Solar System Research Articles

Learning from Arctic Microgrids: Cost and Resiliency Projections for Renewable Energy Expansion with Hydrogen and Battery Storage

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

Engineering Metal Oxide Nanofluids for Enhanced Heat Transfer Performance

Metal oxide nanoparticles (MONPs) have garnered significant attention for their potential to enhance the thermal properties of nanofluids. This review delves into various types of MONPs, including oxides of copper, aluminum, zinc, titanium, and silicon, which have been extensively studied for their superior thermal conductivity, stability, and unique properties. Several synthesis techniques, such as sol-gel, hydrothermal, chemical vapor deposition, and thermal decomposition, are employed to fabricate MONPs with precise control over size, shape, and crystallinity. These nanoparticles are subsequently dispersed in base fluids like water, ethylene glycol, or oil to form nanofluids, utilizing methods such as ultrasonication, mechanical stirring, or chemical reduction. MONP-based nanofluids exhibit enhanced thermal conductivity compared to their base fluids, attributed to factors like increased effective thermal conductivity, Brownian motion of nanoparticles, and interfacial thermal resistance. However, challenges such as nanoparticle agglomeration and sedimentation can hinder their stability and long-term performance. The viscosity of MONP-based nanofluids generally increases with nanoparticle concentration, potentially impacting their flow behavior and pumping requirements. Nevertheless, their superior thermal conductivity often outweighs the viscosity penalty in many applications. MONP-based nanofluids have found diverse applications in various industries. In the energy sector, they are employed in solar thermal systems and heat exchangers to improve energy efficiency. In electronics, they serve as advanced thermal management fluids for cooling electronic devices. Future research directions include developing novel synthesis techniques for producing well-dispersed and stable MONP-based nanofluids, exploring hybrid nanofluids with synergistic effects, and investigating the underlying mechanisms responsible for enhanced thermal properties. By addressing these challenges and continuing to advance the field, MONP-based nanofluids hold the promise of revolutionizing thermal management and energy efficiency in various applications.

A Robust Modeling Analysis of Environmental Factors Influencing the Direct Current, Power, and Voltage of Photovoltaic Systems

Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct current, power, and voltage—of solar panels using advanced robust regression methods: Ridge Regression, Least Absolute Deviation, and M-Estimation. Our results demonstrate that irradiance is the dominant driver of performance, with Ridge Regression coefficients reaching up to 1193 for power. The M-estimation model achieved high predictive accuracy, with R2 Scores of 0.989 for current (Mean Squared Error = 0.0399) and 0.991 for power (Mean Squared Error ≈ 10,445), indicating strong model reliability. voltage prediction was more challenging but still robust (R2 = 0.836, Mean Squared Error = 49.63). Negative impacts from ambient temperature and humidity were consistently observed across models, while wind speed exhibited a beneficial effect by enhancing cooling and thus improving current and power outputs. Soiling was also identified as a critical factor, significantly reducing power and voltage generation. These findings provide quantifiable evidence of how environmental variables shape solar photovoltaic performance and underscore the importance of environmental monitoring and maintenance strategies to optimize energy yield in operational solar power systems.

The Effect of Phase Change Material (PCM) on Efficiency and Productivity in Tubular Solar Distillation Using a Parabolic Trough Collector

This study investigates the effect of Phase Change Material (PCM) on temperature, efficiency, and productivity. The prototype solar distillation system is tube-shaped, with a length of 50 cm and a diameter of 4 inches, using a parabolic trough collector. The PCM used is beeswax, which is wrapped in aluminum foil. The results show that the use of PCM improves freshwater productivity. On the first day, the PCM method showed an 11% increase in production, and on the second day, freshwater production increased by 13%. The production rate with PCM was 0.76 L/m²/day, compared to the conventional method, which had a rate of 0.67 L/m²/day. The system with PCM also achieved high efficiency, reaching 11,47% on the first day and 13,9% on the second day. The system remained stable in the afternoon and evening, maintaining temperatures above 35 °C at night, compared to the system without PCM, where the temperature dropped to 30 °C. The use of PCM significantly enhanced the overall performance of the distillation system.

