Mean Histogram Research Articles

Ai-powered Levenberg-Marquardt neural networks for implementation of novel flux conditions in Carreau nanomaterial stagnation-point flow considering magnetic field and heat source effects

The knacks of artificial intelligence (AI) to manage complex databases effectively transform various fields by enabling sophisticated data analysis and predictive modeling. The presented research work introduces to analyze the flow of Carreau nanofluids focusing on heat and mass transfer mechanism which involves Brownian motion and thermophoresis phenomena based on neural networks backpropagated with Levenberg-Marquardt algorithm (NNs-LMA). The initial nonlinear coupled partial differential equations (PDEs) that describe the Carreau nanofluids model (CNFM) are reformulated into an equivalent system of nonlinear ordinary differential equations (ODEs) by using similarity variables. The governing ODEs are resolved by utilizing the state-of-the-art Adam numerical approach by varying different parameters including Prandtl number Pr from 0.71 to 0.9, stretching parameter m, magnetic parameter M and Brownian motion parameter Nb from 0.4 to 2.4 across multiple CNFM scenarios, leading to the creation of data set for the proposed NNs-LMA. In order to determine the approximate solution for different cases and confirm the accuracy of the suggested NNs-LMA, the training, testing and validation procedures of NNs-LMA are carried out. The effectiveness of the designed procedure NNs-LMA in solving the CNFM is validated through mean square error (MSE) evaluation, histogram analysis and regression studies. Assessing absolute error for concentration characteristics Brownian motion parameter Nb and stretching parameter m ranging from 10-4 to 10-7 and 10-3 to 10-6, respectively. The validity and accuracy of the designed procedure NNs-LMA is reinforced by the error analysis presenting a consistent range of 10-4 to 10-8 between the proposed methodology and reference results. It is revealed that velocity profile shows a decreasing trend against the higher values of magnetic parameter while increasing behavior is visualized for increased values of stretching parameter.

A besyian regularisation neural network approach for hepatitis B virus spread prediction and immune system therapy model

The primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.

Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis.

This study aims to explore the relationship between apparent diffusion coefficient (ADC) and fractional-order calculus (FROC)-specific parameters with prognostic indicators and Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation status in rectal cancer. One hundred fifty-eight patients with rectal cancer were retrospectively enrolled. Histogram measurements of ADC, diffusion coefficient (D), intravoxel diffusion heterogeneity (β), and a microstructural quantity (μ) were estimated for the whole-tumor volume. The relationships between histogram measurements and prognostic indicators were evaluated. The efficacy of histogram measurements, both conducted singly and in conjunction, for evaluating different KRAS mutation statuses was also assessed. The performance of mean and median histogram measurements in evaluating various KRAS mutation statuses was assessed using Receiver Operating Characteristic (ROC) curve analysis. A p-value of less than 0.05 was considered statistically significant. The histogram measurements of ADC, D, β, and μ differed significantly between well-moderately differentiated groups and poorly differentiated groups, T1-2 and T3-4 subgroups, lymph node metastasis (LNM)-negative and LNM-positive subgroups, extranodal extension (ENE)-negative and ENE-positive subgroups, tumor deposit (TD)-negative and TD-positive subgroups, and lymphovascular invasion (LVI)-negative and LVI-positive subgroups. The combination of Dmean, βmean, and μmean achieved the highest performance [The area under the ROC curve (AUC) = 0.904] in evaluating the KRAS mutation status. When assessing parameters from the FROC model as potential biomarkers through histograms, they surpass traditional ADC values in distinguishing prognostic indicators and determining KRAS mutation status in rectal cancer.

The relationship between the collagen score at the anastomotic site of esophageal squamous cell carcinoma and anastomotic leakage.

