Natural Exponential and Three-Dimensional Chaotic System.

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two-dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three-dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics-related problem solving and expands the potential scope of multi-dimensional chaotic systems.

Single-View Measurement Method for Egg Size Based on Small-Batch Images.

Egg size is a crucial indicator for consumer evaluation and quality grading. The main goal of this study is to measure eggs' major and minor axes based on deep learning and single-view metrology. In this paper, we designed an egg-carrying component to obtain the actual outline of eggs. The Segformer algorithm was used to segment egg images in small batches. This study proposes a single-view measurement method suitable for eggs. Experimental results verified that the Segformer could obtain high segmentation accuracy for egg images in small batches. The mean intersection over union of the segmentation model was 96.15%, and the mean pixel accuracy was 97.17%. The R-squared was 0.969 (for the long axis) and 0.926 (for the short axis), obtained through the egg single-view measurement method proposed in this paper.

Corrigendum: Field pest monitoring and forecasting system for pest control.

[This corrects the article DOI: 10.3389/fpls.2022.990965.].

Field pest monitoring and forecasting system for pest control.

Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and laborious. Therefore, developing a method that can more timely and accurately identify insects and obtain insect information. This study designed an image acquisition device that can quickly collect real-time photos of phototactic insects. A pest identification model was established based on a deep learning algorithm. In addition, a model update strategy and a pest outbreak warning method based on the identification results were proposed. Insect images were processed to establish the identification model by removing the background; a laboratory image collection test verified the feasibility. The results showed that the proportion of images with the background completely removed was 90.2%. Dataset 1 was obtained using reared target insects, and the identification accuracy of the ResNet V2 model on the test set was 96%. Furthermore, Dataset 2 was obtained in the cotton field using a designed field device. In exploring the model update strategy, firstly, the T_ResNet V2 model was trained with Dataset 2 using transfer learning based on the ResNet V2 model; its identification accuracy on the test set was 84.6%. Secondly, after reasonably mixing the indoor and field datasets, the SM_ResNet V2 model had an identification accuracy of 85.7%. The cotton pest image acquisition, transmission, and automatic identification system provide a good tool for accurately forecasting pest outbreaks in cotton fields.

Dietary diversity offsets the adverse mortality risk among older indigenous Taiwanese.

BACKGROUND AND OBJECTIVES: The extent to which health and survival inequality between indigenous and nonindigenous older Taiwanese is associated with diet is uncertain. METHODS AND STUDY DESIGN: Participants from the Elderly Nutrition and Health Survey in Taiwan (1999-2000) formed this cohort. Dietary information was collected by 24-hr recall and simplified food frequency questionnaire. Dietary quality was assessed by dietary diversity score (DDS, 0-6). Annual medical service utilization and expenditure were derived from National Health Insurance claims until 2006. Survivorship was ascertained from the National Death Registry until 2008. Cox proportional- hazards models were used to determine the association between aboriginality and mortality in conjunction with dietary diversity. RESULTS: Indigenes (n=156) compared with nonindigenes (n=1182) significantly differed in socio-demography, behaviors and chronic disease prevalences. For up to 8 years, indigenes had a higher mortality rate (46.2% vs 33.6%, p=0.003). Indigenes' nutrient intakes were less for polyunsaturated fat, dietary fiber, vitamins and minerals (but more sodium); food intakes more for meat, with less cooking oil, dairy products and fruits; and a lower DDS, (3.61 vs 4.54). They had a 41% higher mortality risk (HR: 1.41, 95% CI: 1.09-1.81, p=0.008). Control for demographic variables did not change the findings. However, the increase in HR was substantially attenuated by the inclusion of DDS (HR: 1.15, 95% CI: 0.88-1.49, p=0.316). There was no significant interaction between aboriginality and DDS on mortality (p=0.673). CONCLUSIONS: Older indigenous Taiwanese have a higher mortality risk than their majority counterparts. Irrespective of aboriginality, the more diverse diet is associated with a lower risk of mortality.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer.

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.