Designing Smart Perturb and Observe MPPT Controller for 150W Off-Grid PV System

Studying the introduction design of a stand-alone solar photovoltaic (PV) system with a disturbance-and-observation (P&O)-based algorithm for maximum power point tracking (MPPT) is an important topic for improving the performance of solar energy systems. The efficient energy system is designed by using solar photovoltaic panels consisting of a number of cells, using P and O algorithms for effective tracking of maximum power, and performing comparative analysis using the traditional model without the MPPT algorithm. In this approach, the terminal voltage of the photovoltaic cell is always set according to the MPP voltage and duty cycle setting directly in the system. The control loop and calculations have been simplified to reduce the time needed to adjust the console gain. The system has a solar panel, MPPT (maximum power point tracking) with controller, DC converter, and a single-phase VSI (voltage source inverter). The proposed system was simulated using MATLAB/Simulink model.

Colors and Dynamics of a Near-Sun Orbital Asteroid Family: 2021 PH27 and 2025 GN1

Abstract We observed the dynamically similar near-Sun asteroids 2021 PH27 and 2025 GN1 for their optical colors. These objects have the lowest known semimajor axes of any asteroids. 2021 PH27 has the largest general relativistic effects of any known solar system object. The small semimajor axis and very close passage to the Sun suggest the extreme thermal and gravitational environment should highly modify these asteroids’ surfaces. From g’-, r’-, i’-, and z’-band imaging, we find the colors of 2021 PH27 ( g ′ − r ′ = 0.58 ± 0.02 , r ′ − i ′ = 0.12 ± 0.02 , and i ′ − z ′ = − 0.08 ± 0.05 mag) to be between the two major asteroid types: the S and C classes. With a spectral slope of 6.8 ± 0.03 percent per 100 nm, 2021 PH27 is an X-type asteroid and requires albedo or spectral features to further identify its composition. We find the dynamically similar 2025 GN1 also has very similar colors ( g ′ − r ′ = 0.55 ± 0.06 and r ′ − i ′ = 0.14 ± 0.04 ) as 2021 PH27, suggesting these objects are fragments from a once larger parent asteroid or 2021 PH27 is shedding material. The colors are not blue like some other near-Sun asteroids such as 3200 Phaethon that have been interpreted to be from the loss of reddening substances from the extreme temperatures. There is no evidence of activity or a large amplitude period for 2021 PH27, whereas 2025 GN1 might have a more significant rotational light curve. 2025 GN1 may have a very close encounter or hit Venus in about 2300 yr and likely separated from 2021 PH27 in the last ∼10 kyr.

Jurassic constraints on the chaotic Mars–Earth eccentricity cycle linked to the volcanically induced Jenkyns event

Solar system gravitational interactions are embedded in Earth's record of climate, providing a way to bypass the 60 Myr limit imposed by chaos. Presently with a 2.4 Myr period, the Mars-Earth beat cycle of orbital perihelion frequencies is particularly sensitive to chaotic diffusion, potentially varying by more than a million years. Early Mesozoic (252 to 145 Ma) strata provide some constraints on this cycle, with evidence of a swing through most of the solution space from 1.8 Myr at 210 Ma to 2.5 Myr at 190 Ma and back to 1.6 Myr at 180 Ma. However, only the 1.8 Myr cycle is corroborated by geochronologic data and the 1.6 Myr period is disputed. Here, we show that variations in land-plant-dominated stable carbon isotopic ratios (δ13Corg) from the lacustrine, paleo-high-latitude Sangonghe Formation (Junggar Basin, northwestern China), reveal at least three 1.6 Myr Mars-Earth beat cycles centered at 183 Ma, tracking atmospheric CO2 isotopic composition in Earth's exchangeable carbon reservoirs. Furthermore, the middle cycle includes the famous Jenkyns Event, expressed here by poleward migration of cheirolepidaceous conifers driven by CO2 warming from the Karoo-Ferrar large igneous province (LIP). Our data do not, however, support major, LIP-triggered input of isotopically light carbon and instead support CO2 amplification of local processes via warming and ecosystem change. Although requiring additional independent geochronological support, Sangonghe data help provide empirical constraints for filtering orbital solutions, tightening initial conditions, and testing gravitational models, as well as showing how extrinsic cyclical processes interact with a tectonic event, the Karoo-Ferrar LIP.

