Search Results (30,708)

This paper presents a novel ontology-driven digital twin framework specifically designed for aviation maintenance and operations that addresses these challenges through semantic reasoning and explainable decision support. The proposed framework integrates seven interconnected ontologies—structural, functional, behavioral, monitoring, maintenance, lifecycle, and environmental. It collectively provides a comprehensive semantic representation of aircraft systems and their operational context. Each ontology is mathematically formalized using description logics and graph theory, creating a unified knowledge graph that enables transparent, traceable reasoning from sensor observations to maintenance decisions. The digital twin is formally defined as a 6-tuple that incorporates semantic transformation engines, cross-ontology mappings, and dynamic reasoning mechanisms. Unlike traditional data-driven approaches that operate as black boxes, the ontology-driven framework provides explainable inference capabilities essential for regulatory compliance and safety certification in aviation. The semantic foundation enables causal reasoning, rule-based validation, and context-aware maintenance recommendations while supporting standardization and interoperability across manufacturers, airlines, and regulatory bodies. The research contributes a mathematically grounded, semantically transparent framework that bridges the gap between domain knowledge and operational data in aviation maintenance. This work establishes the foundation for next-generation cognitive maintenance systems that can support intelligent, adaptive, and trustworthy operations in modern aviation ecosystems. Full article

Hydrocarbon fluids have a widespread presence in modern society due to their role in the global energy and fuel supply. The ability to distinguish between hydrocarbon fluids from different manufacturing processes is essential in industrial and government settings. Currently, performing such analyses is expensive and time-consuming, as standard practice involves sending samples to a laboratory for gas chromatography-mass spectrometry (GC-MS) analysis. The inherent limitations of traditional separation techniques often make them unsuitable for the demands of real-time process monitoring and control. This work proposes the use of handheld Raman spectroscopy for rapid classification of petroleum- and synthetic-based hydrocarbon fluids. A total of 600 Raman spectra were collected from six different hydraulic fluids and analyzed. Preliminary visual observations revealed reproducible spectral differences between various types of hydraulic fluids. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate the data further. The findings indicate that handheld Raman spectrometers are capable of detecting chemical features of hydrocarbon fluids, supporting the classification of their formulations. Full article

The radiofrequency identification (RFID) interface is used in a controller area network (CAN) bus system to enhance the performance of stacked fuel cells. In addition, a personal computer base logic analyzer (LA) is utilized to monitor and analyze data transmitted over the CAN bus. The LA enables the visualization of digital signals, identification of data patterns, and troubleshooting of communication protocols. The combination of the CAN bus with the RFID interface and LA provides an effective solution for testing and monitoring digital communication systems. The result of this study proves that LA is applied in series-connected fuel cells. The advantages of the RFID CAN bus are validated by modern communication protocols. Full article

Pharmacotherapy in the geriatric population is one of the greatest challenges in modern medicine. Elderly patients, characterized by multimorbidity and the resulting polypharmacy, are significantly more exposed to adverse drug reactions (ADRs), which often lead to hospitalization and a decline in quality of life. Understanding the reasons for this difference requires an analysis of the physiological changes that occur during the aging process at the molecular level. This article presents a perspective on the molecular aspects of geriatric pharmacotherapy, focusing on the fundamental mechanisms that are modified with age. The analysis covers changes in pharmacokinetics, including the role and regulation of cytochrome P450 (CYP) enzymes, whose activity, especially in phase I reactions, is significantly reduced. The age-dependent dysfunction of drug transporters from the ABC (ATP-binding cassette) and SLC (solute carrier) families in key organs such as the intestines, liver and kidneys is discussed, which affects the absorption, distribution and elimination of xenobiotic compounds, including drugs. The article also provides a comprehensive analysis of the blood–brain barrier (BBB), describing changes in neurovascular integrity, including the dysfunction of tight junctions and a decrease in the activity of P-glycoprotein, sometimes referred to as multidrug resistance protein (MDR). This increases the susceptibility of the central nervous system to the penetration and action of drugs. In the realm of pharmacodynamics, changes in the density and sensitivity of key receptors (serotonergic, dopaminergic, adrenergic) are described based on neuroimaging data, explaining the molecular basis for increased sensitivity to certain drug classes, such as anticholinergics. The paper also explores new research perspectives, such as the role of the gut microbiome in modulating pharmacokinetics by influencing gene expression and the importance of pharmacoepigenetics, which dynamically regulates drug response throughout life via changes in DNA methylation and histone modifications. The clinical implications of these molecular changes are also discussed, emphasizing the potential of personalized medicine, including pharmacogenomics, in optimizing therapy and minimizing the risk of adverse reactions. Such an integrated approach, incorporating data from multiple fields (genomics, epigenomics, microbiomics) combined with a comprehensive geriatric assessment, appears to be the future of safe and effective pharmacotherapy in the aging population. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL prediction, they often face two major challenges: an insufficient generalization ability when distribution gaps exist between training data and real-world application scenarios, and the difficulty of comprehensively capturing complex equipment degradation processes with single-modal data. A key challenge in current research is how to effectively fuse multimodal data and leverage transfer learning to address RUL prediction in small-sample and cross-condition scenarios. This paper proposes an innovative deep multimodal fine-tuning regression (DMFR) framework to address these issues. First, the DMFR framework utilizes a Convolutional Neural Network (CNN) and a Transformer Network to extract distinct modal features, thereby achieving a more comprehensive understanding of data degradation patterns. Second, a fusion layer is employed to seamlessly integrate these multimodal features, extracting fused information to identify latent features, which are subsequently utilized in the predictor. Third, a two-stage training algorithm combining supervised pre-training and fine-tuning is proposed to accomplish transfer alignment from the source domain to the target domain. This paper utilized the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbine engine dataset publicly released by NASA to conduct comparative transfer experiments on various RUL prediction methods. The experimental results demonstrate significant performance improvements across all tasks. Full article

