Search Results (554)

Carrier-based aircraft recovery is a critical and challenging phase in maritime operations due to the turbulent airwake generated by aircraft carriers, which significantly increases the workload of flight control systems and pilots. This study investigates the airwake effects of an aircraft carrier under varying wind direction conditions. A high-fidelity mathematical model combining delayed detached-eddy simulation (DDES) with the overset grid method was developed to analyze key flow characteristics, including upwash, downwash, and lateral recirculation. The model ensures precise control of aircraft speed and trajectory during landing while maintaining numerical stability through rigorous mesh optimization. The results indicate that the minimum lift occurs in the downwash region aft of the deck, marking it as the most hazardous zone during landing. Aircraft above the deck are primarily influenced by ground effects, causing a sudden increase in lift that complicates arresting wire engagement. Additionally, the side force on the aircraft undergoes an abrupt reversal during the approach phase. The dual overset mesh technique effectively captures the coupled motion of the hull and aircraft, revealing higher turbulence intensity along the glideslope and a wider range of lift fluctuations compared to stationary hull conditions. These findings provide valuable insights for optimizing carrier-based aircraft recovery procedures, offering more realistic data for simulation training and enhancing pilot preparedness for airwake-induced disturbances. Full article

The presence of polycyclic aromatic hydrocarbons (PAHs) in edible oil has a serious effect on human health and may potentially induce cancer. This study combined thin-layer chromatography and surface-enhanced Raman spectroscopy (TLC-SERS) to rapidly and quantitatively detect PAHs in culinary oil. Machine learning using the principle component analysis-back propagation neural network (PCA-BP) was integrated with TLC-SERS for the detection of PAHs. Ag nanoparticles on diatomite (diatomite/Ag) TLC-SERS substrate were prepared via an in situ growth process and employed as a stationary phase in the TLC channel. The analyte sample was dropped onto the TLC channel for separation and detection. The diatomite/Ag TLC channel demonstrated excellent separation capability and superior SERS performance and successfully detected PAHs from edible oil at a sensitivity of 0.1 ppm. The PCA-BP quantitative analysis model demonstrated outstanding prediction performance. This work demonstrates that the combination of TLC-SERS technology with PCA-BP is an efficient and accurate method for quantitatively detecting PAHs in edible oil, which can effectively improve the quality of food. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Objective: This study aims to investigate the effect of shot selection on the muscle coordination characteristics during basketball shooting. Methods: A three-dimensional motion capture system, force platform, and wireless surface electromyography (sEMG) were used to simultaneously collect shooting data from 14 elite basketball players. An inverse mapping model of sEMG signals and spinal α-motor neuron pool activity was developed based on the Debra muscle segment distribution theory. Non-negative matrix factorization (NMF) and K-means clustering were used to extract muscle coordination features. Results: (1) Significant differences in spinal segment activation timing and amplitude were observed between stationary and jump shots at different distances. In close-range stationary shots, the C5-S3 segments showed higher activation during the TP phase and lower activation during the RP phase. For mid-range shots, the C6-S3 segments exhibited greater activation during the TP phase. In long-range shots, the C7-S3 segments showed higher activation during the TP phase, whereas the L3-S3 segments showed lower activation during the RP phase (p < 0.01). (2) The spatiotemporal structure of muscle coordination modules differed significantly between stationary and jump shots. In terms of spatiotemporal structure, the second and third coordination groups showed stronger activation during the RP phase (p < 0.01). Significant differences in muscle activation levels were also observed between the coordination modules within each group in the spatial structure. Conclusion: Shot selection plays a significant role in shaping neuromuscular control strategies during basketball shooting. Targeted training should focus on addressing the athlete’s specific shooting weaknesses. For stationary shots, the emphasis should be on enhancing lower limb stability, while for jump shots, attention should be directed toward improving core stability and upper limb coordination. Full article

