Search Results (1,840)

Unmanned Aerial Vehicles (UAVs) are widely used in meteorology and logistics due to their unique advantages nowadays. During their lifecycle, uncertainties—such as flight condition variations—can significantly affect both design and performance, making Robust Aerodynamic Design Optimization (RADO) essential. However, existing RADO methodologies face high computational cost of uncertainty analysis and inefficiency of conventional optimization algorithms. To address these challenges, this paper proposed a novel RADO methodology integrating a Stochastic Kriging (SK) surrogate model with the PPO-Clip reinforcement learning algorithm, targeting atmospheric uncertainties encountered by turbojet-powered UAVs in transonic cruise. The SK surrogate model, constructed via Maximin Latin Hypercube Sampling and refined using the Expected Improvement infill criterion, enabled efficient uncertainty quantification. Based on the trained surrogate model, a PPO-Clip-based RADO framework with tailored reward and state transition functions was established. Applied to the RAE2822 airfoil under Mach number perturbations, the methodology demonstrated superior reliability and efficiency compared with L-BFGS-B and PSO algorithms. Full article

The integration of three-dimensional inward-turning inlets with airframes has broad application prospects. This paper develops an integrated design method for the inlet forebody with a controllable incident shock wave shape, aiming at the three-dimensional inward-turning inlet with a circular entrance, and it is applied to the forebody design of a given inward-turning inlet to obtain a three-dimensional inward-turning inlet/forebody matching scheme. Numerical simulation and wind tunnel experiment were carried out to investigate the aerodynamic performance of the inlet. The results show that the inlet/forebody matching scheme successfully realizes both geometric and aerodynamic matching between the inlet and forebody, resulting in a shock-on-lip condition at the design point, with only a 2% reduction in mass flow rate. This indicates that the forebody design and matching method are highly effective. It should be noted that after the forebody matching is achieved, the overall compression effect of the inlet on the airflow is weakened, and both the Mach number and total pressure at the inlet outlet increase slightly. Full article

α-Cyclodextrin/Moringin (α-CD/MOR) is an isothiocyanate showing neuroprotective and antioxidant properties. In this work, we studied in differentiated NSC-34 motor neurons cell line the molecular pathways activated following a treatment of 96 h with α-CD/MOR at different doses, namely 0.5, 5 and 10 μM. Taking advantage of comparative transcriptomic analysis, we retrieved the differentially expressed genes (DEGs) and we mapped DEGs to synaptic genes using the SynGO database. Then, we focused on the biological pathways in which they are involved. We observed that the prolonged treatment with α-CD/MOR significantly modulated biological processes and cellular components associated with synaptic organization. Interestingly, the KEGG pathway “Regulation of actin cytoskeleton” was overrepresented, alongside pathways related to synapses and axon guidance. Specifically, SPIA analysis indicated that the “Regulation of actin cytoskeleton” pathway was found to be activated with the highest dose of α-CD/MOR. Moreover, α-CD/MOR also modulated transcription factors involved in synaptic plasticity, such as Creb1. These results could indicate that α-CD/MOR can influence synaptic functions and organization, being involved in synaptic plasticity through the modulation of actin dynamics. Full article

Sulforaphane (SFN) is a bioactive compound belonging to the isothiocyanate family, known for its neuroprotective properties. While transcriptomic studies have highlighted SFN’s role in regulating gene expression, its impact on alternative splicing (AS), a key regulatory mechanism in neuronal metabolism, remains underexplored. In this study, we investigated whether SFN pre-treatment influences mRNA splicing patterns in an in vitro neuronal model using retinoic acid (RA)-differentiated SH-SY5Y cells. Using a dedicated RNA-seq-based splicing analysis pipeline, we identified 194 differential alternative splicing events (DASEs) associated with SFN treatment. Gene Ontology enrichment revealed significant over-representation of DNA repair processes. To better understand the functional implications, we integrated in silico predictions of premature stop codons, DASE/miRNA hybridizations, and DASE/RNA-binding protein (RBP) motif occurrences. Our findings suggest that SFN may modulate splicing of key DNA repair genes, contributing to protecting neurons against DNA damage. These preliminary results underscore a novel layer of SFN’s molecular effects and propose it as a valuable adjuvant in physiological conditions to enhance cellular health. Further studies are warranted to dissect the mechanistic underpinnings of SFN-mediated AS and its relevance in DNA-damage-related disorders. Full article

