BACKGROUND: Ovine pulmonary adenocarcinoma (OPA) is an infectious lung tumour caused by the Jaagsiekte Sheep Retrovirus. Histological examination is the cornerstone of OPA diagnosis and provides the final morphological basis for diagnosis. However, traditional pathology faces challenges, such as complex image interpretation and reliance on subjective judgment. Ensemble learning models have been increasingly applied to medical image classification. In this study, we constructed a dataset of 69,592 images (OPA: 33,609; non-OPA: 35,983) and divided it by employing a phased dataset division strategy. After evaluating DenseNet, EfficientNet, Res2Net101, and ResNet152, Res2Net101 was selected as the best-performing base model, and ensemble learning was conducted using two strategies: output-layer fusion (Efficient-Res2Net-L) and feature fusion (Efficient-Res2Net). Model performance was evaluated using accuracy, precision, Recall, and F1 score. Anti-peeking validation was conducted using five whole-slide images (three OPA, two non-OPA) not included in the dataset. An additional 600 image blocks were used to compare performance of the model with that of pathologists. RESULTS: Res2Net101 achieved the highest accuracy (94.3%) on the test set, whereas EfficientNet made the fewest misjudgements (11) in the anti-peeking image verification. EfficientNet also outperformed others in the comparison with pathologists (accuracy: 95.0%, specificity: 91.3%, sensitivity: 98.7%). The output-layer fusion model Efficient-Res2Net-L slightly outperformed feature fusion. Efficient-Res2Net showed improved accuracy (96.5%), specificity (93.7%), and sensitivity (99.3%), surpassing the performance of junior pathologists and approaching the performance of senior pathologists, with differences reduced to 2.3% and 5%, respectively. CONCLUSION: The integrated model Efficient-Res2Ne demonstrates high accuracy and robustness. Suspicious lesion areas can be identified through rapid initial diagnosis of tissue slice images, assisting pathologists in efficiently completing the final histological diagnosis. This is a valuable tool for improving diagnostic workflow efficiency.

Urban-scale video analytics systems hold significant promise for traffic monitoring, pedestrian flow estimation and public safety. However, the acquisition and processing of spatio-temporal data extracted from traffic videos pose significant challenges to individual privacy. Existing approaches often rely on trusted tracking pipelines or expose fine-grained data, making them incompatible with strong privacy preservation. In this paper, we introduce GeoPrivd, a geospatial urban traffic video analytics framework that enables declarative spatio-temporal queries over object observations with formal differential privacy guarantees. GeoPrivd enforces privacy without relying on raw trajectories or trusted trackers, using a trajectory slicing and bounding mechanism to limit per-user influence. Queries are expressed in GeoPrivdQL, a high-level domain-specific language with built-in privacy controls. Through static query validation and calibrated noise injection, GeoPrivd ensures privacy-preserving analytics results across diverse analytics tasks. Experiments on real-world urban traffic datasets demonstrate that GeoPrivd consistently outperforms standard differential privacy (DP) baselines in utility, stability, and privacy resiliencea.

Once a mare experiences parturition abnormalities, the outcome between a live foal and a stillborn can change rapidly. Automated detection of mare parturition and timely human intervention is crucial to reducing risks during mare and foal parturition. This paper addresses the challenges of manual monitoring of parturition in large-scale equine facilities due to the unpredictability of mare parturition timing, proposing an algorithm for detecting mare parturition through a balanced multi-scale feature fusion based on an improved Libra RCNN. Initially, a ResNet101 backbone network incorporating the CBAM attention module was used to enhance parturition feature extraction capability; subsequently, a balanced content-aware feature reassembly feature pyramid, CARAFE-BFP, was employed to mitigate data imbalance effects while enhancing the quality of feature map upsampling; finally, the GRoIE module was utilized to merge CARAFE-BFP's multi-scale features, improving the model's perception of multi-scale objectives and minor feature changes. The model achieved a mean average precision of 86.26% in scenarios of imbalanced positive and negative samples of mare parturition data, subtle parturition feature differences, and multi-scale data distribution, with a detection speed of 15.06 images per second and an average recall rate of 98.17%. Moreover, this study employed a statistical method combined with a sliding window mechanism to assess the algorithm's performance in detecting mare parturition in video stream continuous monitoring scenarios, achieving an accuracy rate of 92.75% for mare parturition detection. The algorithm proposed in this study achieved non-contact, stress-free, intensive, and automated detection of mare parturition, also demonstrating the immense potential of artificial intelligence technology in the field of animal production management.

