A lettuce moisture detection method based on terahertz time-domain spectroscopy

ABSTRACT: For non-destructive detection of water stress in lettuce, terahertz time-domain spectroscopy (THz-TDS) was used to quantitatively analyze water content in lettuce. Four gradient lettuce water contents were used . Spectral data of lettuce were collected by a THz-TDS system, and denoised using the S-G derivative, Savitzky-Golay (S-G) smoothing and normalization filtering. The fitting effect of the pretreatment method was better than that of regression fitting, and the S-G derivative fitting effect was obtained. Then a calibration set and a verification set were divided by the Kennan-Stone algorithm, sample set partitioning based on joint X-Y distance (SPXY) algorithm, and the random sampling (RS) algorithm, and the parameters of RS were optimized by regression fitting. The stability competitive adaptive reweighted sampling, iteratively retained information variables and interval combination optimization were used to select characteristic wavelengths, and then continuous projection was used on basis of the three algorithms above. After the successive projection algorithm was re-screened, partial least squares regression was used into modeling. The regression coefficients Rc 2 and RMSEC reach 0.8962 and 412.5% respectively, and Rp 2 and RMSEP of the verification set are 0.8757 and 528.9% respectively.

Reconstruction Algorithm-Based Ultrasonic and Spiral CT Images in Evaluating the Effects of Dexmedetomidine Anesthesia for Acute Abdomen

Objective. The study focused on the application value of iteration reconstruction algorithm-based ultrasound and spiral computed tomography (CT) examinations, and the safety of dexmedetomidine anesthesia in acute abdominal surgery. Methods. 80 cases having the acute abdomen surgery were selected as the research subjects. They were divided into group A (40 cases) and group B (40 cases) according to the anesthetic drugs used in the later period. The experimental group was injected with propofol, remifentanil, and atracurium combined with dexmedetomidine; the control group was injected with propofol, remifentanil, and atracurium only. After the operation, the patient was for observed for the pain, agitation, adverse reactions, heart rate (HR), and blood pressure. All patients received ultrasound and spiral CT examinations, and based on the characteristics of the back-projection algorithm, an accelerated algorithm was established and used to process the image, and according to which, the patient’s condition and curative effects were evaluated. Results. After image reconstruction, the ultrasound and spiral CT images were clearer with less noise and more prominent lesions than before reconstruction. Before image reconstruction, the accuracy rates of ultrasound and spiral CT in diagnosing acute abdomen were 92.3% and 91.1%, respectively. After reconstruction, the corresponding numbers were 96.3% and 98.1%, respectively. After reconstruction, the accuracy of the two methods in diagnosing acute abdomen was significantly improved compared with that before reconstruction, and the difference was statistically significant ( P < 0.05 ). The Ramsay score of the experimental group was significantly higher than that of the control group at each time period, P < 0.05 ; the agitation score and visual analogue scale (VAS) score of the experimental group were significantly lower than the control group at each time period after waking up, P < 0.05 . Conclusion. Reconstruction algorithm-based ultrasound and spiral CT images have high application value in the diagnosis of patients with acute abdomen, and dexmedetomidine has good safety in anesthesia surgery.

Research themes of family and community physicians in Brazil

Family and community medicine is a specialty dedicated to primary care, the cornerstone of effective health systems. Research capacity in primary care varies worldwide, and bibliographic databases such as MEDLINE, Scopus and Web of Science do not index most primary care research coming from Latin America. Our objective was to investigate the research themes of family and community physicians in Brazil, and to correlate the articles' research themes with their authors' trajectories in postgraduate education. For that, we compiled a national list of family and community physicians, retrieved their curricula from the Lattes Platform, compiled a list of journal articles, and obtained their keywords from LILACS and MEDLINE. Treating journal articles and their keywords as the two node types in a bipartite network, we derived research themes using the dual-projection algorithm, combining the Leiden algorithm with hierarchical clustering. We found two research themes to be the largest, most developed, and most central ones: human health and primary care. Authors with a master's or PhD in collective health (public health, epidemiology, and social sciences and humanities in health) were as likely as those with no postgraduate degree to publish articles on primary care. On the other hand, authors with a postgraduate degree in medicine were more likely to publish articles on human health. After discussing the findings in light of previous research and methodological aspects, we conclude there's a relative divide between primary care and clinical research, and the highlight policy implications.

Adaptive Frequency domain Affine Projection Equalizer of MIMO SC-FDMA Based Overlap-Save Method

Design adaptive equalizer using Affine projection algorithm for MIMO SC-FDMA and compare it with MMSE . Both equalizers are used within overlap-save method. Also a turbo decoder is used in conjunction with overlap-save method to enhance the BER performances in 8x8 and 16x16 MIMO SC-FDMA.

Adaptive Frequency domain Affine Projection Equalizer of MIMO SC-FDMA Based Overlap-Save Method

Design adaptive equalizer using Affine projection algorithm for MIMO SC-FDMA and compare it with MMSE . Both equalizers are used within overlap-save method. Also a turbo decoder is used in conjunction with overlap-save method to enhance the BER performances in 8x8 and 16x16 MIMO SC-FDMA.

Estimation of Aboveground Biomass For Winter Wheat In The Later Growth Stage By Combining Digital Texture And Spectral Analysis

Background: Aboveground biomass (AGB) is an important indicator to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly in estimation of AGB at high biomass levels. The present study thus evaluated the ability of spectral features, image textures, combinations thereof to estimate winter wheat AGB. Result: The spectral features were obtained from the wheat canopy reflectance spectra of 400–1000 nm including original wavelengths and seven vegetation indices (VIs), then we screened effective wavelengths (EWs) through successive projection algorithm (SPA) and the optimal vegetation index selected by correlation analysis. The image textures features were extracted by gray level co-occurrence matrix including texture features (TEX) and normalized difference texture index (NDTI), then we selected effective variables including the optimal texture subset (OTEXS) and the optimal normalized difference texture index subset (ONDTIS) through the ranking of feature importance of random forest (RF). Linear regression (LR), partial least squares regression (PLS) and random forest (RF) were established to evaluate the relationship between each calculated feature and AGB. The results demonstrate that the ONDTIS with PLS based on validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R2 = 0.75, RMSE = 0.04). Moreover, the combinations of OTEXS and EWs with LR based on validation datasets exhibited the highest prediction accuracy for the post-seedling stage (R2 = 0.78, RMSE = 0.05). Conclusion: The findings show that the combined use of spectral features and image textures can effectively improve the accuracy for AGB estimation especially in post-seeding stage.

Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.