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The effect of Zn/Mg ratio on the microstructure and properties of Al-Zn-Mg-Cu alloys under single-step and double-step aging processes were investigated by using scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron backscatter diffraction (EBSD), as well as hardness, room temperature tensile, and fracture toughness testing. During single-step aging, as the Zn/Mg ratio increases, the aging response of the alloys accelerates. However, the peak hardness and strength gradually decreased while the average size of precipitates increased. During double-step aging, as the secondary aging time increases, the strength and hardness of the alloys gradually decrease. Additionally, the higher the Zn/Mg ratio, the faster the strength and hardness decline. Under the T74 temper, the alloy with higher Zn/Mg ratios exhibits lower hardness and strength but better fracture toughness and larger precipitate sizes. A simple method was proposed to statistically determine the critical diameter size of GPII zone and η′ phase, which was approximately 4.47 nm. A strength model was developed to predict the yield strength of alloys, considering factors such as solid solution strengthening, grain boundary strengthening, and precipitation strengthening. The model demonstrates good predictive accuracy for both peak-aged and T74 temper alloys, providing valuable guidance for alloy design and microstructure regulation.

The application of traditional Al-Mg-Zn alloy in aerospace field is limited due to poor corrosion resistance for β-phase at grain boundaries and low strength for the lack of T phase. Here we propose a novel high Mg-containing Al-Mg-Zn-Si alloy with a novel alloying strategy of high Mg content combining with a suitable microalloying Si, obtaining small and densely precipitated nanoscale T’ phase in the matrix, co-existing T’ and η’ phases adjacent to grain boundary and T phase at grain boundary. The formation of GPT’ zone in the matrix is dramatically enhanced due to energetically favorable substitution of Al by Si and furtherly facilitates homogeneous growth of T’ phase by solute redistribution. η’ phase is transformed from T’ phase or evolved from GPIIη’ zone along grain boundary due to feasible thermodynamic condition of Mg-poor and Zn-rich domain by distinct Mg and Zn exsolution on grain boundary. The present alloying design makes it possible that differential precipitation behavior of T’ phase and T’ + η’ phases can simultaneously occur in the matrix and adjacent to grain boundary, the alloy has obviously advantaged overall performance over traditional Al-Zn-Mg-(Cu) alloys, which raises a new perspective for developing new high-performance lightweight aluminum alloys.

Using aberration-corrected scanning transmission electron microscopy, a new fully coherent precipitate platelet designated X′′, with a flexible atomic layer FCC structure, was identified in a low Cu/Mg ratio Al-Cu-Mg-Ag alloy. The emergence of the X′′ phase is primarily attributed to the preferential segregation behavior of Mg and Cu atoms towards the {111}α Ag layers. The strengthening mechanism of the X′′ phase is principally driven by the coherency strengthening, yielding efficient reinforcement (99.8 MPa increment per 1 % volume fraction). After dislocation cutting, the X′′ phase maintains coherence with the matrix, minimizing local strain accumulation at the interface. The mechanical tests show that the alloy with the precipitated X′′ phase, despite its lower strength compared to the alloy with the precipitated Ω phase, exhibits superior ductility and work hardening. The study holds promising implications for designing novel Al-Cu-Mg-Ag alloys with a desirable strength-ductility balance.

Abstract In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective.

The microstructure of Al-Cu-Li alloys is influenced by the alloying contents, which in turn affect the ultimate performance. This study examined the microstructure characteristics of Al-Cu-Li alloys with varying Li contents (1.05, 1.30, and 1.66 wt%) in as-cast state, as well as microstructure evolution during homogenization treatments. The investigation employed a range of analytical techniques, including optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), X-ray diffraction (XRD), differential scanning calorimeter (DSC), focused ion beam (FIB), transmission electron microscopy (TEM), and thermodynamic calculation software. The findings indicate that the eutectic phase exhibited significant variations with the Li content changing. Three distinct low-melting phases were identified in 1.05Li alloy, including Ag-containing Al2CuMg, Al7Cu4Li, and Al2Cu. As Li content was elevated to 1.30 wt%, there was a noticeable drop in the quantities of Ag-containing Al2CuMg and Al7Cu4Li phases. Conversely, the Al2Cu phase increased, and a few Al2CuLi phases emerged. When the Li raised to 1.66 wt%, an increased Al2CuLi phase was discovered, which existed along the dendritic edges. However, there were only little Ag-containing Al2CuMg phase, and no Al7Cu4Li phase detected. The initial melting temperatures for the four soluble eutectic phases were systematically arranged in the following order: Ag-containing Al2CuMg