Anastomotic leakage (AL) has always been one of the most serious complications of esophagectomy with gastric conduit reconstruction. There are many strong risk factors for AL in clinical practice. Notably, the tension at the esophagogastric anastomosis and the blood supply to the gastric conduit directly affect the integrity of the anastomosis. However, there has been a lack of quantitative research on the tension and blood supply of the gastric conduit. Changes in extracellular matrix collagen reflect tension and blood supply, which affect the quality of the anastomosis. This study aimed to establish a quantitative collagen score to describe changes in the collagen structure in the extracellular matrix and to identify patients at high risk of postoperative AL. A retrospective study of 213 patients was conducted. Clinical and pathological data were collected at baseline. Optical imaging of the "donut" specimen at the anastomotic gastric end and collagen feature extraction were performed. Least absolute shrinkage and selection operator (LASSO) regression models were used to select the significant collagen features, compute collagen scores, and validate the predictive efficacy of the collagen scores for ALs. LASSO regression analysis revealed three collagen-related parameters in the gastric donuts: histogram mean, histogram variance, and histogram energy. Based on this analysis, we established a formula to calculate the collagen score. The results of the univariate analysis revealed significant differences in the preoperative low albumin values (P=0.002) and collagen scores between the AL and non-AL groups (P=0.001), while the results of the multivariate analysis revealed significant differences in the collagen scores between the AL and non-AL groups (P=0.002). The areas under the curve (AUCs) of the experimental and validation cohorts were 0.978 [95% confidence interval (CI): 0.931-0.996] and 0.900 (95% CI: 0.824-0.951), respectively. The collagen score established herein was shown to be related to AL and can be used to predict AL in patients who underwent esophagectomy.

Modeling of artificial neural network to analyze heat and mass transfer of ternary hybrid nanofluid between two parallel plates with inclined magnetic field

The applications of the artificial neural network (ANN) have become the focus of interest of researchers due to their convenience for accurate modeling, simulation, and efficiency of evaluation. The primary objective of this study is to investigate the characteristics of heat and mass transfer of the ternary hybrid nanofluid flow (THNF), which is squeezed between two parallel plates, using ANN. The plate which lies on x-axis is stretching while the upper plate (UP) can move in upward and downward direction. An inclined magnetic field (MF) is also applied to the lower plate (LP). A system of partial differential equations of flow, energy, and mass transfer is used to simulate the THNF, which is then condensed using similarity substitution to a collection of ordinary differential equations (ODEs). Using the differential transform method (DTM), the resultant nonlinear ODEs in dimensionless form are further solved. The influence of the different varying physical parameters on velocity, temperature, and concentration is graphically presented and discussed. It becomes apparent that the velocity, heat, and mass transfer in squeezing flows are significantly impacted by the inclination angle of the applied MF. To demonstrate the validity of the study, the numerical findings of the Nusselt and the Sherwood numbers are provided. For the accuracy of the used approach, DTM results are compared with results from the numerical approach. The novelty of the current work is to train the neural network with the Levenberg–Marquardt algorithm in the model. To get the estimated output of the model, different scenarios are set for training, testing, and validation. The analysis is done by mean square error (MSE), histogram, fitness curve, and regression (RG). The created ANN model is shown to be reliable due to its exceptional accuracy throughout the training, validation, and testing stages.

Intelligent neural computing to investigate the heat and mass transmission in nanofluidic system between two rotating permeable disks: Supervised learning mechanism

The prime objective of the present study is to investigate the effectiveness and accuracy of a single-trained artificial neural network. The Levenberg-Marquardt Backpropagated networks are tested for heat and mass transmission in magnetized hybrid nanofluid flow between the rotating permeable system. The reference data to generate different cases for distinct scenarios has been obtained using the Adams method in Mathematica using the ND-Solver function. Additionally, the system of highly nonlinear PDE's is achieved and transformed into ODE's. The effect of body forces such as thermophoresis particle diffusion and Brownian motion are incorporated in the hybrid nanofluid system. Configuration under observation is permeable and under constant impact of the magnetic field. Rosseland thermal radiation approximation relation is utilized to investigate the linear impact of radiation on velocity and temperature profile. The accuracy and effectiveness of the proposed ANNLMB are depicted with performance demonstrations. Mean square error, histogram error, and regression plots are generated for all the scenarios to discuss the varying impact of different key study parameters on the performance of the proposed ANNLMB. Furthermore, the generated reference data is distributed in the following manner 82 %, 9 %, and 9 % for training, testing, and validation, respectively.

