System Environment Research Articles

Tracking the influence of a very close secondary star on planetary growth and evolution

Abstract This work investigated the dynamics of planets in binary systems and provided insights into the stability and evolution of these systems. We explored the influence of a nearby secondary star on planetary growth and evolution, focusing on S-type configurations. We tracked the orbits of the planets and analyzed their stability over long timescales, considering various parameters such as mass, eccentricity, and inclination. Our results show that the presence of a secondary star can significantly impact the growth and evolution of planets, leading to changes in their orbits and potential ejection from the system, however, it was possible to identify stable planets even in systems experiencing multiple disturbances. One of the most significant results of the work was the analysis of the increased material in the disc near the primary star, which contributes to planet growth, driven by the density spirals influenced by the binary star.These findings have important implications for the search for habitable exoplanets and emphasize the need for further studies of planetary systems in binary star environments.

Supplemental Low-Irradiance Mono/Polychromatic LED Lighting Significantly Enhances Floral Biology of the Long-Day F1 Hybrid Strawberry ‘Soraya’ (Fragaria x ananassa Duch.)

Floral and vegetative responses of the strawberry (Fragaria x ananassa Duch.) to specific light wavelengths are not well documented. LED lights make it feasible for precise exposure to specific wavelengths during a 24 h cycle to alter growth responses regulated by phytochromes and cryptochromes and thereby potentially enhance fruit productivity in both a controlled environment and field systems or to enhance stolon production for controlled environment propagation. This research developed a systematic method to assess the effects of supplemental, low-irradiance LED lighting on strawberry flowering and vegetative biology. Growth of the long-day F1 seed-propagated cultivar ‘Soraya’ was evaluated during and following 6 or 12 weeks of exposure to supplemental red (660 nm), far-red (730 nm), blue (454 nm), or incandescent lighting at various times during the dark period of a 24 h cycle under a 10 h non-inductive photoperiod at non-inductive temperatures (>27/18 °C, day/night). Treatment effects were monitored via flower mapping and phenology during treatment, field and greenhouse production after treatment, and floral scores derived by ranking treatment effects within the evaluation method and then combining them into a single, simple score. The most promising treatment for enhancing the floral nature of plug plants was exposure to far-red + red light as a 5 h night interruption. This treatment increased inflorescence production in the greenhouse by 285% and resulted in multi-branched, floral plants with the potential for enhancing yield in either greenhouse or field production. Greenhouse runner production increased by 483% following exposure to incandescent lighting at the beginning of the dark period; thus, this treatment or one using a spectral distribution similar to incandescent may be suitable for enhancing vegetative propagation in controlled environments.

Layered quantum secret sharing scheme for private data in cloud environment system

Layered quantum secret sharing scheme for private data in cloud environment system

Optimization Simulation of Mooring System of Floating Offshore Wind Turbine Platform Based on SPH Method

ABSTRACTIn this paper, the Smoothed Particle Hydrodynamics method is used to simulate the dynamic response of the floating offshore wind turbine mooring system in a complex marine environment by using its advantages of dealing with free surface flow and fluid–structure interaction. The single‐cable mooring system and the double‐cable mooring system are introduced into the numerical simulation model of fluid–solid coupling interaction. The first‐order irregular wave and the second‐order regular wave are used to simulate different wave conditions. The operating state of the platform in these two cases is analyzed, and the accurate data such as the velocity change of the geometric center of the platform and the change of six degrees of freedom are obtained. Using visual processing and data analysis, it is found that the optimized double‐cable mooring system structure improves the vibration reduction ability of the platform.

