Search Results (1,039)

Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI) images has attracted the attention of many researchers, and specifically, reducing the bias of models and enhancing robustness is still a very pertinent active topic of attention. Methods: For capturing diverse information from different feature sets, we propose a Features Concatenation-based Brain Tumor Classification (FCBTC) Framework using Hybrid Deep Learning Models. For this, we have chosen three pretrained models—ResNet50; VGG16; and DensetNet121—as the baseline models. Our proposed hybrid models are built by the fusion of feature vectors. Results: The testing phase results show that, for the FCBTC Model-3, values for Precision, Recall, F1-score, and Accuracy are 98.33%, 98.26%, 98.27%, and 98.40%, respectively. This reinforces our idea that feature diversity does improve the classifier’s performance. Conclusions: Comparative performance evaluation of our work shows that, the proposed hybrid FCBTC Models have performed better than other proposed baseline models. Full article

Background: The Nerve Growth Factor (NGF) is essential for neuronal survival and function and represents a key therapeutic target for pain and inflammation-related disorders, as well as for neurodegenerative diseases. Small-molecule antagonists of human NGF (hNGF) offer advantages over monoclonal antibodies, including oral availability and reduced immunogenicity. However, their development is often hindered by solubility challenges, necessitating the use of solvents like dimethyl sulfoxide (DMSO). This study investigates whether DMSO directly interacts with hNGF and affects its receptor-binding properties. Methods: Integrative/hybrid computational and experimental biophysical approaches were used to assess DMSO-NGF interaction by combining machine-learning tools and Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared (FT-IR) spectroscopy, Differential Scanning Fluorimetry (DSF) and Grating-Coupled Interferometry (GCI). These techniques evaluated binding affinity, conformational stability, and receptor-binding dynamics. Results: Our findings demonstrate that DMSO binds hNGF with low affinity in a specific yet non-disruptive manner. Importantly, DMSO does not induce significant conformational changes in hNGF nor affect its interactions with its receptors. Conclusions: These results highlight the importance of considering solvent–protein interactions in drug discovery, as these low-affinity yet specific interactions can affect experimental outcomes and potentially alter the small molecules binding to the target proteins. By characterizing DMSO-NGF interactions, this study provides valuable insights for the development of NGF-targeting small molecules, supporting their potential as effective alternatives to monoclonal antibodies for treating pain, inflammation, and neurodegenerative diseases. Full article

We explore chaos, local dynamics, codimension-one, and codimension-two bifurcations of an asymmetric discrete predator–prey model. More precisely, for all the model’s parameters, it is proved that the model has two boundary fixed points and a trivial fixed point, and also under parametric conditions, it has an interior fixed point. We then constructed the linearized system at these fixed points. We explored the local behavior at equilibria by the linear stability theory. By the series of affine transformations, the center manifold theorem, and bifurcation theory, we investigated the detailed codimensions-one and two bifurcations at equilibria and examined that at boundary fixed points, no flip bifurcation exists. Furthermore, at the interior fixed point, it is proved that the discrete model exhibits codimension-one bifurcations like Neimark–Sacker and flip bifurcations, but fold bifurcation does not exist at this point. Next, for deeper understanding of the complex dynamics of the model, we also studied the codimension-two bifurcation at an interior fixed point and proved that the model exhibits the codimension-two 1:2, 1:3, and 1:4 strong resonances bifurcations. We then investigated the existence of chaos due to the appearance of codimension-one bifurcations like Neimark–Sacker and flip bifurcations by OGY and hybrid control strategies, respectively. The theoretical results are also interpreted biologically. Finally, theoretical findings are confirmed numerically. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