The content of Zn and Mg directly influences the microstructure characteristics of Al–Zn–Mg–Cu alloys. In the present study, the solidification phases of three as-cast Al–Zn–Mg–Cu alloys with different Zn/Mg ratio (1.5, 1.8 and 2.1 at.%, successively) were thoroughly investigated, and their evolutions during homogenizations were analyzed with the aid of optical microscopy (OM), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The results showed that serious dendritic segregation existed in the as-cast microstructures. In the as-cast alloys with three different Zn/Mg ratio, the secondary phases are represented by non-equilibrium intermetallic phases (Mg(Zn,Cu,Al)2 and Al2Cu) and precipitate MgZn2 phase. Thereinto, the Mg(Zn,Cu,Al)2 phase possessed a majority, which was determined through computational analysis of solidification processes. With the increase of Zn/Mg ratio, the amount of Mg(Zn,Cu,Al)2 phase continuously decreased, while the content of Al2Cu phase showed only minor variation. As the Zn/Mg ratio increased, the strength and hardness of the as-cast alloy gradually improved. The Zn/Mg ratio has a significant impact on the dissolution and transformation of the secondary phases during homogenization process. As the Zn/Mg ratio increased, the melting point of the as-cast eutectic gradually decreased. Additionally, due to the higher diffusion rate of Zn compared to Mg and Cu elements, the dissolution rate of the secondary phase increased accordingly. At relatively low temperatures (380, 410, and 440 °C), phase transformation from the Mg(Zn,Cu,Al)2 phase to the Al2CuMg phase occurred in all three alloys. However, phase transformation was only present in Low/Mid-Zn/Mg ratio alloys at 470 °C. These differences are attributed to the varying diffusion rates of Zn, Mg, and Cu elements during the homogenization process. The implementation of a two-stage homogenization process effectively addresses the issue of residual Al2CuMg phase in Low/Mid-Zn/Mg ratio alloys.

In 6000-series Al-Mg-Si alloys, Zn is commonly added to enhance the aging response for improved properties. However, the segregation of Zn to interfaces and its interaction mechanisms with strengthening phases remain unclear. Combining experiments and theoretical calculations, we systematically investigated Zn segregation behavior at the β″/Al interfaces in Al-Mg-Si alloys. Using a modified β″-Mg5Al2Si4 model, we determined that the interface (100)β″//(130)Al has a slightly smaller formation energy of 1.06 kJ/mol, while (001)β″//(3¯20)Al has a relatively smaller interfacial energy of 116 mJ/m2. HAADF-STEM analysis revealed these interfacial morphologies and atomic distributions, showing that Zn atoms not only enter the β″ phase but are also inclined to segregate at the interfaces by occupying the Si3/Al sites. Furthermore, the stability of the β″/Al interfaces and the Zn segregation behavior are well explained at the atomic scale, with calculations showing that stronger hybridization between Zn-3d and Si-3s orbitals facilitates Zn segregation at the interfaces.

In this study, the effect of Sc content on the microstructural characteristics and phase evolution of an Al–Cu–Li alloy under as-cast and homogenization conditions was investigated. The results show the grain refinement effect was distinct when the content of Sc was 0.16 wt%. However, further increased in Sc content resulted in a limited decrease in grain size. Besides, In the 0.16Sc alloy, the flocculent phase with primary Al3(Sc,Zr) phase coexisting with W (Al8-xCu4+xSc) phase appeared, and in the 0.32Sc alloy, the additional blocky primary Al3(Sc,Zr) phase was observed. With the increased of Sc content, the proportion of primary Al3(Sc,Zr) and W phases gradually increased, and the number and size of Al2Cu and Ag-containing Al2CuMg phases at grain boundaries gradually decreased. Two different morphologies of the primary W phase, flocculent and long strip shaped, were revealed within the Sc-containing alloy. The formation of the core-shell structure formed by Al3(Sc,Zr) particles and W phase was generated by the peritectic-eutectic reaction (Liquid + Al3(Sc,Zr)→α-Al + W) and the long strip of W phase was generated by the eutectic reaction (Liquid→α-Al + Al2Cu + W). During the homogenization process, the Al2Cu and Ag-containing Al2CuMg phases were completely re-dissolved after the two previous stages of homogenization, and only a small amount of the spheroidal residual W phase persisted. A subsequent low temperature treatment was employed to allow the Sc atoms that had diffused into the Al matrix to precipitate as Al3(Sc,Zr) dispersoids, resulting in a higher number density of Al3(Sc,Zr) particles for more efficiently utilizing the Sc elements.