Decarbonization of Sewage Sludge Processing Through Solar Thermal Energy Integration

Disposal of sewage sludge results in significant energy consumption and carbon emissions. Greenhouse-type drying methods have been investigated to reduce emissions; however, their use in high capacity plants is limited due to the large amount of space required and the dependence on climatic conditions. This study investigates the energy performance of high temperature hot oil production using concentrated solar power (CSP) systems installed on existing wastewater treatment plant (WWTP) pool areas, and utilizing it as a heat source in an indirect dryer. An annual energy balance was simulated using CSP and dryer models, analysing both a 95% dry matter (DM) condition at the dryer outlet and a 50% DM condition suitable for autothermal combustion. The energy analysis showed that about 28% of the total annual energy required for full drying and about 42% for partial drying could be provided by solar energy. In terms of CO₂ emissions, similar reductions were observed for partial drying and load shifting applications. Complete drying in the plants resulted in emission reductions of 1–3.5 million tonnes, while partial drying resulted in reductions of 2–8 million tonnes. As opposed to greenhouse-based drying systems, this method of integration allows the use of solar energy without requiring additional space. The integration of CSP energy systems into the sewage sludge drying process offers significant benefits in terms of energy savings and emission reductions. Through further optimization of the solar system and maximization of the use of the solar area, zero-emission sludge management could be achieved.

Estimating Solar Wind Charge Exchange Generated Soft X‐Rays in the Jovian Magnetosheath

Abstract Solar Wind Charge eXchange (SWCX) generated soft x‐rays are used extensively to study the interfaces between charged and neutral particles throughout the solar system. This paper outlines the development of a model of Jupiter's magnetosheath using magnetohydrodynamic derived boundary equations and a combination of in situ Juno measurements and numerical models for neutral and charged particle distributions. These are then used to model SWCX emissions in the Jovian magnetosheath for the OVII triplet to determine if the magnetosheath could be imaged in a similar fashion to the planned SMILE mission at Earth. We determine that whole detector counts per minute range from to for various spacecraft, which are 4–5 orders of magnitude weaker than the diffuse soft x‐ray background and, as such, argue that it is infeasible to study the x‐ray emissions from the Jovian magnetosheath. Magnetosheath SWCX emissions would contribute a negligible amount to the diffuse background when performing higher signal x‐ray observations at Jupiter such as x‐ray aurora, emissions from the Io plasma torus, and fluorescence emissions from the Jovian moons in an in‐situ setting, indicating that an x‐ray instrument would be capable of performing observations of these brighter x‐ray targets either inside or outside of the magnetopause.

Bayesian and Monte Carlo approaches to estimating uncertainty for the measurement of the bound-state β− decay of 205Tl81+

Abstract The measurement of the bound-state β decay of 205Tl at the Experimental Storage Ring at GSI, Darmstadt, has recently been reported with substantial impact on the use of 205Pb as an early Solar System chronometer and the low-energy measurement of the solar neutrino spectrum via the LOREX project. Due to the technical challenges in producing a high-purity 205Tl81+ secondary beam, a robust statistical method needed to be developed to estimate the variation in the contaminant 205Pb81+ produced during the fragmentation reaction. Here we show that Bayesian and Monte Carlo methods produced comparable estimates for the contaminant variation, each with unique advantages and challenges given the complex statistical problems for this experiment. We recommend the adoption of such methods in future experiments that exhibit unknown statistical fluctuations.

AI Methods Used in Solar Energy Optimization Over the Last Decade

In light of the increased demand for renewable energy solutions, the application of Artificial Intelligence in solar energy has garnered attention and experienced development. In the last decade, AI methods like machine learning, deep learning, and reinforcement learning have improved solar power forecasting, solar predictive maintenance, and solar control systems to achieve heightened levels of efficiency and reliability. This review systematically integrates recent advances made in the context of applying AI to photovoltaic systems and pronounces major applications, challenges, and performance results. Although AI research has made helpful strides, other issues await solution, such as data heterogeneity, interpretability of AI systems, and real-time adaptability. The paper ends with a brief conclusion and suggestions regarding future work to resolve these issues and take full advantage of the possibilities endowed by AI in solar energy systems.