Considering the importance of free amino acids (FAAs) and biogenic amines (BAs) in the production of fermented beverages (FB), the interest in the quantification of these compounds has been growing. So far, most of the analytical methods developed entail a derivatization step. While this technique allows for the detection of several compounds, it is often associated with scarce accuracy and poor resolution. To counteract the drawbacks, in this study, we aimed to develop a fast, simple, and effective method that combines the use of ultra-high-performance liquid chromatography (UHPLC) and high-resolution mass spectrometry (HRMS) to quantify underivatized FAAs and BAs in FBs. The method was successfully developed and validated: it allowed for the accurate and precise quantification of 20 FAAs—including leucine and isoleucine—and 12 BAs in just 12 min. Its applicability was demonstrated on commercial samples of wines, beers, ciders, saké, and vinegars. Full article

We present a detailed theoretical and experimental study of cascaded double resonance long period gratings (C DR LPGs) for fabricated sensing applications. The matrix description of cascaded LPGs is presented, and several important particular cases are considered related to the regular and turn around point (TAP) gratings. A pulsed CO₂ laser was used to fabricate ordinary and cascaded DR LPGs in a photosensitive optical fiber. The responses of the fabricated C DR LPGs to surrounding refractive index (SRI) temperature as well to longitudinal strain have been studied. A statistical comparison of the SRI sensitivities of ordinary and cascaded DR LPGs is presented to outline the capabilities and advantages of cascaded DR gratings. It was experimentally established that the temperature dependence of the wavelength split at the TAP follows a logarithmic dependence and the sensitivity to temperature is inversely proportional to the temperature itself. We evaluate the temperature stability needed for SRI-based sensing application and the importance of fine-tuning to the operational point slightly after the TAP to ensure maximum sensitivity of the sensor. Full article

A respiratory monitoring sensor based on a balloon-shaped optical fiber is proposed. The sensor consists of a single-mode fiber (SMF) coated with polydimethylsiloxane (PDMS) bent into a balloon shape to form a fiber optic Mach–Zehnder interferometer. The sensor’s sensitivity to temperature enables monitoring of breathing status by recognizing the temperature changes that occur during human respiration. By adjusting the bending radius of the balloon-shaped SMF, high-order modes can be effectively excited to interfere with the core mode. Due to the high thermo-optic coefficient and thermal expansion coefficient of PDMS itself, the balloon-shaped fiber optic sensor can achieve temperature sensitivity. The experimental results show that the temperature sensitivity is −166.29 pm/°C in a temperature range of 30 °C to 60 °C. Finally, the proposed sensor was mounted into a respiratory mask to monitor different breathing states (normal, fast, slow, and oral–nasal breathing transitions) and breathing frequencies. Full article

This study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines. Full article

Hierarchical clustering analysis is applied to the positioning of two simultaneously-occurring intrusion events in the case of a dual Mach-Zehnder interferometer used for intrusion detection. To simulate the two intrusion events, the sensing fibers of the dual Mach-Zehnder interferometer are heavily knocked at two different positions simultaneously. Then the clockwise (CW) and counter-clockwise (CCW) signals are loaded into a personal computer through a data acquisition module, and analyzed by Fourier transform method for determination of the time delay between the two signals. Hierarchical clustering analysis is then employed twice for dividing the data points in a feature space into several clusters according to the conditions required. To locate the two intrusions, the first clustering analysis is performed on the data points formed by signals detected in a 10 ms time period, with the centroid of the largest cluster being the location of a single intrusion event. Then, 100 pairs of CW and CCW signals detected sequentially are analyzed to give 100 locations. These 100 locations and their CP values (each standing for a ratio of a given spectral amplitude to the summation of the spectral amplitudes over the frequency band of 2500 to 5000 Hz) constitute 100 data points in a feature space for the second hierarchical clustering analysis, which then determines the respective locations of the two intrusion events. In the test of a 1036 m long fiber perimeter, we demonstrated an accuracy to within 21.55 m. Full article