The advancement of efficient drug delivery systems continues to pose a significant problem in pharmaceutical sciences, especially for compounds with limited water solubility. Lipid-based systems, including solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs), have emerged as viable options owing to their biocompatibility, capability to safeguard labile chemicals, and potential for prolonged release. Nonetheless, the encapsulation efficiency (EE) and release dynamics of these carriers can be enhanced by including cyclodextrins (CDs)—cyclic oligosaccharides recognized for their ability to form inclusion complexes with hydrophobic compounds. This article offers an extensive analysis of CD-modified SLNs and NLCs as multifunctional drug delivery systems. The article analyses the fundamental principles of these systems, highlighting the pre-complexation of the drug with cyclodextrins before lipid incorporation, co-encapsulation techniques, and surface adsorption after formulation. Attention is concentrated on the physicochemical interactions between cyclodextrins and lipid matrices, which influence essential factors such as particle size, encapsulation efficiency, and colloidal stability. The review includes characterization techniques, such as particle size analysis, zeta potential measurement, drug release studies, and Fourier-transform infrared spectroscopy (FT-IR)/Nuclear Magnetic Resonance (NMR) analyses. The study highlights the application of these systems across many routes of administration, including oral, topical, and mucosal, illustrating their adaptability and potential for targeted delivery. The review outlines current formulation challenges, including stability issues, drug leakage, and scalability concerns, and proposes solutions through advanced approaches, such as stimuli-responsive release mechanisms and computer modeling for system optimization. The study emphasizes the importance of regulatory aspects and outlines future directions in the development of CD-lipid hybrid nanocarriers, showcasing its potential to revolutionize the delivery of poorly soluble drugs. Full article

This essay reads Oji-Cree poet Joshua Whitehead’s full metal indigiqueer in relation to hybridity. Whitehead’s poems are both lyrical and experimental, offering a hybrid poetics that resonates with existing critical discussions of hybridity, but he also extends hybrid poetics in new directions through his engagement with posthuman and Indigenous futurism and through his development of Zoa, the hybridized trickster figure who combines the technological and the biological, who features so prominently throughout the collection. Indigiqueerness emerges in these poems as a hybrid identity positioned not only to survive but to thrive in the twenty-first century and beyond. Full article

Compliant mechanisms are often utilized in precise positioning systems but have not been thoroughly examined in vibration-aided fine CNC machining. This study aims to develop a new 02-DOF flexure stage for vibration-aided fine CNC milling. A hybrid displacement amplifier, featuring a two-lever mechanism, two Scott–Russell mechanisms, and a parallel leading mechanism, was integrated into a symmetric perpendicular series configuration to create an innovative design. The pseudo-rigid body model (PRBM), Lagrangian approach, finite element analysis (FEA), and Firefly optimization algorithm were employed to develop, verify, and optimize the quality response of the new positioner. The PRBM and Lagrangian methods were used to construct an analytical model, while finite element analysis was used to validate the theoretical solution. The primary natural frequency results from theoretical and FEM methods were 318.16 Hz and 308.79 Hz, respectively. The difference between these techniques was 3.04%, demonstrating a reliable modelling strategy. The Firefly optimization approach applied mathematical equations to enhance the key design factors of the mechanism. The prototype was then built, revealing an error of 7.23% between the experimental and simulated frequencies of 331.116 Hz and 308.79 Hz, respectively. The specimen was subsequently mounted on the fabricated optimization positioner, and vibration-assisted fine CNC milling was performed at 100–1000 Hz. At 400 Hz, the specimen achieved ideal surface roughness with a Ra value of 0.187 µm. The developed design is a potential structure that generates non-resonant frequency power for vibration-aided fine CNC milling. Full article

This paper presents a carbon monoxide (CO) detection mechanism achieved through further improvement of the sensing antenna based on hybrid spoof localized surface plasmons (SLSPs) and cavity resonance. Unlike conventional approaches relying on chemical reactions or photoelectric effects, the all-metal configuration detects dielectric variations through microwave-regime resonance frequency shifts, enabling CO/air differentiation with theoretically enhanced robustness and environmental adaptability. The designed system achieves measured figures of merit (FoMs) of 183.2 RIU⁻¹, resolving gases with dielectric contrast below 0.1%. Experimental validation successfully discriminated CO (ε_r = 1.00262) from air (ε_r = 1.00054) under standard atmospheric pressure at 18 °C. Full article

Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise. This review summarizes six cutting-edge nondestructive spectroscopic and imaging technologies, near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging (spanning the UV, visible, and NIR regions, to simultaneously capture both spatial distribution and spectral signatures of sample constituents), terahertz spectroscopy, and nuclear magnetic resonance (NMR), along with their transformative applications. We systematically elucidate the fundamental principles and distinctive merits of each technological approach, with a particular focus on their deep learning-based integration with spectral fusion techniques and hybrid spectral-heterogeneous fusion methodologies. Our analysis reveals that the synergy between spectroscopic technologies and deep learning demonstrates unparalleled superiority in speed, precision, and non-invasiveness. Future research should prioritize three directions: multimodal integration of spectroscopic technologies, edge computing in portable devices, and AI-driven applications, ultimately establishing a high-precision and sustainable food quality inspection system spanning from production to consumption. Full article

Background/Objectives: Hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) is an emerging imaging modality that combines metabolic and anatomical data in a single session, offering promising diagnostic performance in various cardiac conditions. However, its role in inflammatory cardiac diseases (ICDs), such as myopericarditis and endocarditis, remains underexplored. This study aims to evaluate the diagnostic value of PET/MRI in ICDs through a systematic review of the literature, complemented by our institutional experience. Methods: We conducted a systematic review of PubMed, Scopus, and Embase up to April 2025, using keywords including “PET/MRI,” “PET/MR”, “myocarditis”, “endocarditis”, “pericarditis”, “inflammatory heart disease”, and “inflammatory cardiac disease”. Studies reporting the use of hybrid PET/MRI in ICDs were included and analyzed. Additionally, we retrospectively reviewed clinical and imaging data from 33 consecutive patients who underwent PET/MRI in our center for suspected myopericarditis or endocarditis between September 2022 and March 2025. Results: The systematic review identified 12 eligible studies evaluating PET/MRI in ICDs, highlighting its added value in cases with inconclusive findings on standalone MRI or PET. Common advantages reported included improved localization of active inflammation, better characterization of tissue damage, and enhanced diagnostic confidence. In our cohort, hybrid PET/MRI was considered diagnostically useful in 16/21 (76%) of myopericarditis cases and 9/12 (75%) of endocarditis cases, particularly for reclassifying uncertain findings and guiding clinical management. Conclusions: The combined analysis of the current literature and real-world clinical experience supports the diagnostic utility of hybrid PET/MRI in the evaluation of ICDs. This multimodal approach improves the interpretation of equivocal cases, facilitates accurate diagnosis, and may influence therapeutic decisions. However, larger prospective studies are needed to confirm these findings and establish standardized protocols. Full article

High-quality reconstruction of magnetic resonance imaging (MRI) data from undersampled k-space remains a significant challenge in medical imaging. While the integration of compressed sensing and deep learning has notably improved the performance of MRI reconstruction, existing convolutional neural network-based methods are limited by their small receptive fields, which hinders the exploration of global image features. Meanwhile, Swin-Transformer-based approaches struggle with inter-window information interaction and global feature extraction and perform poorly when dealing with complex repetitive structures and similar texture features under undersampling conditions, resulting in suboptimal reconstruction quality. To address these issues, we propose a Symmetry-based Cascaded Dual-Domain Hybrid Attention Network (SCDDHAN). Leveraging the inherent symmetry of medical images, the network combines channel and self-attention to improve global context modeling and local detail restoration. The overlapping window self-attention module is designed with symmetry in mind to improve cross-window information interaction by overlapping adjacent windows and directly linking neighboring regions. This facilitates more accurate detail recovery. The concept of symmetry is deeply embedded in the network design, guiding the model to better capture regular patterns and balanced structures within MRI images. Experimental results demonstrate that under 5× and 10× undersampling conditions, SCDDHAN outperforms existing methods in artifact suppression, achieving more natural edge transitions, clearer complex textures and superior overall performance. This study highlights the potential of integrating symmetry concepts into hybrid attention modules for accelerating MRI reconstruction and offers an efficient, innovative solution for future research in this area. Full article

