Search Results (8,837)

Hairy vetch (Vicia villosa Roth) and smooth vetch (V. villosa Roth var. glabrescens) are important cover crops and legume forage with great economic and ecological values. Due to the large and highly heterozygous genome, full-length transcriptome reconstruction is a cost-effective route to mining their genetic resources. In this study, a hybrid sequencing approach combining SMRT and NGS technologies was applied. The results showed that 28,747 and 40,600 high-quality non-redundant transcripts with an average length of 1808 bp and 1768 bp were generated from hairy vetch and smooth vetch, including 24,864 and 35,035 open reading frames (ORFs), respectively. More than 96% of transcripts were annotated to the public databases, and around 25% of isoforms underwent alternative splicing (AS) events. In addition, 987 and 1587 high-confidence lncRNAs were identified in two vetches. Interestingly, smooth vetch contains more specific transcripts and orthologous clusters than hairy vetch, revealing intraspecific transcript diversity. The phylogeny revealed that they were clustered together and closely related to the genus Pisum. Furthermore, the estimation of Ka/Ks ratios showed that purifying selection was the predominant force. A putative 3-dehydroquinate dehydratase/shikimate dehydrogenase (DHD/SDH) gene underwent strong positive selection and might regulate phenotypic differences between hairy vetch and smooth vetch. Overall, our study provides a vital characterization of two full-length transcriptomes in Vicia villosa, which will be valuable for their molecular research and breeding. Full article

Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments. Full article

We develop a hybrid classical–quantum method for solving the Lorenz system. We use the forward Euler method to discretize the system in time, transforming it into a system of equations. This set of equations is solved by using the Variational Quantum Linear Solver (VQLS) algorithm. We present numerical results comparing the hybrid method with the classical approach for solving the Lorenz system. The simulation results demonstrate that the VQLS method can effectively compute solutions comparable to classical methods. The method is easily extended to solving similar nonlinear differential equations. Full article

Background: Struma ovarii is a rare tumor, a type of ovarian mature teratoma consisting over 50% of its mass in thyroid ectopic tissue; 5% to 10% of cases, as described in the literature, are malignant and well known as malignant struma ovarii or thyroid cancer from struma ovarii. Due to the limited number of malignant struma ovarii cases, the diagnostic and therapeutic approach of malignant struma ovarii lacks in standardization. Methods: We performed a comprehensive search on the English language PubMed and Google Scholar. We used specific controlled keywords “PET CT”, “SPECT CT”, “PET MRI”, “malignant struma ovarii”, “hybrid imaging” and “mature ovarian teratoma”. Upon the retrieval of potential articles, we analyzed them for their eligibility. The inclusion criteria were: articles discussing the role of PET/CT and SPECT/CT hybrid imaging in malignant struma ovarii, full-text articles on the topic of interest and English publications. The exclusion criteria were articles not directly related to the hybrid imaging and not discussing the subject of malignant struma ovarii. Results: A total of 64 articles were screened, 35 duplicates were eliminated, 15 articles excluded and a total number of 14 articles were included for this systematic review, 13 of them being case reports and one being a case report with a systematic review. F-18 FDG PET/CT contributed in seven cases (50%), I-131 NaI SPECT/CT in seven cases (50%) and I-124 NaI PET/CT in two cases (14.29%). In two cases, 131 NaI SPECT/CT and F-18 FDG PET/CT were used as complementary investigation tools. The hybrid imaging methods used as a part of the diagnostic strategy were accompanied by several diagnostic alternatives: ultrasounds, CT, MRI, I-131 NaI WBS and I-123 NaI WBS. Conclusions: There is no consistent or standardized diagnostic and therapeutic approach for malignant struma ovarii. Hybrid imaging methods may be of great value in initial diagnostic and the association of F-18 FDG PET/CT and I-131 NaI SPECT/CT is a successful diagnostic approach. The association of hybrid imaging with other diagnostic imaging alternatives in initial diagnostic and follow up is essential. Full article

Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender leading to a dataset which we will make available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. Full article

Herein, this study reports the development of a multifunctional conductive antibacterial cotton fabric through the utilization of the natural polyphenol-mediated silver mirror reaction. The experimental results demonstrate that polyphenols can effectively facilitate the deposition of silver nanoparticles (AgNPs), resulting in a uniform and durable hybrid nanocoating on the cotton fabric. The effects of polyphenol’s molecular weights on the coating structures and stabilities have been revealed via two distinct approaches: washing resistance and electrochemical testing systems. It has been concluded that lower-molecular-weight phenols induce a compact and dense coating structure, whereas polyphenols such as tannic acid exhibit relatively high stability, achieving an excellent conductivity of 0.2 S/cm and a good washing resistance of 67% over five cycles. The underlying mechanism has been further confirmed by the cyclic voltammetry measurements, suggesting that polyphenols play a significant role in stabilizing AgNPs and preventing their dissolution. Furthermore, the Ag-doped polyphenol-coated fabrics exhibit notable antibacterial properties. By coupling natural polyphenols with typical silver mirror reactions, this study not only offers a sustainable alternative to synthetic chemicals but also presents a promising method to endow cotton textiles with the dual properties of conductivity and antibacterial activity. Full article

Solid particles (SP) suspended in water represent a common contamination that degrades the water quality, not only in drinking water sources. Particles differ in size, nature, and related features like surface charge. Thus, various methods can be utilized for their removal—physical approaches including settling or filtration, chemical coagulation/flocculation, biological microbial degradation, and others. This paper aims to summarize currently available methods for SP removal with special attention devoted to alternative, cost-effective, sustainable, and eco-friendly approaches with low energetic demands where the power of renewable energy sources can be utilized. Besides SP properties, the selection of the proper method (or a sequence of methods) for their separation also depends on the purpose of water treatment. Drinking water production demands technologies with immediate effect and high throughputs, like conventional filtration and coagulation/flocculation (electro- or chemical with alternative coagulant/flocculant agents) or some hybrid approaches to ensure quick and cost-effective decontamination. Such technologies usually imply heavy machinery with high electricity consumption, but current progress allows the construction of smaller facilities powered by solar or wind power plant systems. On the other hand, water decontamination in rivers or ponds can include slower processes based on phytoremediation, being long-term sustainable with minimal energy and cost investments. Full article

This study aims to evaluate the performance of a new hybrid solvent, comprising aqueous MDEA and tetrabutylphosphonium trifluoroacetate ([TBP][TFA]), for CO₂ capture and to optimize its CO₂ absorption efficiency. First, this study focused on predicting the thermodynamic properties of aqueous MDEAs and [TBP][TFA] and their interaction energy with CO₂ using COSMO-RS. Based on the prediction, it aligns with the principle that CO₂ solubility in the MDEA-[TBP][TFA] hybrid solvent decreases as the Henry’s Law constant increases, with the interactions primarily governed by van der Waals forces and hydrogen bonding. The aqueous MDEA-[TBP][TFA] hybrid solvent was prepared in two steps: synthesizing and blending [TBP][TFA] with aqueous MDEAs. The formation and purity of [TBP][TFA] were confirmed through NMR, FT-IR, and Karl Fischer. The heat capacity of the hybrid solvents was lower than their aqueous MDEA solutions. The performance and optimization of CO₂ capture were studied using RSM-FC-CCD design, with the optimal value obtained at 50 wt.% MDEA, 20 wt.% [TBP][TFA], 30 °C, and 30 bar (12.14 mol/kg), aligning with COSMO-RS predictions. A 26% reduction in the heat capacity was achieved with the optimal ratio (wt.%) of the hybrid solvent. These findings suggest that the aqueous MDEA-[TBP][TFA] hybrid solvent is a promising alternative for CO₂ capture, providing a high removal capacity and lower heat capacity for more efficient regeneration compared to commercial aqueous MDEA solutions. Full article

