Search Results (1,936)

Soil hydrological response is crucial for controlling water flow and biogeochemical processes on hillslopes. Understanding soil water dynamics in response to rainfall is essential for accurate hydrological modeling but remains challenging in humid mountainous regions characterized by high antecedent moisture and substantial heterogeneity. We sought to elucidate soil water response patterns to rainfall by estimating lag time, wetting front velocity, rainfall threshold, and preferential flow (PF) frequency in 166 rainfall events across 36 sites on two hillslopes within the Hailuogou catchment, located on the eastern Qinghai–Tibet Plateau. Results indicated that over 90% of the events triggered rapid soil water potential (SWP) responses to depths of 100 cm, with faster responses observed at steeper upslope positions with thinner O horizons. Even light rainfall (2–3 mm) was sufficient to trigger SWP responses. PF was prevalent across the hillslopes, with higher occurrence frequencies at upslope and downslope positions due to steep terrain and consistently moist conditions, respectively. Using the Multivariate Adaptive Regression Splines (MARS) model, we found that site factors (e.g., soil properties and topography) had a greater influence on SWP responses than rainfall characteristics or antecedent soil wetness conditions. These findings highlighted the value of SWP in capturing soil water dynamics and enhancing the understanding and modeling of complex hillslope hydrological processes. Full article

Federated learning is a potential solution for training secure machine learning models on a decentralized network of clients, with an emphasis on privacy. However, the management of system/data heterogeneity and the handling of time-varying client interests still pose challenges to traditional federated learning (FL) approaches. Therefore, we propose the concept of dynamic temporal adaptive clustered federated learning (dy-TACFL) to tackle the issue of of client heterogeneity in time-varying environments. By continuously analyzing and assigning appropriate clusters to the clients with similar behavior, the proposed federated clustering approach increases both prediction accuracy and clustering efficiency. First, a silhouette coefficient-based threshold is used in the temporal adaptive clustering federated learning (TACFL) algorithm to evaluate cluster stability in each round of federated training. Then, an affinity propagation-based dynamic clustering (APD-CFL) algorithm is proposed to adaptively organize clients into an appropriate number of clusters, taking into account the complex underlying pattern. The experimental findings indicate that the proposed time-based adaptive clustered federated learning algorithms can significantly improve prediction accuracy compared to the existing clustered federated learning algorithms. Full article

Images often suffer from various types of noise during their collection and transmission, such as salt-and-pepper, speckle, and Gaussian noise. The wavelet transform (WT) is widely utilized for denoising. However, the decomposition level and threshold significantly impact the quality of the resulting images, but they are difficult to set. This paper uses a modified whale optimization algorithm (MWOA) to optimize the parameters of the WT to achieve better image denoising. The MWOA is enhanced through position updates and mutation to improve the solution quality of WOA and enlarge the search space of the WT. In benchmark images, experimental comparisons with other optimization algorithms like WOA, adaptive cuckoo search (ACS), and social spider optimization (SSO) show that the proposed denoising method achieves superior results in terms of the peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index (SSIM). Full article

This study proposes a high-precision method for retrieving sea surface salinity (SSS) using GF-1 satellite imagery, focusing on Qinzhou Bay along the Guangxi coast. The analysis identified the spectral index B3×B4/(B1×B2) as having the strongest correlation with SSS (R = 0.929). To enhance the performance of the Back Propagation Neural Network (BPNN) model, optimization algorithms including Improved Grey Wolf Optimization (IGWO), Particle Swarm Optimization (PSO), and White Shark Optimization (WSO) were applied. Comparative results show that IGWO significantly optimized network weights and thresholds, yielding superior test performance metrics (MAE = 0.906 psu, MAPE = 4.124%, RMSE = 1.067 psu, and R² = 0.953), demonstrating strong generalization ability. Validation using third-party data indicated accuracy reductions of 10.9% and 8.6% in Qinzhou Bay and Tieshan Port, respectively, highlighting the model’s robustness and broad applicability. SSS retrieval results for Qinzhou Bay in 2023 revealed significant spatial and seasonal variations: the Inner Bay exhibited lower salinity (average 14 psu) from April to September due to freshwater inflows, while salinity increased (average 22 psu) from November to February. The Outer Bay, influenced by its connection to the South China Sea, maintained consistently high salinity levels (25–30 psu) year-round. Additionally, different models showed varying levels of effectiveness in Qinzhou Bay’s complex salinity environment; the IGWO–BPNN model, with its dynamic weight adjustment mechanism, demonstrated superior adaptability in areas with high salinity variability, outperforming other models. These findings suggest that the IGWO–BPNN model provides high accuracy and stability, supporting real-time, precise monitoring in Qinzhou Bay and similar coastal waters, thereby offering robust support for water quality management and marine conservation. Full article