Histogram-based global thresholding method for image binarization

One of the most fundamental issues in image processing is the thresholding (binarization) method. This method is generally used for segmenting regions with different homogeneity in grayscale images. In other words, it performs clustering based on the intensity levels of pixels in an image histogram. This paper presents a new and effective approach to the global thresholding method of grayscale images. In the proposed method, alpha and beta regions are determined using the mean and standard deviation values of an image histogram. The optimum threshold value is obtained by calculating the average of gray-scale values of the alpha and beta regions. The experiments were carried out on three different image sets to demonstrate the effectiveness of the thresholding method. The result of experimental studies show that the proposed method achieves promising performance compared to many traditional, state-of-the-art thresholding and document binarization methods performed in H-DIBCO'14 (Document Image Binarization Competition), Human HT29 colon-cancer cells (BBBC008) and C. elegans live/dead assay (BBBC010) datasets based on various evaluation criteria.

Numerical paradigm to explore the chemically reacting Williamson nanofluid flow with the influence of bioconvection effects using neural networks

This research focuses on using a type of advanced computer program called artificial neural networks (ANNs) using the Levenberg–Marquardt backpropagation technique (LMBPT-NNs) to stimulate the behavior of bioconvective effects on heat and mass transport in non-Newtonian chemically Williamson nanofluid through stretched surface (BEHMT-NNCRWNF-SS). Williamson nanofluid flow refers to specially engineered fluids with nanoparticles dispersed within them and moving when subjected to external forces. Understanding their flow behavior is crucial to effectively use them in various applications, especially in the field of heat and mass transfer and fluid dynamics. Before being solved numerically, a couple of governing partial differential equations are transformed into ordinary differential equations through suitable similarity functions. The finite difference method (FDM) (Lobatto IIIA) is applied for the solution of a nonlinear nanofluidic system by selecting different collocation points. These collocation points play a crucial role in approximating the solution and ensuring accurate results. To address various fluidic problems, it's important to use the appropriate results of FDM to create a reference dataset for the LMBPT-NNs. Statistical analysis should then be performed on the training, testing, and validation of the reference datasets to obtain the optimal scheme for different fluidic flow issues.. The precision of the LMBPT-NNs is checked through ANN tools like mean square error (MSE), regression analysis, curve fitness, and histogram error.

Consistency is key: influence of skull density ratio distribution on the formation of clinically effective lesions and long-term tremor suppression following treatment with MR-guided focused ultrasound.

Skull density ratio (SDR) influences the permeability of the skull to the ultrasound waves used in magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment of tremor. SDR values vary across the skull and the mean value is known to be predictive of sonication thermal increase. The aim of this investigation was to explore the effects of the SDR distribution on clinical outcomes following treatment with MRgFUS. Data from 61 patients with essential or dystonic tremor treated with MRgFUS targeting the ventral intermediate nucleus (Vim) were retrospectively analyzed. Tremor suppression was assessed using the Clinical Rating Scale for Tremor (CRST) and hand tremor score (HTS). Vim ablation volume was measured on the T1-weighted MR image acquired both at 1 day and 12 months after treatment. The numerical distribution of SDR values measured for each element in the ultrasound transducer was quantified by calculating the mean, standard deviation, skewness, entropy, and kurtosis of the SDR histogram. The effect of the SDR metrics on change in CRST and HTS was examined using a linear mixed-effects model. Additionally, the effect of the regional distribution of SDR values was explored in an element-wise analysis between patients with above- and below-average tremor suppression. A significant positive effect was found between SDR kurtosis and improvement in CRST (β = 0.33, p = 0.004) and HTS (β = 0.38, p < 0.001). The effect was found to be significant at 1 month posttreatment (CRST: β = 0.415, p = 0.008; HTS: β = 0.369, p = 0.016), and at the most recent clinical follow-up (CRST: β = 0.395, p < 0.001; HTS: β = 0.386, p < 0.001). One hundred seventy-one significant elements were identified in the element-wise analysis. The mean percentage difference from the mean SDR in these elements was associated with improvement in CRST (β = 0.27, p < 0.008) and HTS (β = 0.27, p < 0.015). Higher SDR kurtosis was associated with increased lesion volume at 12 months (p = 0.040) and less reduction in volume relative to the day-1 lesion volume (p = 0.007). Greater SDR kurtosis was associated with larger, more stable lesions at 12 months posttreatment and increased tremor suppression at long-term follow-up. SDR kurtosis may provide a more meaningful prognostic factor than the mean SDR.

