Search Results (9,121)

Carrier frequency offset (CFO) estimation is crucial for underwater acoustic (UWA) multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. By employing pilot symbols, a CFO estimation scheme utilizing least squares (LS)-based tentative channel estimation and equalization can achieve an improved CFO estimation performance. However, it suffers from performance degradation due to inaccurate tentative channel estimation in scenarios with relatively long channels or a relatively large number of transmitting transducers. To address this problem, we propose a sparse Bayesian learning (SBL)-based CFO estimation scheme, which employs the expectation-maximization SBL (EM-SBL) algorithm as the tentative channel estimator. In addition, to reduce computational complexity caused by matrix inversion, a refined scheme employing variational Bayesian inference (VBI) technology is proposed, which achieves comparable performance to the original scheme with lower complexity. Finally, numerical simulations demonstrate that our proposed schemes can achieve a remarkably low root mean square error (below ${10}^{-2}$) and outperform existing methods across diverse system configurations and simulated channels. Full article

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

Physiologically based pharmacokinetic (PBPK) models allow to simulate the behaviour of compounds in diverse physiological populations. However, the categorization of individuals into distinct populations raises questions regarding the classification criteria. In previous research, simulations of the pharmacokinetics of the mycotoxin aflatoxin B1 (AFB1), were performed in the black South African population, using PBPK modeling. This study investigates the prevalence of clinical CYP450 phenotypes (CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5) across Sub-Saharan Africa (SSA), to determine the feasibility of defining SSA as a single population. SSA was subdivided into Central, East, South and West Africa. The phenotype data were assigned to the different regions and a fifth SSA group was composed of all regions’ weighted means. Available data from literature only covered 7.30% of Central, 56.9% of East, 38.9% of South and 62.9% of West Africa, clearly indicating critical data gaps. A pairwise proportion test was performed between the regions on enzyme phenotype data. When achieving statistical significance (p < 0.05), a Cohen’s d-test was performed to determine the degree of the difference. Next, per region populations were built using SimCYP starting from the available SSA based SouthAfrican_Population FW_Custom population, supplemented with the phenotype data from literature. Simulations were performed using CYP probe substrates in all populations, and derived PK parameters (C_max, T_max, AUC_ss and CL) were plotted in bar charts. Significant differences between the African regions regarding CYP450 phenotype frequencies were shown for CYP2B6, CYP2C19 and CYP2D6. Limited regional data challenge the representation of SSA populations in these models. The scarce availability of in vivo data for SSA regions restricted the ability to fully validate the developed PBPK populations. However, observed literature data from specific SSA regions provided partial validation, indicating that SSA populations should ideally be modelled at a regional level rather than as a single entity. The findings, emerging from the initial AFB1-focused PBPK work, underscore the need for more extensive and region-specific data to enhance model accuracy and predictive value across SSA. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

Meteorological conditions are a key factor affecting airport site selection and operational efficiency. To overcome the limitations of traditional methods in evaluating the joint impact of multiple meteorological elements, this paper aims to develop an airport site selection decision support method based on case-based reasoning (CBR) and multi-meteorological element clustering. Firstly, we propose a universal framework: utilizing K-means clustering to extract typical weather scenarios from historical meteorological data; subsequently, using Zhengzhou Xinzheng International Airport as a case study, a quantitative mapping relationship was established between these weather scenarios and flight operation efficiency (such as delay rate and cancellation rate) to calibrate and validate the model; finally, by calculating the frequency of occurrence of various weather scenarios at candidate sites, the future operational efficiency can be inferred, providing a ranking basis for site selection decisions. The results indicate that low-cloud-base weather has the greatest impact on flight takeoff performance, while good weather has a relatively small impact on flights. This method can effectively and quickly rank the advantages and disadvantages of all candidate airports, providing a reference for airport construction. Full article

If intraday price data are unavailable, then using daily returns to construct realized measures of the variances of lower-frequency returns is a natural substitute for using high-frequency returns in this context. Notably, a suitable application of this approach yields realized measures that are unbiased estimators of the unconditional and conditional variances of holding period returns for any investment horizon. I use a long sample of daily S&P 500 index returns to investigate the merits of constructing realized measures in this fashion. First, I conduct a Monte Carlo study using a data generating process that reproduces the key dynamic properties of index returns. The results of the study suggest that using realized measures constructed from daily returns to estimate the conditional and unconditional variances of lower-frequency returns should lead to substantial increases in efficiency. Next, I fit a multiplicative error model to the realized measures for weekly and monthly index returns to obtain out-of-sample forecasts of their conditional variances. Using the forecasts produced by a generalized autoregressive conditional heteroskedasticity model as a benchmark, I find that the forecasts produced by the multiplicative error model always generate lower mean absolute errors. Furthermore, the improvements in forecasting performance are statistically significant in most cases. Full article