Stimulator of interferon gene (STING) functions importantly as antiviral adaptor protein during the innate immune activation. The role of the dimerization domain (DD) of STING remains obscure although other domains of this molecule have been studied extensively. To clarify the mechanism of black carp STING (bcSTING) in the innate immunity, bcSTING-ΔDD (bcSTING without DD) and bcSTING-ΔCTT (bcSTING without CTT domain) were constructed and analyzed in this manuscript. The reporter assays revealed that the induced transcription of IFN promoters mediated by bcSTING-ΔDD were much higher than that of the wild type bcSTING; however, bcSTING-ΔCTT almost lost the activities to trigger the interferon (IFN) promoters transcription. The mRNA transcriptional levels of IFN and interferon stimulated genes (ISGs) in EPC cells expressing bcSTING-ΔDD were obviously higher than those of EPC cells expressing wild type bcSTING; however, the transcriptional levels of the above cytokines of EPC cells expressing bcSTING-ΔCTT were basically the same as those of control cells. EPC cells overexpressing bcSTING-DD showed stronger antiviral activity than those overexpressing wild-type bcSTING. Furthermore, the co-immunoprecipitation assay identified the self-interaction between bcSTING-ΔDD molecules. And it was interesting that the affinity between bcSTING-ΔDD and bcTBK1 was obviously stronger than that between bcTBK1 and wild-type bcSTING. Thus, our data suggests that DD of black carp STING plays a negative regulatory role in STING-mediated antiviral immunity, which provides a new perspective for further researching the function of fish STING in the innate immunity.

In this work, the evolution of mechanical properties of binary Al–Li alloys with four approximately equal gradient Li contents (0.91–3.98 wt.%) under aging conditions is thoroughly investigated. The alloys undergo aging treatments at 175 °C for x hours (x = 0–120 h), and the peak-aged times of the four alloys are 6 h, 12 h, 48 h and 48 h, respectively, as the Li concentration increases. Both in the solution-treated and peak-aged states, the elastic modulus of binary Al–Li alloys exhibits an approximately linear increase with increasing Li content, consistent with trends predicted by density functional theory (DFT) calculations. Due to the presence of Al3Li precipitates, the modulus of higher-Li-concentration alloys in the peak-aged state increases by approximately 1.4–2.5% compared with that of alloys in the solution-treated state. Additionally, the study finds that increasing Li content significantly enhances the tensile strength and yield strength of the alloy but decreases its ductility, leading to a transition in fracture mode from ductile to brittle, as evidenced by a microscopic analysis of fracture surfaces. Under peak-aged (175 °C/48 h), the alloy with the highest Li content exhibits the maximum tensile strength of 341 MPa and a yield strength of 296 MPa, while its elongation is the lowest at 2.1%. These findings contribute to a deeper understanding of the effects of aging precipitates on the mechanical properties of Al–Li alloys, providing fundamental guidance for the design of future generations of Al–Li alloys.

With the development of society, there is an increasingly urgent demand for light-weight, high-strength, and high-temperature-resistant structural materials. High-entropy alloys (HEAs) owe much of their unusual properties to the selection among three phases: solid solution (SS), intermetallic compound (IM), and mixed SS and IM (SS and IM). Therefore, accurate phase prediction is crucial for guiding the selection of element combinations to form HEAs with desired properties. Light high-entropy alloys (LHEAs), as a significant branch of HEAs, exhibit excellent performance in terms of specific strength. In this study, we employ a machine learning (ML) method to realize the design of light-weight high-entropy alloys based on solid solutions. We determined the Gradient Boosting Classifier model as the best machine learning model through a two-step feature and model selection, in which its accuracy and F1_Score achieve 0.9166 and 0.8923. According to the predicted results, we obtained Al28Li35Mg15Zn10Cu12 LHEAs, which are mainly composed of 90% solid solution. This alloy accords with the prediction results of machine learning. But it is made up of a two-phase solid solution. In order to obtain a light-weight high-entropy alloy dominated by a single solid solution, we designed Al24Li15Mg26Zn9Cu26 LHEAs on the basis of machine learning prediction results accompanied by expert experience. Its main structure includes a single-phase solid solution. Our work provides an alternative approach to the computational design of HEAs and provides a direction for future exploration of light-weight high-entropy alloys.

