamic fouling models—contrasting deterministic, physics-based multi-phase Computational Fluid Dynamics (CFD) with empirical Response Surface Methodology (RSM) and predictive Artificial Intelligence (AI) data-driven models. Beyond this survey, we identify and directly address a critical, previously unquantified gap in the field of SPMD: the absence of an explicit thermodynamic link between transmembrane heat/mass transfer and the nucleation and adhesion processes that govern scaling and fouling, and we further highlight the practical barriers—photothermal coating durability, economic feasibility, and technology readiness—that currently separate laboratory-scale SPMD from field deployment. This holistic synthesis charts future engineering strategies for scalable, fouling-resistant, and optimized solar-driven desalination infrastructure. Full article

This paper presents a generic stochastic simulation framework for evaluating the operational feasibility of solar-powered unmanned aerial vehicles (UAVs) executing an invariant trajectory in high-variability climates. Unlike conventional approaches relying on idealised irradiance conditions, the proposed framework combines a modified ASHRAE radiation model corrected for local bias and variability with a semi-Markov process modelling stochastic transitions between cloud and sunlight states using parametrised state duration times. The environmental model is further extended with diurnal temperature variation and standard atmosphere effects. UAV motion is represented using a rigid body flight dynamics model combined with a cascaded trajectory tracking controller and an energy subsystem incorporating a lithium-ion battery model. Warsaw (Dfb climate) is used as a representative Central European test case characterised by frequent radiation deficits and highly variable atmospheric conditions. The simulations quantify the influence of environmental uncertainty and selected battery capacities on mission success probability across different solar-to-wing area ratios, with the mission entry at 70% initial battery state of charge and no additional manoeuvre losses or external atmospheric perturbations. The evaluations were conducted for a fixed mission start at solar noon on 15 July and were supplemented by an optimised mission scheduling analysis to establish upper flight-time limits. The results demonstrate the strong sensitivity of solar-assisted UAV operations to stochastic cloud conditions and support the design and mission planning for low-altitude long-endurance aircraft. Full article

The main objective of the current study is to assess the techno-economic feasibility, climate change adaptability, and sustainability of photovoltaic energy systems in six large hospitals in Turkey (Adana, Başakşehir, Bursa, Elazig, Gaziantep, and Yozgat) to achieve United Nations recommendations as Sustainable Development Goal 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action). This study aims to determine the impact of long-term climate change on the availability of photovoltaic (PV) energy resources. To achieve this goal, this research was conducted through a multi-step approach combining (1) the detection of long-term climate trends using linear regression on the TerraClimate database, (2) the spatial analysis of photovoltaic solar energy potential using high-resolution satellite imagery (Google Maps) for roof suitability and parking areas, (3) the estimation of photovoltaic electricity generation and the calculation of the capacity factor, (4) the application of the Response Surface Methodology (RSM) based on NASA Giovanni data to model the nonlinear reciprocal relationships between precipitation (R), aerosol optical thickness (AOT), photovoltaic solar energy production, and (5) the techno-economic analysis using the Levelized energy cost (LCOE), payback period, and CO₂ emission reductions. The results show statistically consistent warming trends across all sites with trends for Tmax ranging from +0.0205 to +0.0268 °C/year and for Tmin from +0.0208 to +0.0300 °C/year. The temperature of PV cells increases at a rate of +0.0197 °C/year and the wind speed decreases by −0.0031 to −0.0149 m/s/year, which indicates a reduction in convective cooling. Solar radiation, on the other hand, is relatively constant with small trends ranging from +0.0002 to +0.0566 W/m²/year, and confirms the consistent solar resource availability. Seasonal PV resource potential varies from ~70–95 W/m² in winter to 290–310 W/m² in summer. Furthermore, the installed PV capacities are between 6 MW (Yozgat) and 47 MW (Başakşehir) with capacity factors of 17.0–19.7% and payback periods of 4.31–4.88 years. RSM models have high explanatory power (R² = 0.57–0.74) with AOT as the most important negative driver of PV performance. Consequently, the results show that while the solar resource of Türkiye is stable and highly exploitable, PV efficiency is increasingly determined by climate-induced thermal stress and reduced wind cooling. The study highlights the economic viability, environmental advantages, and strategic relevance of PV systems at hospitals for resilient, low-carbon healthcare infrastructure in future climate scenarios. Full article

