Search Results (384)

Molecular docking is among the most widely used techniques in structure-based drug discovery and has become integral to natural-product research. Despite substantial advances in computational algorithms and artificial intelligence, the methodological quality of published docking studies on natural compounds remains highly variable, and generally accepted methodological guidelines have yet to be established. This review critically evaluates current approaches to molecular docking of natural compounds, examines the specific features of different natural-product classes and protein targets, and offers practical recommendations for the design and validation of docking studies. The review covers the main stages of molecular docking, contemporary search algorithms and scoring functions, protein and ligand preparation, the fundamental limitations of the method, strategies for result validation, and the emerging role of artificial intelligence in computational molecular modeling. A central component is a systematic methodological audit of publications within a prespecified 2025 coverage window, identified by Scopus and PubMed searches executed in August 2026. The audit assessed methodological reporting completeness only and was not intended as a systematic review of biological or pharmacological findings. The audit comprised GPT-assisted structured coding of 1127 publications, detailed full-text assessment of a random sample of 80 studies drawn entirely from the same corpus, and independent human validation of 20 randomly selected Stage 2 publications. In the 80-publication full-text sample, redocking was reported in 7.5% of publications, a qualifying redocking RMSD below 2 Å in 6.2%, positive controls in 87.5%, molecular dynamics in 41.2%, MM/PBSA or MM/GBSA in 12.5%, interaction analysis in 98.8%, and ADMET assessment in 35.0%. Agreement between Stage 1 and Stage 2 was 98.3% across 525 paired criterion decisions. Independent human assessment of the 20-publication validation subsample agreed with Stage 2 in 135 of 140 criterion decisions (96.4%); the five discrepancies all involved AI-coded indeterminate/non-confirmed labels (Unconfirmed or N/A) that the human reviewer classified as No. On the basis of these findings, we propose a practical workflow aimed at improving the reproducibility and methodological rigor of molecular docking studies of natural compounds. A complementary targeted case-enriched validation of redocking and redocking-RMSD classification included 19 publications and two human reviewers. The reviewers reached identical classifications in all 38 criterion decisions; their consensus agreed with Stage 2 in 31 of 38 decisions (81.6%), with seven revisions across four publications. Because this sample was deliberately enriched for informative and ambiguous cases, it was used to examine classification boundaries rather than to estimate prevalence. Because PubMed retrieval was restricted to free full text and the Scopus search used restricted bibliographic fields, these reporting-completeness and validation frequencies primarily characterize an accessibility-enriched corpus and may not fully generalize to subscription-only or otherwise less-accessible journals. By combining a critical appraisal of current methods with a quantitative assessment of published studies and practical recommendations for standardizing the molecular docking of natural compounds, this review offers guidance for researchers in computer-aided drug design. Full article

Bamboo is a renewable material widely used in traditional Chinese musical instruments, but its vibration behavior is sensitive to moisture. This study investigated how moisture content affects the vibration behavior of Jinghu bamboo soundboxes. Three Xipi and three Erhuang specimens were conditioned by soaking and drying to obtain 11 moisture states (M0–M10). Time domain responses, frequency response functions, natural frequencies, damping ratios, mode shapes, and a frequency-mass-based estimated structural stiffness index were evaluated using impact hammer testing and experimental modal analysis. Three individual Xipi soundboxes with nominal storage durations of 3, 46, and 86 years were examined before and after conditioning at 20 °C and 60% relative humidity as an exploratory case comparison. Both types showed stronger vibration responses at intermediate moisture levels, whereas nearly dry and nearly saturated conditions produced weaker responses. Increasing moisture generally lowered resonance frequencies and estimated structural stiffness index, while damping ratios were higher at the moisture extremes. Under the present test conditions, Xipi specimens exhibited a higher estimated structural stiffness index and stronger mid- to high-frequency responses than Erhuang specimens. Given the differences in specimen geometry and mass, these variations are interpreted as reflecting whole-structure dynamic responses rather than intrinsic differences in material properties. In the exploratory comparison, XP-86 showed the largest change, with a 61.17% decrease in fundamental frequency and the development of surface cracks. Given the limited number of independent specimens, the results are interpreted descriptively rather than as statistically generalizable effects. These results s demonstrate the importance of humidity control during the use and preservation of Jinghu soundboxes. Full article