In the present work, the effect of Zn on the aging precipitates and mechanical properties of Al-Cu-Li alloys was investigated by Vickers hardness, tensile tests, transmission electron microscopy (TEM) and differential scanning calorimetry (DSC). The results indicated that the addition of Zn reduced the activation energy of the T1 phase and makes it easier to precipitate. The activation energy of the T1 phase, which was 107.02 ± 1.8 KJ/mol, 94.33 ± 1.7 KJ/mol, 90.33 ± 1.7 KJ/mol and 90.28 ± 1.6 KJ/mol for 0Zn, 0.4Zn, 0.8Zn and 1.2Zn alloy, respectively. The area number density of the T1 precipitate ranged from 97.0 ± 4.4 pcs/μm2 to 118.2 ± 2.8 pcs/μm2 as the Zn content increased from 0 to 1.2 wt.%. Consequently, the addition of Zn promoted the precipitation of the T1 phase. Therefore, the peak hardness and tensile strength of the alloy also increased with the increase in the Zn content, and the hardness of the alloy with Zn content of 1.2 wt.% increased by 16.5 ± 1.4 HV; meanwhile, the ultimate tensile strength increased by 46.5 ± 2.5 MPa. Therefore, the area number density of precipitates increased and improved the strength of the Zn-containing alloy.

The effects of Sn in as-extruded Mg-1.2Ga alloys on the electrochemical and discharge properties were investigated by in-situ electrochemistry, half-cell and air-cell tests to provide new ideas for the development of magnesium alloy anode materials. The addition of Sn promoted grain refinement and deformation precipitation on the extrusion streamlines. Extrusion can effectively reduce the |bc| value of the alloy, improving the cathodic hydrogen control process. After the addition of Sn, more secondary phases are introduced. The redeposition and dissolution of Sn and Ga are enhanced. In conclusion, the GT12 alloy has excellent anode efficiency and average cell voltage, and its maximum discharge capacity (1292.98 mAh·g−1 at 75 mA·cm−2) is 55 mAh·g−1 higher than that of the as-cast alloy, showing great potential for development.

Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.

Antibacterial peptide has been widely developed in cultivation industry as feed additives. However, its functions in reducing the detrimental impacts of soybean meal (SM) remain unknown. In this study, we prepared nano antibacterial peptide CMCS-gcIFN-20H (C-I20) with excellent sustained-release and anti-enzymolysis, and fed mandarin fish (Siniperca chuatsi) with a SM diet supplemented with different levels of C-I20 (320, 160, 80, 40, 0 mg/Kg) for 10 weeks. 160 mg/Kg C-I20 treatment significantly improved the final body weight, weight gain rate and crude protein content of mandarin fish and reduced feed conversion ratio. 160 mg/Kg C-I20-fed fish maintained appropriate goblet cells number and mucin thickness, as well as improved villus length, intestinal cross-sectional area. Based on these advantageous physiological changes, 160 mg/Kg C-I20 treatment effectively reduced multi-type tissue (liver, trunk kidney, head kidney and spleen) injury. The addition of C-I20 did not change the muscle composition and muscle amino acids composition. Interestingly, dietary 160 mg/Kg C-I20 supplementation prevented the reduction in myofiber diameter and change in muscle texture, and effectively increased polyunsaturated fatty acids (especially DHA + EPA) in muscle. In conclusion, dietary C-I20 in a reasonable concentration supplementation effectively alleviates the negative effects of SM by improving the intestinal mucosal barrier. The application of nanopeptide C-I20 is a prospectively novel strategy for promoting aquaculture development.

