Search Results (8,229)

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article

As a core component of industrial robots, the transmission accuracy life (TAL) of rotary vector (RV) reducers constitutes a primary factor determining the high-precision operation of robotic systems. However, current life evaluation methods for RV reducers predominantly rely on conventional bearing strength life calculations, while neglecting its transmission accuracy degradation during operation. To address this limitation, a static analysis model of RV reducers is established, through which a calculation method for transmission accuracy and TAL is presented. Simultaneously, tooth surface and bearing wear models are developed based on Archard’s wear theory. Through coupled analysis of the aforementioned models, the transmission accuracy degradation law of RV reducers is revealed. The results show that during the operation of the RV reducer, the transmission error (TE) maintains relative stability over time, whereas the lost motion (LM) exhibits a continuous increase. Based on this observation, LM is defined as the evaluation metric for TAL, and a novel TAL estimation model is proposed. The feasibility of the developed TAL estimation model is ultimately validated through accelerated transmission accuracy degradation tests on RV reducers. The error between the predicted and experimental results is 11.06%. The proposed TAL estimation model refines the life evaluation methodology for RV reducers, establishing a solid foundation for real-time transmission accuracy compensation in reducer operation. Full article

The United States of America could build 20,000 bases for the Statue of Liberty every year using its construction and demolition waste, and 456 bases using waste glass from jars and bottles. However, some sectors of the population still face a shortage of affordable housing. The challenges of disposing of such large amounts of waste and solving the housing shortage could be addressed together if these materials, considered part of a closed-loop system, were integrated into new building blocks. This research studies compressed earth blocks that incorporate soils and gravels excavated in situ, river sand, crushed concrete from demolition waste, and recycled glass sand. To stabilize the blocks, cement is used at 5, 10, and 15% (by weight). The properties studied include the following: density, apparent porosity, initial water absorption, simple compression, modulus of elasticity, and thermal conductivity. Optical image analysis proved to be a tool for predicting the values of these properties as the stabilizer changed. To assist in decision making regarding the best overall performance of the total 12 mix designs, a ranking system is proposed. The best blocks, which incorporate the otherwise waste materials, exhibited simple compression values up to 7.3 MPa, initial water absorption of 8 g/(cm² × min^0.5) and thermal conductivity of 0.684 W/m·K. Full article

Metal hydrides are emerging hydrogen-storage materials that have attracted much attention for their stability and practicality. The novel magnesium-based metal hydride Mg₂CsH₉ was investigated using the CALYPSO software (version 7.0). First-principles predictive methods were then employed to investigate the structural, mechanical, electronic, optical, and hydrogen-storage properties of Mg₂CsH₉ and its alkali metal substitution structure Mg₂RbH₉. The negative formation energy, compliance with the Born stability criterion, and absence of imaginary modes in the phonon spectrum collectively confirm the thermodynamic, mechanical, and dynamic stability of Mg₂XH₉ (X = Cs, Rb), fulfilling the basic criteria for practical hydrogen-storage applications. Mg₂RbH₉ is particularly outstanding in terms of its hydrogen-storage capacity, with a gravimetric capacity of 6.34 wt% and a volumetric capacity as high as 92.70 g H₂/L, surpassing many conventional materials. The pronounced anisotropic characteristics of both compounds further enhance their practicality and adaptability to complex working conditions. An analysis of Poisson’s ratio revealed that the chemical bonding in both compounds is predominantly ionic. The details of the band structures and density of states indicate that Mg₂CsH₉ and Mg₂RbH₉ are semiconductors. Their optical properties confirm them as being high-refractive-index materials. Full article

Leaves of Gentiana lutea L., traditionally used for treating heart disorders, represent a sustainable and underutilized source of bitter secoiridoids and xanthones, also found in Gentianae radix—an official herbal drug derived from the same, protected species. As root harvesting leads to the destruction of the plant, using the more readily available leaves could help reduce the pressure on this endangered natural resource. This study aimed to optimize the ultrasound-assisted extraction of the secoiridoid swertiamarin and the xanthone isogentisin from G. lutea leaves using response surface methodology (RSM). Subsequently, the stability of the bioactive compounds (swertiamarin, gentiopicrin, mangiferin, isoorientin, isovitexin, and isogentisin) in the optimized extract was monitored over a 30-day period under different storage conditions. The influence of extraction time (5–65 min), ethanol concentration (10–90% v/v), liquid-to-solid ratio (10–50 mL/g), and temperature (20–80 °C) was analyzed at five levels according to a central composite design. The calculated optimal extraction conditions for the simultaneous maximization of swertiamarin and isogentisin yields were 50 min extraction time, 30% v/v ethanol concentration, 30 mL/g liquid-to-solid ratio, and 62.7 °C extraction temperature. Under these conditions, the experimentally obtained yields were 3.75 mg/g dry weight for swertiamarin and 1.57 mg/g dry weight for isogentisin, closely matching the RSM model predictions. The stability study revealed that low-temperature storage preserved major bioactive compounds, whereas mangiferin stability was compromised by elevated temperature and light exposure. The established models support the production of standardized G. lutea leaf extracts and may facilitate the efficient separation and purification of their bioactive compounds, thereby contributing to the further valorization of this valuable plant material. Full article