Parturition and lactation stress greatly affect physiological and metabolic status of postpartum cows. Monitoring feeding behavior can help assess the health status of postpartum cows. This study aimed to explore the changes in feeding behavior, milk yield (MY), serum indexes, and metabolites of dairy cows during three weeks postpartum. Furthermore, the relationships between feeding behavior, milk yield and serum metabolites were investigated. One hundred seventy-eight healthy multiparous Holstein dairy cows were enrolled to continuously record feeding behavior variables, including feeding time (FT), frequency of feeding (FF), and average feeding time (AFT), using the Nedap neck collar devices, wherein the milk yield was recorded. Out of 178 Holstein dairy cows, 20 cows with the same parity number (2 parities), body condition score (3.0 ± 0.25, recorded in 7 days before parturition), and with eutocic calvings, were selected to explore the serum indexes changes on week 1, 2, and 3 postpartum. Then, 7 cows were further selected based on similar calving date (± 7 d) for metabolic transition detection. Compared to week 1 postpartum, the FT and AFT normalized values on week 2 and 3 postpartum were significantly increased (P < 0.001). The normalized values of FT and AFT were significantly and positively correlated with MY (P < 0.01). Furthermore, the serum total protein (TP), total cholesterol (T-Chol) and IgA contents on week 2 and 3 postpartum were significantly increased compared to week 1 postpartum, while the β-hydroxybutyric acid (BHBA) concentration was significantly decreased. Serum malondialdehyde (MDA), interleukin-6 (IL-6), and leptin (LEP) contents of the 2nd week postpartum, were significantly declined compared to the 1st week postpartum. The normalized values of FT was negatively correlated with serum BHBA level, while positively correlated with the contents of TP and T-Chol (P < 0.05). According to UPLC-MS/MS and pathway analysis results, the serum levels of glycerophospholipids changed most in first three weeks postpartum, which were mainly related to glycerophospholipid metabolism. Finally, the correlation analysis showed that 29 glycerophospholipids (GPs) and 3 fatty acyls (FAs) were significantly and positively correlated with the FT and AFT normalized values (P < 0.05). Together, the findings suggest that the feeding behavior variables, such as FT and AFT, could serve as reliable indicators of energy metabolism in dairy cows during the first three weeks postpartum.

With rapid advancements in computing, communication, and storage technologies, innovative machine-learning training methods have emerged. One prominent approach is federated learning, which enables collaborative model training without sharing original data sets and ensures data security and privacy. However, federated learning faces challenges, including low-edge node participation, untrustworthy nodes, and untraceable training data. This research combines federated learning theory with blockchain technology, proposing a hybrid blockchain-based federated learning algorithm and incentive mechanism. The primary contributions are the following: Firstly, existing federated learning algorithms hide training data, making them vulnerable to backdoor attacks. To address this, the proposed algorithm utilizes a federated blockchain for authentication and management, preventing impersonation and false data injection, thereby improving training accuracy. Secondly, participating node identities are authenticated using a consortium blockchain, ensuring data integrity. Training parameters are stored on a public blockchain, enabling training data traceability. Blockchain integration enhances decentralization, security, and privacy. Simulation experiments demonstrate the proposed scheme's superiority in robustness and accuracy compared to traditional methods. This research provides valuable insights for advancing federated learning, addressing challenges, and enhancing its applicability in real-world scenarios.