A synchronous-Buck converter is proposed as part of a maximum power point tracking (MPPT) controller for charging a 12 V battery from a photovoltaic (PV) solar panel. This design incorporates a control module that manages energy flow from the solar panel to the battery. The novel module sequentially integrates MPPT, active current limiting (constant current, CC), and voltage regulation (constant voltage, CV), along with protection mechanisms and low-computational-complexity control. It also incorporates a synchronous-Buck converter with a current PI control loop; this differs from the strategy used in a traditional Buck converter in terms of how much switching losses are reduced. The proposal integrates these two stages and establishes a robust control scheme that enables the battery charging process in photovoltaic systems to adapt to variations in both input and output conditions. Additionally, an irradiance detection logic has been incorporated to ensure that the system operates only when there is sufficient solar radiation is available, thereby extending the system’s lifetime and improving overall energy utilization. This work focuses on the practical integration and experimental validation of a comprehensive maximum-power-point-tracking (MPPT)-based charging system. The main contributions of this study can be summarized as follows: (1) practical integration; (2) experimental validation; (3) hardware-based protection strategy and low-computational-complexity control. To validate the designed system, a 70 W prototype with 90% efficiency was implemented and tested. The experimental results verify that the implemented protection mechanism prevents the battery from discharging unnecessarily during low-irradiance or nighttime conditions. This improves the overall efficiency of the system and extends the battery’s lifetime. Full article

Accurate solar radiation forecasting is essential for photovoltaic energy generation, smart grid stability, and renewable energy management. This study proposes a lightweight hybrid deep learning framework that combines a transformer encoder and Gated Rrecurrent Uunit (GRU) network for short-term solar radiation forecasting in Makkah and Madinah, Saudi Arabia. Hourly meteorological data from the NASA POWER dataset (2020–2025) were utilized, including solar radiation intensity, temperature, humidity, wind speed, cloud amount, rainfall, surface pressure, and dew point temperature. A preprocessing pipeline consisting of missing value treatment, outlier removal, normalization, timestamp alignment, and data cleaning was applied to improve data quality. Feature engineering techniques were incorporated to capture temporal dependency, meteorological interactions, weather dynamics, and solar variability patterns. The transformer encoder was used to learn long-range temporal dependencies through multi-head self-attention, while the GRU layer modeled sequential temporal dynamics efficiently. Hyperparameter optimization was performed using Bayesian optimization with Optuna. The experimental results demonstrate that the proposed transformer GRU framework achieved a Mean Absolute Error (MAE) of 0.014, Root Mean Square Error (RMSE) of 0.0219, and a coefficient of determination (${\mathrm{R}}^2$) of 0.98. The proposed model outperformed ARIMA, LSTM, GRU, and XGBoost models while maintaining stable performance across varying weather conditions and forecasting horizons. Full article

Large episodes of solar activity of the 25th cycle, an extreme Forbush decrease event on 19 January 2026, a series of Forbush effects in November 2025, and an event of ground-level enhancement (GLE 77) on 11 November 2025 have left prominent traces in the monitoring data obtained at a height of 3340 m a.s.l. from the detector facilities of the Tien Shan High-Mountain Cosmic Ray Station. The effects these events have caused on the flux of galactic cosmic rays in the several-GeV energy range, as registered with the standard NM64-type neutron supermonitor, are compared here with their influence on the local neutron and gamma radiation background in the high-mountain environment, which was observed in the counting rate records of the thermal neutron and MeV-order-energy gamma radiation detectors also installed at the station. Full article

