Space Method Research Articles

Different Imaging Evaluating Performances Between Glymphatic System and Motor Symptoms and Levodopa Responsiveness of Parkinson Disease.

Parkinson disease (PD) is defined by its unique motor symptoms, where responsiveness to levodopa (L-DOPA) is fundamental for management. Recent research has highlighted a significant relationship between PD symptoms and glymphatic dysfunction. This study endeavors to clarify the connection between glymphatic system functionality and initial motor symptoms in PD, utilizing imaging biomarkers to determine its predictive capacity for L-DOPA responsiveness (LR). Retrospective study of 86 PD patients with 3.0-T MRI scans (July 2019 to March 2021), assessing the diffusion tensor image analysis along the perivascular space (DTI-ALPS) methods, enlarged perivascular spaces (ePVSs) load, and choroid plexus volume (CPV). Analyzed metrics versus the third part of the Unified Parkinson Disease Rating Scale (UPDRSIII) scores and %LR using linear regression, creating a %LR prediction model for the L-DOPA challenge. Explored relationships with age, sex, Hoehn and Yahr stage, Montreal Cognitive Assessment scores, and Mini-Mental State Examination score. Examined DTI-ALPS index, ePVSs, and CPV interrelations. Pre-L-DOPA, UPDRSIII inversely correlated with DTI-ALPS index (P=0.049), positively with bilateral basal ganglia ePVSs (P<0.001). Age-adjusted BG-ePVSs-UPDRSIII link (P<0.001). Post-L-DOPA, UPDRSIII correlated similarly and CPV was positive. %LR positively linked to DTI-ALPS index (P<0.001), negatively to BG-ePVSs (P=0.04), CPV (P<0.001). Adjusted %LR-DTI-ALPS index positive (P=0.005), %LR-CPV negative (P=0.04). DTI-ALPS index, CPV predicted LCT outcomes (%LR ≥33%) with area under the curves 0.78, 0.79; accuracies 86.01%, 81.4%. The combined model area under the curve is 0.82, with an accuracy of 87.2%. Significant linear correlations were observed (CPV-DTI-ALPS, CPV-ePVSs, DTI-ALPS-ePVSs). A study affirms the link between glymphatic impairment, motor symptoms, and L-DOPA responses in PD. As glymphatic function declines, symptoms worsen, and L-DOPA effectiveness diminishes. The DTI-ALPS index and CPV emerge as potential predictors of PD patient LCT outcomes.

Commutative Quaternion Algebra with Quaternion Fourier Transform-Based Alpha-Rooting Color Image Enhancement

In this paper, we describe the associative and commutative algebra or the (2,2)-model of quaternions with application in color image enhancement. The method of alpha-rooting, which is based on the 2D quaternion discrete Fourier transform (QDFT) is considered. In the (2,2)-model, the aperiodic convolution of quaternion signals can be calculated by the product of their QDFTs. The concept of linear convolution is simple, that is, it is unique, and the reduction of this operation to the multiplication in the frequency domain makes this model very attractive for processing color images. Note that in the traditional quaternion algebra, which is not commutative, the convolution can be chosen in many different ways, and the number of possible QDFTs is infinite. And most importantly, the main property of the traditional Fourier transform that states that the aperiodic convolution is the product of the transform in the frequency domain is not valid. We describe the main property of the (2,2)-model of quaternions, the quaternion exponential functions and convolution. Three methods of alpha-rooting based on the 2D QDFT are presented, and illustrative examples on color image enhancement are given. The image enhancement measures to estimate the quality of the color images are described. Examples of the alpha-rooting enhancement on different color images are given and analyzed with the known histogram equalization and Retinex algorithms. Our experimental results show that the alpha-rooting method in the quaternion space is one of the most effective methods of color image enhancement. Quaternions allow all colors in each pixel to be processed as a whole, rather than individually as is done in traditional processing methods.

Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learning

Abstract In this study, computer vision and machine learning techniques are used to develop an automatic optimization method for low-carbon building landscape space. Firstly, the semantic segmentation of landscape images is carried out using U-Net network to realize the automatic extraction of key landscape features. Then, using the segmentation results, a multi-objective optimization algorithm is developed. The effectiveness of the proposed method is verified by simulation experiments, which not only significantly improves the efficiency and accuracy of landscape space optimization, but also provides valuable optimization suggestions for designers.

Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R2 values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture.

Copula-based reliability estimation of parallel-series system in the multicomponent stress–strength dependent model

Abstract Reliability analysis of stress–strength models usually assumes that the stress and strength variables are independent. However, in numerous real-world scenarios, stress and strength variables exhibit dependence. This paper investigates the reliability estimation in a multicomponent stress–strength model for parallel-series system assuming that the dependence between stress and strength is based on the Clayton copula. The estimators for the unknown parameters and system reliability are derived using the two-step maximum likelihood estimation and the maximum product spacing methods. Additionally, confidence intervals are constructed by utilizing asymptotically normal distribution theory and bootstrap method. Furthermore, Monte Carlo simulations are conducted to compare the effectiveness of the proposed inference methods. Finally, a real dataset is analyzed for illustrative purposes.

MIM waveguide structure consisting of a baffle and a ring cavity embedded in a square metal layer

This paper presents a design for a tunable plasmon refractive index nanosensor based on Fano resonance. The proposed structure comprises a metal–insulator–metal waveguide featuring a silver baffle and a ring cavity embedded in a square (RCSQ). This innovative structural configuration effectively excites dual Fano resonances by leveraging the unique properties of its constituent elements and geometry. The transmission spectrum and electric field at the resonance were simulated using the finite element method (FEM) in two-dimensional space, providing an in-depth explanation of the formation mechanism of the Fano resonance, as well as analyzing how various structural parameters influence sensor characteristics. Simulation results indicate that the Fano peak can be readily tuned by adjusting cavity parameters and refractive indices. Common tuning of the Fano resonance is achievable through modifications to the proportionality constant of the RCSQ. Finally, we optimize and analyze the sensor characteristics of this structure, achieving sensitivity levels up to 2750 nm/RIU with a figure of merit (FOM) of 3.48×104. Consequently, this structure holds significant potential for applications in optical nanosensors.

Associations of ventriculomegaly and white matter hyperintensities with glymphatic dysfunction in idiopathic normal pressure hydrocephalus.

To investigate glymphatic function in idiopathic normal pressure hydrocephalus (iNPH) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and to explore the associations of ALPS index with ventriculomegaly and white matter hyperintensities (WMH). This study included 41 patients with iNPH and 40 age- and sex-matched normal controls (NCs). All participants underwent brain MRI. Based on DTI, we then calculated the ALPS index to obtain the water diffusivity along the perivascular space. Ventricular volume and WMH were also determined. Differences in the diffusivities and ALPS indexes between the iNPH and NC groups were investigated; associations of the DTI-ALPS index with ventriculomegaly and WMH were analysed. Patients with iNPH had a lower ALPS index than NCs (p < 0.001). The ALPS index was significantly correlated with the normalised ventricular volume (r = -0.446, p = 0.004), but not with total WMH volume (r = -0.246, p = 0.126). Further regression analyses indicated that the reduced ALPS index was associated with increased ventricular volume (β = -7.158, p = 0.016), but not with normalised WMH volume (β = -2.796, p = 0.161). The receiver operating characteristic analysis demonstrated the ALPS index's excellent diagnostic performance for iNPH (the optimal cut-off point = 1.322; sensitivity, 100.0%; specificity, 87.5%; AUC = 0.980). Patients with iNPH had a lower ALPS index, which may suggest impaired glymphatic function. This study demonstrated an association of DTI-ALPS index with ventriculomegaly, but not WMH in patients with iNPH. Question Glymphatic dysfunction is crucial in idiopathic normal pressure hydrocephalus (iNPH) development, yet its associations with neuroimaging features remains unclear. Findings Diffusion tensor image analysis along the perivascular space (DTI-ALPS) revealed a reduced ALPS index in idiopathic normal pressure hydrocephalus, negatively correlating with ventricular volume. Clinical relevance DTI-ALPS enables non-invasive assessment of glymphatic function and its relationship with neuroimaging characteristics in idiopathic normal pressure hydrocephalus, facilitating the investigation of glymphatic dysfunction in iNPH pathophysiology.