Indonesian Local Wisdom-Based Physics Learning: Strategy and Media

Aim. This systematic literature review (SLR) research explores strategies and media for learning physics based on local wisdom in Indonesia at all levels of education. Methods. The review involved Scopus-indexed journals and conference papers published between 2014 and 2024. SLR was performed by defining research questions, setting eligibility criteria, searching and selecting documents, reviewing documents in-depth, and disseminating results. The document search and selection strategy employed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, which comprised identification, screening, eligibility, and included stages. Results. The analysis results indicate that the strategies and media are primarily designed to enhance cognitive learning outcomes. Additionally, various Indonesian local wisdoms containing physics concepts are dispersed across Java, Bali, Sumatra, Kalimantan, Sulawesi, Maluku, Nusa Tenggara, and Papua, Indonesia. Physics concepts are related to electromagnetism, fluids, heat and temperature, measurement, mechanics, nuclear physics, oscillations and waves, optics, quantity and unit, and the solar system. Additionally, most research related to local wisdom concerns traditional games. Conclusion. Various print and digital learning media support the learning strategy. The findings of this study may serve as a foundation for developing strategies and media for further local wisdom-based physics learning.

Hybrid Optimal Adaptive Maximum Entropy Reinforcement Learning for Balanced Islanding Detection in Distributed Generation

ABSTRACTGrid‐connected solar systems confront the persistent challenge of islanding, a phenomenon wherein the system persists in generating power even after disconnection from the main grid. Detecting islanding poses multifaceted challenges, encompassing model complexity, non‐detection zones, power quality ramifications, nuisance tripping occurrences, and synchronization and timing issues. In response, this research proposes an innovative solution that integrates passive and active techniques with a Reinforcement Learning algorithm. The passive component harnesses DWT for signal processing, while the active component utilizes a pioneer evolutionary algorithm (PEA) for adaptive optimization. This fusion results in the development of the Hybrid Optimal Adaptive Maximum Entropy Reinforcement Learning (HOAMERL) algorithm, engineered to enhance islanding detection efficiency and accuracy. To evaluate the method, it is simulated in IEEE‐14 bus environment. Additionally, the power system incorporated with a low‐pass filter to optimize nuisance tripping and a Static VAR Compensator (SVC) to improve power quality. The research findings unveil compelling outcomes, with a tripping time of 45 ms, a detection time of 1.29 ms, a notable reduction in total harmonic distortion (THD) by 0.90%, and an impressive detection accuracy of 99.92%. These findings highlight the HOAMERL algorithm's remarkable capability to address the intricate challenges of islanding detection, signifying its potential to significantly elevate the reliability and performance of grid‐connected solar systems in real‐world applications.

Design of a complex dual-axis solar tracker with an integrated solar PV monitoring system

The object of this study is two-axis tracker systems for tracking the position of the Sun and monitoring the parameters of photovoltaic panels. The task addressed is the optimization of photovoltaic panel positioning to improve their efficiency and maximize electricity generation, specifically selecting the optimal tracking algorithm while accounting for implementation cost and payback period under variable climatic conditions. The essence of the results is the design and deployment of a system that controls panel tilt and azimuth angles according to the chosen tracking algorithm, while performing online monitoring of key photovoltaic converter operating parameters and meteorological data. By accurately calculating the Sun’s trajectory and employing dual-axis tracking, the number of unnecessary movements is reduced, which lowers the tracker’s energy consumption and drive wear, thereby improving system reliability and reducing operational costs. These findings are associated with the use of dual-axis algorithms with precise solar-position calculations, as well as by the implementation of a web interface and an integrated database for collecting statistical data on tracking performance. The system provides real-time data collection and analysis, allowing the tracking algorithm to be changed and its effectiveness to be evaluated for a specific location or climatic zone. The user-friendly web interface enables users to access information in the form of plots and sensor readings. In practice, the designed system can be used for long-term monitoring of tracking efficiency, to analyze return on investment, as well as plan operational expenditures. Experimental studies showed that the dual-axis tracker with a precise solar-position calculation algorithm increases energy generation efficiency on a spring sunny day in the western region of Ukraine by more than 25%

Exploring the Functional Properties of Leaves of Moringa oleifera Lam. Cultivated in Sicily Using Precision Agriculture Technologies for Potential Use as a Food Ingredient