In the present study, the effects of varying heating and cooling rates during the solution treatment process on the microstructure and properties of AA7050 alloy wires were investigated using tensile tests, metallographic microscopy, electron backscattered diffraction, and transmission electron microscopy. It was found that the recrystallized grain size of the alloy, subjected to method of rapid heating, exhibited a smaller and more uniform distribution in comparison to method of slow heating. The low density of η′ strengthening phases after the artificial aging treatment was formed using air cooling method. Meanwhile, by using the water quenching method sufficient solute atoms and more nucleation sites were provided resulting in a large number of η′ strengthening phases being formed. In addition, the alloy processed using the water quenching method displayed higher strength than that treated using the air cooling method for the T6 and T73 states. Furthermore, coarse precipitates formed and less clusters were observed in the matrix, while high density nanoscale clusters and no continuous precipitation are formed when using the water quenching method.

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy’s components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm−3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys.

A quantitative relationship between microstructure and tensile properties of Al–Zn–Mg–Cu alloys with various alloying degrees was investigated in this study. Three kinds of high strength Al–Zn–Mg–Cu alloys with little strength difference were used for the microstructural characteristic and statistical analysis. A strength model with high accuracy was established through the systematical calculation of strengthening theories, which was validated by the microstructure and ultimate tensile strength of a testing sample. The model gives the precise ratio of the contribution of precipitation hardening and even the component achieved from the bypass mechanism. Moreover, guidance on the regulation of precipitation phases was proposed from the strength model. It can be disclosed that the alloys will show the best strength when the precipitates of 7xxx series Al alloy are distributed near the right of critical radius, which is identified as 2.5 nm in this model.

Large-scale sheep farming has conventionally relied on barcodes and ear tags, devices that can be difficult to implement and maintain, for sheep identification and tracking. Biological data have also been used for tracking in recent years but have not been widely adopted due to the difficulty and high costs of data collection. To address these issues, a noncontact facial recognition technique is proposed in this study, in which training data were acquired in natural conditions using a series of video cameras, as Dupo sheep walked freely through a gate. A key frame extraction algorithm was then applied to automatically generate sheep face data sets representing various poses. An improved MobilenetV2 framework, termed Order-MobilenetV2 (O-MobilenetV2), was developed from an existing advanced convolutional neural network and used to improve the performance of feature extraction. In addition, O-MobilenetV2 includes a unique conv3x3 deep convolution module, which facilitated higher accuracy while reducing the number of required calculations by approximately two-thirds. A series of validation tests were performed in which the algorithm identified individual sheep using facial features, with the proposed model achieving the highest accuracy (95.88%) among comparable algorithms. In addition to high accuracy and low processing times, this approach does not require significant data pre-processing, which is common among other models and prohibitive for large sheep populations. This combination of simple operation, low equipment costs, and high robustness to variable sheep postures and environmental conditions makes our proposed technique a viable new strategy for sheep facial recognition and tracking.

The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters.

Advanced aluminum-lithium alloys are the key structural materials urgently needed for the development of light-weighted aircraft in the aerospace field. In this study, we employ a machine learning approach accompanied by domain knowledge to realize the accelerated design of aluminum-lithium alloy with high specific modulus and specific strength by identifying an optimal combination of key features through a three-step feature filtering of datasets containing 145 alloys. The maximum specific modulus in the experimental alloys that screened from the predicted results increases by 4 % compared with the maximum specific modulus in the comparative dataset. The specific modulus of the designed alloy with the best comprehensive performance increased by 12.6 % compared with the widely used 2195-T8 alloy while maintaining a similar specific strength. Machine learning shows appealing feasibility and reliability in the field of materials design.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study developed a large-scale benchmark dataset, Sheepface-107, comprising 5350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol was developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

Grass carp reovirus (GCRV) seriously threatens the grass carp (Ctenopharyngodon idella) industry. Prophylactic GCRV vaccines prepared by virus-like particle (VLP) assembly biotechnology can improve effectiveness and safety. The highly immunogenic candidate antigens of GCRV vaccines that have been generally considered are the outer capsid proteins VP4, VP56, and VP35. In this study, VP4, VP56, and VP35 were expressed in an Escherichia coli expression system and a Pichia pastoris expression system. The successful assembly of uniform, stable, and non-toxic VP4/VP56/VP35 VLPs was confirmed through various assays. After vaccination and GCRV infection, the survival rate in the VLPs + adjuvant Astragalus polysaccharide (APS) group was the highest (62%), 40% higher than that in control group (22%). Through the antibody levels, tissue viral load, and antioxidant immunity assays, the P. pastoris VLP vaccine effectively improved IgM levels, alleviated tissue virus load, and regulated antioxidant immune-related indicators. The treatment with P. pastoris VLPs enhanced the mRNA expression of important immune-related genes in the head kidney, as measured by qRT-PCR assay. Upon hematoxylin-eosin staining examination, relatively reduced tissue pathological damage was observed in the VLPs + APS group. The novel vaccine using P. pastoris VLPs as an effective green biological agent provides a prospective strategy for the control of fish viral diseases.