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters. Full article

E. coli attaches to, and forms biofilms on various surfaces, including latex and polystyrene, contributing to nosocomial spread. E. coli responds to both exogenous and endogenous insulin, which induces behavioral changes. Human insulin, a quorum signal surrogate for microbial insulin, may affect the ability of E. coli to interact with latex and polystyrene in the presence of various sugars. E. coli ATCC 25923 was grown in peptone (1%) yeast nitrogen base broth to either the logarithmic or stationary growth phase. Adherence to latex was determined using 6 × 6 mm latex squares placed in a suspension of washed cells (10³ CFU/mL; 30 min; 37 °C) in buffer containing insulin at 2, 20, and 200 µU/mL (Humulin^® R; Lilly) with and without mannose, galactose, fructose, sorbose, arabinose, xylose, lactose, maltose, melibiose, glucose-6-phosphate, glucose-1-phosphate, and glucosamine at concentrations reported to affect behavioral response. Attachment levels to latex were determined by the press plate method. Biofilm levels were measured in a similar fashion but with overnight cultures in flat bottom uncoated polystyrene plates. Controls were media, insulin, sugar, or buffer alone. Glucose served as the positive control. Overall, the stationary phase cells’ adherence to latex was greater, regardless of the test condition, than was measured for the logarithmic phase cells. The effect of insulin on adherence to latex was insulin and sugar concentration dependent. The addition of insulin (200 µU/mL) resulted in a significantly (p < 0.05) increased adherence to latex and biofilm formation on polystyrene compared with sugar alone for 12 of the 13 sugars tested with stationary phase bacteria and 10 of the 13 sugars tested with logarithmic phase bacteria. Adherence in response to sorbose was the only sugar tested that was unaffected by insulin. These findings show that insulin enhances E. coli’s association with materials in common usage in medical environments in a nutrition-dependent manner. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

Background/Objectives: Sustained drug exposure is a key factor in the treatment of patients infected with human immunodeficiency virus (HIV) or hepatitis B virus (HBV) in order to achieve the intended virological response. Although influenced also by other parameters, adherence to the treatment scheme is the most important for adequate drug exposure. This can be assessed by therapeutic drug monitoring (TDM). Tenofovir (TFV) is a nucleotide analogue used in the treatment of both HIV and HBV. Although various analytical methods for the quantification of tenofovir prodrugs have been published, there is limited literature on methods for simultaneous TFV and its active metabolite, tenofovir diphosphate (TFVDP) direct determination. Methods: In this study, we describe a novel micro-liquid-chromatography-mass spectrometry (micro-LC-MS/MS) method for TDM of TFV and TFVDP in biological matrices (whole blood, plasma). The challenging separation of the high-polarity analytes was resolved on an amino stationary phase, eluted in HILIC (hydrophilic interaction liquid chromatography) mode. The sample preparation included a clean-up step with hexane for the removal of lipophilic compounds and then protein precipitation with organic solvent. Results: The achieved low limits of quantification in blood were 0.25 ng/mL for TFV, and 0.5 ng/mL for TFVDP. Linearity, accuracy (91.63–109.18%), precision (2.48–14.08), and stability were validated for whole blood matrix, meeting the guidelines performance criteria. Samples collected from treated patients were analyzed, with results being in accordance with the reported pharmacokinetics. Conclusions: The new method is adequate for analyzing samples in a clinical set-up. The measurement of both TFV and TFVDP improves clinical decision by an in-depth evaluation of long-term adherence, and together with viral load and resistance data helps guiding the treatment towards the intended virological suppression. Full article

