Expansion cycle rocket engines have unintelligible and sensitive dynamic behavior. Contrary to other types of rocket engine which have gas generator, Expansion cycle rocket engines utilizes mass flow of fuel propellant to provide power for rotating turbo pump. Which contributes to a complicated and difficult ignitions process in these engines. Priority and delay process in opening of control valves is important to prevent aforementioned phenomena. As opening and closing of control valves cause dynamic process in rocket engine, whose effects are expensive and difficult to predict by experimental tests. Therefore, dynamic modelling plays a key role in development of Expansion cycle rocket engines and may decrees future expenses. In this article RL-10 rocket engine with sufficient data for validation has been chosen. The main goal of this article is dynamic modelling of Expansion cycle rocket engine using mathematical non-Linear models. Modelling results yield that the presented non-Linear model is valid.

In seismic data processing, the processing steps are completely affected by the data quality. Reflection seismic data are often affected by various noises including random and coherent noises. Low signal to noise ratio can produce problems for stacking and migration steps, which ultimately leads to poor interpretation. There are many methods that can be used for noise removal or attenuation of seismic data. The basic assumption of the Fourier transform is that it considers stationary signal, thus, for non-stationary signals, it is not always applicable. Based on this fact that the wavelet transform decomposes a function by translation and stretching, it can provide time-scale representation of a signal. In this paper, we have used SURE-LET method for noise removal in the wavelet transform domain. In the SURE-LET method, any assumptions of noise free signals are avoided.

In recent years there has been a growing interest to employ non-Linear predictive techniques and models in speech coding to further reduce bit-rate and therefore channel bandwidth. Usually neural nets are used for this purpose that result in an additional up to 3dB reduction in the excitation signal energy. Non-Linear prediction can also be performed based on Volterra series Expansion wherein the Expansion is usually limited to first and second terms, for simplicity (quadratic prediction). Early studies have shown that employing Volterra filters results in a much higher reduction in excitation signal energy (6 to 10 dB), as compared with neural nets. But, because of instability, this reduction can not be materialized in terms of bit-rate reduction or signal to noise improvement. This instability in the decoder is triggered by computational errors (i.e. due to quantization of the excitation signal) and high sensitivity of algorithms to these errors.In the original work, presented here, the instability in the codec is studied in both forward and backward prediction schemes using LS and LMS algorithms respectively. It is shown that stability can be obtained at the cost of losing most of saving in excitation signal energy where final reduction level is as much as for neural nets. With forward prediction, after stabilizing, in spite of a small increasing in the operational complexity for 20 to 45% of frames including the quadratic term will be beneficial. So a scheme is developed to perform non-Linear prediction only on these frames. This algorithm results in an improvement of up to 4 dB in final signal to noise ratio. Sequential backward quadrant prediction, although much more interesting from implementation point of view, does not lead to an appreciable better performance over Linear prediction.

In order to detect targets upon sea surface or near it, marine radars should be capable of distinguishing signals of target reflections from the sea clutter. Our proposed method in this paper relates to detection of dissimilar marine targets in an inhomogeneous environment with clutter and non-stationary noises, and is based on adaptive Thresholding determination methods. The variance and the mean values of the noise level have been estimated in this paper, based on non-stationary, statistical methods and Thresholding has been carried out using the suggested two-pole recursive filter. Making the rate of false alarm constant, the concerned threshold resolves the hypothesis of existence or absence of the target signal. Performance of the mentioned algorithm has been compared with the well-known conventional method as CA-CFAR in terms of decreasing the losses and increasing calculation speed. The algorithm provided for detection of signal has been implemented as a part of signal-processing algorithms of some practical marine radar. The results obtained from the algorithm performance in a real environment indicate appropriate workability of this method in heterogeneous environment and non-stationary interference.

Purpose: To compare the results of the new strategy Swedish Interactive Thresholding Algorithm (SITA) Faster to the results of SITA Standard in patients with glaucoma. Methods: This was a cross-sectional study of 49 patients with glaucoma and previous experience with standard automated perimetry. Two consecutive tests were performed in random order, one with SITA Standard and another one with SITA Faster, in the studied eye of each patient. Comparisons were made for test time, mean deviation (MD), visual field index (VFI), and number of depressed points in pattern deviation map and total deviation map for every level of significance. Results: The average test time was 56% shorter with SITA Faster (P < 0. 001). The intraclass correlation coefficient (ICC) for MD and VFI showed excellent agreement between both strategies, ICC = 0. 98 (95% confidence interval [CI]: 0. 96, 0. 99) and ICC = 0. 97 (95% CI: 0. 95, 0. 99), respectively. For the number of depressed points in total deviation map and pattern deviation map, ICC demonstrated good agreement with values between 0. 8 and 0. 95. Conclusions: Our study shows that SITA Faster is a shorter test with strong agreement with SITA Standard parameters. These results suggest that SITA Faster could replace SITA Standard for glaucoma diagnosis.