Type II grass carp reovirus (GCRV-II) with high pathogenicity and infectivity causes severe hemorrhagic disease, which leads to extensive death in the grass carp and black carp aquaculture. However, the early invasion portal remains unclear. In this study, we explored the invasion portal, time, and pathway of GCRV-II by immersion infection in grass carp. Through the detection of the infected grass carp external body surface tissues, most of them could be detected to carry GCRV-II within 45 min except for the skin covered by scales. Further shortening the duration of infection, we proved that GCRV-II rapidly invades through the nostril (especially), gill, and skin on head at only 5 min post-immersion, rather than merely by adhesion. Subsequently, visual localization investigations of GCRV-II were conducted on the nostril, olfactory system (olfactory bulb and olfactory tract), and brain via immunofluorescence microscopy and transmission electron microscopy. We found that few viruses were located in the nostril at 5 min post-immersion infection, while a significantly increased quantity of viruses were distributed in all of the examined tissues at 45 min. Furthermore, the semi-qRT-PCR and Western blotting results of different infection times confirmed that GCRV-II invades grass carp via the nostril–olfactory system–brain axis and then viral replication unfolds. These results revealed the infection mechanism of GCRV-II in terms of the invasion portal, time, and pathway in grass carp. This study aims to understand the invasion mode of GCRV-II in grass carp, thus providing theoretical support for the prevention and control strategies of hemorrhagic disease.

Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations, reduce data dimensionality, and simplify the model structure, a BP neural network model for air quality level prediction optimized by stepwise discriminant analysis (STEPDISC) and principal component analysis (PCA) is proposed with 12 factors of historical daily meteorology and air pollutants in Bayannur city as samples. The results showed that, at the significance level of 0.01, the STEPDISC method retained 9 significant impact factors. The PCA method made an orthogonal linear combination of the 9 factors to form the principal components, and the contribution of the top 5 principal components were 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%. At a contribution threshold of 0.85, the top 5 principal component scores were used as input nodes to construct the STEPDISC-PCA-BP model, which had a prediction accuracy of 85.5%.Compared with the PCA-BP and BP model, which had a prediction accuracy of 61.8% and 56.7%, respectively, the STEPDISC-PCA-BP model has a higher prediction accuracy, shorter time, and lower complexity of structure and data dimensionality, and can provide the necessary technical support for the local air quality improvement.

The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior recognition capabilities. This review summarizes the recent application of machine vision technology, machine learning, and deep learning in the main behavior recognition of dairy cows. The authors summarized identity recognition technology based on facial features, muzzle prints, and body features of dairy cows; motion behavior recognition technology such as lying, standing, walking, drinking, eating, rumination, estrus; and the recognition of common diseases such as lameness and mastitis. Based on current research results, machine vision technology will become one of the important technological means for the intelligent breeding of dairy cows. Finally, the author also summarized the advantages of this technology in intelligent dairy farming, as well as the problems and challenges faced in the next development.

Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.

In this study, the effects of Cu addition on artificial age hardening behavior and mechanical properties of Al-1.2Mg-1.2Si-(xCu) alloy was investigated quantitatively and qualitatively by Vickers hardness, tensile test, and transmission electron microscope. The results indicated that Cu addition enhanced the aging response of the alloy at 175 °C. With the increase in Cu content, the time for the alloys to reach peak aging decreased from 12 h to 10 h and 8 h. The tensile strength of the alloy was obviously improved with Cu added in which was 421 MPa of 0Cu alloy, 448 MPa of 0.18Cu alloy, and 459 MPa of 0.37Cu alloy. The results of TEM observation revealed that the addition of 0.37Cu changed the aging precipitation sequence of the alloy, in which the precipitation sequence of 0Cu and 0.18Cu alloy was SSSS→GP zones/pre-β″→β″→β″ + β′, 0.37Cu alloy was SSSS→GP zones/pre-β″→β″ + L→β″ + L + Q′. Moreover, with the addition of Cu, the number density and volume fraction of precipitates of the Al-1.2Mg-1.2Si-(xCu) alloy was evidently increased. The number density was increased from 0.23 × 1023/m3 to 0.73 × 1023/m3 in the initial aging stage and from 1.9 × 1023/m3 to 5.5 × 1023/m3 in the peak aging stage. The volume fraction was increased from 0.27% to 0.59% in the early aging stage and from 4.05% to 5.36% in the peak aging stage. It indicated that Cu addition promoted the precipitation of strengthening precipitates and boosted the mechanical properties of the alloy accordingly.