The Development of Yunnan “New Folk Songs”

Background and Aim: This study investigates the evolution of the “New Folk Song” in Yunnan and analyzes how historical, cultural, and social changes have influenced their development. Yunnan folk songs are a cultural product with local characteristics and close to the development of the working people in Yunnan that is close to the people's daily life. The "New Folk Songs" of Yunnan is a musical form combining “nation” and “modern” innovation. In the process of development, it is constantly endowed with new definitions and connotations and presents the situation of diversified development. Due to the special historical and social environment of Yunnan, a large number of “New Folk Songs” with rich themes and excellent quality have emerged in Yunnan during the period of the late Qing Dynasty and the early Republic of China, the Revolutionary War, the “Cultural Revolution”, and the era of peace and development in recent years. The results show that Yunnan's “New Folk Songs” evolved through the integration of modern and traditional elements, reflecting the unique social and cultural dynamics of Yunnan over the past hundred years. To study the development of “New Folk Songs” in Yunnan, this study is not only a record of musical life in different periods, but also the development trajectory of different musical works in Yunnan across the century, but also a study of the development history of music creation in Yunnan. Materials and Methods: This Article aimed to study related literature to find out the development of Yunnan's “New Folk Songs”. The instrument for collecting data was documentary research. Relevant journals, academic papers, books, magazines, and online information related to the development of Yunnan's “New Folk Songs” are reviewed and organized. From the perspective of creation and music ontology, the style characteristics and creation characteristics of Yunnan "new folk songs" in different periods are analyzed, to explore the development of Yunnan “New Folk Songs” in Yunnan. Results: Throughout the development of “New Folk Songs” in Yunnan, its creation characteristics are contemporary, national, and regional; music works have the style characteristics of Yunnan minority elements. This paper will analyze and study the above characteristics in depth. This study is beneficial to improve the reader's understanding of Yunnan's “New Folk Songs” and understanding, through the development of “New Folk Songs” in Yunnan, to explore the development prospect of “New Folk Songs”, Yunnan for music workers can provide certain reference and reference, also for the Yunnan “New Folk Songs” and singing practice, teaching practice provides reference way, also further promote Yunnan traditional folk song inheritance and the sustainable development of “New Folk Songs”. At the same time, through this study, it is hoped to help the exploration of Yunnan's “New Folk Songs” style, and also to promote the inheritance of the existing Yunnan traditional folk music culture and establish awareness of the exploration and protection of Yunnan local music materials. Conclusion: The development of “New Folk Songs” in Yunnan is reflected in the creative characteristics and style characteristics changing with the changes of the times. The social environment, economic situation, and policy system of a country in an era will often affect the artistic creation of the creators with subjective or objective factors. Therefore, In this paper, the horizontal melody and vertical harmonic layout of music works in the budding period of Yunnan “New Folk Songs” (The Late Qing Dynasty and The Early Republic of China), The War Period (1931-1949), The 17th years of the Founding of the People's Republic of China (1949-1966), The Ten Years of the “Cultural Revolution” (1966-1976), and The Innovation and Development period (1978-2020), Relying on the musical activities in various historical periods, Analyzing the common and personalized creation characteristics of creators and the formation process of Yunnan local ethnic creation concept. Thus, the different purposes of creation, the different contents of the times, the different style characteristics, the different forms of expression, and then to understand the development of “New Folk Songs” in Yunnan.

The Diverse Applications of Aerospace Drones: From Defense to Humanitarian and Commercial Uses

This research investigates the extensive applications of aerospace drones across defense, humanitarian, and commercial sectors, showcasing their transformative impact on modern industry. Aerospace drones have become pivotal tools in diverse settings—from defense and tactical operations to humanitarian aid and infrastructure management—offering operational efficiencies, cost savings, enhanced safety, and innovative solutions to complex challenges. This study explores defense applications such as military reconnaissance, where Vertical Take-Off and Landing (VTOL) drones enable effective surveillance in challenging terrains. In humanitarian settings, drones are utilized for disaster response in earthquake and flood zones, providing critical functions like search-and-rescue, real-time damage assessment, and medical supply delivery. Commercially, drones support long-range infrastructure inspections, smart agriculture, and environmental monitoring, driven by advancements in imaging and remote sensing. The project emphasizes the technical challenges and ethical considerations associated with drone deployment, particularly the need for robust AI and autonomous navigation systems in dynamic and unpredictable environments. Collaborations with international aerospace firms and humanitarian organizations have further refined drone integration strategies in both civilian and military contexts. The research concludes with a discussion on regulatory frameworks and environmental impacts, aiming to provide a comprehensive view of thefuture of aerospace drones and their role in addressing global challenges.