Background/Objectives: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to that of unimodal approaches. Nonetheless, there remains a paucity of in-depth investigations analyzing the inherent relationship between EEG and fNIRS and constructing brain networks to improve the performance of emotion recognition. Methods: In this study, we introduce an innovative method to construct hybrid brain networks in the source space based on simultaneous EEG-fNIRS signals for emotion recognition. Specifically, we perform source localization on EEG signals to derive the EEG source signals. Subsequently, causal brain networks are established in the source space by analyzing the Granger causality between the EEG source signals, while coupled brain networks in the source space are formed by assessing the coupling strength between the EEG source signals and the fNIRS signals. The resultant causal brain networks and coupled brain networks are integrated to create hybrid brain networks in the source space, which serve as features for emotion recognition. Results: The effectiveness of our proposed method is validated on multiple emotion datasets. The experimental results indicate that the recognition performance of our approach significantly surpasses that of the baseline method. Conclusions: This work offers a novel perspective on the fusion of EEG and fNIRS signals in an emotion-evoked experimental paradigm and provides a feasible solution for enhancing emotion recognition performance. Full article

Background: In recent years, microarray datasets have been used to store information about human genes and methods used to express the genes in order to successfully diagnose cancer disease in the early stages. However, most of the microarray datasets typically contain thousands of redundant, irrelevant, and noisy genes, which raises a great challenge for effectively applying the machine learning algorithms to these high-dimensional microarray datasets. Methods: To address this challenge, this paper introduces a proposed hybrid filter and differential evolution-based feature selection to choose only the most influential genes or features of high-dimensional microarray datasets to improve cancer diagnoses and classification. The proposed approach is a two-phase hybrid feature selection model constructed using selecting the top-ranked features by some popular filter feature selection methods and then further identifying the most optimal features conducted by differential evolution (DE) optimization. Accordingly, some popular machine learning algorithms are trained using the final training microarray datasets with only the best features in order to produce outstanding cancer classification results. Four high-dimensional cancerous microarray datasets were used in this study to evaluate the proposed method, which are Breast, Lung, Central Nervous System (CNS), and Brain cancer datasets. Results: The experimental results demonstrate that the classification accuracy results achieved by the proposed hybrid filter-DE over filter methods increased to 100%, 100%, 93%, and 98% on Brain, CNS, Breast and Lung, respectively. Furthermore, applying the suggested DE-based feature selection contributed to removing around 50% of the features selected by using the filter methods for these four cancerous microarray datasets. The average improvement percentages of accuracy achieved by the proposed methods were up to 42.47%, 57.45%, 16.28% and 43.57% compared to the previous works that are 41.43%, 53.66%, 17.53%, 61.70% on Brain, CNS, Lung and Breast datasets, respectively. Conclusions: Compared to the previous works, the proposed methods accomplished better improvement percentages on Brain and CNS datasets, comparable improvement percentages on Lung dataset, and less improvement percentages on Breast dataset. Full article

Grounding faults are a common type of fault in train auxiliary power supply systems (APS). Timely identification and localization of these faults are crucial for ensuring the stable operation of electric locomotives and the safety of passengers. Therefore, this paper proposes a fault diagnosis method for grounding faults (GFs) that integrates mechanistic insights with data-driven feature extraction. Firstly, this paper analyzes the mechanisms of grounding faults and summarizes the characteristics of their time–frequency distribution. Then, a Short-Time Fourier Transform (STFT) is employed to derive a frequency signature vector enabling classification into three principal categories. Concurrently, a time series sliding window approach is applied to extract time domain indicators for further subdivision of fault types. Finally, a time–frequency hybrid-driven diagnostic model framework is constructed by integrating the frequency distribution with the spatiotemporal map, and validation is conducted using an experimental platform that replicates system fault scenarios with a hardware-in-the-loop (HIL) simulation and executes the real-time diagnostic frameworks on a DSP diagnostic board card. The results demonstrate that the proposed method can detect and accurately locate grounding faults in real time. Full article