Solar stills are considered an effective solution to produce fresh drinking water from saline water. Solar stills utilize solar energy, which is available in abundant quantity for long periods across Middle Eastern countries like Oman. In this study, two single-slope passive solar stills are fabricated with two configurations: a Conventional Solar Still (CSS) and a Modified Solar Still (MSS). The CSS is the basic model, whereas the MSS is a model obtained by incorporating copper tubes that are filled with phase change material (PCM) combined with nano copper oxide particles, which are attached inside the basin. The objective of this study is to compare the performance of the two systems from energy, exergy, and economical aspects. The solar stills were fabricated according to the geometrical conditions of the city Nizwa, Oman, and the standards for the fabrication of each solar still component. The highlights of this research are comparing the performance of the CSS and MSS under the prevailing atmospheric conditions of the city Nizwa, Oman, and investigating the effects of the nano materials and phase change materials used in the MSS on its performance. The results of the study reveal certain important facts; for example, higher thermal conductivity of the copper tubes increases the heat transfer and evaporation of saline water inside the basin. The freshwater production in the MSS was higher than in the CSS, with an average difference of about 79.75%. This difference in freshwater production is due to the accumulated heat storage and release of heat from the PCM material combined with nanoparticles during reduced solar radiation. The nanoparticles contributed to an increase in the heat transfer rate of the PCM. The presence of copper tubes filled with nano-PCM in the MSS influences and increases both energy and exergy efficiencies to around 30 to 35% and 1 to 1.5% compared to those in the CSS. The increased efficiencies in the MSS are due to its improved evaporation and condensation rates, which enhance the energy utilization in the process of converting saline water to freshwater. Full article

The dynamic nature of daylight and occupant position can cause issues such as overheating and visual discomfort that must be monitored during real-time use. Dynamic adaptive facades have emerged as a promising solution for educational buildings to prevent daylight glare and enhance daylight performance for students’ comfort. Based on recent progress in the construction and development of renewable energy technologies, adaptive facades can act as collectors of electrical energy in the form of photovoltaic cells to provide the required energy, in addition to improving internal performance. This research seeks to maximize the performance of a professional adaptive facade called Pioneer Adaptive Dynamic Facade (PADF) by searching for solutions based on the kinetics and type of adaptability of the facade and considering the position of the occupants. The solution of adaptability and type of mobility has been developed based on the extraction of the PADF control strategies. In this study, three strategies based on the season, the occupant’s position, and manual control are used to control the PADF. The methodology of this research is designed to compare the PADF in strict competition with regular shades such as simple horizontal louvers. The results show that the strategies for adaptive control of the PADF, in addition to improving the comfort conditions, reducing the cooling load, and providing sufficient daylight for the space, provide the maximum amount of solar radiation for the production of electricity. Full article

The water use efficiency (WUE) in North China is undergoing rapid changes due to climate warming and vegetation “greening”, significantly impacting the ecosystem’s carbon and water cycles. Existing research lacks quantitative analysis of WUE or an understanding of future trends. This study selected the rapidly greening Agro-Pastoral Ecotone of Northern China (APENC) as a case study, utilizing linear regression, Hurst index analysis, and residual analysis to analyze the past and future changes and driving mechanisms of WUE. The results indicated that: (1) The multi-year (2001–2023) annual mean WUE in the APENC spatially ranged from 0.32 to 2.50 g C kg⁻¹ H₂O. (2) Gross primary productivity (GPP), evapotranspiration (ET), and WUE showed significant increasing trends of 10.22 g C m⁻² yr⁻², 5.62 kg H₂O m⁻² yr⁻², and 0.01 g C kg⁻¹ H₂O yr⁻¹, respectively. (3) Precipitation had highly positive impacts on GPP and ET, while non-climatic factors (land use, human activities, etc.) explained 62% of WUE variations in the APENC, and energy conditions (air temperature and solar radiation) were not the decisive factor of WUE. (4) The Hurst exponent of WUE indicates that WUE in the APENC region generally exhibits anti-persistent behavior. In terms of future trends, WUE is projected to shift from rising to declining in 58.9% of the region, while 28.5% is expected to continue increasing. Full article

