A Predefined-Time Adaptive Zeroing Neural Network for Solving Time-Varying Linear Equations and Its Application to UR 5 Robot.

Time-varying linear equations (TVLEs) play a fundamental role in the engineering field and are of great practical value. Existing methods for the TVLE still have issues with long computation time and insufficient noise resistance. Zeroing neural network (ZNN) with parallel distribution and interference tolerance traits can mitigate these deficiencies and thus are good candidates for the TVLE. Therefore, a new predefined-time adaptive ZNN (PTAZNN) model is proposed for addressing the TVLE in this article. Unlike previous ZNN models with time-varying parameters, the PTAZNN model adopts a novel error-based adaptive parameter, which makes the convergence process more rapid and avoids unnecessary waste of computational resources caused by large parameters. Moreover, the stability, convergence, and robustness of the PTAZNN model are rigorously analyzed. Two numerical examples reflect that the PTAZNN model possesses shorter convergence time and better robustness compared with several variable-parameter ZNN models. In addition, the PTAZNN model is applied to solve the inverse kinematic solution of UR 5 robot on the simulation platform CoppeliaSim, and the results further indicate the feasibility of this model intuitively.

A review of the recent advances for the in ovo sexing of chicken embryos using optical sensing techniques.

The culling of day-old male chicks has caused ethical and economic concerns. Traditional approaches for detecting the in ovo sex of chicken embryos involve opening the eggshell and inner membrane, which are destructive, time-consuming, and inefficient. Therefore, noncontact optical sensing techniques have been examined for the in ovo sexing of chicken embryos. Compared with traditional methods, optical sensing can increase determination throughput and frequency for the rapid sexing of chicken embryos. This paper presented a comprehensive review of the different optical sensing techniques used for the in ovo sexing of chicken embryos, including visible and near-infrared (Vis-NIR) spectroscopy, hyperspectral imaging, Raman spectroscopy, fluorescence spectroscopy, and machine vision, discussing their advantages and disadvantages. In addition, the latest research regarding different detection algorithms and models for the in ovo sexing of chicken embryos was summarized. Therefore, this paper provides updated information regarding the optical sensing techniques that can be used in the poultry industry and related research.

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment.

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

A Fixed-Time Noise-Tolerance ZNN Model for Time-Variant Inequality-Constrained Quaternion Matrix Least-Squares Problem.

Presently, numerical algorithms for solving quaternion least-squares problems have been intensively studied and utilized in various disciplines. However, they are unsuitable for solving the corresponding time-variant problems, and thus few studies have explored the solution to the time-variant inequality-constrained quaternion matrix least-squares problem (TVIQLS). To do so, this article designs a fixed-time noise-tolerance zeroing neural network (FTNTZNN) model to determine the solution of the TVIQLS in a complex environment by exploiting the integral structure and the improved activation function (AF). The FTNTZNN model is immune to the effects of initial values and external noise, which is much superior to the conventional zeroing neural network (CZNN) models. Besides, detailed theoretical derivations about the global stability, the fixed-time (FXT) convergence, and the robustness of the FTNTZNN model are provided. Simulation results indicate that the FTNTZNN model has a shorter convergence time and superior robustness compared to other zeroing neural network (ZNN) models activated by ordinary AFs. At last, the construction method of the FTNTZNN model is successfully applied to the synchronization of Lorenz chaotic systems (LCSs), which shows the practical application value of the FTNTZNN model.

A Segmented Variable-Parameter ZNN for Dynamic Quadratic Minimization With Improved Convergence and Robustness.

As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides, the existing noise-tolerant ZNN model is not good enough to deal with time-varying noise. Therefore, a new-type segmented VPZNN (SVPZNN) for handling the dynamic quadratic minimization issue (DQMI) is presented in this work. Unlike the previous ZNNs, the SVPZNN includes an integral term and a nonlinear activation function, in addition to two specially constructed time-varying piecewise parameters. This structure keeps the time-varying parameters stable and makes the model have strong noise tolerance capability. Besides, theoretical analysis on SVPZNN is proposed to determine the upper bound of convergence time in the absence or presence of noise interference. Numerical simulations verify that SVPZNN has shorter convergence time and better robustness than existing ZNN models when handling DQMI.

Airborne bacterial communities in the poultry farm and their relevance with environmental factors and antibiotic resistance genes.

