Dynamic analysis of a fuzzy Bobwhite quail population model under g-division law.

This paper is concerned with a kind of Bobwhite quail population model x n + 1 = A + B x n + x n x n - 1 x n - 2 , n = 0 , 1 , ⋯ , where the parameters and initial values are positive parabolic fuzzy numbers. According to g-division of fuzzy sets and based on the symmetrical parabolic fuzzy numbers, the conditional stability of this model is proved. Besides the existence, boundedness and persistence of its unique positive fuzzy solution. When some fuzzy stability conditions are satisfied, the model evolution exhibits oscillations with return to a fixed fuzzy equilibrium no matter what the initial value is. This phenomena provided a vivid counterexample to Allee effect in density-dependent populations of organisms. As a supplement, two numerical examples with data-table are interspersed to illustrate the effectiveness. Our findings have been verified precise with collected northern bobwhite data in Texas, and will help to form some efficient density estimates for wildlife populations of universal applications.

Superfast, large-scale harvesting of cellulose molecules via ethanol pre-swelling engineering of natural fibers.

Cellulose molecules, as the basic unit of biomass cellulose, have demonstrated advancements in versatile engineering and modification of cellulose toward sustainable and promising materials in our low-carbon society. However, harvesting high-quality cellulose molecules from natural cellulosic fibers (CF) remains challenging due to strong hydrogen bonds and unique crystalline structure, which limit solvents (such as ionic liquid, IL) transport and diffusion within CF, making the process energy/time-intensively. Herein, we superfast and sustainably engineer biomass fibers into high-performance cellulose molecules via ethanol pre-swelling of CF followed by IL treatment in the microwave (MW) system. Ethanol-pre-swelled cellulosic fibers (SCF) feature modified morphological and structural distinctions, with improved fiber width, pore size, and specific surface area. The ethanol in the SCF structure is appropriately removed through MW heating and cooling, leaving transport and diffusion pathways of IL within the SCF. Such strategy enables the superfast (140 s) and large-scale (kilogram level) harvesting of cellulose molecules with high molecular weight, resulting in high-performance, versatile cellulose ionogel with a 300 % increase in strength and 1027 % in toughness, monitoring human movement, external pressure, and temperature. Our strategy paves the way for time/energy-effectively, sustainably harvesting high-quality polymer molecules from natural sources beyond cellulose toward versatile and advanced materials.

Two stream Non-Local CNN-LSTM network for the auxiliary assessment of mental retardation.

At present, the assessment of mental retardation is mainly based on clinical interview, which requires the participation of experienced psychiatrist and is laborious. Studies have shown that there are correlations between mental retardation and abnormal behaviors (such as, hyperkinetic, tics, stereotypes, etc.). On the basis of this fact, a two stream Non-Local CNN-LSTM network has been proposed to learn the features of upper body behavior and facial expression of patients, thus, to achieve the preliminary screening of mental retardation. Specifically, RGB and optical flow are extracted separately from interview videos, and a two stream network based on contribution mechanism is designed to effectively fuse the information of two kinds of images, which may update the network in a new approach of alternating iteration training to find the optimal model. Besides, by introducing non-local mechanism and adopting it to the network, the global feature sensing can be established more effectively to reduce the background interference for video clip in a short time zone. Experiments on clinical video dataset show that the performance of proposed model is better than other prevalent deep learning methods of behavioral feature learning, the accuracy reaches 89.15% in basic experiment, and is further improved to 89.52% in the supplementary experiment. Furthermore, the experimental results show that this method still has a lot of room for improvement. In general, our work indicates that the proposed model has potential value for the clinical diagnosis and screening of mental retardation.

A lightweight CNN and Transformer hybrid model for mental retardation screening among children from spontaneous speech.

Mental retardation (MR) is a group of mental disorders characterized by low intelligence and social adjustment difficulties. Early diagnosis is beneficial for the timely intervention of children with MR to ease the degree of disability. Children with MR always have impaired speech functions compared to normal children, which is significant for clinical diagnosis. On the basis of this, our study proposes a spontaneous speech-based framework (MT-Net) for screening MR, which merges mobile inverted bottleneck convolutional blocks (MBConv) and visual Transformer blocks. MT-Net takes log-mel spectrograms converted from raw interview speech as data source, and utilizes MBConv and visual Transformer to learn low-level and high-level features well. In addition, SpecAugment, a data augmentation strategy, has been used to expand our audio dataset to further enhance the performance of MT-Net. The experimental results show that our proposed MT-Net outperforms Transformer networks (ViT) and convolutional neural networks (ResNet18, MobileNetV2, EfficientNetV2), achieving accuracy of 91.60% after using SpecAugment. Our proposed MT-Net has fewer parameters, low computing consumption and high prediction accuracy, which is expected to be an auxiliary screening tool for MR.

On the System of High Order Rational Difference Equations.

This paper is concerned with the boundedness, persistence, and global asymptotic behavior of positive solution for a system of two rational difference equations x n+1 = A + (x n /∑ i=1 (k) y n-i ), y n+1 = B + (y n /∑ i=1 (k) x n-i ), n = 0,1,…, k ∈ {1,2,…}, where A, B ∈ (0, ∞), x -i ∈ (0, ∞), and y -i ∈ (0, ∞), i = 0,1, 2,…, k.