Accurate forecasting of photovoltaic (PV) power generation is of critical importance for grid stability, energy management, and production planning in renewable energy systems. However, the high variability of atmospheric conditions and sudden fluctuations in solar irradiance significantly limit the performance of conventional forecasting models. In this study, a hybrid photovoltaic power forecasting model based on the XGBoost algorithm, whose hyperparameters are optimized using the Artificial Vulture Optimization Algorithm (AVOA) and enhanced with the physics-based Radiation Stability and Efficiency Index (RSEI), designated as RSEI-XGBoost-AVOA, is proposed. The proposed approach provides a forecasting framework that is not only data-driven but also sensitive to physical processes by jointly modeling the temporal stability of solar irradiance and intraday generation dynamics. In this context, irradiance variability is represented through statistical measures, while intraday generation behavior is modeled using a sinusoidal efficiency function, and this structure is made more flexible through parameters optimized by AVOA. The model performance was evaluated separately for four different seasons using real data obtained from a 25 MW photovoltaic power plant in Türkiye. The results show that the proposed hybrid (RSEI-XGBoost-AVOA) model produces lower error values than both the standard XGBoost model and the AVOA-optimized model across all seasons. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values of the model were obtained as 4.15% and 0.4157 MW in summer, 6.07% and 0.2574 MW in winter, 8.03% and 0.7343 MW in spring, and 8.51% and 0.6135 MW in fall, respectively. These findings demonstrate that the proposed approach provides stable and generalizable forecasting performance even under highly variable atmospheric conditions and can be used as an effective decision-support tool for photovoltaic grid integration, short-term generation planning, and real-time energy management applications. Full article

The growing demand for sustainable urban infrastructure requires reliable and energy-efficient lighting solutions, particularly for urban parks characterized by dynamic energy consumption patterns. This study proposes an integrated analytical framework combining deterministic energy system modeling, a novel interaction-oriented metric, and machine learning techniques to evaluate hybrid photovoltaic (PV)–wind systems for urban park lighting applications. Multi-year hourly meteorological and lighting-demand data from millet gardens in Balıkesir and Çanakkale, Türkiye, were analyzed. A Hybrid Energy Synergy Index (HESI) was introduced to quantify the degree of concurrent contribution and coordination between PV and wind resources relative to lighting demand. The results show substantial demand variability, with an average daily demand of approximately 1609 kWh and peak values exceeding 25,600 kWh. Photovoltaic generation dominated total renewable energy production (92.95%), whereas wind energy contributed 7.05%. The average HESI value (≈0.113) indicated weak source coordination, accompanied by persistent energy deficits that occasionally exceeded −20,000 kWh. Reliability analysis revealed severe system inadequacy, with a reliability rate of only 0.000547 (0.055%). Within the evaluated design space, the highest-performing configuration consisted of 49 PV panels and 19 wind turbines; however, reliability improvements remained limited, indicating that capacity expansion alone is insufficient to ensure satisfactory performance. Machine learning models achieved high predictive accuracy for HESI forecasting (R² = 0.9902; MAE = 0.0027), while explainable artificial intelligence identified solar radiation and wind speed as the dominant environmental drivers. The results highlight the importance of integrating renewable energy capacity planning with energy storage support, improved source coordination, and adaptive energy management strategies to enhance the reliability of sustainable urban lighting systems. Full article