Background/Objectives: Tacrolimus is the most used immunosuppressive agent in solid organ transplantation due to its efficacy in preventing acute rejection, but it has a narrow therapeutic range, and overexposure often leads to toxicities, including neurological side effects like tremors. Tremor affects up to 54% of renal transplant patients under tacrolimus. Extended-release tacrolimus (LCPT) has demonstrated efficacy in reducing tremor severity, as evidenced by studies employing quality of life (QoL) questionnaires, the Fahn–Tolosa–Marin (FTM) scale, and Accelerometer devices. The objectives of this study were to evaluate the benefits of the conversion to LCPT formulation in kidney transplant recipients experiencing tremors on prolonged-release tacrolimus (PR-TAC) treatment and to validate the DyCare device, a wearable wireless sensor for tremors. Results: The DyCare device measured tremor frequencies of 8.74 ± 0.11 Hz and 1.36 ± 0.08° and 17.38 ± 1.16°, as root mean square (RMSx100 for accelerometer and Gyroscope, respectively) in PR-TAC patients. After switching ten patients to LCPT, tremor severity significantly decreased, as confirmed by DyCare and the QoL in the Essential Tremor Questionnaire (QUEST). Additionally, LCPT allowed a 34% reduction in tacrolimus dosage while maintaining therapeutic trough concentrations. Immunological and pharmacodynamic biomarkers (p-miR-210-3p, p-IL10, p-IL12p70, p-IFNγ uCXCL10, NFAT-regulated gene expression) confirmed stable immunosuppression post-conversion. Conclusions: The conversion to the LCPT formulation significantly reduced tremors in kidney transplant recipients without altering their immunological status, as confirmed through a panel of immunologic and pharmacodynamic biomarkers. The DyCare device enables a precise quantification of tremors in transplant recipients, allowing physicians to optimize treatment strategies. Full article

Major stratospheric sudden warmings are key processes in the coupling between the stratosphere and the troposphere, exerting a direct influence on mid-latitude climate variability. This study evaluates projected changes in the frequency of these phenomena during the 2006–2100 period using six high-top general circulation models from the CMIP5 project under the Representative Concentration Pathway scenarios 2.6, 4.5, and 8.5. The analysis combines the full future period with a moving-window approach of 27 and 48 years, compared against both the satellite-era (1979–2005) and extended historical (1958–2005) periods. This methodology reveals that model responses are highly heterogeneous, with alternating periods of significant increases and decreases in event frequency, partially modulated by internal variability. The magnitude and statistical significance of the projected changes strongly depend on the chosen historical reference period, and most models tend to reproduce displacement-type polar vortex events preferentially over split-type events. These results indicate that assessments based solely on multi-model means or long aggregated periods may mask subperiods with robust signals, although some of these may arise by chance given the 5% significance threshold. This underscores the need for temporally resolved analyses to improve the understanding of stratospheric variability and its potential impact on climate predictability. Full article