With the growing demand for global energy transition, integrated energy systems (IESs) have emerged as a key pathway for sustainable development due to their deep coupling of multi-energy flows. Accurate load forecasting is crucial for IES optimization and scheduling, yet conventional methods struggle with complex spatio-temporal correlations and long-term dependencies. This study proposes ST-ScaleFusion, a multi-scale time–frequency complementary hybrid model to enhance comprehensive energy load forecasting accuracy. The model features three core modules: a multi-scale decomposition hybrid module for fine-grained extraction of multi-time-scale features via hierarchical down-sampling and seasonal-trend decoupling; a frequency domain interpolation forecasting (FI) module using complex linear projection for amplitude-phase joint modeling to capture long-term patterns and suppress noise; and an FI sub-module extending series length via frequency domain interpolation to adapt to non-stationary loads. Experiments on 2021–2023 multi-energy load and meteorological data from the Arizona State University Tempe campus show that ST-ScaleFusion achieves 24 h forecasting MAE values of 667.67 kW for electric load, 1073.93 kW/h for cooling load, and 85.73 kW for heating load, outperforming models like TimesNet and TSMixer. Robust in long-step (96 h) forecasting, it reduces MAE by 30% compared to conventional methods, offering an efficient tool for real-time IES scheduling and risk decision-making. Full article

β-carotene, a high-value carotenoid widely used in the food, pharmaceutical, and cosmetics industries, is naturally synthesized by the microalga Dunaliella sp. However, the efficient extraction and purification of β-carotene from microalgae biomass remain a technical challenge. This study presents the development of a scalable and efficient isolation method employing high-performance countercurrent chromatography (HPCCC) to recover β-carotene from Dunaliella sp. The separation process was optimized by integrating two elution strategies (reverse phase and extrusion) using a biphasic solvent system of n-heptane and methanol (1:1, v/v). The upper phase served as the stationary phase, while the lower phase was used as the mobile phase. Two consecutive injections of 800 mg of microalgal extract each resulted in the isolation of 225.4 mg of β-carotene with a purity of 97% and a recovery of 98%. The developed HPCCC approach represents an efficient method for β-carotene purification and serves as a promising model for future scale-up in microalgae-based production platforms. Full article

The unregulated consumption of corticosteroids causes significant adverse effects on human health. Therefore, it is important to develop methodologies that allow their analysis in pharmaceutical matrices with competitive analysis times and costs. The determination of corticosteroids by micellar electrokinetic chromatography (MEKC) using a background electrolyte (BGE) composed of phosphate buffer and a micellar pseudo-stationary phase constituted by a mixture of surfactants is proposed as an alternative quantification technique. The variables involved in the BGE: phosphate concentration, surfactant (sodium dodecyl sulfate (SDS) or sodium lauryl ether sulfate (SLES)), sodium taurocholate (STC) and the pH value were optimized using a Taguchi L₉ (3⁴) experimental design. Employing the optimal BGE, the separation of the three corticosteroids is possible in a linear range of 1.05–10.0 mg L⁻¹, with limits of detection (LOD) of 0.28–0.35 mg L⁻¹. The relative standard deviation (RSD) values obtained for the repeatability (n = 3) and intermediate precision (n = 9) were less than 5.0%. Pharmaceutical formulations (ointments, injectable solution and ophthalmic solution) were analyzed using the proposed methodology (MEKC) and the official methodology (high-performance liquid chromatography, HPLC), and no significant differences were found between the corticosteroid contents obtained from both methods. Full article

The application of fractional calculus in power electronics modeling provides an innovative method for improving inverter performance. This paper presents a three-phase inverter topology with fractional-order LC filter characteristics, analyzes its frequency response, and develops mathematical models in both stationary and rotating reference frames. Based on these models, a dual closed-loop decoupling control strategy for voltage and current is designed to enhance system stability and dynamic performance. In the power control loop, fractional-order virtual synchronous generator control (FOVSG) is employed. Observations show that increasing the fractional-order of the rotor leads to a higher transient frequency variation rate. To address this, an adaptive rotational inertia control scheme is integrated into the FOVSG structure (ADJ-FOVSG), enabling real-time adjustment of inertia to suppress transient frequency fluctuations. Experimental results demonstrate that when the reference active power changes, ADJ-FOVSG effectively suppresses power overshoot. Compared to traditional VSG, ADJ-FOVSG reduces the power regulation time by approximately 34.5% and decreases the peak frequency deviation by approximately 37.2%. Compared to the adaptive rotational inertia control in traditional VSG, ADJ-FOVSG improves regulation time by about 24% and reduces peak frequency deviation by roughly 24.4%. Full article