Cancer risk estimation remains one of the main unresolved challenges in human spaceflight beyond low Earth orbit, where astronauts are exposed to galactic cosmic rays, solar particle events, and high-linear energy transfer (high-LET) secondary radiation. This narrative review summarizes the principal quantitative models used to estimate radiation-induced cancer risk in astronauts, including particle fluence-based cross-sections, mixture models, risk of exposure-induced death (REID)-based operational frameworks, uncertainty distribution approaches, and ensemble models. Early studies estimated 1-year excess cancer mortality at solar minimum as 1.3% in women and 1.1% in men under 10 g/cm² aluminum shielding, whereas later models projected non-leukemia lifetime cancer incidence after 1 Sv dose equivalent/effective dose between 2.20% and 2.98%, depending on sex and age. Earlier REID-based models suggested that the historical 3% REID threshold could be exceeded after approximately 18 months in women and 24 months in men under unfavorable solar conditions, whereas the current NASA radiation standard uses a universal career-effective dose limit of 600 mSv, applied regardless of sex or age. More recent revisions of the NASA Space Cancer Risk model and non-targeted effect scenarios suggest that exploration mission risks may be higher than previously estimated, while uncertainty remains substantial, especially for high-LET radiobiology, mixed-field exposure, and the transfer of terrestrial epidemiological data to the spaceflight setting. Future progress may also involve exploring quantitative imaging biomarkers and tomographic assessments as complementary tools for longitudinal monitoring and early detection of radiation-related tissue changes, although these approaches are not yet validated as components of operational astronaut cancer risk models. Full article

Perovskite solar cells (PSCs) have emerged as a disruptive photovoltaic technology for aerospace and extreme environment applications, driven by their substantial power-to-weight ratio and mechanical flexibility. However, continuous operation under harsh conditions, characterized by the AM0 spectrum, deep vacuum, extreme thermal cycling, and ionizing radiation, exposes the fundamental thermodynamic instability of traditional organic–inorganic hybrid perovskites. This comprehensive review systematically synthesizes 131 recent studies to provide a holistic framework for designing ultrastable, radiation-hardened PSCs. We critically examine the underlying degradation mechanisms, including vacuum-induced volatile desorption, UV-triggered halide segregation, and thermomechanical fracture at buried interfaces. To overcome these critical barriers, we highlight advanced engineering strategies: the transition to all-inorganic CsPbX₃ and lead-free double/chalcogenide perovskites (e.g., Cs₂SnI₆, CaHfS₃), the implementation of dopant-free inorganic transport layers coupled with self-assembled monolayers (SAMs) for cascade band alignment, and the integration of polymeric scaffolds for fracture energy toughening. Furthermore, we emphasize the imperative shift toward solvent-free vacuum deposition techniques (ALD, PLD). A distinctive focus of this review is the integration of Artificial Intelligence; specifically, we evaluate Deep Learning architectures, such as Long Short-Term Memory (LSTM) networks, for predictive State of Health (SOH) monitoring, underscoring the vital transition from simulated to empirical datasets. Finally, coupled with Material Flow Cost Accounting (MFCA), this review outlines a strategic roadmap for the commercialization and deployment of autonomous, self-diagnosing photovoltaic platforms in next-generation satellite and deep-space missions. Full article

Solar radiation can induce circumferential temperature gradients and thermoelastic bending moments in lightweight spacecraft appendages. For spinning spacecraft, this thermal excitation is periodically modulated by spin motion and may amplify flexible response when the modulation frequency approaches a structural frequency. This study investigates the spin-modulated thermoelastic response of a spacecraft with a circular thin-walled flexible appendage under attitude-dependent solar radiation. A reduced-order rigid-flexible-thermal model is formulated by coupling rigid-body attitude motion, assumed-mode appendage deformation, first-harmonic circumferential temperature perturbations, and the resulting generalised thermoelastic bending moment. Long-time simulations, spin-period response sampling, degraded-model comparisons and modal energy/work diagnostics are used to identify the dominant response mechanism. The results show that the post-transient flexible response is governed mainly by spin rate, while the initial solar-incidence angle modifies the local response classification in the sampled high-spin region. Representative high-spin loss-of-admissibility cases lie near the first bending-frequency region, where the retained structural energy and positive thermoelastic work input are concentrated mainly in the first bending mode. Removing deformation-dependent solar-incidence feedback does not eliminate these cases, whereas suppressing thermoelastic bending does. Thus, spin-modulated thermoelastic bending is the essential pathway by which attitude-dependent solar radiation amplifies appendage response. Full article