Abstract B125: Neutralizing acidosis with L-DOS47 urease immunoconjugate enhances responses to anti-PD1 checkpoint blockade in a preclinical orthotopic model of pancreatic adenocarcinoma

Abstract Background: Acidosis in the tumor microenvironment is an important immunosuppressive mechanism that leads to tumor growth. We have shown that neutralization of tumor pH using sodium bicarbonate improves responses to immune checkpoint blockade (ICB) in pre-clinical models, but phase I/IIa clinical trials failed due to poor patient compliance. Thus, we aimed to test clinically translatable agents to neutralize tumor acidity and improve cancer outcomes in response to ICB. We tested L-DOS47, a targeted urease immunoconjugate designed to directly neutralize tumor acidity that has been well-tolerated in phase I/IIa trials. The nanobody component of L-DOS47 binds carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6), a cell surface protein highly expressed in gastrointestinal and lung cancers; the urease moiety cleaves endogenous urea into two NH4+ and one CO2 thereby raising local pH. Materials and Methods: Immunohistochemistry screening for CEACAM6 expression and L-DOS47 binding was performed on tumor microarrays from pancreatic ductal adenocarcinoma (PDAC) cases (n=10 plus 2 normal tumor adjacent cases). We established a preclinical in vivo PDAC model by orthotopically inoculating human CEACAM6-expressing KPC961 into immunocompetent mice. Pharmacodynamic studies were performed using chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) to measure tumor extracellular pH (pHe) at baseline (pre-L-DOS47) and at various time points post-L-DOS47 (4, 18, 24, 48, 72 and 96 hours). Mean tumor pHe and histograms of pHe distributions were compared among groups. Additional biological replicates were conducted to assess therapeutic efficacy. For these experiments, mice were randomized, after measurable tumors are established, into groups with equal tumor volume averages to initiate twice-weekly therapies with 300 µg anti-PD1 or 90 µg/kg L-DOS47 monotherapies, 300 µg anti-PD1 + 90 µg/kg L-DOS47 compared to a control group receiving no therapy. Tumor volumes were measured weekly by ultrasound imaging up to 4 weeks. Results: Immunohistochemistry screening of patient tumor microarrays confirmed high CEACAM6 expression and L-DOS47 binding to PDAC tissues. CEST-MRI results showed that L-DOS47 increased tumor pHe from 4h to 96h post-injection; however, pHe increases were only evident in tumors with baseline pHe ≤ 6.60. Efficacy studies in the PDAC model showed that the combination of anti-PD1 + L-DOS47 significantly reduced tumor growth when compared with control, L-DOS47 and anti-PD1 groups at week 3 (p&amp;lt;.0001; &amp;lt;.0001 and 0.0368, respectively) and week 4 (p&amp;lt;.0001; &amp;lt;.0001 and 0.0142, respectively). Conclusion: These studies provide strong evidence that L-DOS47, by neutralizing acidic tumor pH, significantly improves responses to anti-PD1 immunotherapy. Citation Format: Bruna V Jardim-Perassi, Pietro Irrera, Dominique Abrahams, Christopher J Whelan, Matthew S Beatty, Samantha R Byrne, Andrew A Odeja, Dario L Longo, Young Ou, Kim Gaspar, Martin Koebel, Shari A Pilon-Thomas, Christof Boehler, Gabrielle M Siegers, Robert J Gillies, Arig A Ibrahim-Hashim. Neutralizing acidosis with L-DOS47 urease immunoconjugate enhances responses to anti-PD1 checkpoint blockade in a preclinical orthotopic model of pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B125.

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

Integration of adaptive segmentation with heuristic-aided novel ensemble-based deep learning model for lung cancer detection using CT images

In recent days people are affected with lung cancer in, and the severe stage of this disease leads to death for human beings. Lung cancer is the second most typical cancer type to be found worldwide. Pulmonary nodules present in the lung can be used to identify cancer metastases because these nodules are visible in the lungs. Cancer diagnosis and region segmentation are the most important procedures because the prosperous prediction-affected area can accurately identify the variation in cancer and normal cell. By analyzing the lung nodules present in the image, the radiologists missed several useful low-density and small nodules, and this may tend to the diagnose process very difficult, and the radiologists needs more time to decide the prediction of affected lung nodules. Due to the radiologist’s physical inspection time and the possibility of missing nodules, automatic identification is needed to address these issues. In order to achieve this, a new hybrid deep learning model is developed for lung cancer detection with the help of CT images. At first, input images like CT images are gathered from the standard data sources. Once the images are collected, it undergoes for the pre-processing stage, where it is accomplished by Weighted mean histogram equalization and mean filtering. Consequently, a novel hybrid segmentation model is developed, in which Adaptive fuzzy clustering is incorporated with the Optimized region growing; here, the parameters are optimized by Improved Harris Hawks Optimization (IHHO). At last, the classification is accomplished by Ensemble-based Deep Learning Model (EDLM) that is constructed by VGG-16, Residual Network (ResNet) and Gated Recurrent Unit (GRU), in which the hyperparameters are tuned optimally by an improved HHO algorithm. The experimental outcomes and its performance analysis elucidate the effectiveness of the suggested detection model aids to early recognition of lung cancer.