In the era of VUCA, cultivating and enhancing the resilience of high-end manufacturing enterprises is critical. Based on existing research, this paper defines enterprise resilience at the beginning and constructs an enterprise resilience evaluation index system that includes three segmented capabilities: recognition and resistance, adaptation and adjustment, and recovery and rebound. Finally, the relationship between human capital investment, technological innovation, and high-end enterprise resilience is empirically studied. The research results show that human capital investment positively affects the resilience of high-end manufacturing enterprises, with breakthrough innovation and progressive innovation playing a mediating role. Digital transformation positively moderates the impact of human capital investment on the resilience of high-end manufacturing enterprises. Further, there is a higher threshold between human capital investment and technological innovation in improving the resilience of high-end manufacturing enterprises. Human capital investment has a significantly positive effect on high-end manufacturing enterprises’ ability to resist risks and adapt to adjustments but has no significant impact on recovery and rebound ability. Breakthrough and progressive innovation partially mediate the impact of human capital investment on the ability to resist risks and adapt to adjustments, while breakthrough innovation has no significant impact on the recovery of the rebound ability; however, progressive innovation completely mediates the relationship between human capital investment and the recovery of rebound ability. Compared with Chinese non-state-owned enterprises, state-owned enterprises’ efforts to increase investment in human capital only positively impact their ability to resist risks. Compared with large-scale enterprises, the increase in human capital investment in small-scale enterprises only has a significant positive impact on the ability to resist risks. Based on the above, this paper suggests that high-end manufacturing enterprises should enhance their strategic focus and constantly strengthen their investment in human capital and technological innovation; at the same time, they should further optimize the structure of human capital investment and introduce and cultivate cutting-edge talents. Full article

In the field of biomedical engineering, the issue of drug delivery constitutes a multifaceted and demanding endeavor for healthcare professionals. The intravenous administration of pharmacological agents to patients and the normalization of average arterial blood pressure (AABP) to desired thresholds represents a prevalent approach employed within clinical settings. The automated closed-loop infusion of vasoactive drugs for the purpose of modulating blood pressure (BP) in patients suffering from acute hypertension has been the focus of rigorous investigation in recent years. In previous works where model-based and fuzzy controllers are used to control AABP, model-based controllers rely on the precise mathematical model, while fuzzy controllers entail complexity due to rule sets. To overcome these challenges, this paper presents an adaptive closed-loop drug delivery system to control AABP by adjusting the infusion rate, as well as a communication time delay (CTD) for analyzing the wireless connectivity and interruption in transferring feedback data as a new insight. Firstly, a nonlinear backstepping controller (NBC) is developed to control AABP by continuously adjusting vasoactive drugs using real-time feedback. Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. Besides the various analyses such as normal condition (without CTD strategy), stability, and hybrid noise, a CTD analysis is implemented to illustrate the functionality of the system in a wireless manner and interruption in real-time feedback data. Full article

After the lifting of the COVID-19 pandemic restrictions, urban socio-economic development has been continuously recovering. Researchers’ attention to urban vitality recovery has increased. However, few studies have paid attention to the recovery and driving of urban vitality in university fringe areas. This study aims to address this gap by exploring the driving mechanisms of urban vitality recovery in the university fringe areas using both linear and nonlinear models. The results reveal the following: (1) The recovery of urban vitality in university fringe areas follows a distinct pattern where central urban areas with greater openness recover more rapidly, while university fringe areas farther from the city center with stricter management experience slower recovery. (2) The fitting coefficients of the student enrollment, school area, the density of various POIs, and opening hours are 0.0020, −0.0105, −0.0053, and 0.0041 respectively. These variables exhibit a more pronounced linear relationship, and the significance level is quite high. Recovery effects also express significant spatial heterogeneity. (3) Both university opening hours and school area show a nonlinear positive relationship with the urban vitality recovery of university fringe areas, demonstrating a clear threshold effect. This relationship is characterized by slow growth at lower values, rapid acceleration once a critical threshold is reached, and eventual stabilization at higher values. This study offers targeted strategies for urban planning, fostering more responsive and adaptive urban governance that aligns with the evolving needs of urban development. Full article