Accurate precipitation data are fundamental for reliable hydrological modeling, water resources management, and climate resilience planning. Although extensive hydrometeorological datasets exist in Brazil, including those maintained by the Brazilian National Water and Sanitation Agency (ANA) and the National Institute of Meteorology (INMET), raw data acquisition and preprocessing often pose significant bottlenecks for researchers. To address this gap, this paper introduces a computational framework designed to streamline the management, visualization, and evaluation of Brazilian rainfall datasets. The platform provides station-level daily precipitation series for 602 INMET automatic stations, covering the period from May 2000 to April 2025 and comprising 113,102 station-months, of which 72,527 (64.1%) satisfy a strict completeness criterion under which a calendar month is retained only if every one of its days is observed. The platform includes an interactive map for station visualization and selection, a dataset explorer with date filtering, time series visualization, and data download capabilities, and a dedicated analytical interface for hydrological and statistical analyses. The analytical modules include monthly precipitation climatology, probability distribution fitting for annual maximum daily precipitation, Standardized Precipitation Index at the one-month scale (SPI-1), and Intensity–Duration–Frequency (IDF) curves. The main limitations affecting the interpretation of these outputs are stated explicitly: record-length limitations are explicitly communicated to users through station-specific warnings and informational notices, since no station in the network reaches the preferred 30-year span for robust SPI estimation, and the IDF module is scoped as a screening and pre-design instrument because its disaggregation coefficients were calibrated for a single Brazilian state. By bridging the gap between raw data availability and actionable insights, this open-source tool optimizes hydro-meteorological workflows and supports environmental assessments. Full article

Conventional quasi-steady-state models treat a self-adaptive STATCOM and line-commutated converter (SLCC) station as an LCC with an external reactive-power source, which cannot fully represent valve-side coupling. This paper develops a three-phase stationary-frame differential model for the SLCC and derives quasi-steady-state expressions for the average DC voltage and fundamental displacement angle. The non-commutation equivalent voltage is decomposed into fundamental and nonfundamental components. The fundamental component is retained in the power-flow model, while a control-informed harmonic extension evaluates the corresponding average DC-voltage correction over the tested operating domain. A positive-sequence fundamental-frequency formulation calculates the commutation overlap angle, and a first-zero diagnostic identifies control-sensitive conditions associated with the fast SVG voltage response. When the fast commutation-direction voltage reaches zero or reverses before current transfer is completed, a control-equivalent effective-area formulation provides an alternative low-order representation. The station equations are incorporated into a sequential AC/DC power-flow algorithm and validated against the engineering PSCAD/EMTDC main-circuit and control model of the Yangzhou–Zhenjiang HVDC Phase II project. Across the stable tested operating points, the phase-aware EMT-derived harmonic DC-voltage correction ranges from 0.585% to 1.245%, remaining below the adopted 2% screening threshold. The control-informed estimate follows the EMT-derived correction, whereas the phase-independent conservative bound reaches 2.128% at high controller gain. Across eight cases with available PSCAD reference values, the control-equivalent formulation reduces the mean and maximum overlap-angle errors from $0.90°$ and $1.37°$ to $0.78°$ and $1.09°$. For the benchmark power-flow cases, the maximum relative errors are 1.3% for the SLCC bridge reactive power and 1.0% for the SVG reactive-power output, and the calculation converges without sustained oscillation. A representative operating-point calculation is completed in approximately 3 s with the quasi-steady-state (QSS) formulation, compared with about 15 min for the engineering EMT benchmark. Full article

Non-contact vital sign monitoring using millimeter-wave radar has emerged as a promising alternative to contact-based devices for continuous healthcare and elderly care applications. However, accurate heart rate estimation remains challenging because the weak cardiac-induced chest displacement is approximately an order of magnitude smaller than respiratory motion, and its fundamental frequency is frequently masked by higher-order respiratory harmonics. Here we propose a signal processing framework that addresses this challenge through three integrated stages: a slow-time phase correlation method that enhances the signal-to-noise ratio by coherently aggregating vital sign energy from adjacent range bins; an adaptive harmonic matching filtering approach based on complementary ensemble empirical mode decomposition that isolates and suppresses respiratory harmonic interference; and autocorrelation-based heart rate estimation. Experimental results obtained with a 77 GHz FMCW radar demonstrate that the proposed method achieves heart rate estimates within 5% error of reference wearable sensors in the presence of respiratory harmonics, with robustness confirmed through long-duration testing. This framework provides a practical solution for reliable radar-based heart rate monitoring without requiring subject-specific calibration or specialized hardware modifications. Full article

