The goal of this paper is to further our understanding of the nature of functional features in Creoles while focusing on how the functional exponent is morphologically realized, assuming a late-insertion-based exoskeletal model in the language mixing...
15 July 2025
Study design: Trends in the utilization of Mandibulo-Maxillary Fixation (MMF) are shifting nowadays from tooth-borne devices over specialized screws to hybrid MMF devices. Hybrid MMF devices come in self-made Erich arch bar modifications and commerci...
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute o...
Image dehazing is critical for visual enhancement and a wide range of computer vision applications. Despite significant advancements, challenges remain in preserving fine details and adapting to diverse, non-uniformly degraded scenes. To address thes...
In the present study, we propose a method for bio-nano hybrid formation by coupling a redox metalloprotein, Azurin, with CdSe-ZnS quantum dot for the development of a nanoscale resistive memory device. The covalent interaction between the two nanomat...
Tannins are eco-friendly, bio-sourced, natural, and highly reactive polyphenols. In the past decades, the understanding of their versatile properties has grown substantially alongside a continuously broadening of the tannins’ application scope....
The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT ima...
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract differe...
Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodolo...
The concepts hybrid and hybridization are common in many scientific fields, as in the taxonomic parts of botany and zoology, in modern genetic, and in the quantum–mechanical theory of atomic–molecular orbitals, which are of foremost relevance in most...
The gamut of natural polymers, from polysaccharides to proteins, exhibit peculiar features and multiple functionalities that are being exploited to engineer advanced nanocomposites and hybrid materials [...]
Today, MXenes with fascinating electronic, thermal, optical, and mechanical features have been broadly studied for biomedical applications, such as drug/gene delivery, photothermal/photodynamic therapy, antimicrobials/antivirals, sensing, tissue engi...
The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function–support vector regression and...
Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, wel...
We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in th...
Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of da...
Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image e...
By utilizing nanoparticles to upload and interact with several pharmaceuticals in varying methods, the primary obstacles associated with loading two or more medications or cargos with different characteristics may be addressed. Therefore, it is feasi...
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hy...
Hyperspectral image (HSI) classification has been extensively applied for analyzing remotely sensed images. HSI data consist of multiple bands that provide abundant spatial information. Convolutional neural networks (CNNs) have emerged as powerful de...
The classification of breast cancer using machine learning techniques has become a critical tool in modern medical diagnostics. This study analyzes the performance of hybrid models that combine traditional machine learning algorithms (TMLAs) with a c...
To address the application requirements of Visual Simultaneous Localization and Mapping (VSLAM) in intracavitary environments and the scarcity of gold-standard datasets for deep learning methods, this study proposes a hybrid attention mechanism combi...
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as...
Low-light image enhancement aims to improve the perceptual quality of images captured under low-light conditions. This paper proposes a novel generative adversarial network to enhance low-light image quality. Firstly, it designs a generator consistin...
The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimi...
Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuato...
Cystic fibrosis (CF) is a genetic disease affecting the lungs and pancreas and causing progressive damage. CF is caused by mutations abolishing the function of CFTR, a protein whose role is chloride’s mobilization in the epithelial cells of var...
The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemb...
Metastasis remains an unsolved clinical challenge. Two crucial features of metastasizing cancer cells are (a) their ability to dynamically move along the epithelial–hybrid–mesenchymal spectrum and (b) their tumor initiation potential or s...
The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability a...
Hybrid organic-inorganic catalysts have been extensively investigated by several research groups in the last decades, as they allow combining the structural robust-ness of inorganic solids with the versatility of organic chemistry. Within the field o...
In comparison to bolted joints, structural bonds are the desirable joining method for light-weight composite structures. To achieve a broad implementation of this technology in safety critical structures, the issues of structural bonds due to their c...
To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and ap...
(1) Background: Graphene oxide is a new carbon-based material that contains functional groups (carboxyl, hydroxyl, carbonyl, epoxy) and therefore can be easily functionalized with organic compounds of interest, yielding hybrid materials with importan...
The issue of renewable energy curtailment poses a crucial challenge to its effective utilization. To address this challenge, mitigating the impact of the intermittency and volatility of wind and solar energy is essential. In this context, this paper...
Multi-modal image fusion is a methodology that combines image features from multiple types of sensors, effectively improving the quality and content of fused images. However, most existing deep learning fusion methods need to integrate global or loca...
Nanotechnology offers outstanding potential for future biomedical applications. In particular, due to their unique characteristics, hybrid nanomaterials have recently been investigated as promising platforms for imaging and therapeutic applications....
Novel magnetic-antimicrobial-fluorescent multifunctional hybrid microspheres with well-defined nanostructure were synthesized by the aid of a poly(glycidyl methacrylate) (PGMA) template. The hybrid microspheres were fully characterized by scanning el...
Hybrid varieties can provide the boost needed to increase stagnant wheat yields through heterosis. The lack of an efficient hybridization system, which can lower the cost of goods of hybrid seed production, has been a major impediment to commercializ...
Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than solub...
Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interfe...
Unimodal brain–computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) of...
In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based imag...
Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundan...
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attent...
Deep learning has become indispensable for identifying hierarchical spatial features (SFs), which are crucial for linking neurological disorders to brain functionality, from functional Magnetic Resonance Imaging (fMRI). Unfortunately, existing method...
High-dimensional structured datasets are common in fields such as semiconductor manufacturing, healthcare, and finance, where redundant and irrelevant features often increase computational cost and reduce predictive accuracy. Feature selection mitiga...
Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great...
RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA–protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed...
This paper considers problems associated with the large size of the hyperspectral pansharpening network and difficulties associated with learning its spatial-spectral features. We propose a deep mutual-learning-based framework (SSML) for spectral-spa...
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