Search Results (20,564)

This study presents a computational fluid dynamics (CFD) simulation to investigate fluid flow and heat transfer within the growth zone of gallium nitride crystals synthesized via the alkaline ammonothermal method, with particular emphasis on the influence of seed crystal arrangement and installation geometry. The model analyzes temperature and velocity distributions, highlighting how seed configuration affects turbulent and transitional flow behavior. Key findings demonstrate the effectiveness of CFD in evaluating and optimizing growth zone design. Both simulation and experimental results show that achieving more uniform flow and temperature fields leads to more consistent growth rates and improved structural crystal quality. Furthermore, the study underscores the critical role of installation geometry in shaping flow characteristics such as velocity distribution, temperature gradients, and their transient fluctuations, factors essential for optimizing the ammonothermal crystallization process. Full article

Urban lifeline Natech events are coupled systems composed of multiple risks and entities with complex dynamic transmission chains. Predicting risk evolution probabilities is the core task for achieving the safety management of urban lifeline Natech events. First, the risk evolution mechanism is analyzed, where urban lifeline Natech events exhibit spatial evolution characteristics, which involves dissecting the parallel and synergistic effects of risk evolution in spatial dimensions. Next, based on fitting marginal probability distribution functions for natural hazard and urban lifeline risk evolution, a Multi-dimensional Copula (MdC) function for the joint probability distribution of urban lifeline Natech event risk evolution is constructed. Building upon the MdC function, a Markov Chain Monte Carlo (MCMC) model for predicting risk evolution probabilities of urban lifeline Natech events is developed using the Metropolis–Hastings (M-H) algorithm and Gibbs sampling. Finally, taking the 2021 Zhengzhou ‘7·20’ catastrophic rainstorm as a case study, joint probability distribution functions for risk evolution under Rainfall-Wind speed scenarios are fitted for traffic, electric, communication, water supply, and drainage systems (including different risk transmission chains). Numerical simulations of joint probability distributions for risk evolution are conducted, and visualizations of joint probability predictions for risk evolution are generated. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

Accurate State of Health (SOH) estimation is critical for ensuring the safe operation of lithium-ion batteries. However, current data-driven approaches face significant challenges: insufficient feature extraction and ambiguous physical meaning compromise prediction accuracy, while initialization sensitivity to noise undermines stability; the inherent nonlinearity and temporal complexity of battery degradation data further lead to slow convergence or susceptibility to local optima. To address these limitations, this study proposes an enhanced CNN-BIGRU model. The model replaces conventional random initialization with a Hiking Optimization Algorithm (HOA) to identify superior initial weights, significantly improving early training stability. Furthermore, it integrates an Attention mechanism to dynamically weight features, strengthening the capture of key degradation characteristics. Rigorous experimental validation, utilizing multi-dimensional features extracted from the NASA dataset, demonstrates the model’s superior convergence speed and prediction accuracy compared to the CNN-BIGRU-Attention benchmark. Compared with other methods, the HOA-CNN-BIRGU-Attention model proposed in this study has a higher prediction accuracy and better robustness under different conditions, and the RMSEs on the NASA dataset are all controlled within 0.01, with R2 kept above 0.91. The RMSEs on the University of Maryland dataset are all below 0.006, with R2 kept above 0.98. Compared with the CNN-BIGRU-ATTENTION baseline model without HOA optimization, the RMSE is reduced by at least 0.15% across different battery groups in the NASA dataset. Full article

To reveal how the altitude gradient regulates the effects of rhizosphere microbial dynamics on ecosystem multifunctionality in Hylodesmum podocarpum, a field experiment was conducted across four elevation transects (a.s. 896–1805 m) in the Qinling Mountains. The results showed that rhizosphere soil exhibited peak microbial diversity richness at 1805 m (HB4), with bacterial communities showing a strong interspecific cooperative relationship, while the fungal communities showed a competitive relationship. In addition, this study found the assembly process to be different. Bacterial assemblages changed from random processes (HB1, HB2, HB3) to deterministic processes (HB4), whereas fungal assemblages remained stochastic processes across all elevations. Our results also revealed that synergistic interactions among soil carbon, phosphorus, and nitrogen nutrient functions collectively enhanced nutrient-centered soil multifunctionality. Notably, carbon and phosphorus nutrient functions emerged as the primary drivers of soil multifunctionality. Further mechanistic analysis revealed that while soil pH exerted significant control over both carbon and nitrogen nutrient functions, microbial mediation exhibited functional specialization: bacterial communities predominantly regulated carbon cycling, whereas fungal communities played a more comprehensive role in modulating carbon, nitrogen, and phosphorus dynamics along with overall ecosystem multifunctionality. This finding suggested that altitude gradients indirectly affect the characteristics of the microbial community by regulating soil nutrient status, thereby driving changes in ecosystem multifunctionality. This finding provides new insights into how nutrients regulate ecosystem functions through microbial pathways. Full article

This study conducts a simulation-based assessment of a recently commissioned office building in the Republic of Korea, representing a typical public office facility. The building was modeled using EnergyPlus 23.1.0 after construction, although no validation was performed due to the absence of metered consumption data. Previous approaches relying on simplified methods such as the Radiant Time Series (RTS), which neglect dynamic building behavior, have often led to overestimated cooling and heating loads. This has emerged as a major obstacle in designing energy-efficient buildings within the context of compact and smart cities pursuing carbon neutrality. Consequently, the trend in building performance analysis is shifting toward dynamic simulations and digital twin-based design methodologies. Furthermore, electrification of buildings without adequate thermal load assessment may also contribute to overdesign, irrespective of urban environmental characteristics. From an urban planning standpoint, there is a growing need for performance criteria that reflect occupant behavior and actual usage patterns. However, dynamics-based building studies remain scarce in the Republic of Korea. In this context, the present study demonstrates that passive design strategies, implemented through systematic changes in envelope materials, HVAC operational standards, and compliance with ASHRAE 90.1 criteria, can significantly enhance thermal comfort and indoor air quality. The simulation results show that energy consumption can be reduced by over 36.21% without compromising occupant health or comfort. These findings underscore the importance of thermal load understanding prior to electrification and highlight the potential of LEED-aligned passive strategies for achieving high-performance, low-energy buildings. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