Since the 1990s, cannabis has experienced a gradual easing of access restrictions, accompanied by the expansion of its legalization and commercialization. This shift has led to the proliferation of cannabis-based products, available as cosmetics, food supplements, and pharmaceutical dosage forms. Consequently, there has been a growing demand for reliable and reproducible extraction techniques alongside precise analytical methods for detecting and quantifying cannabinoids, both of which are essential for ensuring consumer safety and product quality. Given the variability in extraction and quantification techniques across laboratories, significant attention has recently been directed toward method validation. Validated methods ensure precise cannabinoid measurement in cannabis-based products, supporting compliance with dosage guidelines and legal limits. Thus, this review highlights recent advancements in these areas, with a particular focus on High-Performance Liquid Chromatography (HPLC) coupled with Ultraviolet (UV) detection, as it is considered the gold standard for cannabinoid analysis included in cannabis monographs present in several pharmacopeias. The research focused on studies published between January 2022 and December 2024, sourced from PubMed, Scopus, and Web of Science, that employed an HPLC-UV analytical technique for the detection of phytocannabinoids. Additionally, the review examines cannabinoid extraction techniques and the validation methodologies used by the authors in the selected papers. Notably, ultrasound extraction has emerged as the most widely utilized technique across various matrices, with Deep Eutectic Solvents (DESs) offering a promising, efficient, and environmentally friendly extraction alternative. Analytical chromatographic separations continue to be predominantly conducted using C18 reversed-phase columns. Nevertheless, in recent years, researchers have explored various stationary phases, particularly to achieve the enantioseparation of cannabinoids. Full article

Porous materials are used in many important applications, such as separation technologies, catalysis, and chromatography. They may be obtained from various monomers via diverse polymerization techniques and a wide range of synthesis parameters. The study is devoted to the synthesis and characterization of crosslinked porous polymeric spheres containing pyrrolidone subunits. To achieve this goal, two methods were applied: direct synthesis from N-vinyl-2-pyrrolidone (NVP) with ethylene glycol dimethacrylate (EGDMA) and via a modification reaction of porous poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) with pyrrolidone (P). The polymerization was carried out with the use of different molar ratios of the monomers. In order to obtain highly porous materials, pore-forming diluents (toluene, dodecane, and dodecan-1-ol) were used. The synthesized copolymers were characterized using size distribution analysis, ATR-FTIR spectroscopy, scanning electron microscopy, thermogravimetry, and inverse gas chromatography. Determined by the nitrogen adsorption/desorption method, the specific surface area was in the range of 55–468 m²/g. The good thermal properties of the poly(VP-co-EGDMA) copolymers allowed them to be applied as the stationary phase in gas chromatography. Full article

The antihypertensive drugs indapamide, atenolol, metoprolol, propranolol and bisoprolol were considered in this research. Because they have structures that are affected by pH, developing a chromatographic method was challenging. Based on the speciation diagram of these compounds versus pH scale, a mixed-mode stationary phase (hydrophobic stationary phase, C18 and strong cation exchanger (SCX)) was our first choice. Design of Experiments (DoE) was used to estimate how various factors such as pH, mobile phase composition and flow rate influenced chromatographic performance. As a result, the separation was achieved in 24 min using an aqueous phosphate buffer phase (pH 7.20) and 20 mM triethylamine, with methanol being used as organic modifier (30%). Their retention mechanism was investigated. The new method was validated in term of linearity, limits of detection and quantification, precision, accuracy, and robustness. The method was applied to river water samples, and good results were obtained. Full article