Accurate monophonic pitch estimation is central to music and speech systems, with practical applications often requiring small models that remain robust to noise and timbre variations. This paper proposes a Frequency Convolution Network (FrCN) for fundamental frequency and voicing estimation from Variable-Q Transform (VQT) features. The model applies dilated one-dimensional convolutions along the log-frequency axis, giving a large frequency receptive field with only 17.8k parameters. This design allows the model to compare harmonic evidence across frequency while keeping the parameter count low. The model is trained with spectral-domain noise augmentation and frequency scaling augmentation, which encourage noise invariance and pitch equivariance. Experiments on MDB-stem-synth, PTDB-TUG, and MIR-1K show that FrCN improves over a CNN baseline in clean and noisy conditions. On clean in-domain evaluation, FrCN reaches 98.8% RPA50 on MDB, 93.1% on PTDB, and 97.5% on MIR-1K. At 0 dB SNR, FrCN keeps RPA50 above 80% on all three in-domain validation sets. The model is also competitive with recent pitch estimation systems while using far fewer parameters. These results show that frequency axis dilation is effective for low-parameter, noise-robust pitch estimation. Full article

This study compares the elastic modal characteristics of reinforced-concrete cantilever beams containing four and six 12 mm longitudinal reinforcing bars using three-dimensional finite element analysis (FEA) and an exploratory machine learning (ML) exercise. Beam geometry, material properties, bond assumptions, and boundary conditions were held constant. Mesh refinement from a 50 mm to a 25 mm nominal element size changed the fundamental natural frequency by 0.18%, satisfying the adopted 1% convergence criterion. The Fine mesh FEA fundamental frequency of 36.048 Hz differed by 1.49% from the Euler–Bernoulli transformed-section estimate of 35.52 Hz. Block Lanczos extraction provided the first six natural frequencies and corresponding normalised mode shapes. Increasing the longitudinal steel area from 452.39 to 678.58 mm² changed the calculated frequencies by −0.02% to +0.19%, while the two layouts exhibited similar mode-shape topology under the assumed intact, linear-elastic, perfectly bonded conditions. Four regression algorithms were fitted to the 12 correlated mode–configuration records using only mode number and reinforcement count as inputs and natural frequency as the output. The reported R², MAE, and RMSE values describe complete dataset fitting or interpolation and do not establish generalisation to unseen configurations. No physical modal displacement, strain, or stress amplitude is reported because eigenvector scaling is arbitrary and no forced-response analysis with defined excitation and damping was performed. Full article

Accurate estimation of subsurface acoustic impedance is fundamental to quantitative reservoir characterization in seismic exploration. Nevertheless, a single network architecture cannot adequately represent both the local details and the global trends of seismic records within a unified framework, while the severe scarcity of annotated well-log data substantially constrains the generalization capability and predictive accuracy of deep-learning-based inversion approaches. To overcome these limitations, a semi-supervised acoustic impedance inversion framework based on a hybrid deep learning architecture is proposed. The framework employs a cascaded architecture consisting of a multi-scale depthwise separable convolution with channel attention (MSDSE) module and a convolution-augmented Transformer encoder. Seismic data are first processed by the MSDSE module to extract local multi-scale temporal features, and are subsequently passed to the convolution-augmented Transformer encoder, which captures global long-range sequence dependencies while retaining complementary local temporal information. The two modules progress hierarchically and jointly achieve a feature representation that spans from local details to global trends, and the initial low-frequency model is fused with the network output via channel-wise concatenation. Meanwhile, an initial-model constraint together with a physical-consistency constraint are simultaneously imposed within the loss function, thereby improving training stability while fully leveraging the physical information embedded in unlabeled traces. Experiments on both synthetic and field data confirm the effectiveness of the proposed method. The results show that, even with a small number of labels, the method produces stable impedance estimates and outperforms conventional deep learning methods in both generalization and prediction accuracy. Full article