Combustion instability poses a significant challenge in aerospace propulsion systems, particularly in afterburners that employ bluff-body flame stabilizers. The flame transfer function (FTF) is essential for characterizing the dynamic response of flames to perturbations, which is critical for predicting and controlling these instabilities. This study experimentally investigates the effect of varying the number of fuel injection holes (N = 3, 4, 5, 6) on the FTF and flame dynamics in a model afterburner combustor. Using acoustic excitations, the FTF was measured across a range of frequencies, with flame behavior analyzed via high-speed imaging and chemiluminescence techniques. Results reveal that the FTF gain exhibits dual-peak characteristics, initially decreasing and then increasing with higher N values. The frequencies of these gain peaks shift to higher values as N increases, while the time delay between velocity and heat release rate fluctuations decreases, indicating a faster flame response. Flame morphology analysis shows that higher N leads to shorter, taller flames due to enhanced fuel distribution and mixing. Detailed examination of flame dynamics indicates that different pulsation modes dominate at various frequencies, elucidating the observed FTF behavior. This research provides novel insights into the optimization of fuel injection configurations to enhance combustion stability in afterburners, advancing the development of more reliable and efficient aerospace propulsion systems. Full article

Reliable short-term electricity usage prediction is essential for preserving the stability of topologically symmetric power networks and their dynamic supply–demand equilibrium. To tackle this challenge, this paper proposes a novel approach derived from the standard Extreme Learning Machine (ELM) by integrating an enhanced Crayfish Optimization Algorithm (DSYCOA). This algorithm combines Logistic chaotic mapping, local precise search, and dynamic parameter adjustment strategies designed to achieve a dynamic balance between exploration and exploitation, thereby optimizing the initial thresholds and weights of the ELM. Consequently, a new short-term power load forecasting model, namely the DSYCOA-ELM model, is developed. Experimental validation demonstrates that the improved DSYCOA exhibits fast convergence speed and high convergence accuracy, and successfully harmonizes global exploration and local exploitation capabilities while maintaining an empirical balance between exploration and exploitation. To additionally verify the effectiveness of DSYCOA in improving ELM, this paper conducts simulation comparison experiments among six models, including DSYCOA-ELM, ELM, and ELM improved by BWO (BWO-ELM). The findings demonstrate that the DSYCOA-ELM model outperforms the other five forecasting models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other indicators. Specifically, in terms of MAPE, DSYCOA-ELM reduces the error by 96.9% compared to ELM. This model demonstrates feasibility and effectiveness in solving the problem of short-term power load prediction, providing critical support for maintaining the stability of grid topological symmetry and supply–demand balance. Full article

Traditional internal support systems for deep foundation pits often suffer from issues such as insufficient stiffness, excessive displacement, and large support areas. To address these problems, the authors developed a novel spatial steel joist internal support system. Based on a large-scale field model test, this study investigates the bearing characteristics of the proposed system in deep foundation pits. A stiffness formulation for the novel support system was analytically derived and experimentally validated through a calibrated finite element model. After validation with test results, the effects of different vertical prestressing forces on the structure were analyzed. The results indicate that the proposed system provides significant support in deep foundation pits. The application of both horizontal and vertical prestressing increases the internal forces within the support structure while reducing overall displacement. The numerical predictions of horizontal displacement, bending moment, and the axial force distribution of the main support, as well as their development trends, align well with the model test results. Moreover, increasing the prestressing force of the steel tie rods effectively controls the deformation of the vertical arch support and enhances the stability of the spatial structure. The derived stiffness formula shows a small error compared with the finite element results, demonstrating its high accuracy. Furthermore, the diagonal support increases the stiffness of the lower chord bar support by 28.24%. Full article

Spectral models, due to their stability and efficiency, have become one of the most popular approaches for implementing numerical weather prediction systems. Given the complexity of these models, they often require the use of multi-node computing resources for parallel processing to meet the stringent real-time requirements. However, as the number of nodes increases, the efficiency of inter-node communication becomes a critical bottleneck. In the case of the Yin-He Global Spectral Model (YHGSM), developed by the National University of Defense Technology, communication overhead is very high during the Fourier transform section, which consists of the transform itself and the subsequent transposition from the z-μ decomposition to the z-m decomposition. To address this challenge, we introduce an optimized scheme that overlaps communication with computation. By grouping corresponding communication and computation tasks, this approach leverages non-blocking communication techniques within MPI, combined with the use of asynchronous communication progress threads. Our experimental results demonstrate that this scheme can reduce execution time by up to 30% compared to the non-overlapped version, thereby significantly hiding communication overhead and enhancing the efficiency of YHGSM. Full article