Cropland abandonment, a global challenge, necessitates comprehensive monitoring to achieve the zero hunger goal. Prior monitoring approaches to cropland abandonment often face constraints in resolution, time series, drivers, prediction, or a combination of these. Here, we proposed an artificial intelligence framework to comprehensively monitor cropland abandonment and tested the framework in Hengyang City, China. Specifically, we first mapped land cover at 30 m resolution from 1985 to 2023 using Landsat, stable sample points, and a machine learning model. Subsequently, we constructed the extent, time, and frequency of cropland abandonment from 1986 to 2022 by analyzing pixel-level land-use trajectories. Finally, we quantified the drivers of cropland abandonment using machine learning models and predicted the spatial distribution of cropland abandonment risk from 2032 to 2062. Our results indicated that the abandonment maps achieved overall accuracies of 0.88 and 0.78 for identifying abandonment locations and timing, respectively. From 1986 to 2022, the proportion of cropland abandonment ranged between 0.15% and 4.06%, with an annual average abandonment rate of 1.32%. Additionally, the duration of abandonment varied from 2 to 38 years, averaging approximately 14 years, indicating widespread cropland abandonment in the study area. Furthermore, 62.99% of the abandoned cropland experienced abandonment once, 27.17% experienced it twice, and only 0.23% experienced it five times or more. Over 50% of cropland abandonment remained unreclaimed or reused. During the study period, tree cover, soil pH, soil total phosphorus, potential crop yield, and the multiresolution index of valley bottom flatness emerged as the five most important environmental covariates, with relative importances of 0.087, 0.074, 0.068, 0.050, and 0.043, respectively. Temporally, cropland abandonment in 1992 was influenced by transportation inaccessibility and low agricultural productivity, soil quality degradation became an additional factor by 2010, and synergistic effects of all three drivers were observed from 2012 to 2022. Notably, most cropland had a low abandonment risk (mean: 0.36), with only 0.37% exceeding 0.7, primarily distributed in transitional zones between cropland and non-cropland. Future risk predictions suggested a gradual decline in both risk values and the spatial extent of cropland abandonment from 2032 to 2062. In summary, we developed a comprehensive framework for monitoring cropland abandonment using artificial intelligence technology, which can be used in national or regional land-use policies, warning systems, and food security planning. Full article

There are close relationships between vertical positions in space and auditory experiences of fundamental frequency (F0), which even very young children seem to use. Like adults, they associate higher or rising F0 values with upper positions in space and vice versa for lower or falling F0 values. Here, we tested whether caregivers’ speech capitalizes on these associations to guide the attention of very young children (18 to 24 months). Together with their child, caregivers saw four different objects (in the corners of a computer screen). Caregivers produced standardized sentences (e.g., “Look at the hat.”). We observed a significantly increased maximum F0 (but no differences in mean or minimum F0) at the onset of the utterance when the to be named object appeared at an upper position compared to the same object at a lower position. Furthermore, exploratory analyses of F0 dynamics of caregivers’ utterances indicated that the higher F0 maximum was part of a more prominent F0 increase for objects appearing at an upper compared to a lower position. Adults without a child present and less experience in interacting with children did not show systematic pitch modulation when addressing an imagined child in the same study set-up. Thus, caregivers appear to systematically modulate F0 dynamics to provide an effective language environment when they are interacting with their child. Full article

Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address these problems. We propose a compact spatial representation (CSR) to robustly generate local spatial multihypothesis features from part of the 2D pose sequence. Specifically, CSR models spatial constraints based on body parts and incorporates 2D Gaussian filters and nonparametric reduction to improve spatial features against low-quality 2D poses and reduce the computational cost of subsequent temporal encoding. We design a residual-based Hybrid Adaptive Fusion module that combines multihypothesis features with global frequency domain features to accurately estimate the 3D human pose with minimal computational cost. We realize CGFusionFormer with a PoseFormer-like transformer backbone. Extensive experiments on the challenging Human3.6M and MPI-INF-3DHP benchmarks show that our method outperforms prior transformer-based variants in short receptive fields and achieves a superior accuracy–efficiency trade-off. On Human3.6M (sequence length 27, 3 input frames), it achieves 47.6 mm Mean Per Joint Position Error (MPJPE) at only 71.3 MFLOPs, representing about a 40 percent reduction in computation compared with PoseFormerV2 while attaining better accuracy. On MPI-INF-3DHP (81-frame sequences), it reaches 97.9 Percentage of Correct Keypoints (PCK), 78.5 Area Under the Curve (AUC), and 27.2 mm MPJPE, matching the best PCK and achieving the lowest MPJPE among the compared methods under the same setting. Full article

In recent years, Bionic reading has been introduced as a means to combat superficial reading and low comprehension rates. This paper investigates eye movements between participants who read a passage in standard font and an additional Bionic font passage. It was found that Bionic font does not significantly change eye movements when reading. Fixation durations, number of fixations and reading speeds were not significantly different between the two formats. Furthermore, fixations were spread throughout the word and not only on leading characters, even when using Bionic font; hence, participants were not able to “auto-complete” the words. Additionally, Bionic font did not facilitate easier processing of low-frequency or unfamiliar words. Overall, it would appear that Bionic font, in the short term, does not affect reading. Further investigation is needed to determine whether a long-term intervention with Bionic font is more meaningful than standard interventions. Full article