This paper presents a novel fractional-order hybrid observer framework for robust sensorless control of brushless DC (BLDC) motor drives in unmanned aerial vehicle (UAV) propulsion systems, addressing the fundamental limitations of conventional integer-order observers through the lens of fractional calculus. The proposed architecture synergistically integrates high-frequency square-wave signal injection for zero/low-speed operation with an adaptive fractional-order extended Kalman filter (AFEKF) augmented by online stator resistance and flux linkage estimation, capitalizing on the memory and hereditary properties inherent to fractional-order systems. A minimum-order current observer enables accurate three-phase current reconstruction using a single DC-link sensor, substantially reducing hardware complexity and cost. The complete algorithm is implemented on an STM32H7 microcontroller and experimentally validated on a 1.5 kW drone propulsion testbench and in-flight platform. Results demonstrate reliable startup under 50% rated load, stable operation from standstill to 5000 RPM on the UAV motor (and validated up to 22,000 RPM on a high-speed test motor, <4° electrical position error at 5 kRPM, and strong robustness against 35% stator resistance variation. In-flight tests confirm improved thrust smoothness and hover stability compared to conventional sensorless strategies. The proposed fractional-order architecture offers a practical, resilient, and computationally feasible solution for next-generation autonomous aerial systems, establishing a new paradigm for observer design in electric propulsion. Full article

In modern power systems, DC offset and odd-order harmonics degrade grid phase and frequency estimation accuracy. To address this problem, this paper proposes an enhanced grid parameter estimation scheme based on cascaded delayed signal cancellation (CDSC). Unlike the conventional CDSC, the proposed method introduces a redesigned delay configuration, significantly reducing the inherent filtering delay while preserving the rejection capability for the DC component and targeted odd-order harmonics. Meanwhile, an amplitude-phase compensation strategy is developed to correct the magnitude attenuation and phase distortion introduced by the CDSC filter, which allows accurate extraction of the fundamental grid component. Furthermore, a frequency-adaptive module is integrated to maintain stable grid synchronization under off-nominal frequency deviations. Finally, experimental results verify the effectiveness of the proposed method. Full article

This work presents, for the first time, visibility measurements carried out in five locations in Colombia (Yopal, San Andrés, Montería, Puerto Carreño, and Villavicencio) during their respective months of lowest visibility, using Vaisala FD70 forward-scatter sensors with a 1-min integration time. The cumulative distributions (CDs) of visibility obtained are fundamental for estimating fog attenuation in free-space optical (FSO) communication systems operating at optical frequencies. Additionally, the performance of visibility prediction models reported in the literature (the Sousa, Queluz, and Rodrigues model, hereafter referred to as the SQR model, and the Pinto I and Pinto II models) is evaluated within the Colombian climatic context. It was observed that the Pinto I and Pinto II models exhibited the best overall performance among the existing models, with a root mean square error (RMSE) value close to 11.46 km for the set of locations, while the locality of San Andrés presented the most adverse conditions, with visibilities below 50 m for 0.01% of the time during the critical month of November. A link-budget analysis shows that the use of visibility prediction models can lead to severe underdimensioning or overdimensioning of FSO links, highlighting the need for locally measured visibility data. Full article

Background: Population sex ratios and age structure are fundamental demographic parameters in shorebird studies, and their accurate estimation depends on unbiased capture methods. Methods: To identify potential capture biases, we evaluated the randomness of mist-net recaptures between sex and age classes of the Western Sandpiper (Calidris mauri) in La Paz Lagoon, Baja California Sur, Mexico. Results: A total of 856 individuals were captured and banded, of which 89 (10.4%) were recaptured. Observed and expected recapture frequencies did not differ significantly between sexes (67 observed vs. 62 expected males; 22 observed vs. 26 expected females), indicating no sex-related bias in recapture probability. In contrast, significant differences were detected between age classes (20 observed vs. 42 expected adults; 69 observed vs. 46 expected juveniles), demonstrating a clear age-related bias. The elapsed time between initial capture and recapture was similar for males and females but differed significantly between age classes: adults were most frequently recaptured after 51–75 days, whereas juveniles were recaptured predominantly after 1–25 days. Conclusions: Juveniles were more likely than adults to be captured and recaptured. The higher capture probability of juveniles may be associated with their greater mobility, resulting from their limited experience and lower social status. Full article