Levenberg–Marquardt neural network-based intelligent computation for the non-Newtonian polymer during forward roll coating

Scientists and researchers widely recognize the effectiveness of artificial intelligence (AI)-based machine learning and intelligent computing solvers, demonstrating qualities such as resilience, robustness, stability, and rapid convergence. One particularly significant and rapidly growing field within AI is artificial neural networks. This research uses a supervised neural network model based on Levenberg–Marquardt backpropagation (LMB-SNNs) to examine the Sisko fluid model for the forward roll coating process (SFM-FRCP). A suitable transformation is applied to the partial differential equations based SFM-FRCP mathematical model, resulting in a set of nonlinear ordinary differential equations. The perturbation method has been used to find the analytical solutions for the velocity profile, pressure gradient, and pressure profile. A dataset for varying the pertinent parameters is generated, and the LMB-SNNs technique has been used to estimate the velocity profile, pressure gradient, and pressure profile behavior during FRCP for numerous scenarios. The numerical solution for SFM-FRCP in different scenarios, such as the validation, training, and testing procedures of LMB-SNNs, is carried out. Moreover, the state transition index, fitness outline, mean square error, histogram error, and regression presentation also endorse the strength and reliability of the solver LMB-SNNs for SFM-FRCP. The comparative analyses and performance studies through outputs of regression drawings, absolute error, and error histograms validate the effectiveness of the suggested solver LMB-SNNs. The method's precision is verified by the closest numerical outputs of both built and dataset values with similar levels 10−11–10−14. Furthermore, it has been observed that as the non-Newtonian parameter increases, the fluid velocity decreases. The research work carried out in this paper is original and fills a gap in the existing research by showing the rheological properties of the Sisko fluid model and the implementation of the LMB-SNNs during the FRCP.

Investigation on the physical dose filtered by linear energy transfer for treatment plan evaluation in carbon ion therapy.

Large tumor size has been reported as a predicting factor for inferior clinical outcome in carbon ion radiotherapy (CIRT). Besides the clinical factors accompanied with such tumors, larger tumors receive typically more low linear energy transfer (LET) contributions than small ones which may be the underlying physical cause. Although dose averaged LET is often used as a single parameter descriptor to quantify the beam quality, there is no evidence that this parameter is the optimal clinical predictor for the complex mixed radiation fields in CIRT. Purpose of this study was to investigate on a novel dosimetric quantity, namely high-LET-dose ( , the physical dose filtered based on an LET threshold) as a single parameter estimator to differentiate between carbon ion treatment plans (cTP) with a small and large tumorvolume. Ten cTPs with a planning target volume, (large) and nine with a (small) were selected for this study. To find a reasonable LET threshold ( ) that results in a significant difference in terms of , the voxel based normalized high-LET-dose ( ) distribution in the clinical target volume (CTV) was studied on a subset (12 out of 19 cTPs) for 18 LET thresholds, using standard distribution descriptors (mean, variance and skewness). The classical dose volume histogram concept was used to evaluate the and distributions within the target of all 19 cTPs at the before determined . Statistical significance of the difference between the two groups in terms of mean and volume histogram parameters was evaluated by means of (two-sided) t-test or Mann-Whitney-U-test. In addition, the minimum target coverage at the above determined was compared and validated against three other thresholds to verify its potential in differentiation between small and large volumetumors. An of approximately was found to be a reasonable threshold to classify the two groups. At this threshold, the and were significantly larger ( ) in small CTVs. For the small tumor group, the near-minimum and median (and ) in the CTV were in average (0.31 ± 0.08) and (0.46 ± 0.06), respectively. For the large tumors, these parameters were (0.20 ± 0.01) and (0.28 ± 0.02). The difference between the two groups in terms of mean near-minimum and median ( ) was 2.7 Gy (11%) and 5.0 Gy (18%),respectively. The feasibility of high-LET-dose based evaluation was shown in this study where a lower was found in cTPs with a large tumor size. Further investigation is needed to draw clinical conclusions. The proposed methodology in this work can be utilized for future high-LET-dose basedstudies.