Apple leaf diseases severely affect the quality and yield of apples, and accurate classification is crucial for reducing losses. However, in natural environments, the similarity between backgrounds and lesion areas makes it difficult for existing models to balance lightweight design and high accuracy, limiting their practical applications. In order to resolve the aforementioned problem, this paper introduces a lightweight converged attention multi-branch network named LCAMNet. The network integrates depthwise separable convolutions and structural re-parameterization techniques to achieve efficient modeling. To avoid feature loss caused by single downsampling operations, a dual-branch downsampling module is designed. A multi-scale structure is introduced to enhance lesion feature diversity representation. An improved triplet attention mechanism is utilized to better capture deep lesion features. Furthermore, a dataset named SCEBD is constructed, containing multiple common disease types and interference factors under natural environments, realistically reflecting orchard conditions. Experimental results show that LCAMNet achieves 92.60% accuracy on the SCEBD and 95.31% on a public dataset, with only 0.03 GFLOPs and 1.30M parameters. The model maintains high accuracy while remaining lightweight, enabling effective apple leaf disease classification in natural environments on devices with limited resources.

Body condition scoring (BCS) in dairy goats serves as an objective method for quantifying the reserves of body tissues, namely adipose and muscle tissues, and evaluating overall health. Recognized as a dependable and feasible welfare indicator, BCS is crucial for managing animal health. This study introduced an automated BCS model for dairy goats, leveraging computer vision and deep learning via the YOLO v5 algorithm. The model distinguished waist phenotypic characteristics, analyzed milk quality, and assessed blood biochemical indices across different body conditions. Demonstrating high precision, the model achieved Precision (P), Recall (R), and F1 scores of 78.5 %, 82.0 % and 81.7 %, respectively. It effectively identified underweight, moderate, and overweight groups with identification rates of 85.2 %, 79 %and 71.2 % respectively, and maintained a deviation rate from manual assessments of <= 10 %. Notably, the waist region's grayscale parameters and brightness levels correlated positively with body condition scores, while the depth of indentation exhibited a negative correlation. Milk yield showed no significant variation (3-4 kg), but milk protein content was highest in the average condition group. Lipid and liver metabolism markers varied significantly with body condition, underscoring physiological impacts. This model not only confirmed the robustness of YOLO v5 for animal welfare assessment but also early intervention strategies are used in the management of dairy goats, in line with the principles of precision animal husbandry, particularly beneficial during the critical mid-lactation period. This work underscored the significant potential of integrating advanced technologies into everyday agricultural practices to enhance animal welfare and farm management.

Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive to obstructions and alterations in the cattle’s appearance, such as back defacement. The current open-set identification methods exhibit low discriminatory stability for new individuals. These limitations significantly hinder the application and promotion of the model. To address these challenges, this paper proposes a prototype network-based incremental identification framework for Holstein cattle to achieve stable identification of new individuals under small sample conditions. Firstly, we design a feature extraction network, ResWTA, which integrates wavelet convolution with a spatial attention mechanism. This design enhances the model’s response to low-level features by adjusting the convolutional receptive field, thereby improving its feature extraction capabilities. Secondly, we construct a few-shot augmented prototype network to bolster the framework’s robustness for incremental identification. Lastly, we systematically evaluate the effects of various loss functions, prototype computation methods, and distance metrics on identification performance. The experimental results indicate that utilizing ResWTA as the feature extraction network achieves a top-1 accuracy of 97.43% and a top-5 accuracy of 99.54%. Furthermore, introducing the few-shot augmented prototype network enhances the top-1 accuracy by 4.77%. When combined with the Triplet loss function and the Manhattan distance metric, the identification accuracy of the framework can reach up to 94.33%. Notably, this combination reduces the incremental learning forgetfulness by 4.89% compared to the baseline model, while improving the average incremental accuracy by 2.4%. The proposed method not only facilitates incremental identification of Holstein cattle but also significantly bolsters the robustness of the identification process, thereby providing effective technical support for intelligent farm management.

Individual recognition of Holstein cows is the basis for realizing precision dairy farming. Current machine vision individual recognition systems usually rely on fixed vertical illumination and top-view camera perspectives or require complex camera systems, and these requirements limit their promotion in practical applications. To solve this problem, a lightweight Holstein cow individual recognition feature extraction network named CowBackNet is designed in this paper. This network is not affected by camera angle and lighting changes and is suitable for farm environments. Secondly, a fusion multi-attention mechanism approach was adopted to integrate the attention mechanism, inverse residual structure, and depth-separable convolution technique to design a new feature extraction module, LightCBAM. This module was placed in the corresponding layer of CowBackNet to enhance the model’s ability to extract the key features of the cow’s back image from different viewpoints. In addition, the CowBack dataset was constructed in this study to verify the model’s ability to be applied in real scenarios, containing Holstein cowback images in real production environments under different viewpoints. The experimental results show that when using CowBackNet as a feature extraction network, the recognition accuracy reaches 88.30%, FLOPs are 0.727 G, and the model size is only 6.096 MB. Compared with the classical EfficientNetV2, the accuracy of CowBackNet is improved by 11.69%, the FLOPs are reduced by 0.001 G, and the number of parameters is also reduced by 14.6%. Therefore, the model developed in this paper shows good robustness in shooting angle, light change, and real production data, which not only improves the recognition accuracy but also optimizes the computational efficiency of the model, which is of great practical application value for realizing precision farming.

