Egg rejection and egg recognition mechanisms in Oriental Reed Warblers

Background:Nest parasitism by cuckoos(Cuculus spp.)results in enormous reproductive failure and forces hosts to evolve antiparasitic strategies,i.e.,recognition of own eggs and rejection of cuckoo eggs.There are often sexual conflicts between male and female individuals in the expression of antiparasitic behavior due to the differences in reproductive inputs and division of labor.Methods:By adding a foreign egg made of blue soft clay to the host nest during early incubation period in the field,and by removing several host eggs and adding experimental eggs to control the proportion of two egg types in the nest,we examined egg rejection ability,egg recognition mechanism and sexual difference in egg rejection of the Oriental Reed Warbler(Acrocephalus orientalis),one of the major hosts of Common Cuckoos(Cuculus canorus).Results:Our results indicated that Oriental Reed Warblers can recognize and reject nearly 100%(73/75)of the nonmimetic eggs made of blue soft clay,and they could reject foreign eggs with 100%accuracy,regardless of the ratio of experimental eggs and its own eggs in the nest.Furthermore,all cases of egg rejections recorded by videos were only carried out by females.Conclusions:Oriental Reed Warblers have a high egg recognition ability and show a true recognition mechanism.Only female warblers perform egg rejection,suggesting that the sex for host egg incubation seems to play an important role in the evolution of egg recognition mechanisms.

EEG Emotion Recognition Using an Attention Mechanism Based on an Optimized Hybrid Model

Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.

A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

In many existing multi-view gait recognition methods based on images or video sequences,gait sequences are usually used to superimpose and synthesize images and construct energy-like template.However,information may be lost during the process of compositing image and capture EMG signals.Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection.To better solve the problems,a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed.Firstly,the sliding time window method is used to capture EMG signals.Then,the back-propagation learning algorithm is used to train each layer of convolution,which improves the learning ability of the convolutional neural network.Finally,the channel attention mechanism is integrated into the neural network,which will improve the ability of expressing gait features.And a classifier is used to classify gait.As can be shown from experimental results on two public datasets,OULP and CASIA-B,the recognition rate of the proposed method can be achieved at 88.44%and 97.25%respectively.As can be shown from the comparative experimental results,the proposed method has better recognition effect than several other newer convolutional neural network methods.Therefore,the combination of convolutional neural network and channel attention mechanism is of great value for gait recognition.

Recognition mechanism and sequence optimization of organophosphorus pesticides aptamers for better monitoring contaminations in food

Aptamers as a kind of biological recognition element have shown great potential in monitoring and the rapid quantification of organophosphorus pesticides(OPPs). However, molecules of OPPs are structurally similar and original aptamers selected by systematic evolution of ligands by exponential enrichment are usually long-chain bases, which hamper the further application under OPPs-aptamer recognition. The aim of the research was to develop a new strategy to design oligonucleotide sequences for binding OPPs by combination of experimental and molecular modeling methods. 3D models of aptamers binding OPPs were constructed, and binding energy and the most probable binding site for the OPPs were then determined by molecular docking, and the binding sites were further confirmed by the results of 2-AP replaced experiments. Based on the docking results, a new aptamer for detection 4 representative OPPs with only 29 bases was designed by reasonable truncation and mutation of the reported aptamer(named S4-29). The interaction between this new aptamer and OPPs were analyzed by molecular docking, microscale thermophoresis, circular dichroism and fluorometric analysis. The results revealed that the new aptamer exhibit more superior recognition performance to OPPs, which can be promote the monitoring ability of OPPs contaminations in food.

Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism

Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.

Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.

Theoretical study of mutual control mechanism between magnetization and polarization in multiferroic materials

The mutual control mechanism between magnetization and polarization in multiferroic materials is studied. The system contains a ferromagnetic sublattice and a ferroelectric sublattice. To describe the magneto–electric coupling, we propose a linear coupling Hamiltonian between ferromagnetism and ferroelectricity without microscopic derivation. This coupling enables one to retrieve the hysteresis loops measured experimentally. The thermodynamic properties of the system are calculated, such as the temperature dependences of the magnetization, polarization, internal energy and free energy.The ferromagnetic and ferroelectric hysteresis loops driven by either a magnetic or an electric field are calculated, and the magnetic spin and pseudo-spin are always flipped synchronously under the external magnetic and electric field. Our theoretical results are in agreement with the experiments.

Recognition of an additional mechanism leading to myocardial abnormalities

A growing body of evidence explicitly suggests the significant role of inflammatory processes in the development and progressive deterioration of vascular diseases and cardiomyopathies.1-3 In recent years, a large variety of infections have been reported to be associated with the development of cardiomyopathy; the pathogenic factors include rickets, bacteria, protozoa and other parasites,and also, at least 17 viruses.2。

Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

In the actual complex environment,the recognition accuracy of crop leaf disease is often not high.Inspired by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition accuracy.TSPI-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal stream.PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf datasets.Furthermore,the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed.The experimental results show that as the number of interactions increases,the recognition accuracy of the network also increases.Finally,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