Leveraging Machine Learning for Cybersecurity: Techniques, Challenges, and Future Directions

This study aims to explore the application of machine learning (ML) in enhancing cybersecurity measures, focusing specifically on intrusion detection, malware detection, fraud detection, and anomaly detection systems. A systematic review of 743 scholarly papers was conducted, from which 115 were selected for in-depth analysis. The review process involved evaluating advancements in ML techniques within the context of cybersecurity. The findings reveal that ML-driven systems significantly enhance the automation of security processes, improve the recognition of novel threats, and reduce human error in cyber threat management. However, challenges such as adversarial attacks and the need for high-quality model training pose significant barriers to the broader adoption of ML in cybersecurity. The study discusses the implications of these findings, emphasizing the necessity for developing robust and adaptive ML models that can withstand adversarial threats while improving integration across various cybersecurity applications. The insights gained from this research provide a comprehensive overview of the potential benefits and challenges of implementing ML in cybersecurity, highlighting the need for continuous innovation in threat detection mechanisms. This review acknowledges limitations such as the predominance of literature from Western contexts, potentially overlooking insights from other regions. Furthermore, the complexity of implementing ML systems in dynamic cyber environments remains a critical challenge. Future research should concentrate on refining ML algorithms to enhance resilience against adversarial threats, exploring the integration of emerging technologies for improved cybersecurity, and addressing gaps in the existing literature regarding the life cycle of ML models in real-world applications.

A FUZZY APPROACH FOR INFORMATION QUALITY ASSESSMENT IN BIG DATA ENVIRONMENT

Information quality assessment is very important for enterprise development, especially under big data environment. This paper combines the characteristics of big data to construct an information quality evaluation index system in a big data environment. The information quality assessment system includes fourteen indicators consisting of four aspects: reliability, relevance, timeliness, completeness, etc. Then, we set up Five examples to test and verify the information quality evaluation index system with a fuzzy approach, and the examples demonstrate the rationality and feasibility of the evaluation index system. The results of the study have certain theoretical and practical significance for the assessment of the quality of accounting data in a big data environment. Keywords: big data, data quality, fuzzy set, fuzzy logic, fuzzy approach.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

Two-Dimensional Differential Positioning with Global Navigation Satellite System Signal Frequency Division Relay Forwarding to Parallel Leaky Coaxial Cables in Tunnel

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.

The Removal and Mitigation Effects of Biochar on Microplastics in Water and Soils: Application and Mechanism Analysis

Due to their widespread distribution, microplastics (MPs) are endangering the soil ecological environment system, causing water pollution and altering the soil’s physicochemical and microbiological features. Because of its unique pore structure and strong stability, biochar is widely used as an adsorbent. However, the effects of MP–biochar interactions in water and soil environment are still unclear. This review outlines the application and mechanism of biochar as an adsorbent in a water environment for the removal of MPs. Also, biochar serves as remediation material for MPs in soils as it mitigates the adverse effects of MPs on soil properties, enzyme activities and soil microbial community. It was found that woody biochar had the highest yield and was more effective in adsorbing MPs. Further research should focus on the combined effects of biochar and MPs, the environmental risks of biochar, the modification of biochar application of MP-removal technologies, the characterization of MP properties, the remediation of combined contamination of MPs and other pollutants, and the transportation of MPs.

Exoplanets across galactic stellar populations with PLATO. Estimating exoplanet yields around FGK stars for the thin disk, thick disk, and stellar halo