This study aims to establish a methodology that integrates experimental measurements with finite element analysis (FEA) to investigate the mechanical behavior and dynamic characteristics of soft–hard laminated composites fabricated via additive manufacturing (AM) under dynamic excitation. A hybrid AM technique was employed, using the PolyJet process based on stereolithography (SLA) to fabricate composite beam structures composed of alternating soft and hard materials. Initially, impact tests using a steel ball on cantilever beams made of hard material were conducted to inversely calculate the first natural frequency via time–frequency analysis, thereby identifying Young’s modulus and Poisson’s ratio. For the viscoelastic soft material, tensile and stress relaxation tests were performed to construct a Generalized Maxwell Model, from which the Prony series parameters were derived. Subsequently, symmetric and asymmetric multilayer composite beams were fabricated and subjected to impact testing. The experimental results were compared with FEA simulations to evaluate the accuracy and validity of the identified material parameters of different structural configurations under vibration modes. The research focuses on the time- and frequency-dependent stiffness response of the composite by hard and soft materials and integrating this behavior into structural dynamic simulations. The specific objectives of the study include (1) establishing the Prony series parameters for the soft material integrated with hard material and implementing them in the FE model, (2) validating the accuracy of resonant frequencies and dynamic responses through combined experimental and simulation, (3) analyzing the influence of composite material symmetry and thickness ratio on dynamic modals, and (4) comparing simulation results with experimental measurements to assess the reliability and accuracy of the proposed modeling framework. Full article

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes. Full article

This study presents an integrated experimental and theoretical investigation of two pharmacologically significant neolignans—magnolol and honokiol—with the aim of characterizing their structural and spectroscopic properties in detail. Experimental Fourier-transform infrared (FT-IR), ultraviolet–visible (UV-Vis), and nuclear magnetic resonance (¹H NMR) spectra were recorded and analyzed. To support and interpret these findings, a series of density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were conducted using several hybrid and long-range corrected functionals (B3LYP, CAM-B3LYP, M06X, PW6B95D3, and ωB97XD). Implicit solvation effects were modeled using the CPCM approach across a variety of solvents. The theoretical spectra were systematically compared to experimental data to determine the most reliable computational approaches. Additionally, natural bond orbital (NBO) analysis, molecular electrostatic potential (MEP) mapping, and frontier molecular orbital (FMO) visualization were performed to explore electronic properties and reactivity descriptors. The results provide valuable insight into the structure–spectrum relationships of magnolol and honokiol and establish a computational benchmark for further studies on neolignan analogues. Full article

This study presents the successful fabrication of lead zirconate titanate (PZT) thin films on silicon (Si) substrates using a hybrid deposition method combining spin-coating and RF sputtering techniques. Initially, a PZT layer was deposited through four successive spin-coating cycles, followed by an additional layer formed via RF sputtering. The resulting multilayer structure was annealed at ${700 }^{°}$C for 2 h to improve crystallinity. Comprehensive material characterization was conducted using XRD, SEM, cross-sectional SEM, EDX, and UV–VIS absorbance spectroscopy. The analyses confirmed the formation of a well-crystallized perovskite phase, a uniform surface morphology, and an optical band gap of approximately 3.55 eV, supporting its suitability for sensing applications. Building upon these findings, a multilayer PZT-based touch sensor was fabricated and electrically characterized. Low-frequency I–V measurements demonstrated consistent and repeatable polarization behavior under cyclic loading conditions. In addition, $\left|Z\right|$–f measurements were performed to assess the sensor’s dynamic electrical behavior. Although expected dielectric responses were observed, the absence of distinct anti-resonance peaks suggested non-idealities linked to ${\mathrm{Ag}}^{+}$ ion diffusion from the electrode layers. To account for these effects, the classical Butterworth–Van Dyke (BVD) equivalent circuit model was extended with additional inductive and resistive components representing parasitic pathways. This modified model provided excellent agreement with the measured impedance and phase data, offering deeper insight into the interplay between material degradation and electrical performance. Overall, the developed sensor structure exhibits strong potential for use in piezoelectric sensing applications, particularly for tactile and pressure-based interfaces. Full article