The accelerating occurrence and environmental dissemination of bacteria, gas pollutants and antibiotic resistance genes (ARGs) in aerosols of poultry farms have become emerging environmental issues due to their potential threat to animals, workers, and the communities located near such farms. Here, aerosol samples were gathered from inside and outside of the chicken house in winter with a transportable high-flow bioaerosol sampler. Then, 16S rRNA gene amplicon sequencing was used to categorize the bacteria in air samples, and the abundance of 12 ARG subtypes was researched via the real-time quantitative polymerase chain reaction (qPCR). Results indicated that the bacterial richness and diversity and total absolute abundance of ARGs were similar in the bioaerosols from indoor and downwind site of the poultry farm. The zoonotic pathogens, Staphylococcus and Corynebacterium, were detected both inside and outside of the chicken house, and the four most abundant target genes were blaTEM, tetQ, ermB and sul1 in aerosols. Moreover, the correlation between the bacterial communities and environmental factors, such as NH3 and H2S concentrations, wind speed, temperature and relative humidity, was analyzed. The result revealed that the indoor bacteria community was positively associated with temperature and concentrations of air pollutants (NH3 and H2S), and could spread from confinement buildings to the ambient atmosphere through wind. In addition, the network analysis result showed that the airborne bacteria might significantly contribute in shaping the ARGs' profiles in bioaerosol from inside and outside of the poultry house. Overall, our results revealed the airborne bacterial communities and their associated influencing factors in the micro-environment (inside of the chicken house and nearby the boundary of the farm), and brought a new perspective for studying the gas pollutants and bioaerosol from poultry farms in winter.

Effects of dietary energy levels on rumen fermentation, microbiota, and gastrointestinal morphology in growing ewes.

This study investigated whether dietary metabolizable energy (ME) could generate dynamical effects on rumen fermentation, gastrointestinal tract (GIT) morphology, and microbial composition of growing ewes. A total of twenty-eight female Hu lambs were randomly allotted to two treatments with different dietary ME levels: 9.17 (FEA) and 10.41 MJ/kg (FEB). These lambs were further made ready for a 67-day feeding trial. Results showed that the molar proportions of butyrate (p = .020), iso-valerate (p = .028), and valerate (p = .005) were significantly higher in the FEB group than those in the FEA group. The results of the GIT morphologic properties showed that the villus height (VH) (p = .005) was significantly higher and crypt depth was significantly deeper (CD) (p = .005) in the duodenum and that the rumen papillary height (PH) was significantly higher (p = .020) in FEB group compared with the FEA group. High-throughput sequencing results showed that 1826 operational taxonomic units (OTUs) were obtained and that the OTU number (p = .039), the ACE (p = .035), and Chao1 indices (p = .005) were lower in the FEB group. Moreover, 76 genera belonging to 21 phyla were detected in all samples; the relative abundance of Papillibacter (p = .036) and Flexilinea (p = .046) was significantly lower in the high energy group, whereas the relative abundance of unidentified Lachnospiraceae (p = .019), Acetitomaculum (p = .029), unidentified Veillonellaceae (p = .017), Anaerovibrio (p = .005), and Succinivibrio (p = .035) was significantly higher in the FEB group at the genus level. Furthermore, the relative abundance of genes and metabolic pathways were predicted by PICRUSt. The relative abundance of gene families related to carbohydrate metabolism was particularly higher (p = .027) in the FEB group. In summary, these results reveal that the dietary energy levels altered the composition and function of rumen microbiota and GIT morphology in growing female Hu sheep and provide a reference for optimizing diet formula and 10.41MJ/kg of ME level has been recommended in the growing period.

Receiver-Side TCP Countermeasure in Cellular Networks.

Cellular-based networks keep large buffers at base stations to smooth out the bursty data traffic, which has a negative impact on the user's Quality of Experience (QoE). With the boom of smart vehicles and phones, this has drawn growing attention. For this paper, we first conducted experiments to reveal the large delays, thus long flow completion time (FCT), caused by the large buffer in the cellular networks. Then, a receiver-side transmission control protocol (TCP) countermeasure named Delay-based Flow Control algorithm with Service Differentiation (DFCSD) was proposed to target interactive applications requiring high throughput and low delay in cellular networks by limiting the standing queue size and decreasing the amount of packets that are dropped in the eNodeB in Long Term Evolution (LTE). DFCSD stems from delay-based congestion control algorithms but works at the receiver side to avoid the performance degradation of the delay-based algorithms when competing with loss-based mechanisms. In addition, it is derived based on the TCP fluid model to maximize the network utility. Furthermore, DFCSD also takes service differentiation into consideration based on the size of competing flows to shorten their completion time, thus improving user QoE. Simulation results confirmed that DFCSD is compatible with existing TCP algorithms, significantly reduces the latency of TCP flows, and increases network throughput.