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Abstract Developing a comprehensive magnetic theory for correlated itinerant magnets poses challenges due to the difficulty in reconciling both local moments and itinerant electrons. In this work, we investigate the microscopic process of magnetic phase transition in ferromagnetic metal Fe3−δ GeTe2. We find that Hund’s coupling is crucial for establishing ferromagnetic order. During the ferromagnetic transition, we observe the formation of quasiparticle flat bands and an opposing tendency in spectral weight transfer, primarily between the lower and upper Hubbard bands, across the two spin channels. Moreover, our results indicate that one of the inequivalent Fe sites exhibits Mott physics, while the other Fe site exhibits Hund’s physics, attributable to their distinct atomic environments. We suggest that ferromagnetic order reduces spin fluctuations and makes flat bands near the Fermi level more distinct. The hybridization between the distinctly flat bands and other itinerant bands offers a possible way to form heavy fermion behavior in ferromagnets. The complex interactions of competing orders drive correlated magnetic metals to a new frontier for discovering outstanding quantum states.

Abstract Hydrogen embrittlement reduces the durability of the structural steels required for the hydrogen economy. Understanding how hydrogen interacts with the materials plays a crucial role in managing the embrittlement problems. Theoretical models have indicated that carbon vacancies in metal carbide precipitates are effective hydrogen traps in steels. Increasing the number of carbon vacancies in individual metal carbides is important since the overall hydrogen trapping capacity can be leveraged by introducing abundant metal carbides in steels. To verify this concept, we compare a reference steel containing titanium carbides (TiCs), which lack carbon vacancies, with an experimental steel added with molybdenum (Mo), which form Ti-Mo carbides comprising more carbon vacancies than TiCs. We employ theoretical and experimental techniques to examine the hydrogen trapping behavior of the carbides, demonstrating adding Mo alters the hydrogen trapping mechanism, enabling hydrogen to access carbon vacancy traps within the carbides, leading to an increase in trapping capacity.

ABSTRACT: The enclosed multistory poultry housing is a type of poultry enclosure widely used in industrial caged chicken breeding. Accurate identification and detection of the comb and eyes of caged chickens in poultry farms using this type of enclosure can enhance managers’ understanding of the health of caged chickens. However, the accuracy of image detection of caged chickens will be affected by the enclosure's entrance, which will reduce the precision. Therefore, this paper proposes a cage-gate removal algorithm based on big data and deep learning Cyclic Consistent Migration Neural Network (CCMNN). The method achieves automatic elimination and restoration of some key information in the image through the CCMNN network. The Structural Similarity Index Measure (SSIM) between the recovered and original images on the test set is 91.14%. Peak signal-to-noise ratio (PSNR) is 25.34dB. To verify the practicability of the proposed method, the performance of the target detection algorithm is analyzed both before and after applying the CCMNN network in detecting the combs and eyes of caged chickens. Different YOLOv8 detection algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, and YOLOv8x, were used to verify the algorithm proposed in this paper. The experimental results demonstrate that compared to images without CCMNN processing, the precision of comb detection of caged chickens is improved by 11, 11.3, 12.8, and 10.2%. Similarly, the precision of eye detection for caged chickens is improved by 2.4, 10.2, 6.8, and 9%. Therefore, more complete outline images of caged chickens can be obtained using this algorithm and the precision in detecting the comb and eyes of caged chickens can be enhanced. These advancements in the algorithm offer valuable insights for future poultry researchers aiming to deploy enhanced detection equipment, thereby contributing to the accurate assessment of poultry production and farm conditions.

The classification of silkworm cocoons is essential prior to silk reeling and serves as a key step in improving the quality of raw silk. At present, cocoon classification mainly relies on manual sorting, which is labor-intensive and inefficient. In this paper, a cocoon detection algorithm S-YOLOv8_c based on the cooperation of MobileSAM and YOLOv8 for the mountage cocoons was proposed. The MobileSAM with a designed area thresholding algorithm was used for the semantic segmentation of mountage cocoon images, which could mitigate the effect of complex backgrounds and maximize the discriminability of cocoon features. Subsequently, the BiFPN was added to the neck of YOLOv8 to improve the multiscale feature fusion capability. The loss function was replaced with the WIoU, and a dynamic non-monotonic focusing mechanism was introduced to improve the generalization ability. In addition, the GAM was incorporated into the head to focus on detailed cocoon information. Finally, the S-YOLOv8_c achieved a good detection accuracy on the test set, with a mAP of 95.8%. Furthermore, to experimentally validate the sorting ability, we deployed the proposed model onto the self-developed Cartesian coordinate automatic cocoon harvester, which indicated that it would effectively meet the requirements of accurate and efficient cocoon sorting.