Temporal vision is a vital aspect of human perception, encompassing the ability to detect changes in light and motion over time. Optical scattering, or straylight, influences temporal visual acuity and the critical flicker fusion (CFF) threshold, with potential implications for cognitive visual processing. This study investigates how scattering affects CFF using an Arduino-based psychophysical device and electroencephalogram (EEG) recordings to analyze brain activity during CFF tasks under scattering-induced effects. A cohort of 30 participants was tested under conditions of induced scattering to determine its effect on temporal vision. Findings indicate a significant enhancement in temporal resolution under scattering conditions, suggesting that scattering may modulate the temporal aspects of visual perception, potentially by altering neural activity at the temporal and frontal brain lobes. A compensation mechanism is proposed to explain neural adaptations to scattering based on reduced electrical activity in the visual cortex and increased wave oscillations in the temporal lobe. Finally, the combination of the Arduino-based flicker visual stimulator and EEG revealed the excitatory/inhibitory stimulation capabilities of the high-frequency beta oscillation based on the alternation of an achromatic and a chromatic stimulus displayed in the CFF. Full article

This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is achieved by comparing zonotopic sets and evaluating a fault signature matrix. The method accounts for parametric uncertainty and measurement noise, ensuring robustness. Experimental validation on a hydraulic pipeline demonstrated high precision (up to 99.24%), recall (up to 99.20%), and low false positive rates (as low as 0.76%) across various fault scenarios and operational points. Full article

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

This article presents an efficient and accurate adaptive time-stepping finite difference method (FDM) for solving the Landau–Lifshitz (LL) equation, which is an important mathematical model in understanding magnetic materials and processes. Our proposed algorithm strategically selects an adaptive time step, ensuring that the maximum displacement falls within a predefined tolerance threshold. Furthermore, this adaptive approach allows the utilization of larger time steps near equilibrium states and results in faster computations. For example, we introduce a numerical test where the adaptive time step decreases from 3.05×10−7 to 3.52×10−9. If a uniform time step is applied, around a 100 times smaller time step must be applied at unnecessary cases. To demonstrate the high performance of our proposed algorithm, we conduct several characteristic benchmark tests. The computational results confirm that the proposed algorithm is efficient and accurate. Overall, our adaptive time-stepping FDM offers a promising solution for accurately and efficiently solving the LL equation and contributes to advancements in the understanding and analysis of magnetic phenomena. Full article

To support the coexistence of long-term evolution (LTE)-license-assisted access (LAA) and wireless local area network (WLAN) in unlicensed bands, the load-based listen-before-talk (LB-LBT) scheme has been developed, incorporating channel sensing and backoff functions similar to those used in WLAN. In the LB-LBT scheme, the contention window size and clear channel assessment (CCA) threshold of the LTE-LAA eNodeB (eNB) significantly influences its transmission probability and the interference caused by concurrent WLAN transmissions outside the CCA range. However, most existing LB-LBT schemes use fixed contention window sizes and CCA thresholds, irrespective of the channel congestion status. To address this limitation, in this paper, we propose a channel measurement-based LBT (CM-LBT) scheme to enhance overall system throughput while ensuring fairness between LTE-LAA and WLAN systems. Our proposed CM-LBT scheme adaptively adjusts the contention window size and CCA threshold of LTE-LAA eNB in the LB-LBT scheme, according to the current channel access activities of LTE-LAA and WLAN systems. Through simulations, we evaluate the performance of our proposed CM-LBT scheme against existing LBT schemes by assessing the throughput of LTE-LAA and WLAN systems, as well as the fairness between them, using a reward function. Full article

In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability. Full article

To address the difficulty in extracting fault features from dual-channel signals, this work proposes a multichannel signal fusion processing method based on Flexible Tensor Singular Spectrum Decomposition (FTSSD) with adaptive embedding dimension selection. Firstly, the optimal embedding dimension of the trajectory tensor is adaptively determined using the proposed Trajectory Dimension Ratio (TDR) index. Once the optimal embedding dimension is obtained, the multichannel signals are represented as an optimal trajectory tensor. Then, FTSSD is employed to decompose the tensor and extract feature component signals. Moreover, by setting a residual threshold or maximum number of components to control the iterative process, the precision and rationality of the decomposition are ensured. Finally, all component signals are reconstructed, and their waveforms and spectra are comprehensively analyzed. The experimental results demonstrate that the proposed adaptive embedding FTSSD algorithm achieves a high accuracy and robustness in multichannel signal decomposition and feature extraction, making it suitable for the multicomponent analysis of complex dynamic signals such as mechanical fault diagnosis and vibration analysis. Full article

Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images. Full article