Search

High-velocity air-fuel spray （HVAF） technology was used to prepare two kinds of Al-based amorphous alloy coatings with high porosity （HP） of 1.36% and low porosity （LP） of 0.86%. The effects of porosities on the corrosion behavior of Al-based amorphous alloy coatings were studied. The porosities and microstructures of the coatings were analyzed by scanning electron microscope （SEM） and X-ray diffraction （XRD） combined with three-dimensional X-ray tomography （3D XRT）. The corrosion properties of the coatings were studied by electrochemical testing system and contact angle measuring instrument. The passive film components of the two coatings were analyzed by X-ray photoelectron spectroscopy （XPS）. The results show that the self-corrosion current density （Icorr） and pitting potential （Epit） of the LP coatings are 3.0×10-6 A/cm2 and -0.40 V， respectively. The Icorr and Epit for HP coatings are 6.0×10－6 A/cm2 and -0.47 V， respectively， signifying that the LP coating has better resistance to the localized corrosion； the charge transfer resistance （Rct） of LP coatings is about twice than that of HP coating； the LP coating has a larger contact angle， which proves that LP coating has better hydrophobicity and stronger corrosion resistance； the contents of RE2O3 in the passive film formed on the LP coating are larger， which further indicates that LP coating has better corrosion resistance.

NiCoCrAlYNbHfTa powders were prepared by using the argon gas atomization method through the composite modification of Nb, Hf and Ta elements. The oxidation performance of the prepared powders was compared with that of commercial NiCoCrAlY powder at 1200 ℃ for 24, 48 h and 72 h. The phase, microstructure, and elemental distribution of the powders were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectrometry (EDS). The isothermal oxidation experiments were conducted to compare the high-temperature oxidation performance of the two powders. The results indicate that the prepared powders have good sphericity, and the elemental distribution within the spheres is uniform. The NiCoCrAlYNbHfTa powders consist primarily of the β phase and the γ/γ' phases, and commercially available NiCoCrAlY powders are composed of the γ/γ' phase.NiCoCrAlYNbHfTa powder can rapidly form a dense oxide layer, effectively slowing the diffusion and consumption of Al. Conversely, commercial NiCoCrAlY powder consumes Al at a faster rate after isothermal oxidation. The two powders show similar overall trends in oxidation mass gain curves. However, commercial NiCoCrAlY powder has a longer initial oxidation period with rapid depletion of the Al element. In comparison with the commercial powder, the NiCoCrAlYNbHfTa powder exhibits superior high-temperature oxidation and anti-sintering performance.

Simple Summary: In the duck breeding process, the rapid and accurate identification of gender is currently a key challenge. This study proposes a method based on sound signal discrimination to address this issue. The vocalizations of one-day-old male and female ducklings were extracted and the signal features were obtained through professional processing. Classification models were established using a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). In total, 70% of the dataset was used for training, and 30% was used for testing. The test results showed that the BPNN model had the highest accuracy rate of 93.33% in identifying male ducks, while the CNN model had the highest accuracy rate of 95% in identifying female ducks. From the perspective of overall model performance, the CNN model performed the best, with the highest accuracy rate and F1-score of 84.15% and 84.32%, respectively. This study can provide a technical reference and a foundation for future voice recognition technologies for different poultry breeds and applications. Gender recognition is an important part of the duck industry. Currently, the gender identification of ducks mainly relies on manual labor, which is highly labor-intensive. This study aims to propose a novel method for distinguishing between males and females based on the characteristic sound parameters for day-old ducks. The effective data from the sounds of day-old ducks were recorded and extracted using the endpoint detection method. The 12-dimensional Mel-frequency cepstral coefficients (MFCCs) with first-order and second-order difference coefficients in the effective sound signals of the ducks were calculated, and a total of 36-dimensional feature vectors were obtained. These data were used as input information to train three classification models, include a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). The training results show that the accuracies of the BPNN, DNN, and CNN were 83.87%, 83.94%, and 84.15%, respectively, and that the three classification models could identify the sounds of male and female ducks. The prediction results showed that the prediction accuracies of the BPNN, DNN, and CNN were 93.33%, 91.67%, and 95.0%, respectively, which shows that the scheme for distinguishing between male and female ducks via sound had high accuracy. Moreover, the CNN demonstrated the best recognition effect. The method proposed in this study can provide some support for developing an efficient technique for gender identification in duck production. [ABSTRACT FROM AUTHOR]

Simple Summary: Efficient breeding of meat ducks using three-dimensional and multi-layer cages is a novel approach being actively explored in China. In this process, timely and accurate detection of abnormal situations among ducks is crucial for optimizing and refining the cage-rearing system, and ensuring animal health and welfare. This study focused on the overturned and dead status of cage-reared ducks using YOLOv8 as the basic network. By introducing GAM and Wise-IoU loss functions, we proposed an abnormal-situation recognition method for cage-reared ducks based on YOLOv8-ACRD. Building on this, we refined the identification of key body parts of cage-reared ducks, focusing on six key points: head, beak, chest, tail, left foot, and right foot. This resulted in the development of an abnormal posture estimation model for cage-reared ducks, based on HRNet-48. Furthermore, through multiple tests and comparative verification experiments, it was confirmed that the proposed method exhibited high detection accuracy, generalization ability, and robust comprehensive performance. The method proposed in this study for perceiving abnormal situations in cage-reared ducks not only provides foundational information for the progress and improvement of the meat duck cage-reared system but also offers technological references for the intelligent breeding of other cage-reared poultry. Overturning and death are common abnormalities in cage-reared ducks. To achieve timely and accurate detection, this study focused on 10-day-old cage-reared ducks, which are prone to these conditions, and established prior data on such situations. Using the original YOLOv8 as the base network, multiple GAM attention mechanisms were embedded into the feature fusion part (neck) to enhance the network's focus on the abnormal regions in images of cage-reared ducks. Additionally, the Wise-IoU loss function replaced the CIoU loss function by employing a dynamic non-monotonic focusing mechanism to balance the data samples and mitigate excessive penalties from geometric parameters in the model. The image brightness was adjusted by factors of 0.85 and 1.25, and mainstream object-detection algorithms were adopted to test and compare the generalization and performance of the proposed method. Based on six key points around the head, beak, chest, tail, left foot, and right foot of cage-reared ducks, the body structure of the abnormal ducks was refined. Accurate estimation of the overturning and dead postures was achieved using the HRNet-48. The results demonstrated that the proposed method accurately recognized these states, achieving a mean Average Precision (mAP) value of 0.924, which was 1.65% higher than that of the original YOLOv8. The method effectively addressed the recognition interference caused by lighting differences, and exhibited an excellent generalization ability and comprehensive detection performance. Furthermore, the proposed abnormal cage-reared duck pose-estimation model achieved an Object Key point Similarity (OKS) value of 0.921, with a single-frame processing time of 0.528 s, accurately detecting multiple key points of the abnormal cage-reared duck bodies and generating correct posture expressions. [ABSTRACT FROM AUTHOR]