This study aimed to evaluate the microbiological quality and functional properties of Moringa oleifera Lam. leaves from plants cultivated in Sicily, with the objective of exploring their potential use in functional food production. Precision agriculture techniques, including unmanned aerial vehicle-based multispectral remote sensing, were used to determine the optimal harvesting time for M. oleifera. After harvesting, leaves were dried using a smart solar dryer system based on a wireless sensor network and milled with a laboratory centrifugal mill to produce powdered M. oleifera leaves (PMOLs). Plate counts showed no colonies of undesired microorganisms in PMOLs. The MiSeq Illumina analysis revealed that the class Alphaproteobacteria was dominant (83.20% of Relative Abundance) among bacterial groups found in PMOLs. The hydroalcoholic extract from PMOLs exhibited strong redox-active properties in solution assays and provided antioxidant protection in a cell-based lipid peroxidation model (CAA50: 5.42 μg/mL). Additionally, it showed antiproliferative activity against three human tumour epithelial cell lines (HepG2, Caco-2, and MCF-7), with GI50 values ranging from 121.03 to 237.75 μg/mL. The aromatic profile of PMOLs includes seven phytochemical groups: alcohols, aldehydes, ketones, esters, acids, terpenes, and hydrocarbons. The most representative compounds were terpenes (27.5%), ketones (25.3%), and alcohols (14.5%). Results suggest that PMOLs can serve as a natural additive for functional foods.

Core-Shell Engineering of One-Dimensional Cadmium Sulfide for Solar Energy Conversion.

Fabricating efficient photocatalysts that can be used in solar-to-fuel conversion and to enhance the photochemical reaction rate is essential to the current energy crisis and climate changes due to the excessive usage of nonrenewable fossil fuels. To attain high photo-to-chemical conversion efficiency, it is important to fabricate cost-effective and durable catalysts with high activity. One-dimensional cadmium sulfides (1D CdS), with higher surface area, charge carrier separation along the linear direction, and visible light harvesting properties, are promising candidates for converting solar energy to H2, reducing CO2 to commodity chemicals, and remediating environmental pollutants. The main disadvantage of CdS is photocorrosion due to the leaching of S2- ions during the photochemical reactions, and further charge recombination rate leads to low quantum efficiency. Therefore, the implementation of core-shell heterostructured morphology, i.e., the growth of the shell on the surface of the 1D CdS, which offers unique features such as protection of CdS from photocorrosion, a tunable interface between the core CdS and shell, and photogenerated charge carrier separation via heterojunctions, provides additional active sites and enhanced visible light harvesting. Therefore, the viability of the core-shell synthesis strategy and synergetic effects offer a new way of designing photocatalysts with enhanced stability and improved charge separation in solar energy conversion systems. This review highlights some critical aspects of synthesizing 1D CdS core-shell heterostructures, underlying reaction mechanisms, and their performance in photoredox reactions. Finally, some challenges and considerations in the fabrication of 1D CdS-based core-shell nanostructures that can overcome the current barriers in industrial applications are discussed.

Enhanced Sliding Mode Control for Dual MPPT Systems Integrated with Three-Level T-Type PV Inverters

Dual Maximum Power Point Tracking (MPPT) inverters are essential in residential and small commercial solar power systems, optimizing power extraction from two independent solar panel arrays to enhance efficiency and energy harvesting. On the other hand, the Three-Level T-Type Voltage Source Inverter (3L T-Type VSI) is known for its reduced switching losses, improved harmonic distortion, and reduced part count in comparison to other three-level topologies. In this paper, a novel architecture is proposed to integrate the dual MPPT structure directly to each DC-side split capacitor of the 3L T-Type VSI, taking advantage of the intrinsic characteristics of the inverter’s topology. Further performance enhancement is achieved by integrating a classical MPPT strategy to the control framework to make it feasible for a real-case grid integration. The combination of these methods ensures faster and stable tracking under dynamic irradiance conditions. Considering that strategies dedicated to balancing the DC-link capacitor’s voltage slightly affect the AC-side current waveform, an enhanced sliding mode control (SMC) strategy tailored for dual MPPT and 3L T-Type VSI is deployed, combining the simplicity of conventional PI controllers used in the independent MPPT-based DC-DC converters with the superior robustness and dynamic performance of SMC. Real-time results obtained using the OPAL-RT Hardware-in-the-Loop platform validated the performance of the proposed control strategy under realistic test scenarios. The current THD was maintained below 4.8% even under highly distorted grid conditions, and the controller achieved a steady state within approximately 15 ms following perturbations in the DC-link voltage, sudden irradiance variations, and voltage sags and swells. Additionally, the power factor remained unitary, enhancing power transfer from the renewable source to the grid. The proposed system was able to achieve efficient power extraction while maintaining high power quality (PQ) standards for the output, positioning it as a practical and flexible solution for advanced solar PV systems.

