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The development of new hydro turbines or the optimization of traditional hydro turbines is one of the research directions that scholars have been interested in to broaden the source of hydropower and improve energy efficiency. In our previous work, a Francis turbine was used in the water supply systems of high-rise buildings to properly control water pressure inside the pipeline and recover excess water energy. In this paper, an optimization study of the volute for the sectional shape, design method, inlet parameters, outlet width, and tongue was conducted using numerical simulations to improve the hydro turbine performance. The simulated results show that a circular section could better enhance the flow velocity inside the volute and make the flow velocity distribution more uniform at the volute outlet, and the equal mean velocity method is a more suitable volute design method in this special condition. The results of range analysis show that the water head reduction increases significantly with the increase in the inlet height, and both inlet height and outlet width have a significant effect on the efficiency. In addition, in the tongue optimization, a larger tilt angle corresponds to smaller output power and water head reduction; a larger stretch length in the limited angle range corresponds to larger output power and lower efficiency. Ultimately, the output power of the hydro turbine was increased by 18.65% and the efficiency by 2.1% through the volute optimization.

High order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low-rank matrix from missing or corrupted observations. This is a new model to describe the high order statistics of PSK signals. Unlike traditional uniformly Nyquist samples, our method uses the advanced optimization technique, which is guaranteed to find the low-rank matrix by simultaneously fixing the missing entries. Simulation results demonstrate that our method achieves accurate estimates of the major portion of the high order statistics. The new technique can be used to fulfil automatic modulation recognition (AMR) and rough estimations of parameters. More specifically, low-rank structure of PSK signals is studied. In contrast to the existing $l_{1}$ optimization criteria, our method proposed here is computationally more efficient and provides high accuracy.

Sugar-free food has been gaining popularity because of low-calorie content. But sugar replacement by high-intensity sweeteners can negatively affect sensory. In this study, the effect of the addition of sucralose (Suc), stevioside (Ste), and erythritol (Ery) as sugar substitutes on the sensory profile and overall acceptance of ice cream were evaluated by penalty analysis (PA) based on the check-all-that apply (CATA) method, with those of the partial least squares (PLS) regression. Twelve sweetening agents of ice cream samples were presented to 106 consumers who answered on an overall liking question using the 15-point hedonic scale and a CATA question with 32 attributes that described the sensory characteristics of ice cream. The results showed that mixed sweeteners (60%Suc+20%Ste+20%Ery or 60%Suc+10%Ste+30%Ery) can present an advantageous performance when used separately, and making ice cream similar to that of sucrose (Sac) added. Adding Suc, Ste, and Ery to ice cream hardly felt bitterness, astringency, and chemical-like sensations of the sweetening agent. The significant difference between different sweeteners is the intensity and speed of sweetness. Developing combination of high-potency sweeteners that can make sweetness appear quickly could open up new ways to design sugar-free ice cream. [ABSTRACT FROM AUTHOR]

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.

In this paper, the inerter is introduced to form the "inerter-spring-damper" landing gear (ISDLG) based on the "spring-damper" landing gear (SDLG), and the taxiing dynamic models of both are established. Taking fuselage acceleration, buffer stroke, and dynamic tire load as performance indexes, the parameters of the ISDLG are optimized using a multi-objective genetic optimization algorithm. In MATLAB/Simulink environment, SDLG and ISDLG are simulated in the time domain and frequency domain. The results show that compared with SDLG, the values of performance indexes of ISDLG are all reduced, especially at low frequencies. This demonstrates that ISDLG can effectively suppress the low frequency vibration of the fuselage and improve the ride comfort and handling stability of the aircraft when it taxis on the ground. In addition, the performance indexes of the two models will deteriorate with the increase of speed and the deterioration of road grade, but the extent to which performance indexes of ISDLG are superior to those of SDLG is increasingly evident. Through comparison, it is also found that ISDLG can effectively improve the vibration caused by the deterioration of the road grade. [ABSTRACT FROM AUTHOR]

Anxiety and depression are frequently noticed in glioma patients, while few studies report this issue in recurrent glioma patients. Hence, this study aimed to evaluate the prevalence of anxiety and depression, as well as their risk factors and prognostic value in recurrent glioma patients. Eighty recurrent glioma patients, 40 newly-diagnosed glioma patients, and 40 healthy controls were enrolled in this study. Then, the Hospital Anxiety and Depression Scale for anxiety (HADS-A) and for depression (HADS-D) were used to assess the anxiety and depression status of all subjects. The HADS-A score (8.6 ± 3.3 vs. 7.0 ± 2.9 vs. 4.3 ± 2.5), anxiety rate (58.8% vs. 32.5% vs. 10.0%), HADS-D score (7.9 ± 3.0 vs. 6.9 ± 3.1 vs. 4.0 ± 2.6), and depression rate (45.0% vs. 30.0% vs. 7.5%) were all highest in recurrent glioma patients, followed by newly-diagnosed glioma patients, and were lowest in healthy controls (all P < 0.001). Furthermore, female sex (vs. male sex) was independently correlated with anxiety (odds ratio (OR): 3.042, P = 0.029); meanwhile, higher World Health Organization (WHO) pathological grade was independently correlated with depression (OR: 2.573, P = 0.019) in recurrent glioma patients. Additionally, anxiety was correlated with shortened progression-free survival (PFS) (P = 0.028) and overall survival (OS) (P = 0.047), while depression only had a correlation trend with shortened PFS (without statistical significance) (P = 0.069) and was associated with shortened OS (P = 0.035) in recurrent glioma patients. The prevalence of anxiety and depression is high in recurrent glioma patients, which relates to gender, WHO pathological grade, and estimates worsen survival. [ABSTRACT FROM AUTHOR]