Heat stress is one of the most important environmental stressors facing poultry production. The presence of heat stress will reduce the antioxidant capacity and immunity of poultry, thereby seriously affecting the health and performance of poultry. The paper proposes an improved FPN-DenseNet-SOLO model for poultry heat stress state detection. The model uses Efficient Channel Attention (ECA) and DropBlock regularization to optimize the DenseNet-169 network to enhance the extraction of poultry heat stress features and suppress the extraction of invalid background features. The model takes the SOLOv2 model as the main frame, and uses the optimized DenseNet-169 as the backbone network to integrate the Feature Pyramid Network to detect and segment instances on the semantic branch and mask branch. In the validation phase, the performance of FPN-DenseNet-SOLO was tested with a test set consisting of 12,740 images of poultry heat stress and normal state, and it was compared with commonly used object detection models (Mask R CNN, Faster RCNN and SOLOv2 model). The results showed that when the DenseNet-169 network lacked the ECA module and the DropBlock regularization module, the original model recognition accuracy was 0.884; when the ECA module was introduced, the model's recognition accuracy improved to 0.919. Not only that, the recall, AP0.5, AP0.75 and mean average precision of the FPN-DenseNet-SOLO model on the test set were all higher than other networks. The recall is 0.954, which is 15, 8.8, and 4.2% higher than the recall of Mask R CNN, Faster R CNN and SOLOv2, respectively. Therefore, the study can achieve accurate segmentation of poultry under normal and heat stress conditions, and provide technical support for the precise breeding of poultry.

The behaviors of dairy cows, such as feeding, ruminating, running, resting (standing, lying), head-shaking, drinking, and walking, can indicate their health status. In this study, a multi-sensor was used to collect data of cow’s multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system equipped with geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities was designed. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments with different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.

The harvesting straw feed crops (silage corn, alfalfa, herbaceous mulberry, etc.) was tedious, high-labor-cost, and large-nutrient-loss. A self-propelled straw forage crop harvester, which could realize the integration of cutting, flattening and modulating, chopping, and throwing straw forage crops, was designed. The cutting angle could freely be adjusted between 0° and 8°. The max rotation speed of the flattening roller could reach 590 r/min and could be adjusted consecutively by the hydraulic control device. To verify the performance of this machine, several harvesting experiments of alfalfa, silage corn, and herbaceous mulberry with different moisture, were carried out on this machine. During the experiment, the average working speed of the machine was 1.6 m/s, the cutting height was 40–80 mm, and the flattening rate was 97.14%. It is determined that the suitable cutting speed for harvesting alfalfa is 2131 r/min; the suitable cutting speed for harvesting silage corn is 836 r/min; the suitable cutting speed for harvesting herb mulberry is 1045 r/min. The design of the machine can not only improve labor productivity and reduce the nutrient loss of forage crop but also support the silage harvesting machinery and equipment for forage crop.

Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems of labor-intensive, labor-intensive, and inability to push material in time at night, etc., in this study, an autonomous navigation pusher robot based on 3D lidar is designed, and an obstacle avoidance strategy based on the improved artificial potential field method is proposed. Firstly, the 3D point cloud data of the barn is collected by the self-designed pushing robot, the point cloud data of the area of interest is extracted using a direct-pass filtering algorithm, and the 3D point cloud of the barn is segmented using a height threshold. Secondly, the Least-Squares Method (LSM) and Random Sample Consensus (RANSAC) were used to extract fence lines, and then the boundary contour features were extracted by projection onto the ground. Finally, a target influence factor is added to the repulsive potential field function to determine the principle of optimal selection of the parameters of the improved artificial potential field method and the repulsive direction, and to clarify the optimal obstacle avoidance strategy for the pusher robot. It can verify the obstacle avoidance effect of the improved algorithm. The experimental results showed that under three different environments: no noise, Gaussian noise, and artificial noise, the fence lines were extracted using RANSAC. Taking the change in the slope as an indicator, the obtained results were about −0.058, 0.058, and −0.061, respectively. The slope obtained by the RANSAC method has less variation compared to the no-noise group. Compared with LSM, the extraction results did not change significantly, indicating that RANSAC has a certain resistance to various noises, but RANSAC performs better in extraction effect and real-time performance. The simulation and actual test results show that the improved artificial potential field method can select reasonable parameters and repulsive force directions. The optimized path increases the shortest distance of the obstacle point cloud from the navigation path from 0.18 to 0.41 m, where the average time is 0.059 s, and the standard deviation is 0.007 s. This shows that the optimization method can optimize the path in real time to avoid obstacles, basically meet the requirements of security and real-time performance, and effectively avoid the local minimum problem. This research will provide corresponding technical references for pusher robots to overcome the problems existing in the process of autonomous navigation and pushing operation in complex open scenarios.