The shear strength parameters of landslide zones are the necessary data basis for landslide stability evaluation and landslide surge disaster chain research. It is important to determine the physical and mechanical parameters of landslide zones scientifically and reasonably. In this study, four small residual landslide deposits near the Hei Duo Village road in Diebu County, Gansu Province, were investigated. The research involved detailed field investigations, the construction of landslide engineering geological models, and the use of the transfer coefficient method for simultaneous/inverse inversion and sensitivity analysis of the strength parameters of the four landslides. Based on the inversion results, an analysis of the landslide formation mechanism was conducted. The inversion results yielded the shear strength parameters of the sliding surface soil as c = 30.12 kPa and φ = 21.08°. It was found that the excavation at the base of the slope is the direct triggering factor for the landslides, with the 3# landslide being the most affected by the base excavation. In terms of the type of movement, all four landslides belong to the retrogressive landslide, with the maximum shear strain increment mainly concentrated at the slope angle after excavation. The slope body experiences shear failure, which is in good agreement with the field conditions. The study provides reference for stability prediction and disaster prevention and control of reservoir bank slope. Full article

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman filtering to decompose AQI data into features and residuals, effectively mitigating volatility at the initial stage. For residual components that continue to exhibit substantial fluctuations, a secondary decomposition is conducted using variational mode decomposition (VMD), further optimized by the particle swarm optimization (PSO) algorithm to enhance stability. To overcome the limited predictive capabilities of single models, this hybrid framework integrates bidirectional gated recurrent units (BiGRU) with convolutional neural networks (CNNs) and convolutional attention modules, thereby improving prediction accuracy and feature fusion. Experimental results demonstrate the superior performance of KL-PV-CBGRU, achieving R² values of 0.993, 0.963, 0.935, and 0.940 and corresponding MAE values of 2.397, 8.668, 11.001, and 14.035 at 1 h, 8 h, 16 h, and 24 h intervals, respectively, in Shijiazhuang—surpassing all benchmark models. Ablation studies further confirm the critical roles of both the secondary decomposition process and the hybrid architecture in enhancing predictive accuracy. Additionally, comparative experiments conducted in Beijing validate the model’s strong transferability and consistent outperformance over competing models, highlighting its robust generalization capability. These findings underscore the potential of the KL-PV-CBGRU model as a powerful and reliable tool for air quality forecasting across varied urban settings. Full article

Tensins (TNS1–4) are pivotal molecular scaffolds bridging the actin cytoskeleton to integrin-based adhesions, orchestrating signal transduction and governing cellular processes in cancer. Structurally, the N-terminal actin-binding domain (ABD) in TNS1–3 enables cytoskeletal regulation and interactions with regulators like the Rho GAP DLC1, while ABD-deficient TNS4 functions as a focal adhesion signal amplifier. Functionally, TNS1–3 exhibit context-dependent duality as tumor promoters or suppressors, dictated by tissue-specific microenvironments and signaling crosstalk. In contrast, TNS4 acts predominantly as an oncoprotein across carcinomas by stabilizing epidermal growth factor receptor (EGFR), driving epithelial–mesenchymal transition and invasion, and sustaining proliferation. Clinically, tensin dysregulation correlates with metastasis and poor prognosis: TNS2 serves as a diagnostic biomarker for gastrointestinal stromal tumors, aberrant TNS1/TNS3 expression predicts metastasis risk, and TNS4 is recurrently embedded in multi-gene prognostic signatures. This review synthesizes their structural basis, regulatory mechanisms, and clinical relevance, highlighting context-dependent switches and TNS4’s therapeutic potential. Full article

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4B satellite plays a critical role in numerical weather prediction and extreme weather monitoring. To meet the requirements of quantitative remote sensing and high-precision operational applications for radiometric calibration accuracy, this study, based on pre-launch calibration experiments, conducts a novel modeling analysis of the coupling between stray radiation at the input side and the system’s nonlinearity, and proposes a correction method for nonlinear coupling errors. This method explicitly models and physically traces the calibration residuals caused by stray radiation introduced via non-nominal optical paths under the effect of system nonlinearity, which are related to the radiance of the observed target. Experimental results show that, within the brightness temperature range of 200–320 K, the calibration bias is reduced from approximately 0.7 to 0.3–0.4 K, with good consistency and stability observed across channels and pixels. Full article