Search Results (20,809)

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

The development of slicing equipment for Fritillariae Thunbergii Bulbus (FTB) has been constrained by the absence of precise and reliable simulation model parameters, which has hindered the optimization of structural design through simulation techniques. Taking FTB as the research object, this study aims to resolve this issue by conducting the calibration and experimental validation of the discrete element parameters for FTB. Both intrinsic and contact parameters were obtained through physical experiments, on the basis of which a discrete element model for FTB was established by using the Hertz–Mindlin with bonding model. To validate the calibrated bonding parameters of this model, the maximum shear force was selected as the evaluation index. Significant influencing factors were identified and analyzed through a single-factor test, a two-level factorial test, and the steepest ascent method. Response surface methodology was then applied for experimental design and parameter optimization. Finally, shear and compression tests were conducted to verify the accuracy of calibrated parameters. The results show that the mechanical properties of FTB are significantly affected by the normal stiffness per unit area, the tangential stiffness per unit area, and the bonding radius, with optimal values of 1.438 × 10⁸ N·m⁻³, 0.447 × 10⁸ N·m⁻³, and 1.362 mm, respectively. The relative errors in the shear and compression tests were all within 5.18%. The maximum error between the simulated and measured maximum shear force under three different types of blades was less than 5.11%. The percentages of the average shear force of the oblique blade were reduced by 52.23% and 29.55% compared with the flat and arc blades, respectively, while the force variation trends for FTB remained consistent. These findings confirm the reliability of the simulation parameters and establish a theoretical basis for optimizing the structural design of slicing equipment for FTB. Full article

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights. Full article

The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature domains or shallow time-frequency fusion, struggle to comprehensively capture the spatio-temporal-frequency characteristics of the signals, thereby limiting decoding accuracy. To address these limitations, this paper proposes a dual-branch neural network architecture with multi-domain feature fusion, the dual-branch spatio-temporal-frequency fusion convolutional network with Transformer (DB-STFFCNet). The DB-STFFCNet model consists of three modules: the spatiotemporal feature extraction module (STFE), the frequency feature extraction module (FFE), and the feature fusion and classification module. The STFE module employs a lightweight multi-dimensional attention network combined with a temporal Transformer encoder, capable of simultaneously modeling local fine-grained features and global spatiotemporal dependencies, effectively integrating spatiotemporal information and enhancing feature representation. The FFE module constructs a hierarchical feature refinement structure by leveraging the fast Fourier transform (FFT) and multi-scale frequency convolutions, while a frequency-domain Transformer encoder captures the global dependencies among frequency domain features, thus improving the model’s ability to represent key frequency information. Finally, the fusion module effectively consolidates the spatiotemporal and frequency features to achieve accurate classification. To evaluate the feasibility of the proposed method, experiments were conducted on the BCI Competition IV-2a and IV-2b public datasets, achieving accuracies of 83.13% and 89.54%, respectively, outperforming existing methods. This study provides a novel solution for joint time-frequency representation learning in EEG analysis. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. In particular, attention is necessary for satellite and/or drone images. Analyzing image elements by classifying the given classes is important for obtaining information about space for autonomous systems, identifying landscape elements, or monitoring and maintaining the infrastructure and environment. Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. In addition, the classifier is guided by an attention mechanism for focusing more on different channels, spatial information, and even feature pyramid mechanisms. Atrous convolutional operators were also used in such an architecture as better context feature extractors. The proposed classifier architecture is the main element of the modeled framework for satellite data analysis, which is based on the possibility of training depending on the client’s desire. The proposed methodology was evaluated on three publicly available classification datasets for remote sensing: satellite images, Visual Terrain Recognition, and USTC SmokeRS, where the proposed model achieved accuracy scores of 97.8%, 100.0%, and 92.4%, respectively. The obtained results indicate the effectiveness of the proposed attention mechanisms across different remote sensing challenges. Full article

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

The process of feature selection is a critical component of any decision-making system incorporating machine or deep learning models applied to multidimensional data. Feature selection on input data can be performed using a variety of techniques, such as correlation-based methods, wrapper-based methods, or embedded methods. However, many conventionally used approaches do not support backwards interpretability of the selected features, making their application in real-world scenarios impractical and difficult to implement. This work addresses that limitation by proposing a novel correlation-based strategy for feature selection in regression tasks, based on a three-dimensional visualization of correlation analysis results—referred to as three-dimensional correlation graphs. The main objective of this study is the design, implementation, and experimental evaluation of this graphical model through a case study using a multidimensional dataset with 28 attributes. The experiments assess the clarity of the visualizations and their impact on regression model performance, demonstrating that the approach reduces dimensionality while maintaining or improving predictive accuracy, enhances interpretability by uncovering hidden relationships, and achieves better or comparable results to conventional feature selection methods. Full article

With the wide application of airdrop technology in rescue activities in civil and aerospace fields, the importance of accurate airdrop is increasing. This work comprehensively analyzes the interactive mechanisms among multiple models affecting airdrops, including wind field distribution, drag force effect, and the parachute opening process. By integrating key parameters across various dimensions of these models, a multidimensional parameter dynamic evolution (MPDE) target prediction method for aerial delivery parachutes in radar-detected wind fields is proposed, and the Runge–Kutta method is applied to dynamically solve for the final landing point of the target. In order to verify the performance of the method, this work carries out field airdrop experiments based on the radar-measured meteorological data. To evaluate the impact of model input errors on prediction methods, this work analyzes the influence mechanism of the wind field detection error on the airdrop prediction method via the Relative Gain Array (RGA) and verifies the analytical results using the numerical simulation method. The experimental results indicate that the optimized MPDE method exhibits higher accuracy than the widely used linear airdrop target prediction method, with the accuracy improved by 52.03%. Additionally, under wind field detection errors, the linear prediction method demonstrates stronger robustness. The airdrop error shows a trigonometric relationship with the angle between the synthetic wind direction and the heading, and the phase of the function will shift according to the difference in errors. The sensitivity of the MPDE method to wind field errors is positively correlated with the size of its object parachute area. Full article