The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms.

The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms’ low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.

We investigate a reduced scaling full-potential DFT method based on the multiple scattering theory (MST) code MuST, which is released online (https://github.com/mstsuite/MuST) very recently. First, we test the accuracy by calculating structural properties of typical body-centered cubic (BCC) metals (V, Nb, and Mo). It is shown that the calculated lattice parameters, bulk moduli, and elastic constants agree with those obtained from the VASP, WIEN2k, EMTO, and Elk codes. Second, we test the locally self-consistent multiple scattering (LSMS) mode, which achieves reduced scaling by neglecting the multiple scattering processes beyond a cut-off radius. In the case of Nb, the accuracy of 0.5 mRy/atom can be achieved with a cut-off radius of 20 Bohr, even when small deformations are imposed on the lattice. Despite that the calculation of valence states based on MST exhibits linear scaling, the whole computational procedure has an overall scaling of about O(N1.6), due to the fact that the updating of Coulomb potential scales almost as O(N2). Nevertheless, it can be still expected that MuST would provide a reliable and accessible way to large-scale first-principles simulations of metals and alloys.

Simulation analysis and parameter optimization are performed for the loading and mixing devices of a self-propelled total mixed ration mixer. To reveal the three-dimensional movement of silage material under the action of the loading cutter roller, the latter is modeled using SolidWorks software. ANSYS/LS-DYNA software is used to simulate the process of silage cutting, which is modeled using smoothed particle hydrodynamics coupled with the finite element method. The cutting force and power consumption are simulated, and the behavior of the equivalent strain of the silage is determined. The results showed that silage was broken up mainly by extrusion and shear force due to the loading cutter roller. The power consumption according to the simulation is consistent with the value from an empirical formula, confirming the validity of the proposed modeling method. To study the mixing performance and obtain the optimum parameters of the mixing device, the Hertz–Mindlin model is used for the interaction between material particles and mixing device. A three-factor, five-level method is used to optimize the mixing performance. Material-mixing time, loading rate, and auger speed are chosen as experimental factors and mixed uniformity as an evaluation index. It is found that auger speed and material mixing time have significant effects on mixing uniformity. These results provide reference values allowing the analysis of the crushing of silage and selection of the optimum parameters for mixing performance.

In view of such a phenomenon that nowadays cotton breeding and seeding mainly depend on artificial planting in China, a kind of combined air suction cotton breed seeder was designed, which is mainly composed of combined air suction seed metering device, three-way switch device, waste seed recovery device, vacuum fan, and so on. It can sow two rows at a time, and the line spacing, plant spacing, and sowing depth can be continuously adjusted to achieve precise sowing. The model of “suck before and blow after” is adopted in seed clearing, which makes seed clearing both clean and rapid. The force, speed, and acceleration of cotton seed are theoretically analyzed when the seed metering device is filled with seeds and then determines the main structural parameters and motion parameters of the device. This paper takes Lumianyan No. 37 as the research object, adopts the single-factor test and the quadratic regression rotation orthogonal test, selects the forward speed, air pressure, and groove plate width of the air suction cup as the experimental factors, and conducts an experiment of the seeder’s performance, by analyzing the influence law of the seeder’s velocity on its seeding variation coefficient. This paper concludes that the coefficient of variation is small when the velocity is 1.2–1.5 m/s and finds the optimal range of unit velocity when sowing and finally builds the mathematical models including multiples index and missing index. After parameter optimization, this paper makes the optimal parameters for sure; that is, the seeder’s velocity ranges from 1.3 to 1.5 m/s, wind pressure ranges from 2.5 to 2.8 kPa, and the width of the air suction groove ranges from 2.6 to 2.8 mm. At this time, the multiples index and missing index of the combined air suction cotton breed seeder are less than 3%, whose sum total is less than 5%. In the field test, the seed clearing performance of the seed clearing system is tested, and the seed clearing rate has reached 100%, meeting the seeding requirements in cotton tablets.