Correlation between Multiparametric MR Imaging and Molecular Genetics in Pontine Pediatric High-Grade Glioma.

Molecular profiling is a crucial feature in the "integrated diagnosis" of CNS tumors. We aimed to determine whether radiomics could distinguish molecular types of pontine pediatric high-grade gliomas that have similar/overlapping phenotypes on conventional anatomic MR images. Baseline MR images from children with pontine pediatric high-grade gliomas were analyzed. Retrospective imaging studies included standard precontrast and postcontrast sequences and DTI. Imaging analyses included median, mean, mode, skewness, and kurtosis of the ADC histogram of the tumor volume based on T2 FLAIR and enhancement at baseline. Histone H3 mutations were identified through immunohistochemistry and/or Sanger or next-generation DNA sequencing. The log-rank test identified imaging factors prognostic of survival from the time of diagnosis. Wilcoxon rank-sum and Fisher exact tests compared imaging predictors among groups. Eighty-three patients had pretreatment MR imaging and evaluable tissue sampling. The median age was 6 years (range, 0.7-17 years); 50 tumors had a K27M mutation in H3-3A, and 11, in H3C2/3. Seven tumors had histone H3 K27 alteration, but the specific gene was unknown. Fifteen were H3 wild-type. Overall survival was significantly higher in H3C2/3- compared with H3-3A-mutant tumors (P = .003) and in wild-type tumors compared with any histone mutation (P = .001). Lower overall survival was observed in patients with enhancing tumors (P = .02) compared with those without enhancement. H3C2/3-mutant tumors showed higher mean, median, and mode ADC_total values (P < .001) and ADC_enhancement (P < .004), with lower ADC_total skewness and kurtosis (P < .003) relative to H3-3A-mutant tumors. ADC histogram parameters are correlated with histone H3 mutation status in pontine pediatric high-grade glioma.

Soft computing paradigm for heat and mass transfer characteristics of nanofluid in magnetohydrodynamic (MHD) boundary layer over a vertical cone under the convective boundary condition

ABSTRACT In this article, the Levenberg-Marquardt back propagation technique under the framework of neural networks (LMBT-NN) is incorporated. The heat and mass transfer characteristics of nanofluid (HMT-CNF) in a magneto-hydrodynamic (MHD) boundary layer over a vertical cone under convective boundary conditions have been investigated. The similarity transformation has been employed with the goal of transforming nonlinear partial differential equations (PDEs) into the system of ordinary differential equations (ODEs). A set of suggested data (LMBT-NN) is produced for some scenarios by modifying the radiation parameter (R), magnetic field parameter (M), buoyancy ratio parameter (Nr), chemical reaction parameter (Cr), thermophoresis factor (Nt), Lewis number (Le), and Brownian motion factor (Nb) within the applicability of the state-of-the-art Adams numerical technique. Using the (LMBT-NN) training, testing, and validation technique, the approximate solution of distinct instances has been validated, and for excellence, the proposed model has equated. To justify the proposed methodology (LMBT-NN), different error plots and numerical illustrations based on mean square errors, histogram plots, and regression analysis representations are prepared. With a correctness level ranging from E-9 to E-10, the recommended method has been observed based on the closeness of the suggested and reference outcomes.