Fused behavior recognition model based on attention mechanism

With the rapid development of deep learning technology,behavior recognition based on video streams has made great progress in recent years.However,there are also some problems that must be solved:(1)In order to improve behavior recognition performance,the models have tended to become deeper,wider,and more complex.However,some new problems have been introduced also,such as that their real-time performance decreases;(2)Some actions in existing datasets are so similar that they are difficult to distinguish.To solve these problems,the ResNet34-3DRes18 model,which is a lightweight and efficient two-dimensional(2D)and three-dimensional(3D)fused model,is constructed in this study.The model used 2D convolutional neural network(2DCNN)to obtain the feature maps of input images and 3D convolutional neural network(3DCNN)to process the temporal relationships between frames,which made the model not only make use of 3DCNN’s advantages on video temporal modeling but reduced model complexity.Compared with state-of-the-art models,this method has shown excellent performance at a faster speed.Furthermore,to distinguish between similar motions in the datasets,an attention gate mechanism is added,and a Res34-SE-IM-Net attention recognition model is constructed.The Res34-SE-IM-Net achieved 71.85%,92.196%,and 36.5%top-1 accuracy(The predicting label obtained from model is the largest one in the output probability vector.If the label is the same as the target label of the motion,the classification is correct.)respectively on the test sets of the HMDB51,UCF101,and Something-Something v1 datasets.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

Video expression recognition based on frame-level attention mechanism

Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.

Thoughts and Practice on the Education Projects of Sino-foreign Credit Mutual Recognition in Private Undergraduate Universities

International development is a challenge that each university must face.The educational mechanism of private undergraduate universities is flexible,and has certain advantages in expanding international education programs.Sino-foreign credit mutual recognition programs are more common in private undergraduate universities.With the continuous development of resources and models on international education cooperation,the forms of Sino-foreign credits mutual recognition cooperation are becoming more diversified.The rapid development of Chinese and foreign credit recognition education programs in private undergraduate universities requires scientific and advanced management concepts and support.Young private universities have short international development time and lack of experience.Therefore,relevant issues should be more researched.This paper analyzes the problems and challenges in the education mode and management of Sino-foreign credit mutual recognition projects in private undergraduate universities,and puts forward relevant countermeasures.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Attention Guided Food Recognition via Multi-Stage Local Feature Fusion

The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.

Analytical modeling and approaches of multihelix cables incorporating with interwire mutual contacts

This study aims to develop an analytical model based on the curve beam theory to capture the mechanical response of a multihelix cable considering the internal contact displacements.Accordingly,a double-helix cable subjected to axial tension and torsion is analyzed,and both the line and point contacts between the neighboring wires and strands are considered via an equivalent homogenized approach.Then,the proposed theoretical model is extended to a hierarchical multihelix cable with mutual contact displacements by constructing a recursive relationship between the high-and low-level multihelix structures.The global tensile stiffness and torsional stiffness of the double-helix cable are successfully evaluated.The results are validated by a finite element(FE)model,and are found to be consistent with the findings of previous studies.It is shown that the contact deformations in multihelix cables significantly affect their equivalent mechanical stiffness,and the contact displacements are remarkably enhanced as the helix angles increase.This study provides insights into the interwire/interstrand mutual contact effects on global and local responses.

Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

Object detection in unmanned aerial vehicle(UAV)aerial images has become increasingly important in military and civil applications.General object detection models are not robust enough against interclass similarity and intraclass variability of small objects,and UAV-specific nuisances such as uncontrolledweather conditions.Unlike previous approaches focusing on high-level semantic information,we report the importance of underlying features to improve detection accuracy and robustness fromthe information-theoretic perspective.Specifically,we propose a robust and discriminative feature learning approach through mutual information maximization(RD-MIM),which can be integrated into numerous object detection methods for aerial images.Firstly,we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain.Then,we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories.Finally,we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields.We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)datasets to prove the effectiveness of the proposed method.The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods,achieving relative growth rates of 51.0%and 39.4%in corruption robustness,respectively.Our code is available at https://github.com/cq100/RD-MIM(accessed on 2 August 2024).

Preparation and interaction mechanism analysis of single‑chain fragment variables against phenylethanolamine A

This is the first report on the screening,expression,and recognition mechanism analysis of single-chain fragment variable(scFv)against phenylethanolamine A(PEAA),a newly emergedβ-adrenergic agonist illegally used as a feed additive for growth promotion.The PEAA-specific scFv scFv,called scFv-32,was screened from hybridoma cell lines by phage display and was found to be optimally expressed in the E.coli system.The ic-ELISA results revealed an IC_(50)value of 10.34μg/L for scFv-32 and no cross-reactivity with otherβ-adrenergic agonists.Homology modeling and molecular docking revealed the key binding sites VAL178,TYP228,and ASP229.One hydrogen bond,two pisigma bonds,and one pi-pi bond maintain the formation of the antibody‒drug complex.Alanine scanning mutagenesis of the three predicted key binding sites showed that the mutants completely lost their recognition activity,which confirmed the accuracy of the theoretical analysis.These results are valuable for the preparation of scFvs and the analysis of the molecular recognition mechanism of antigen-antibodies.

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour recognition.These applications have dramatically increased the diversity of IoT systems.Specifically,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension.Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial information.However,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos.In this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing systems.In particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and UCF101.The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.