Timely and accurate detection of ear tag dropout is crucial for standardized precision breeding, health monitoring, and breeding evaluation. Reserve breeding pigs exhibit high activity levels and frequent interactions, leading to a higher prevalence of ear tag dropout. However, detection is challenging due to motion blur, small tag size, and significant target scale variations. To address this, we propose a motion blur-aware multi-scale framework, Adapt-Cascade. First, a Weight-Adaptive Attention Module (WAAM) enhances the extraction of motion blur features. Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. Third, a Feature-Guided Multi-Scale Region Proposal strategy (FGMS-RP) strengthens multi-scale target detection. Integrated into the Cascade Mask R-CNN framework with Focal Loss, Adapt-Cascade achieves 93.46% accuracy at 19.2 frames per second in detecting ear tag dropout in reserve breeding pigs. This model provides a high-accuracy solution for intelligent pig farm management.

IntroductionThe timely and precise detection of foliar diseases in potatoes, a food crop of worldwide importance, is essential to safeguarding agricultural output. In complex field environments, traditional recognition methods encounter significant challenges, including the difficulty in extracting features from small and diverse early-stage lesions, blurred edge features due to gradual transitions between diseased and healthy tissues, and degraded robustness from background interference such as leaf texture and varying illumination.MethodsTo address these limitations, this study proposes an optimized lightweight convolutional neural network architecture, termed LDL-MobileNetV3S. The model is built upon the MobileNetV3 Small backbone and incorporates three innovative modules: a Lightweight Multi-scale Lite Fusion (LF) module to enhance the perception of small lesions through cross-layer connections, a Dynamic Dilated Convolution (DDC) module that employs deformable convolutions to adaptively capture pathological features with blurred boundaries, and a Lightweight Attention (LA) module designed to suppress background interference by assigning spatially adaptive weights.ResultsExperimental results demonstrate that the proposed model achieves a recognition accuracy of 94.89%, with corresponding Precision, Recall, and F1-score values of 93.54%, 92.53%, and 92.77%, respectively. Notably, these results are attained under a highly compact model configuration, requiring only 6.17 MB of storage and comprising 1.50 million parameters. This is substantially smaller than benchmark models such as EfficientNet-B0 (15.61 MB / 3.83 M parameters) and ConvNeXt Tiny (106 MB / 27.8 M parameters).ConclusionThe proposed LDL-MobileNetV3S model demonstrates superior performance and efficiency compared to several existing lightweight models. This study provides a cost-effective and high-accuracy solution for potato leaf disease diagnosis, which is particularly suitable for deployment on intelligent diagnostic devices operating in resource-limited field environments.