Integration Design of Chinese Painting and Architectural Space Based on Virtual Reality Technology

Chinese painting elements are important elements in modern architectural design, which facilitate the integration of the entire building into the environment and present the scroll style spatial hierarchy of Chinese painting. Therefore, it is necessary to research on the integration design method of Chinese painting and architectural space based on virtual reality technology. First, the architectural space and landscape design plan images integrated with Chinese painting are imported into VTK software. Second, the median filtering method is used to filter the imported images, and a secure multi-party calculation method is used to construct building and landscape models, thus generating a database of architectural spatial layout element models. Then, for the 3D models in the database, Chinese painting elements are applied to architectural space design. Model rendering is achieved through lighting, color, atomization, and other processing to enhance the visual appeal of the models. Next, using the rendered 3D models of buildings and landscapes, a virtual interactive architectural spatial environment background is achieved through the freehand artistic conception concept and framing techniques in Chinese painting. Finally, we design a multi-sensory visual interactive interface based on virtual reality to display the results of the architectural spatial environment scenery to users. The experimental results show that this method has high modeling and rendering accuracy, and can achieve landscape layout browsing from different angles, improving user experience.

Dust removal method for open space of fully mechanized mining face: A cutting area pressure balance method

Dust removal method for open space of fully mechanized mining face: A cutting area pressure balance method

Characterizations of Toeplitz Algebras and Operators with Series of Symbols and Essential Commutants

In the perfect method of RaffaelHagger [22] we show the characterization of the Toeplitz algebra, that generated by Toeplitz operators with series of bounded symbols on the Fock space 𝐹1+𝜖1+𝜖 and other spaces. We show that the Toeplitz algebra coincides with each of the algebras generated by band-dominated, sufficiently localized and weakly localized operators, respectively. We determine its essential commutant and its essential bicommutant with respect to Toeplitz operators of series of symbols. For 𝜖=1see Xia. However, Xia's ideas are mostly connected to Hilbert space theory and methods which are not applicable for 𝜖≠1. We completely use the result of Fulsche to generalize Xia's theorems under the light of [22].

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system. Nevertheless, manual examination is sometimes arduous, time-consuming, and prone to errors. Deep learning-based methods have recently demonstrated encouraging results in several areas, such as image categorization and natural language mining. The majority of deep learning techniques developed for medical image analysis rely on convolutional modules to extract the inherent structure of images within a certain local receptive field. Furthermore, transformer-based models have been utilized to tackle medical image processing problems by capitalizing on global connections among distant pixels in the images. Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. This model is designed to accurately identify and classify diabetic retinopathy lesions displayed in fundus images. Furthermore, the vision mamba component incorporates the bidirectional state space method and positional embedding to enable the location sensitivity of visual data samples and meet the conditions for global relationship context. An evaluation of the suggested method was carried out by comparison experiments between state-of-the-art algorithms and the proposed methodology. Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. Hence, the suggested approach may be regarded as a helpful tool for therapeutic processes.

Controllable design and evaluation of Voronoi-based irregular porous scaffolds for bone restoration

The Voronoi-based irregular porous scaffolds (VIPS) for bone restoration often suffer from uncontrollable structural characteristics and mechanical properties. In this study, a method using spherical spaces to control the randomness of the seed points distribution was proposed to achieve controllable design of the VIPS. The irregularity ε, porosity P and average pore size D̅ of the VIPS were calculated by parametric design software Grasshopper, and the apparent elastic modulus E, equivalent compressive strength S of the VIPS were obtained by quasi-static compressive test. The functional relationships between the design parameters (spherical radius to lattice spacing ratio R/a, seed points number N and pore size coefficient K) and ε, P, D̅, E, S were established through statistical analyses. The results indicated that the VIPS designed based on the spherical space method had high controllability. Design parameters in the range of R/a∈0,0.5, N∈64,1000, and K∈0.4,0.9 allow control over the irregularity, porosity, average pore size, elastic modulus, and compressive strength of VIPS in the range of 0–0.32, 37%–96%, 200–1200 μm, 0.8–5.4GPa, 15–240MPa, respectively, and all of which could meet the structural and mechanical requirements of the VIPS for bone restoration.