Implantable biomaterials are widely used in modern medicine, especially in orthopaedics, cardiovascular surgery, dentistry, and plastic and reconstructive surgery. The issue of the interaction of implants with body tissues and the risk of infection associated with them is one of the most studied and topical issues in medicine. It is very important to find a biomaterial that effectively combines both microbiology and tissue compatibility aspects. The aim of this research work was to determine the adhesion and colonization rates of Staphylococcus epidermidis, Pseudomonas aeruginosa, and Candida albicans on smooth, microtextured, and macro-textured silicone biomaterials in an in vitro study. A total of 90 silicone biomaterial samples were used, 30 for each type of biomaterial. In each of the biomaterial groups, half of the samples (n = 15) were used to determine the adhesion intensity and the other half to determine the colonization intensity on the active surface of the biomaterial samples. The study found that Staphylococcus epidermidis and Pseudomonas aeruginosa had the highest adhesion intensity on the macro-textured implant, while Candida albicans adhered best to smooth. Among the microorganisms, Pseudomonas aeruginosa demonstrated the highest colonization rate, followed by Staphylococcus epidermidis and then Candida albicans. The most intensive colonization of microorganisms was on the macro-textured implant, then on the micro-textured, and then on the smooth. The smooth and micro-textured implants did not show statistically significant differences in the intensity of adhesion and colonization. The biomaterials did not show pro-oxidant or anti-oxidant properties, and no lipid peroxidation was induced by the biomaterials. Full article

Highly efficient shielding materials, transparent in the visible and IR ranges are becoming important in practice. This stimulates the development of cheap methods for creating transparent conductors with low sheet resistance and high optical transparency. This work presents a complex approach based on preliminary modeling of the shielding characteristics of two-layer sandwich structures based on irregular aluminum mesh (IAM) formed by the cracked template method. Experimentally measured spectral dependences of the transmission coefficient of single-layer IAM are used as a reference point for modeling. According to the simulation results, two types of sandwich structures were designed using IAM, with varying filling factors and a fixed PMMA layer thickness of 4 mm. The experimentally measured shielding characteristics of the sandwich structures in the range of 0.01–7 GHz are in good agreement with the calculated data. The obtained structures demonstrate a shielding efficiency of 55.96 dB and 65.55 dB at a frequency of 3.5 GHz (the average range of 5G communications). At the same time, their optical transparency at a wavelength of 550 nm are 84.07% and 75.78%, respectively. Our sandwich structures show electromagnetic shielding performance and uniform diffraction pattern. It gives them an advantage over structures based on regular meshes. The obtained results highlight the prospect of the proposed comprehensive approach for obtaining highly efficient, low-cost optically transparent shielding structures. Such materials are needed for modern wireless communication systems and metrology applications. Full article

We present the conceptual framework of the Astronomical Hub (AstroHub), a unified platform combining various optical instruments at a single observatory. Its major approach lies in arranging conditions for research groups to install telescopes and equipment and participate in joint projects. AstroHub is planned to integrate Virtual Observatory (VO) tools, FAIR data principles, and a telescope network to create a powerful and attractive ecosystem for both robust near-Earth object (NEO) monitoring and diverse deep space research. We provide an overview of the AstroHub development directions in the case study of the Assy-Turgen Observatory. Full article