Dementia is a broad category of neurodegenerative pathologies characterized by a progressive decline in two or more cognitive domains, including memory, language, executive and visuospatial functions, personality, and behavior, resulting in the loss of the ability to perform instrumental and/or basic daily activities. One of the most common types of dementia is Alzheimer’s disease. Current approved treatments for Alzheimer’s disease are mainly limited to alleviating cognitive, behavioral, and psychological deficits. To date, four drugs belonging to two families have been approved for the treatment of Alzheimer’s disease: acetylcholinesterase inhibitors (donepezil, galantamine, rivastigmine) and antiglutamatergic drugs (memantine). Drugs delay the progression of the disease, but they cause a number of side effects. Many scientific studies have focused on finding natural products with potential neuroprotective properties and no or minimal cytotoxicity that can support current drug therapy. The main objective of this review is to analyze and describe the neuroprotective potential of selected groups of natural substances (polyphenols, alkaloids, terpenoids) in one of the commonly performed in vivo studies, the scopolamine-induced animal model of Alzheimer’s disease. The article is a review of literature reports from the last 5 years, and the information collected indicates that the neuroprotective activity of natural compounds may prove to be a potential alternative or add-on for Alzheimer’s disease therapy. Full article

The present study reports the outcome of an experimental study of organic vapor trailing edge flows. As a working fluid, the organic vapor Novec 649 was used under representative pressure and temperature conditions for organic Rankine cycle (ORC) turbine applications characterized by values of the fundamental derivative of gas dynamics below unity. An idealized vane configuration was placed in the test section of a closed-loop organic vapor wind tunnel. The effect of the Reynolds number was assessed independently from the Mach number by charging the closed wind tunnel. The airfoil surface roughness and the trailing edge shape were evaluated by experimenting with different test blades. The flow and the loss behavior were obtained using Pitot probes, static wall pressure taps, and background-oriented schlieren (BOS) optics. Isentropic exit Mach numbers up to 1.5 were investigated. Features predicted via a simple flow model proposed by Denton and Xu in 1989 were observed for organic vapor flows. Still, roughness affected the downstream loss behavior significantly due to shockwave boundary-layer interactions and flow separation. The new experimental results obtained for this organic vapor are compared with correlations from the literature and available loss data. Full article

The development of compact high-speed low-pressure turbines with high efficiencies requires the characterization of the secondary flow structures and the interaction of cavity purge and leakage flows with the mainstream. During the SPLEEN project funded by the European Union’s Horizon 2020, the von Karman Institute and Safran Aircraft Engines performed detailed measurements of low-pressure turbines in engine-realistic conditions (i.e., low Reynolds and high exit Mach numbers considering background turbulence, wakes, row interactions, and leakages). The SPLEEN project is thus a fundamental contribution to the progress of high-speed low-pressure turbines by delivering unique experimental databases, essential to characterize the time-resolved 3D turbine flow, and new critical knowledge to mature the design of 3D technological effects. Being able to simulate the flow and associated losses in such a configuration is both challenging and of paramount importance to help the understanding of the flow physics complementing experimental measurements. This paper focuses on the high-fidelity numerical simulation of one of the SPLEEN configuration consisting of a linear blade cascade. The objective is to provide a validated numerical setup in terms of computational domain, boundary conditions, mesh resolution and numerical scheme to reproduce the experimental results. By mean of wall-resolved large-eddy simulations, the design point characterized by an exit Mach number of 0.9 and an exit Reynolds number of 70,000 with a turbulence level of 2.4% is investigated for the baseline configuration without purge and without wake generator. The results show that the considered computational domain and the associated inlet total pressure profile play a critical role on the development of secondary flows. The isentropic Mach number distribution around the blade is shown to be robust to the mesh and numerical scheme. The development of the wake and secondary flow fields are drastically influenced by the mesh resolution and numerical scheme, impacting the resulting losses. Full article