Fuzzy Logic Controlled PV Microgrid for a Sports Complex in Saudi Arabia

This paper proposes a fuzzy logic based central control facility for the isolated microgrid designed for the sports complex in a university set up in Saudi Arabia. The design aspects of the solar photo voltaic energy conversion system (PVES) for the sports complex facility are also included in the paper. Diesel generator is utilized as a backup system. The research is conducted to evaluate the effect of the proposed microgrid over the existing grid power supply in the minimization of carbon emissions and its alignment with the nation’s objectives in the implementation of renewable energy-based systems in Saudi Arabia. The fuzzy logic-based system set up takes into consideration the intermittent characteristics of solar PVES output and accordingly efficiently regulate power generation, consumption and the energy storage in the sports complex. The simulation results indicate the importance and significance of the implementation of microgrid set up for major facilities in the educational institutions in Saudi Arabia.

Physics of the Universe in a Model with Minimum Initial Entropy III Solar System

The third part of the review paper examines the physical processes that have occurred in the Solar System from its formation to the present, within the framework of the Universe with minimal initial entropy (UMIE) model. In this case, the expansion of space and the formation of the Universe as a hierarchical system are taken into account and the following conclusions are made: According to the UMIE model, the Universe is a component of the Super-Universe, which consists of four layers: the zero-dimensional world (space-time quantum, World-1), the one-dimensional world (World-2), the two-dimensional world (World-3) and our three-dimensional world (World-4). The space-time quantum has constant dimensions, and all other layers expand at a constant speed equal to the speed of light. The mass of a cosmic body and the distance from the planet to the Sun increase proportionally over time. This fact ensures a constant speed of movement of planets and small bodies in their orbit, which are constantly moving away from the Sun. The new model describes the sequence of planet formation, taking into account the resonant interactions between the planets' orbits, which results in the distance from the planet to the star following a geometric progression. The increase in the mass of cosmic bodies occurs at a constant rate due to the birth of bineutrons in the vicinity of atomic nuclei. Heavy chemical elements are located in the center of the Sun and planets. When their mass increases to a critical level, constantly active radiation processes and nuclear explosions occur in the nuclei of the Sun and planets. Radial fluxes of electrons and protons, which arose as a result of radiation processes and nuclear explosions in the nucleus, cause the appearance of a magnetic field around the Sun and planets, and also cause differential rotation of the Sun. It is shown that all atoms from the table of chemical elements continue to be formed throughout the volume of cosmic bodies. This leads to the creation of all possible compounds of chemical elements, as well as to the appearance of water on Earth. The crystallization of chemical elements and molecules in the Earth's magma leads to the formation of minerals, or a nuclear explosion occurs, which is responsible for the appearance of deep-focus earthquakes.

Proposed Solar-Powered Motion Sensor for Farm Monitoring and Surveillance: A Solar-Powered Assisted Process

Background The farming industry faces continuous threats from pest control and farm security issues because rodents cause significant damage to crops and disrupt farm operations. Traditional pest control methods require continuous human interaction which proves both resource-intensive and inefficient. Modern agricultural practices benefit from sustainable solutions through the combination of renewable energy with smart technologies. Method The research presents an innovative solar-powered motion-sensor system that utilizes OpenCV-based image analysis to detect and classify rodent intruders on farmland autonomously. The system depends on solar panels for energy autonomy while employing computer vision to monitor threats in real time and classify them. Results The system demonstrates its ability to detect and prevent rodent intruders according to initial testing results. The OpenCV system uses motion sensor signals to analyze movement patterns before distinguishing rodents from other detected objects. The solar-powered system operates continuously which decreases human intervention needs and enhances farm surveillance capabilities. The model demonstrates its capability to defend crops from rodent damage and enhance farm resistance against land degradation threats. Conclusion The proposed system demonstrates progress in uniting renewable energy systems with smart surveillance technologies to mitigate agricultural risks. The current system encounters problems with detecting wild animals beyond rodents as well as tracking rodent activity beneath ground level. Future developments could include improved pest capture systems alongside enhanced surveillance features for detecting both unauthorized human intruders and large animals. The research shows that solar power systems need to be connected with automated monitoring technology to create sustainable agricultural operations that are efficient and resilient.