This paper presents a modification of an image encryption algorithm combining chaos and the Fibonacci matrix by integrating artificial intelligence via a Generative Pre-Trained Transformer (GPT). The goal is to improve the robustness of the algorithm by dynamically adapting the parameters of the chaotic system and generating cryptographic keys based on image characteristics. The proposed methodology includes two main innovations: the implementation of GPT for automated generation of the initial parameters of the chaotic system, which allows for greater variability and security in encryption, and the use of GPT for dynamic determination of the Fibonacci Q-matrix, which provides additional complexity and increased resistance to attacks. The method is realized in the MATLAB (R2023a) environment through integration with OpenAI API and the MATLAB–Python interface for requesting GPT models. The efficiency and reliability of the modified algorithm are compared with those of standard chaotic encryption algorithms, and its robustness to various cryptographic attacks is also studied. Full article

This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications. Full article

The large-scale trajectory simulation serves as a fundamental basis for the mission planning of a rocket-powered vehicle swarm. However, the traditional flight trajectory calculation method for a rocket-powered vehicle, which employs strict dynamic and kinematic models, often struggles to meet the temporal requirements of mission planning. To address the challenges of timely computation and intelligent optimization, a segmented training strategy, derived from the domain knowledge of the multi-stage flight characteristics of a rocket-powered vehicle, is integrated into the deep neural network (DNN) method. A high-precision trajectory prediction model that fuses multi-DNN is proposed, which can rapidly generate high-precision trajectory data without depending on accurate dynamic models. Based on the determination of the characteristic parameters derived from rocket-powered trajectory theory, a homemade dataset is constructed through a traditional computation method and utilized to train the DNN model. Extensive and varying numerical simulations are given to substantiate the predictive accuracy, adaptability, and stability of the proposed DNN-based method, and the corresponding comparative tests further demonstrate the effectiveness of the segmented strategy. Additionally, the real-time computational capability is also confirmed by computing the simulation of generating full trajectory data. Full article

Most electromagnetic invisibility devices are designed while relying on rigid structures, which have limitations in adapting to complex curved surfaces and dynamic deployment. In contrast, flexible invisibility structures have great application value due to their bendable and easy-to-fit characteristics. In this paper, we propose a flexible metasurface suitable for broadband SAR (Synthetic Aperture Radar) imaging invisibility, which realizes multi-domain joint regulation of electromagnetic waves by designing two subwavelength unit structures with differentiated reflection characteristics and combining array inverse optimization methods. The metasurface employs a sponge-like dielectric substrate and integrates resistive ink to construct a resonant structure, which can suppress electromagnetic scattering through joint phase and amplitude modulation, achieving low detectability of targets in UAV (Unmanned Aerial Vehicle) detection scenarios. Indoor microwave anechoic chamber tests and outdoor UAV-borne SAR experiments verify its stable invisibility performance in a wide frequency band, providing theoretical and experimental support for the application of flexible metasurfaces in dynamic electromagnetic detection countermeasures. Full article

Age-related physiological and cognitive changes significantly affect older adults’ participation in day-to-day functioning. This interview study aimed to uncover and illuminate the intricate dynamics between individuals’ responses to aging restrictions and day-to-day functioning, and how they relate to successful aging. We used a qualitative research design to explore the various responses to aging decline and their implications for daily functioning among older adults. Eighteen in-depth interviews were conducted with older adults, focusing on their occupational characteristics, needs, and responses to aging constraints. The transcripts were analyzed using principles of constructivist grounded theory. Three main categories were identified regarding older adults’ responses to the decline in abilities that come with age: (a) acceptance, reflecting the individual’s ability to adapt to the age-related changes and constraints; (b) personal resources, including a positive mindset and self-efficacy; and (c) coping strategies, including meaningful roles and occupational adaptation. This study’s findings indicate three types of responses to aging restrictions that may contribute to greater engagement in daily life and, consequently, be a key to successful aging. Developing individually tailored interventions that focus on occupational adaptations according to individual needs and preferences is vital in helping older adults maintain their daily functioning and quality of life. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article

Hydrogels are water-rich polymeric networks mimicking the body’s extracellular matrix, making them highly biocompatible and ideal for precision medicine. Their “tunable” and “smart” properties enable the precise adjustment of mechanical, chemical, and physical characteristics, allowing responses to specific stimuli such as pH or temperature. These versatile materials offer significant advantages over traditional drug delivery by facilitating targeted, localized, and on-demand therapies. Applications range from diagnostics and wound healing to tissue engineering and, notably, cancer therapy, where they deliver anti-cancer agents directly to tumors, minimizing systemic toxicity. Hydrogels’ design involves careful material selection and crosslinking techniques, which dictate properties like swelling, degradation, and porosity—all crucial for their effectiveness. The development of self-healing, tough, and bio-functional hydrogels represents a significant step forward, promising advanced biomaterials that can actively sense, react to, and engage in complex biological processes for a tailored therapeutic approach. Beyond their mechanical resilience and adaptability, these hydrogels open avenues for next-generation therapies, such as dynamic wound dressings that adapt to healing stages, injectable scaffolds that remodel with growing tissue, or smart drug delivery systems that respond to real-time biochemical cues. Full article