The vast majority of exoplanet discoveries to date have occurred around stars in the solar neighbourhood, with chemical compositions comparable to that of the Sun. However, models suggest that planetary systems in different Galactic environments, with varying dynamical histories and chemical abundances, may exhibit distinct characteristics, which can help improve our understanding of planet formation processes. This study aims to assess the potential of the upcoming PLATO mission to investigate exoplanet populations around stars in diverse Galactic environments, specifically focusing on the Milky Way thin disk, thick disk, and stellar halo. We aim to quantify PLATO's ability to detect planets in each environment and determine how these observations could constrain planet formation models. Beginning with the all-sky PLATO Input Catalogue, we kinematically classified the 2.4 million FGK stars into their respective Galactic components. For the sub-sample of stars in the long-observation LOPS2 and LOPN1 PLATO fields, we estimated planet occurrence rates using the New Generation Planet Population Synthesis (NGPPS) dataset. Combining these estimates with a PLATO detection efficiency model, we predicted the expected planet yields for each Galactic environment during a nominal 2+2 year mission. Based on our analysis, PLATO is likely to detect at least 400 exoplanets around the alpha -enriched thick disk stars. The majority of those planets are expected to be super-Earths and sub-Neptunes with radii between 2 and 10 $R_ and orbital periods between 2 and 50 days, which is ideal for studying the link between the radius valley and stellar chemistry. For the metal-poor halo, PLATO is likely to detect between 1 and 80 planets with periods between 10 and 50 days, depending on the potential existence of a metallicity threshold for planet formation. The PLATO fields contain more than 3,400 potential target stars with Fe/H < -0.6, which will help improve our understanding of planets around metal-poor stars. We identified a specific target list of 47 (kinematically classified) halo stars in the high-priority, high-signal-to-noise PLATO P1 sample, offering prime opportunities in the search for planets in metal-poor environments. PLATO's unique capabilities and large field of view position it as a valuable tool for studying planet formation across the diverse Galactic environments of the Milky Way. By probing exoplanet populations around stars with a varying chemical composition, PLATO will provide helpful insights into the connection between stellar chemistry and planet formation.

An efficient neural network LEACH protocol to extended lifetime of wireless sensor networks

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol’s potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.

Fault in Converter Interfaced Micro Grid Using Detection and Identification of Hybrid Technique

ABSTRACTThis paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments.

A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments

Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy.

Ultrastrong coupling limit to quantum mean force Gibbs state for anharmonic environment.

The equilibrium state of a quantum system can deviate from the Gibbs state if the system-environment (SE) coupling is not weak. An analytical expression for this mean force Gibbs state (MFGS) is known in the ultrastrong coupling (USC) regime for the Caldeira-Leggett (CL) model that assumes a harmonic environment. Here, we derive analytical expressions for the MFGS in the USC regime for more general SEmodels. For all the generalized models considered here, we find the USC state to be diagonal in the basis set by the SEinteraction, just like in the CL case. While for the generic model considered, the corresponding USC-MFGS is found to alter from the CL result, we do identify a class of models more general than the CL model for which the CL-USC result remains unchanged. We also provide numerical verification for our results. These results provide key tools for the study of strong coupling quantum thermodynamics and several quantum chemistry and biology problems under more realistic SEmodels, going beyond the CL model.

A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble Classifier

A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble Classifier

Retraction Note: Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment

Retraction Note: Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment

A Study of the Effect of Temperature on the Capacitance Characteristics of a Metal-μhemisphere Resonant Gyroscope

Metal-μhemispherical resonant gyros (M-μHRGs) are widely used in highly dynamic navigation systems in extreme environments due to their high accuracy and structural stability. However, the effect of temperature variations on the capacitance characteristics of M-μHRGs has not been fully investigated, which is crucial for optimizing the performance of the gyro. This study aims to systematically analyze the effect of temperature on the static and dynamic capacitances of M-μHRGs. In this study, an M-μHRG structure based on a 16-tooth metal oscillator is designed, and conducted simulation experiments using non-contact capacitance measurement method and COMSOL Multiphysics 6.2 finite element simulation software in the temperature range of 233.15 K to 343.15 K. The modeling analysis of the static capacitance takes into account the thermal expansion effect, and the results show that static capacitance remains stable across the measured temperature range, with minimal effect from temperature. The dynamic capacitance exhibits significant nonlinear variations under different temperature conditions, especially in the two end temperature intervals (below 273.15 K and above 313.15 K), where the capacitance values show local extremes and fluctuations. In order to capture this nonlinear behavior, the experimental data were smoothed and fitted using the LOESS method, revealing a complex trend of the capacitance variation with temperature. The results show that the M-μHRG has good capacitance stability in the mid-temperature range, but its dynamic performance is significantly affected at extreme temperatures. This study provides a theoretical reference for the optimal design of M-μHRGs in high- and low-temperature environments.