We numerically investigate high-efficiency third-harmonic generation via cascaded ${\chi }^{\left(2\right)}$ processes in orientation-patterned Gallium Arsenide waveguides comprising two quasi-phase-matched sections. The first section is phase-matched for second-harmonic generation ($\omega \to 2\omega$) and the second for sum-frequency generation ($\omega +2\omega \to 3\omega$). Assuming a continuous-wave pump at $\lambda =10.61\mathsf{\mu }$m, the structure converts the fundamental to the third harmonic at ${\lambda }_{3\omega }\approx 3.54\mathsf{\mu }$m, within the atmospheric transmission window. By solving the coupled-wave equations, we optimize the number of domains in each section and estimate THG conversion efficiencies exceeding 90% at a pump intensity of $\sim 100\mathrm{MW}/{\mathrm{cm}}^2$ in a total crystal length $\approx$$1.6$cm. Using temperature-dependent parameters from the Sellmeier equations for GaAs, we also address thermal tuning of quasi-phase-matching over the range 22–300 °C and pinpoint robustness against fabrication tolerances of $\pm 10$ domains and $\pm 2\%$ in domain length. Our results indicate two-section orientation-patterned GaAs as a suitable platform for high-efficiency frequency tripling from mid-infrared. Full article

Background and Objective: Maximal oxygen uptake (VO₂max) is a fundamental indicator of cardiorespiratory fitness in sports medicine, essential for athletic profiling and training prescription. However, traditional direct measurements require exhaustive physical testing, which induces considerable physiological stress, limits testing frequency, and increases the risk of injury. To address this problem, this study aimed to eliminate the need for exhaustive protocols by developing a highly precise, non-invasive digital prediction model for VO₂max utilizing readily available anthropometric data and acute metabolic biomarkers (blood glucose and lactate kinetics). Methods: To overcome the limitations of a small initial empirical sample (n = 16) and prevent model overfitting, the original dataset was statistically augmented to create a robust synthetic cohort (n = 200) using Multivariate Normal Distribution and k-Nearest Neighbors (k-NN) algorithms. Three different machine learning models (Multiple Linear Regression [MLR], Random Forest [RF], and Support Vector Regression [SVR]) were trained using such parameters as sex, height, weight, baseline/pre-exercise, and net (Δ) glucose and lactate concentrations. Evaluation of the performance of the models included tenfold cross-validation and Bland–Altman analysis as a measure of clinical agreement. Results: Among the three algorithms used, the highest correlation coefficient (R² = 0.939) was observed for SVR, along with the lowest error metrics (RMSE = 1.442 mL/kg/min, MAPE = 2.78%). Moreover, SVR showed remarkable performance in predicting VO₂max of female (R² = 0.890) and male (R² = 0.702) athletes separately. Also, Bland–Altman analysis proved almost zero-bias estimation with 95% limits of agreement ranging between −4.40 and 4.43 mL/kg/min. In order to bypass the black-box problem of complex algorithms for practical application in the field, the MLR model was used for the development of the Beta-Weighted Lacto-Glycemic Equation (β-LGE). Conclusions: In this work, a unique dual-model approach was introduced, with the SVR algorithm serving as a high-performance backend of a digital tool for sport technologists and Beta-Weighted LGE providing a practical calculation formula for coaches. This innovative approach allowed for a completely non-exhaustive profiling of an athlete’s VO₂max using only minimally invasive metabolic measurements. Full article

Large-scale genomic initiatives like the UK Biobank have revolutionized our understanding of human disease. These studies typically assume that blood-derived DNA faithfully reflects an individual’s germline genome. However, this assumption is challenged by somatic mutations arising from processes like clonal hematopoiesis. Although standard bioinformatics pipelines employ variant allele frequency (VAF)-based filtering to mitigate such contamination, the efficacy of these approaches requires systematic evaluation. By systematically analyzing germline genome data from large cohorts through applications of mutational signatures, we revealed critical limitations in current filtering methodologies. We found that the mutational spectrum of rare “germline” variants is highly similar to that of somatic mutations. Furthermore, we uncovered that these variants show significant associations with phenotypes such as age, sex, and smoking status, established drivers of somatic mutagenesis. Notably, our multivariable regression models estimated that these somatic artifacts contribute to a substantial excess burden, such as 4.73 mutations per megabase (mut/Mb) in males compared to females, a magnitude exceeding the mutation burden of many cancers. Although the precise absolute size of this contamination may vary depending on specific pathologies and individual environmental exposures, this persistent somatic contamination introduces substantial confounder effects, posing a risk of spurious associations and reverse causality in genetic studies. Our work underscores the urgent reconsideration of two fundamental aspects of genomic research: (1) refinement of variant filtering strategies to better distinguish true germline variants from somatic contaminants, and (2) incorporation of somatic mutagenesis factors as essential covariates in study design. Our findings provide basic guidance for improving the accuracy and interpretability of large-scale genomic studies. Full article