Using the ab initio calculations, we study the lattice distortion of HfNbTaTiVC5, HfNbTaTiZrC5 and MoNbTaTiVC5 high-entropy carbide (HEC) ceramics. Results indicate that the Bader atomic radius and charge transfer in HECs is very close to those from binary carbide. The degree of lattice distortion strongly depends on the alloying element. The Bader atomic radius can excellently describe the lattice distortion in HEC. Further, the corresponding atomic radius and formation enthalpy of binary carbides may be indicators to predict the single-phase HECs.

The ordered L12 phase generally appears as dispersed precipitates in FCC multi-principal element alloys (MPEAs). In this work, a single L12 ordered intermetallic is obtained in the annealed equimolar FeCoNiV alloy. Experiments show that the FCC to L12 ordering transformation occurs below 778±2 °C, accompanied by lattice contraction, anti-phase boundaries (APBs) formation and low-temperature magnetic moment decrease. A strong linear strain hardening rate up to 5.0 GPa is obtained during the tensile deformation for L12 ordered alloy at room temperature. The ab initio calculations based on the exact muffin‑tin orbitals in combination with coherent potential approximation predict that V atoms prefer to occupy the cubic corner positions of L12 sublattice, and the FCC phase becomes more stable with respect to L12 phase above 849 °C considering the competition between enthalpy and entropy. The ab initio molecular dynamic simulations indicate that V atoms prefer to bond with the unlike atoms (Fe, Co and Ni) in liquid FeCoNiV, which is consistent with the cubic corner site occupation of V in L12 ordered structure. The present work demonstrates a new design strategy of MPEAs which focus on single phase ductile intermetallic compounds. Keywords: High-entropy alloys, Ordering transformation, FeCoNiV, Ab initio calculations, Liquid structure

Similar to the importance of XRD in experiments, ab initio calculations, as a powerful tool, have been applied to predict the new potential materials and investigate the intrinsic properties of materials in theory. As a typical solid-solution material, the large degree of uncertainty of high-entropy alloys (HEAs) results in the difficulty of ab initio calculations application to HEAs. The present review focuses on the available ab initio based solid-solution models (virtual lattice approximation, coherent potential approximation, special quasirandom structure, similar local atomic environment, maximum-entropy method, and hybrid Monte Carlo/molecular dynamics) and their applications and limits in single phase HEAs.

The stability, electronic structures, elastic and thermal properties of the ternary YMgX4 (X=Co, Ni, Cu) compounds are investigated by first principle calculations in combination with the quasi-harmonic Debye model. The three compounds are predicted to be stable according to the formation enthalpies (ΔH), elastic constants (cij) and phonon dispersion models. The elastic constants and polycrystalline elastic moduli reveal the relatively low anisotropy of these compounds. The electronic structures and bonding characters are analyzed by the electron densities of states and difference charge densities in the (110) plane. The comparisons between the phonon density of state and the quasi-harmonic Debye model imply the heat capacities (Cv) and vibrational entropies (S) are close for YMgCo4 and YMgCu4, whereas large deviation for YMgNi4. A possible reason is that YMgNi4 has the slightly strong directional bondings, relative to YMgCo4 and YMgCu4.

We investigate the elastic moduli, ideal tensile strength, and thermodynamic properties of TiVNb and AlTiVNb refractory medium-entropy alloys (HEAs) by using ab initio alloy theories: the coherent potential approximation (CPA), the special quasi-random supercell (SQS), and a 432-atom supercell (SC). We find that with increasing number of alloy components, the SQS elastic constants become sensitive to the supercell size. The predicted elastic moduli are consistent with the available experiments. Aluminum doping decreases the stability of the body centered cubic phase. The ideal tensile strength calculation indicates that adding equiatomic Al to TiVNb random solid solution increases the intrinsic strength (ideal strain increase from 9.6% to 11.8%) and decreases the intrinsic strength (from 9.6 to 5.7GPa). Based on the equation of states calculated by the CPA and SC methods, the thermodynamic properties obtained by the two ab initio methods are assessed. The L21 AlTiVNb (Ti-Al-V-Nb) alloy is predicted to be thermodynamically and dynamically stable with respect to the solid solution. [ABSTRACT FROM AUTHOR]