Modeling and performance analysis of MWCNT bundle interconnect based on state space method

Abstract To efficiently analyze the time-domain and frequency-domain characteristics of novel interconnects, this paper focuses on multi-walled carbon nanotube (MWCNT) bundle interconnects as the primary subject of research and proposes a state space model that is applicable to carbon nanotube interconnects, graphene nanoribbon interconnects and various improved structures. Using the state space method, the equivalent circuit model of single MWCNT bundle interconnect that primarily adopts voltage-mode signaling (VMS) and current-mode signaling (CMS) is solved, respectively. Additionally, the transient output responses and frequency responses of the interconnect at local, intermediate, and global levels for 14 nm technology node are analyzed to evaluate the delay and 3 dB bandwidth performance for different modes. And the noise peak and power dissipation are also discussed. The proposed state space method is found to be highly accurate and effective by comparing with the simulation results from the SPICE environment in ADS and ABCD parameter matrix method. Furthermore, the method presented in this paper is suitable for solving multi-line coupled interconnects and can provide some meaningful theoretical guidance for analyzing the electrical characteristics of novel interconnects.

Nematic versus Kekulé Phases in Twisted Bilayer Graphene under Hydrostatic Pressure.

We address the precise determination of the phase diagram of magic angle twisted bilayer graphene under hydrostatic pressure within a self-consistent Hartree-Fock method in real space, including all the remote bands of the system. We further present a novel algorithm that maps the full real-space density matrix to a 4×4 density matrix based on a SU(4) symmetry of sublattice and valley degrees of freedom. We find a quantum critical point between a nematic and a Kekulé phase, and show also that our microscopic approach displays a strong particle-hole asymmetry in the weak coupling regime. We arrive then at the prediction that the superconductivity should be Ising-like in the hole-doped nematic regime, with spin-valley locking, and spin-triplet in the electron-doped regime.

Glymphatic system dysfunction in nondialysis-dependent ESRD patients with diabetic kidney disease: associations with clinical characteristics and cognitive function

Objectives To investigate glymphatic function in nondialysis-dependent ESRD (ND-ESRD) patients with diabetic kidney disease (DKD) or non-DKD using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and choroid plexus (CP) volume to explore the associations among DTI-ALPS index, CP volume, clinical characteristics, and cognitive function. Methods 25 DKD patients, 25 non-DKD patients, and 25 healthy control (HC) were included in this study. All participants underwent cerebral MRI and cognitive assessments. Bilateral DTI-ALPS index and CP volume were calculated and were compared among three groups. Correlations among the DTI-ALPS index, CP volume, clinical characteristics, and cognitive function were analyzed. Results DKD patients had significantly lower left DTI-ALPS index (p = 0.001) and mean DTI-ALPS index (p = 0.003) than non-DKD patients. In ND-ESRD patients, there was a significant positive correlation between the left DTI-ALPS index and phosphorus (r = 0.377, p = 0.007) and attention score (r = 0.434, p = 0.002). In DKD group, the mean DTI-ALPS was positively correlated with total cholesterol (r = 0.631, p = 0.001). In non-DKD group, there was a significant positive correlation between the left DTI-ALPS index and phosphorus (r = 0.696, p < 0.001). Both DKD and non-DKD patients exhibited significant higher CP volume than HC group. In non-DKD group, CP volume was negatively correlated with mean DTI-ALPS (r = −0.535, p = 0.006) and attention score of MoCA (r = −0.538, p = 0.006). Conclusions Glymphatic system dysfunction may contribute to the pathogenesis of clinical and cognitive impairment in ND-ESRD patients especially with DKD.