X-ray/gamma radiation shielding properties of Aluminium-Barium[sbnd]Zinc Oxide nanoparticles synthesized via low temperature solution combustion method

For the first time Aluminium-BariumZinc oxide nanocomposite (ZABONC) was synthesized by solution combustion method where calcination was carried out at low temperatures (6000C) to study the electromagnetic (EM) (X/γ) radiation shielding properties. Further for characterization purpose standard techniques like PXRD, SEM, UV-VISIBLE, FTIR were used to find phase purity, functional groups, surface morphology, and to do structural analysis and energy band gap determination. The PXRD pattern shows (hkl) planes corresponding to spinel cubic phase of ZnAl2O4, cubic Ba(NO3)2, α and γ phase of Al2O3 which clearly confirms the formation of complex nano composite. From SEM histogram mean size of nano particles was calculated and is in the order of 17 nm. Wood and Tauc’s relation direct energy band gap calculation gives energy gap of 2.9 eV. In addition, EM (X/γ) shielding properties were measured and compared with the theoretical ones using standard procedures (NaI (Tl) detector and multi channel analyzer MCA). For energy above 356 keV the measured shielding parameters agree well with the theory, while below this value slight deviation is observed, due to the influence of atomic/crystallite size of the ZABONC. Hence synthesized ZABONC can be used as a shielding material in EM (X/γ) radiation shielding.

Aircraft piston engine load distribution in steady state operating conditions

The article presents the results of analysis of operational parameters of piston engine CA 912 ULT which is a propulsion system of ultralight gyroplane Tercel produced by Aviation Artur Trendak. Research was conducted under normal operating conditions of the autogyro and data was collected from 15 independent tests including a total of 14 flight hours conducted during training flights. Engine and flight parameters were recorded at 9 Hz during each flight using on-board Fligh Data Recorded system. Collective results were presented to show the statistical analyses of the individual parameters by determining the mean values, standard deviations and histograms of the distribution of these parameters. The analyses included engine speed, charge pressure, air temperature, oil temperature and exhaust temperature. Histograms of operating points defined by both engine speed and manifold air pressure were also determined. Analyses were carried out to determine the contribution of steady-state and transient engine operation. It was also shown that the dynamics of engine work in real operating conditions is small. Detailed analyses were conducted for steady-state conditions. The value of the engine load index was determined and its statistical analyses were carried out. It was shown that the engine operating points are concentrated mainly in the range of idle and power above 50% of nominal power. The most frequent range is 50% to 80% of nominal power.

Steganography Encryption Secret Message in Video Raster Using DNA and Chaotic Map

Recently, much secured data has been sent across the internet and networks. Steganography is very important because it conceals secure data in images, texts, audios, protocols, videos, or other mediums. Video steganography is the method of concealing data in frames of video format. A video is a collection of frames or images used for hidden script messages. This paper proposes a technique to encrypt secret messages using DNA and a 3D chaotic map in video frames using the raster method. This technique uses three steps: Firstly, converting video frames into raster to extract features from each frame. Secondly, encryption of secret messages using encoded forms of DNA bases, inverse/inverse complements of DNA, and utilizing 3D chaotic maps, Thirdly, hiding encryption secret messages in that raster video frame by using a secret key in the four corners of each video frame. This technique hides large amounts of secret data because the video frame is large and accepts any message size. The outcomes are efficient, robust, highly secure, and can tolerate high capacity. These outcomes have been obtained from a set of tests like: peak signal to noise ratio (PSNR), mean square error (MSE), entropy, correlation coefficient, and histogram and capacity.

Monitoring of Chlorophylls during the Maturation Stage of Plums by Multivariate Calibration of RGB Data from Digital Images

The methodology developed in this study was based on digital imaging processing of plums harvested in eight different weeks during their ripening process. Mean RGB data, histograms, and matrices of RGB data were used to characterise the ripening stage of the plums, in both qualitative and quantitative approaches, by using classification and quantification chemometric methods. An exploratory analysis of data was performed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) in RGB histograms and matrices data, respectively, showing differences in the colour features since the fourth week of harvesting. In the case of the quantitative approach, high correlation was achieved between the histogram data, using partial least squares (PLS), and total chlorophyll content. In addition, between three-way matrixes and total chlorophyll content, good correlations were obtained applying unfolded-PLS (U-PLS) and N-way-PLS (N-PLS). The most accurate results were obtained on the green channel. Analytical parameters obtained were good, with determination coefficients (R2) higher than 0.91 for all models in the first and second-order multivariate analysis. In addition, relative errors of prediction (REPs) were lower than 12% in all models for the green channel. Therefore, the proposed method was a satisfactory alternative to destructive physiological and biochemical methods in the determination of total chlorophylls in plum samples. In the routine analysis, first-order multivariate calibration with PLS analysis is a good option due to the simplicity of data processing.