The 2195 aluminum alloy is widely used in cryogenic storage tanks for space vehicles, where it can reach a temperature of 20 K. In order to explore the reasons for the increased strength of 2195 aluminum alloys at cryogenic temperatures, uniaxial tensile tests were conducted in the range of 20 K–298 K. Tensile fracture was observed. In addition, the microstructures under different temperatures were observed using EBSD (electron back-scattered diffraction) and TEM (transmission electron microscopy) techniques, and the dislocation density of the material was quantitatively characterized using the modified Williamsone–Hall method based on XRD (X-ray diffraction) analysis. The results indicated that the ultimate strength increased at an increasing rate with the temperature decrease, while the elongation increase was insignificant. The fracture’s surface exhibited that dimple characteristics seemed to be unapparent while the quantity of tearing ridges was enhanced by the temperature decrease. Meanwhile, the fracture mode changed from ductile to brittle fracture. The microdeformation degree revealed by KAM images showed an aggravating trend, and the deformation tended to be more uniform. The increasingly enhanced dislocation density quantitatively revealed by the modified Williamsone–Hall method also proved this and that the increase in dislocations had a similar trend to that of tensile strength, which was furtherly revealed by TEM images. This indicated that the more regions are involved in deformation, the more dislocations are generated in the material during deformation, resulting in an increase in strength at cryogenic temperatures.

The 2198 Al–Li alloy has unique superiority in mechanical performance and has been extensively used in the aerospace field. In this study, the hot deformation behavior of the 2198 Al–Li alloy was investigated on a Gleeble-1500 thermomechanical simulator with a strain rate of 0.01–10 s−1 in the temperature range of 330–510 °C. The Arrhenius constitutive equation of the alloy was established based on the true stress–strain curves to describe the rheology behaviors during the deformation of the alloy. The processing maps under the strain of 0.2–0.8 were constructed, which indicates the efficiency of power dissipation and instability of the deformed alloy. It was found that the instability domains are more likely to occur in the regions of low deformation temperature and high strain rate, corresponding to the high Zener–Hollomon (Z) parameter. The microstructure evolution of the studied alloy with different Z parameters was characterized. Then, the dynamic recrystallization (DRX) behavior was studied by electron backscatter diffraction, and the misorientation angle of deformed specimens was analyzed. The effect of different deformation temperatures and strain rates on the microstructure of the alloy and the behavior of dislocations and precipitations were investigated by transmission electron microscopy. The results demonstrate that continuous dynamic recrystallization (CDRX) and geomatic dynamic recrystallization (GDRX) mainly occur at the deformation conditions of a low Z value, and discontinuous dynamic recrystallization (DDRX) is likely to occur with increasing Z values.

The effect of trace Sc and Zr on grain refinement of Al-5.0Mg-3.0Zn as-cast alloy was investigated by optical microscopy and scanning electron microscopy with EDS. The results indicated that the critical quantities of Sc and Zr for Al-Mg-Zn alloy to produce a significant refining effect were determined and the total mass fraction of Sc and Zr was not less than 0.27, and the mass fraction of Sc was more than 0.13. The average grain size of the as-cast alloy ranged from 30 to 44 μm. The as-cast microstructure refinement of the alloy was related to the number density of Al3(Sc, Zr) particles and the critical nucleation work of grains. Furthermore, based on first-principles calculations, the formation enthalpies of different Al3(Sc, Zr) particles and the interface stabilities have been theoretically discussed. The experimental phenomenon of increasing the particle number by the simultaneous addition of Sc and Zr was well explained. Thus, this investigation presented a better insight into the grain refining mechanism from experiments and theoretical calculations.