The efficient and targeted delivery of pharmaceutical substances remains a major challenge in modern therapeutics. Traditional drug delivery systems often suffer from limited bioavailability, rapid clearance, and off-target effects. Red blood cells (erythrocytes), due to their long circulation time, biocompatibility, and immune-evasive properties, have emerged as promising carriers in the development of novel nanotechnology-based drug delivery platforms.A comprehensive literature review was conducted, analyzing recent studies on erythrocyte membrane-coated nanoparticles, their interactions with loaded therapeutic agents, and their performance in vitro and in vivo. Special focus was given to applications in chemotherapy, photodynamic therapy (PDT), photothermal therapy (PTT), and immunotherapy. Erythrocyte-based nanocarriers demonstrated improved circulation times, reduced immune clearance, and enhanced targeting capabilities compared to traditional nanoparticles. Encapsulation of nanoparticles within erythrocyte membranes preserved the functional integrity of the carrier while minimizing systemic toxicity. However, challenges such as membrane stability, hemocompatibility, and the potential for nanoparticle-induced hemoglobin dysfunction were identified as areas requiring further research. In conclusion, erythrocyte membrane-coated nanoparticles represent a unique and promising strategy for drug delivery, combining the natural advantages of red blood cells with the versatility of nanotechnology. Full article

We present a novel deep learning framework for high-resolution semantic segmentation, designed to interpret complex visual environments such as cities, rural areas, and natural landscapes. Our method integrates conic geometric embeddings, which is a mathematical approach for capturing spatial relationships, with belief-aware learning, a strategy that adapts model predictions when input data are uncertain or change over time. A multi-scale refinement process further improves boundary accuracy and detail preservation. The proposed model, built on a hybrid Vision Transformer (ViT) backbone and trained end-to-end using adaptive optimization, is evaluated on four benchmark datasets including EDEN, OpenEarthMap, Cityscapes, and iSAID. It achieves 88.94% Accuracy and $R^2$ of 0.859 on EDEN, while surpassing 85.3% Accuracy on Cityscapes. Ablation studies demonstrate that removing Conic Output Embeddings causes drops in Accuracy of up to 2.77% and increases in RMSE, emphasizing their contribution to frequency-aware generalization across diverse conditions. On OpenEarthMap, our model achieves a mean IoU of 73.21%, outperforming previous baselines by 2.9%, and on iSAID, it reaches 80.75% mIoU with improved boundary adherence. Beyond technical performance, the framework enables practical applications such as automated landscape analysis, urban growth monitoring, and sustainable environmental planning. Its consistent results across three independent runs demonstrate both robustness and reproducibility, offering a reliable tool for large-scale geospatial and environmental modeling. Full article

Glioblastoma multiforme (GBM), the most aggressive primary brain tumor in adults, has a poor prognosis due to rapid recurrence and treatment resistance. This review examines the evolution of radiotherapy (RT) for GBM management, from whole-brain RT to modern techniques like intensity-modulated RT (IMRT) and volumetric modulated arc therapy (VMAT), guided by 2023 European Society for Radiotherapy and Oncology (ESTRO)-European Association of Neuro-Oncology (EANO) and 2025 American Society for Radiation Oncology (ASTRO) recommendations. The standard Stupp protocol (60 Gy/30 fractions with temozolomide [TMZ]) improves overall survival (OS) to 14.6 months, with greater benefits in O6-methylguanine-DNA methyltransferase (MGMT)-methylated tumors (21.7 months). Tumor Treating Fields (TTFields) extend median overall survival (mOS) to 31.6 months in MGMT-methylated patients and 20.9 months overall in supratentorial GBM (EF-14 trial). However, 80–90% of recurrences occur within 2 cm of the irradiated field due to tumor infiltration and radioresistance driven by epidermal growth factor receptor (EGFR) amplification, phosphatase and tensin homolog (PTEN) mutations, cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletions, tumor hypoxia, and tumor stem cells. Pseudoprogression, distinguished using Response Assessment in Neuro-Oncology (RANO) criteria and positron emission tomography (PET), complicates response evaluation. Targeted therapies (e.g., bevacizumab; PARP inhibitors) and immunotherapies (e.g., pembrolizumab; oncolytic viruses), alongside advanced imaging (multiparametric magnetic resonance imaging [MRI], amino acid PET), support personalized RT. Ongoing trials evaluating reirradiation, hypofractionation, stereotactic radiosurgery, neoadjuvant therapies, proton therapy (PT), boron neutron capture therapy (BNCT), and AI-driven planning aim to enhance efficacy for GBM IDH-wildtype, but phase III trials are needed to improve survival and quality of life. Full article