Optimization Problem for Probabilistic Time Intervals of Quasi-Deterministic Output and Self-Similar Input Data Packet Flow in Telecommunication Networks

Introduction. When managing traffic at the packet level in modern telecommunication networks, it is proposed to use methods that transform a self-similar stochastic packet flow into a quasi-deterministic one. To do this, it is required to apply complex probabilistic laws of distribution of self-similar flows. From the literature, methods of balancing the network load are known, which, with the problem indicated above, contribute to increasing the efficiency of telecommunication systems. However, there is no strictly mathematical solution to find out the optimal probabilistic characteristics of the output flow, based on the input flow. The presented research is intended to fill this gap. Its objective is to create a method for determining the optimal probabilistic characteristics of the packet flow, using the minimum value of the proximity measure of the self-similar input and quasi-deterministic output flows.Materials and Methods. To solve the research problem, the parameters of the output flow distribution were selected so that the approximation function was close to 𝛿𝛿-function. The Kullback-Leibler divergence was used as a proximity measure of the input and output distributions of time intervals. Methods of set theory, metric spaces, multidimensional optimization, and teletraffic were used. The solution algorithm included minimization of the Kullback-Leibler divergence and the limit passage to 𝛿𝛿-function.Results. A probability distribution is shown — an approximation of 𝛿𝛿-function, which maintains the equality of time intervals of a quasi-deterministic output packet flow. A method for transforming a self-similar input flow into a quasideterministic output flow is presented. The Kullback–Leibler divergence was used as a measure of their proximity. The minimum of the Kullback-Leibler divergence between the input and output flows with a normal distribution was achieved in the case of equality of the mathematical expectations of these flows. Using the passage to the limit, it has been established that time interval T between packets of the quasi-deterministic output flow must be equal to the mathematical expectation of the time intervals between packets of the input self-similar flow. To obtain a quasi-deterministic flow, the passage to the limit is performed for the found value of the mathematical expectation at σ → 0.Discussion and Conclusion. The application of this method will reduce the negative impact of self-similarity of network traffic on the efficiency of the telecommunication network. The use of quasi-deterministic flows makes it possible to predict the load of network resources, which can be the basis for improving the quality of user service. Two difficulties associated with calculations and practical implementation of the solution are eliminated. Firstly, it is difficult to use the delta function as a function of the output flow distribution density. Secondly, there are no ideal deterministic flows in the operation of telecommunication networks. The proposed method has great potential in the design and optimization of communication networks.

Axisymmetric Free Vibration of Functionally Graded Piezoelectric Circular Plates

An analytical solution is presented for axisymmetric free vibration analysis of a functionally graded piezoelectric circular plate on the basis of the three-dimensional elastic theory of piezoelectric materials. The material properties are assumed to follow an exponential law distribution through the thickness of the circular plate. The state space equations for the free vibration behavior of the functionally graded piezoelectric circular plate are developed based on the state space method. The finite Hankel transform is utilized to obtain an ordinary differential equation with variable coefficients. By virtue of the proposed exponential law model, we have ordinary differential equations with constant coefficients. Then, the free vibration behaviors of the functionally graded piezoelectric circular plate with two kinds of boundary conditions are investigated. Some numerical examples are given to validate the accuracy and stability of the present model. The influences of the exponential factor and thickness-to-span ratio on the natural frequency of the functionally graded piezoelectric circular plate, constrained by different boundary conditions, are discussed in detail.

A CsWRKY48 Gene from Tea Plants Intercropped with Chinese Chestnut Plays an Important Role in Resistance to Biotic and Abiotic Stresses.

Tea plant (Camellia sinensis) is an important horticultural crop. The quality and productivity of tea plants is always threatened by various adverse environmental factors. Numerous studies have shown that intercropping tea plants with other plants can greatly improve the quality of their products. The intercropping system of Chinese chestnut (Castanea mollissima) and tea plants is an agricultural planting model in which the two species are grown on the same piece of land following a specific spacing and cultivation method. Based on a comparative transcriptome analysis between Chinese chestnut tea intercropped plantations and a pure tea plantation, it was found that the expression levels of the WRKY genes were significantly upregulated under the intercropping pattern. In this study, we cloned a candidate gene, CsWRKY48, and verified its functions in tobacco (Nicotiana tabacum) via heterologous transformation. The contents of protective enzyme activities and osmoregulatory substances were significantly increased, and the trichomes length and density were improved in the transgenic tobacco lines. This phenotype offered an enhanced resistance to both low temperatures and aphids for transgenic lines overexpressing CsWRKY48. Further analysis indicated that the CsWRKY48 transcription factor might interact with other regulators, such as CBF, ERF, MYC, and MYB, to enhance the resistance of tea plants to biotic and abiotic stresses. These findings not only confirm the elevated resistance of tea plants under intercropping, but also indicate a potential regulatory network mediated by the WRKY transcription factor.