In wind tunnel tests for aerospace and bridge engineering, the accurate prediction of Mach number remains a core challenge to ensure the reliability of airflow dynamics characterization. Pure data-driven models often fail to meet high-precision prediction requirements due to the lack of physical mechanism constraints and insufficient generalization capability. This paper proposes a physical information-based long short-term memory network (P-LSTM), which constructs a physical loss function by embedding isentropic flow equations from gas dynamics, thereby constraining the Mach number prediction solution space within the physically feasible domain. This approach effectively balances the neural network’s ability to capture temporal features with the interpretability of physical mechanisms. Aiming at the scarcity of data in new wind tunnel scenarios, an adaptive weight transfer learning method (AWTL) is further proposed, realizing efficient knowledge transfer across different-scale wind tunnels via cross-domain data calibration, adaptive source-domain weight reweighting, and target-domain fine-tuning. Experimental results show that the P-LSTM method achieves a 50.65–62.54% reduction in RMSE, 48.00–54.05% in MAE, and 47.88–73.68% in MD compared with traditional LSTM for Mach number prediction in the 0.6 m continuous wind tunnel flow field. The AWTL model also outperforms the direct training model significantly in the 2.4 m continuous wind tunnel, with RMSE, MAE, and MD reduced by 85.26%, 95.12%, and 71.14%, respectively. These results validate that the proposed models achieve high-precision Mach number prediction with strong generalization capability. Full article

This work analyzes the aero-elastic oscillations of cantilever beams reinforced by carbon nanotubes (CNTs). Four different distributions of single-walled CNTs are assumed as the reinforcing phase, in the thickness direction of the polymeric matrix. A modified third-order piston theory is used as an accurate tool to model the supersonic air flow, rather than a first-order piston theory. The nonlinear dynamic equation governing the problem accounts for Von Kármán-type nonlinearities, and it is derived from Hamilton’s principle. Then, the Galerkin decomposition technique is adopted to discretize the nonlinear partial differential equation into a nonlinear ordinary differential equation. This is solved analytically according to a multiple time scale method. A comprehensive parametric analysis was conducted to assess the influence of CNT volume fraction, beam slenderness, Mach number, and thickness ratio on the fundamental frequency and lateral dynamic deflection. Results indicate that FG-X reinforcement yields the highest frequency response and lateral deflection, followed by UD and FG-A patterns, whereas FG-O consistently exhibits the lowest performance metrics. An increase in CNT volume fraction and a reduction in slenderness ratio enhance the system’s stiffness and frequency response up to a critical threshold, beyond which a damped beating phenomenon emerges. Moreover, higher Mach numbers and greater thickness ratios significantly amplify both frequency response and lateral deflections, although damping rates tend to decrease. These findings provide valuable insights into the optimization of CNTR composite structures for advanced aeroelastic applications under supersonic conditions, as useful for many engineering applications. Full article

Landscape metrics are one of the main tools for studying changes in the landscape and the ecological structure of the territory. However, the calculation of some metrics yields significantly different values depending on the configuration of the “Cell neighborhood” (CN) used. This makes the comparison of different analysis results often impossible. In fact, although the metrics are defined in the same way for all software, the choice of a CN with four cells, which includes only the elements on the same row or column, or eight cells, which also includes the cells on the diagonal, changes their value. QGIS’ LecoS plugin uses the value eight while GRASS’ r.li module uses the value four and these values are not modifiable by users. A previous study has shown how the value of the CN used for the calculation of landscape metrics is rarely explicit in scientific publications and its value cannot always be deduced from the indication of the software used. The difference in value for the same metric depends on the CN configuration and on the compactness of the patches, which can be expressed through the Aggregation Index (AI), of the investigated landscape. The scope of this paper is to explore the possibility of deriving an analytical relationship between the Aggregation Index and the variation in the values of some landscape metrics as the CN varies. The numerical experiments carried out in this research demonstrate that it is possible to estimate the differences in landscape metrics evaluated with a four and eight CN configuration using polynomials only for few metrics and only for some intervals of AI values. This analysis combines different Free and Open Source Software (FOSS) systems: GRASS GIS for the creation of test maps and R landscapemetrics package for the calculation of landscape metrics and the successive statistical analysis. Full article