Hygroscopic Expansion is an important property of restorative materials which play a significant role in the clinical performance of the materials. The aim of this study was to evaluate the hygroscopic Expansion of a conventional glass-ionomer cement (Fuji II) , a resin-modified glass-ionomer cement (Fuji II LC), a composite resin (Z100) and a compomer (Compoglass). Ten samples (6x4 mm) were made using a two-part stainless steel mould. Samples were immersed in distilled water or natural saliva during the period of the study. The length of each sample was measured after preparation at: 24 hours, 1 week, 2 weeks, 1 month, 2 months and 3 months. The Linear hygroscopic Expansion was presented in the form of percentage for the length changes of the specimens in relation to the baseline measurement. Results indicated that Fuji II has no significant Expansion in both storage medias during the study, whereas Fuji II LC exhibited the highest Expansion rate among the materials tested. It was also found that the pattern of the Expansion of Compoglass was similar to that of the composite resin tested. It was concluded that the Linear hygroscopic Expansion of the resin-modified glass-ionomer was more than the other materials and natural saliva had no effect on the magnitude of the Expansion.

Background: Tooth damage is one of the most common reasons that patients visit the dentists. Nowadays composites are increasingly used in dentistry because they have the colour of tooth and are more compatible with tooth tissue. Hygroscopic Expansion is one of the most reasons that reduce the efficacy of the composite. Main goal of this article is to compare the hygroscopic Expansion of the three common composites.Methods: In this longitudinal study, laboratory data from three common composites (Z25, P90, Kalore) that were kept in two different environments (distilled water and natural mouth saliva) for three months were used. Their cylinder length was measured 18 times after preparation of 540 samples and their hygroscopic Expansion was comprised using multilevel Linear mixed model by SAS 9.1.3 software.Results: In this study both, time (p<0.001) and composite (p=0.032) were statistically significant and environment did not have significant effect. Composite Z250 had the lowest hygroscopic Expansion between the three underlying composites. Also, random effect of the intercept which showed the effect of unobserved factors on the response, was significant (p<0.001).Conclusions: Results showed that in addition to the time and composite type, there are other factors that do not exist in the model but affect the hygroscopic Expansion. Nevertheless it’s better to choose the best composite that have the lowest hygroscopic Expansion (like Z250) during the time.

Out-of-domain intent detection in natural language understanding systems faces significant challenges from suboptimal threshold selection and signal degradation through inappropriate normalization techniques. This paper presents an adaptive ensemble Thresholding framework that substantially extends our previous conference work by addressing fundamental limitations in existing variational autoencoder-based detection methods. Our approach combines reconstruction loss from variational autoencoders with classifier confidence scores to create a unified detection signal that captures both semantic deviation and prediction uncertainty. The framework incorporates a novel smart scaling strategy that preserves natural separation ratios between in-domain and out-of-domain samples, preventing the signal destruction caused by standard normalization approaches. Through systematic parameter optimization using grid search techniques, the method adaptively determines optimal ensemble weights and threshold selection strategies tailored to specific dataset characteristics. We evaluate our framework across multiple datasets with varying semantic complexity and domain structures, demonstrating consistent performance improvements over baseline variational autoencoder approaches and recent state-of-the-art methods. Compared to our previous VAE-based approach, the framework demonstrates an average performance gain of 3.15 percentage points across all evaluation metrics. Our analysis reveals that ensemble scaling strategy significantly impacts detection performance, with proper signal preservation being more critical than sophisticated threshold selection methods. This work provides a principled approach to adaptive ensemble learning for out-of-domain detection, offering a robust solution that generalizes effectively across diverse datasets and linguistic contexts including low-resource languages like Persian.

This paper addresses the problem of speech enhancement in wavelet domain. After decomposition of noisy signal into wavelet sub-bands, an adaptive Thresholding process is applied on wavelet coefficients. In the proposed technique, small threshold value and hard thrsholding function are used in sub-bands with high speech energy; vice versa, in sub-bands with low speech energy, large threshold value and soft Thresholding function are employed. For other sub-bands (between above two extreme cases for speech energy), we use an adaptive Thresholding function that is actually between soft- and hard-Thresholding functions. The threshold value and Thresholding function are determined by a parameter related to the ratio of speech and noise powers in each sub-band. Our extensive experiments show the superiority of proposed method in removing the background noise and reduction of speech distortion. It was also shown that both wavelet tree structure and wavelet type affect on the performance of speech de-noising system.

The Linear Expansion, early-age compressive strength and setting times of the binary mixtures of gypsum and Portland cement clinkers of relatively low C3A-contents were investigated. For this reason, type 1, 2, and 5 of Portland cement-clinkers were selected and a number of binary mixtures were designed. At relatively lower percentages of gypsum (about 5%), the early strength behavior is improved. Results obtained for compressive strength of mixtures with 5% gypsum confirm the possibility of achieving 28- and 90-day compressive strengths up to values higher than 100 MPa and 130 MPa, respectively. At relatively higher percentages of gypsum (more than 25%), excessive Expansion caused by ettringite formation results in the formation of micro-cracks effectively weakening the strength behavior. The work suggests that type S expansive cements could be produced from Portland cement clinkers of relatively low C3Acontents.