Reconstruction of electron velocity distribution function and Gibbs entropy from electron cyclotron emission in magnetized plasmas

Abstract We propose a method for reconstructing the fluctuation components of the electron velocity distribution function, and the electron entropy, which is a functional of the fluctuation components of the electron velocity distribution function, using the harmonic spectrum from pure X-mode electron cyclotron emission (ECE) in optically thin plasmas. This formulation employs the maximum entropy method in velocity space using the Hankel transform, which converts from v⊥ space to p space (where, v⊥ and p are the electron velocity perpendicular to the background magnetic field and the index of the wavenumber in velocity space). Numerical tests validated the effectiveness of the proposed method, which is applicable across a wide range of magnetized plasma conditions, including conditions with both non-relativistic and relativistic electrons, except in cases of harmonic overlap or under optically thick conditions. Notably, this method does not require radiometer calibration for ECE measurements. This method fascilitates the experimental evaluation of electron entropy transport in fusion plasma experiments. Moreover, when combined with measurements in k-space (spatial distribution), this approach enables entropy distribution acquisition in phase space (k-p space).

Adaptive high flow oxygen therapy: New concept

Background High-flow oxygen therapy (HFOT) is increasingly utilized in clinical settings due to its potential to improve oxygenation, patient comfort and possibly outcomes in different respiratory failure states. The potential benefits of HFOT include matching the patients’ flow to reduce the work of breathing, creating airway pressure, humidification, and reducing dead space. However, it has some shortcomings including the difficulty of setting the flow to match patients’ efforts, the continuous flow does not represent normal physiologic spontaneous breathing, and the lack of traditional airway pressure monitoring. We are testing a new adaptive high-flow oxygen delivery technique “Auto Positive Airway Pressure” (Auto-PAP) where the flow delivered from the ventilator adapts to patients’ efforts taking into consideration the patients’ dead space Methods A bench study using ASL 5000 simulator, we created a single-compartment active model of a male with IBW 70 kg, with compliance of 40 ml/cmH20 and resistance of 10 cmH2o/L/s. The respiratory rate was set at 20 bpm, with inspiratory time of 1 second. Muscle pressure (Pmus) was gradually increased by increments of 5 cmH2O from 5 to 50. Adaptive high flow mode (Auto PAP) using a Bellavista 1000e ventilator (Zoll MA, USA) using a large bore nasal cannula to an adult-sized mannequin nose that was connected to the lung simulator. Pearson correlation coefficient was used in correlating the Pmus to the maximum, mean flow and pressure, and the simulator to the ventilator flow. Results There was significant strong correlation between the Pmus and the maximum flow R: 0.949, CI (0.794, 0.988) P &lt; 0.001, and mean flow R: 0.955, P &lt; 0.001, CI (0.816, 0.989). There was a significant strong correlation with Pmus and the maximum pressure R: 0.972, P &lt; 0.001, CI (0.883, 0.993) and mean pressure R: 0.942, P &lt; 0.001, CI (0.768, 0.986). There was significant strong correlation between the simulator and the ventilator flow R: 0.961, P &lt; 0.001. There was significant strong correlation between the simulator and the ventilator mean pressure R: 0.951, P &lt; 0.001, CI (0.799, 0.988). There was significant strong correlation between the mean flow from the ventilator to the mean airway pressure measured at the simulator R: 0.936, P &lt; 0.001 CI (0.747, 0.985). Conclusion Results suggest a significant correlation between the flow and pressure from adaptive HFOT and the patient muscle effort, indicating that the flow and pressure increase in response to the patients’ effort. This may reduce respiratory muscle workload compared to traditional high flow oxygen therapies and reduce the need for multiple manual adjustment of the flow. These findings underscore the potential of this technology to enhance respiratory support while minimizing patient effort. Further research is warranted to validate these findings in real patient cohorts in diverse clinical scenarios. Keywords: High flow oxygen therapy, mean flow, mean pressure