Voltage supraharmonics present in the electrical grid can trigger chain reactions in grid-connected household and industrial power supplies equipped with Power Factor Correction (PFC). A single source of voltage supraharmonics may significantly increase the current in switching devices with PFC, leading to higher-amplitude disturbances throughout the electrical network. When addressing issues in a real low-voltage (LV) grid, it was observed that activation of a single device emitting supraharmonics caused oscillating currents across all feeders connected to the transformer’s busbars, matching the frequency of the supraharmonic source. To investigate this phenomenon further, the grid voltage containing supraharmonics was replicated in a controlled laboratory environment and used to supply various power electronic devices. The laboratory results closely mirrored those observed in the field. Supraharmonics present in the supply voltage caused current oscillations in the power electronic devices at the same frequency. Moreover, the amplitude of the observed current oscillations increased with the amplitude of the injected supply voltage supraharmonics. In some cases, the root mean square (RMS) value of the current drawn by the power electronic devices doubled, indicating a substantial impact on device behaviour and potential implications for grid stability and energy efficiency. Full article

Background/Objectives: Beetroot supplementation is a rich source of inorganic nitrate and has been proposed to enhance nitric oxide bioavailability and support cardiovascular recovery after exercise. This study aimed to evaluate the effects of beetroot supplementation on post-exercise cardiovascular and autonomic recovery in postmenopausal women. Methods: A systematic review and meta-analysis were conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Searches were performed in PubMed, Scopus, and Web of Science databases from inception to July 2025. Ten trials involving postmenopausal women were included. Outcomes assessed included cardiovascular measures (blood pressure and vascular function), autonomic parameters derived from heart rate variability (HRV)—specifically the root mean square of successive differences (RMSSD), the standard deviation of normal-to-normal intervals (SDNN), and high-frequency power (HF)—as well as physical performance (peak oxygen uptake [VO_2peak or VO_2max] and functional fitness tests). Four independent reviewers extracted data, assessed risk of bias, and evaluated the certainty of evidence using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Results: Pooled analyses from two trials (n = 54) revealed a statistically significant improvement in RMSSD with beetroot supplementation (mean difference: 6.68 ms; 95% CI: 0.86 to 12.50; p = 0.02), suggesting enhanced parasympathetic reactivation after exercise. No significant effects were detected for HF (mean difference: 61.75 ms²; 95% CI: −70.92 to 194.43; p = 0.36) or SDNN (mean difference: 6.20 ms; 95% CI: −9.69 to 22.09; p = 0.44). Substantial to considerable heterogeneity was identified across outcomes (I² = 73–86%). Certainty of evidence was rated moderate for RMSSD, low for SDNN, and very low for HF. Conclusions: Beetroot supplementation may enhance post-exercise autonomic recovery in postmenopausal women, primarily through improvements in RMSSD. However, further trials with standardized protocols, larger samples, and longer intervention durations are required to clarify its impact on broader HRV domains, cardiovascular function, and clinical outcomes. Full article

Soil electrical conductivity (EC) and water content are key indicators of soil health, influencing nutrient availability, salinity stress, and crop productivity. Monitoring these parameters is critical for precision agriculture. However, most existing measurement systems are costly, which restricts their use in practical field conditions. The aim of this study was to develop and validate a low-cost, portable system for simultaneous measurement of soil EC, water content, and temperature, while maintaining accuracy comparable to laboratory-grade instruments. The system was designed with four electrodes arranged in two pairs and employed an AC bipolar pulse method with a constant-current circuit, precision rectifier, and peak detector to minimize electrode polarization. Experiments were carried out in sandy loam soil at water contents of 13%, 18%, and 22% and KNO₃ concentrations of 0, 0.1, 0.2, and 0.4 M. Measurements from the developed system were benchmarked against a professional impedance analyzer (E4990A). The findings demonstrated that EC increased with both frequency and water content. At 100 Hz, the mean error compared with the analyzer was 8.95%, rising slightly to 9.98% at 10 kHz. A strong linear relationship was observed between EC and KNO₃ concentration at 100 Hz (R² = 0.9898), and for the same salt concentration (0.1 M KNO₃) at 100 Hz, EC increased from ~0.26 mS/cm at 13% water content to ~0.43 mS/cm at 22%. In conclusion, the developed system consistently achieved <10% error while maintaining a cost of ~€55, significantly lower than commercial devices. These results confirm its potential as an affordable and reliable tool for soil salinity and water content monitoring in precision agriculture. Full article