This study introduces a novel hybrid model combining Bayesian Stochastic Partial Differential Equations (SPDE) with deep learning, specifically Convolutional Neural Networks (CNN) and Deep Feedforward Neural Networks (DFFNN), to predict PM_2.5 concentrations. Traditional models often fail to account for non-linear relationships and complex spatial dependencies, critical in urban settings. By integrating SPDE’s spatial-temporal structure with neural networks’ capacity for non-linearity, our model significantly outperforms standalone methods. Accurately predicting air pollution supports sustainable public health strategies and targeted interventions, which are critical for mitigating the adverse health effects of PM_2.5, particularly in urban areas heavily impacted by climate change. The hybrid model was applied to the Pleasant Run Airshed in Indianapolis, Indiana, utilizing a comprehensive dataset that included PM_2.5 sensor data, meteorological variables, and land-use information. By combining SPDE’s ability to model spatial-temporal structures with the adaptive power of neural networks, the model achieved a high level of predictive accuracy, significantly outperforming standalone methods. Additionally, the model’s interpretability was enhanced through the use of SHAP (Shapley Additive Explanations) values, which provided insights into the contribution of each variable to the model’s predictions. This framework holds the potential for improving air quality monitoring and supports more targeted public health interventions and policy-making efforts. Full article

This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical. Full article

Stunting remains a significant public health issue in Aceh, Indonesia, and is influenced by various socio-economic and environmental factors. This study aims to address key challenges in accurately classifying stunting prevalence, predicting future trends, and optimizing clustering methods to support more effective interventions. To this end, we propose a novel hybrid machine learning framework that integrates classification, predictive modeling, and clustering optimization. Support Vector Machines (SVM) with Radial Basis Function (RBF) and Sigmoid kernels were employed to improve the classification accuracy, with the RBF kernel outperforming the Sigmoid kernel, achieving an accuracy rate of 91.3% compared with 85.6%. This provides a more reliable tool for identifying high-risk populations. Furthermore, linear regression was used for predictive modeling, yielding a low Mean Squared Error (MSE) of 0.137, demonstrating robust predictive accuracy for future stunting prevalence. Finally, the clustering process was optimized using a weighted-product approach to enhance the efficiency of K-Medoids. This optimization reduced the number of iterations from seven to three and improved the Calinski–Harabasz Index from 85.2 to 93.7. This comprehensive framework not only enhances the classification, prediction, and clustering of results but also delivers actionable insights for targeted public health interventions and policymaking aimed at reducing stunting in Aceh. Full article

The aim of the present study was to apply an updated benchmark dose (BMD) approach to estimate reference urinary cadmium (U-Cd) for renal tubular and glomerular effects. This cross-sectional survey was conducted 30 years ago in 30 men and 44 women living in a Cd-polluted area and in 18 men and 18 women living in a non-polluted area. We applied an updated hybrid approach to estimate the BMDs and 95% lower confidence limits (BMDLs) of U-Cd for creatinine (Cr) clearance (CrCl), estimated glomerular filtration rate (eGFR), β2-microglobulin (β2-MG), and β2-MG tubular reabsorption (%TRβ2-MG). Using a benchmark response (BMR) of 5%, we estimated the BMDLs of U-Cd for adverse renal effect markers to be 2.9 (eGFR), 1.8 (β2-MG), 1.8 (%TRβ2-MG < 95%), and 3.6 μg/g Cr (%TRβ2-MG < 90%) in men, and 3.5 (CrCl), 2.5 (β2-MG), 2.6 (%TRβ2-MG < 95%), and 3.9 μg/g Cr (%TRβ2-MG < 90%) in women. The obtained BMDLs for tubular effects were 1.8–3.6 µg/g Cr and for glomerular effects were 2.9–3.5 µg/g Cr; these are not very high compared to the exposure levels in the general population. The BMDLs calculated in this study provide important information for measures regarding protecting general inhabitants or workers from the adverse health effects of Cd exposure. Full article