Abstract Chenopodium album L. (CAL) has many bioactive compounds and pharmacological activities. Fermentation is the preferred modification method of releasing target bioactive. We prepared two crude polysaccharides: SLC (the polysaccharide from the unfermented stem and leaves of CAL) and FSLC (the polysaccharide from the fermented stem and leaves of CAL). In vitro antioxidant and composition of SLC and FSLC were compared, and the effects of FSLC on antioxidant activity in the IPEC-J2 cells model and development-promoting activity in the zebrafish model were evaluated. The results revealed that FSLC possesses stronger DPPH, hydroxyl radical scavenging, and reducing power than SLC. The levels of total polysaccharide, polyphenol, and flavonoid, as well as the molar ratio of glucuronic acid increased in FSLC. Compared with SLC, the relative contents of protocatechuic acid, protocatechualdehyde, gentisic acid, vanillic acid, p-coumaric acid, quercetin, ferulic acid methyl ester, hispidulin, diosmetin, cinnamic acid, isorhamnetin, syringic acid and kaempferol in FSLC increased. In IPEC-J2 cells, antioxidant enzyme activities and GSH levels were significantly increased, while the MDA level was reduced by treatment with 0.25 mg/mL FSLC. In the zebrafish model, treatment with 25 ~ 300 μg/mL of FSLC had no harmful impact on the morphology and viability of embryos at 12 ~72 hpf. At 48 and 60 hpf, treatment with 100 ~ 300 μg/mL FSLC increased the hatching rate of embryos. At 72hpf, treatment with 100 ~ 300 μg/mL FSLC could relieve morphological abnormalities caused by LPS in zebrafish and improve the hatching rate of embryos. Together, these results provide useful information on the potential for applying polysaccharides from the stems and leaves of CAL as natural feed additives to exert its antioxidant and development-promoting functions. Graphical Abstract

The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model’s ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.

Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications.

The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs.

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms’ deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.

Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

The study showcases the process of deep learning operated in agriculture, including Deep IoT, which makes the procedure easier using the deep neural system. The use of the IoT in the agrarian sectors makes the evolution of firms more effective. The application of the IoT detector supports the making of grade derivatives in the husbandry department. Marketing of crop finance is two other operations of smart agriculture that help for better harvest farming. Through the IoT technology in the farming industry, agriculturalists can get notifications about the temperature and climate. The method needs professional and qualified employees in the division to properly monitor the system and the methods. The submission of the proper nourishment for the proper crop increases the life duration of the harvest and makes the crop free from menace. The velocity of the manufacture of undeveloped items can also be improved by using the IoT. The function of the BDA and IoT has enlarged for the healthier construction of farming items. The foreword of elegant farming in the rural industry requires more capable and qualified trainers to give the personnel proper teaching. The urbanization of the farming process and the use of elegant and modern technology are well-designed in the time of "Agriculture 3.0".

The close-coupled selective catalytic reduction (cc-SCR) catalyst is an effective technology to reduce tailpipe NOx emission during cold start. This paper investigated the optimal ammonia storage under steady and transient state in the cc-SCR. The study showed that a trade-off between NOx conversion efficiency and ammonia slip is observed on the pareto solutions under steady state, and the optimal ammonia storage is calculated with ammonia slip less than 10 μL/L based on the China Ⅵ emission legislation. The rapid temperature increase will lead to severe ammonia slip in the transient test cycle. A simplified 0-D calculation method on ammonia slip under transient state is proposed based on kinetic model of ammonia adsorption and desorption. In addition, the effect of ammonia storage, catalyst temperature and temperature increasing rate on ammonia slip are analyzed. The optimal ammonia storage is calculated with maximum ammonia slip less than 100 μL/L according to the oxidation efficiency of ammonia slip catalyst (ASC) downstream cc-SCR. It was found that the optimal ammonia storage under transient state is much lower than that under steady state in cc-SCR at lower temperature, and a phase diagram is established to analyze the influence of temperature and temperature increasing rate on optimal ammonia storage.

为确定并联混合动力涡扇发动机的能量利用效率,并对不同电功率输入下发动机工作特性与性能参数的变化规律进行研究,本文从理论上对发动机有效循环功的来源进行划分,并由此分别定义并联混合动力涡扇发动机的电能利用率和燃油利用率;使用航空发动机性能仿真软件PROOSIS搭建基于CFM56-7B26发动机的并联混合动力涡扇发动机零维模型,模拟不同电功率输入条件下的发动机稳态性能.研究结果表明:并联混合动力涡扇发动机内涵道的循环类型仍然是基于布雷顿循环的实际循环,输入的电功率对发动机外涵道推力的贡献占比远高于其对内涵道推力的贡献;电能利用率始终明显高于燃油利用率,这也是并联混合动力涡扇发动机实现节能的主要原因;当输入的电功率增加时,发动机的涵道比增加,涡轮前温度与总压比降低,各部件的稳态工作点将发生移动,可能造成部件效率的下降.由于电功率的输入以及电能的高利用效率,并联混合动力涡扇发动机的油耗和能耗均低于常规涡扇发动机.