Background/Objectives: The integration of machine learning (ML) and artificial intelligence (AI) has revolutionized the pharmaceutical industry by improving drug discovery, development and manufacturing processes. Based on literature data, an ML model was developed by our group to predict the formation of binary co-amorphous systems (COAMSs) for inhalation therapy. The model’s ability to develop a dry powder formulation with the necessary properties for a predicted co-amorphous combination was evaluated. Methods: An extended experimental validation of the ML model by co-milling and X-ray diffraction analysis for 18 API-API (active pharmaceutical ingredient) combinations is presented. Additionally, one COAMS of rifampicin (RIF) and ethambutol (ETH), two first-line tuberculosis (TB) drugs are developed further for inhalation therapy. Results: The ML model has shown an accuracy of 79% in predicting suitable combinations for 35 APIs used in inhalation therapy; experimental accuracy was demonstrated to be 72%. The study confirmed the successful development of stable COAMSs of RIF-ETH either via spray-drying or co-milling. In particular, the milled COAMSs showed better aerosolization properties (higher ED and FPF with lower standard deviation). Further, RIF-ETH COAMSs show much more reproducible results in terms of drug quantity dissolved over time. Conclusions: ML has been shown to be a suitable tool to predict COAMSs that can be developed for TB treatment by inhalation to save time and cost during the experimental screening phase. Full article

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets. Full article

The Engine Electronic Controller (EEC), as the core component of an aircraft engine control system, is vulnerable to rapid failure when exposed to thermal shock during engine fire incidents, potentially leading to catastrophic aviation accidents. To address this issue, this study conducts both numerical simulations and experimental investigations to evaluate the thermal performance of the EEC under thermal shock conditions, exploring the weaknesses of the EEC chassis under high-temperature thermal shock and the damage to important internal electronic components. A three-dimensional finite element model of the EEC was established to simulate its behavior under a thermal shock of 1100 °C. Simulation results reveal that the aluminum alloy chassis wall cannot withstand the extreme thermal load, resulting in failure of the internal electronic components within the first 5 min of exposure, thereby rendering the EEC inoperative. In contrast, when the chassis wall is made of stainless steel, all components and internal electronics remain functional throughout the initial 5 min thermal shock period. Experimental results show that the temperature evolution and component failure patterns under both scenarios align well with the simulation outcomes, thus validating the model’s accuracy. Full article

Objectives: The aim of the study was to assess the utility of contrast-enhanced ultrasound (CEUS), using both qualitative and quantitative perfusion analysis, in differentiating subtypes of renal cell carcinoma (RCC). Methods: This prospective, single-center study includes 91 patients with histologically confirmed RCC. We performed a CEUS within one week prior to nephrectomy. Qualitative parameters (enhancement pattern, heterogeneity, pseudocapsule) and quantitative perfusion metrics were assessed. Logistic regression models were developed to evaluate the diagnostic performance of CEUS in differentiating high-grade (clear cell RCC) from low-grade RCC (papillary and chromophobe). Results: Qualitative CEUS findings showed that hyperenhancement and isoenhancement were significantly associated with high-grade RCC (OR = 38.3 and OR = 7.8, respectively; p < 0.001 and p = 0.014). Hypoenhancement was predominant in low-grade RCC (80.0%). Quantitative parameters, including peak enhancement and wash-in/wash-out area under the curve, significantly differed between tumor grades (p < 0.001). A model using qualitative parameters alone achieved an AUC of 0.847 and 81.9% accuracy. Adding quantitative metrics marginally improved performance (AUC 0.912, accuracy 86.2%), though not significantly. Conclusions: CEUS provides valuable diagnostic information in differentiating RCC subtypes, with qualitative parameters alone demonstrating strong predictive power. While quantitative analysis slightly enhances diagnostic accuracy, its added value may be limited by technical challenges. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

Background/Objectives: Recent breakthroughs in three-dimensional (3D) printing and high-resolution imaging have opened up new possibilities in personalized medicine, surgical planning, and forensic reconstruction. This study breaks new ground by evaluating the integration of high-resolution peripheral quantitative computed tomography (HR-pQCT) with multimodal imaging and additive manufacturing to assess a chronic, infected gunshot injury in the knee joint of a red deer. This unique approach serves as a translational model for complex skeletal trauma. Methods: Multimodal imaging—including clinical CT, MRI, and HR-pQCT—was used to characterise the extent of osseous and soft tissue damage. Histopathological and molecular analyses were performed to confirm the infectious agent. HR-pQCT datasets were segmented and processed for 3D printing using PolyJet, stereolithography (SLA), and fused deposition modelling (FDM). Printed models were quantitatively benchmarked through 3D surface deviation analysis. Results: Imaging revealed comminuted fractures, cortical and trabecular degradation, and soft tissue involvement, consistent with chronic osteomyelitis. Sphingomonas sp., a bacterium that forms biofilms, was identified as the pathogen. Among the printing methods, PolyJet and SLA demonstrated the highest anatomical accuracy, whereas FDM exhibited greater geometric deviation. Conclusions: HR-pQCT-guided 3D printing provides a powerful tool for the anatomical visualisation and quantitative assessment of complex bone pathology. This approach not only enhances diagnostic precision but also supports applications in surgical rehearsal and forensic analysis. It illustrates the potential of digital imaging and additive manufacturing to advance orthopaedic and trauma care, inspiring future research and applications in the field. Full article