Characterization Of The Mechanical Performance Of The AE44-2 And AE44-4 High Pressure Die Cast Mg-Rare Earth Alloys

This thesis aims to study the local mechanical properties of high pressure die cast (HPDC) AE44-2 and AE44-4 alloys at 25˚C and 200˚C and their microstructures. The chemical composition of the precipitates and grain size, the effect of cooling rate on the grains, the relationship between the grain size and mechanical properties, and the creep resistance of these two HPDC alloys were studied and discussed. In this thesis, the spherical micro-indentation, constant Berkovich indentation tests, and tensile tests were performed on the specimens at 25˚C and 200˚C to probe their stress-strain response and creep behavior. This study used a new analytical technique to deduce the stress-strain curves from the spherical indentation tests. This thesis suggests that the mechanical properties of these two alloys have a complicated dependence on the grain size. Although, with different RE additions, these two alloys have a similar indentation creep resistance at 25˚C and 200˚C

Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing Systems (NeurIPS) 201

New insights in to the ameliorative effects of zinc and iron oxide nanoparticles to arsenic stressed spinach (Spinacia oleracea L.)

Nanotechnology is capturing great interest worldwide due to their stirring applications in various fields and also individual application of iron oxide nanoparticle (FeO−NPs) and zinc oxide nanoparticle (ZnO−NPs) have been studied in many literatures. However, the combined application of FeO and ZnO−NPs is a novel approach and studied in only few studies. For this purpose, a pot experiment was conducted to examine the plant growth and biomass, photosynthetic pigments, gas exchange attributes, oxidative stress and response of antioxidant compounds (enzymatic and nonenzymatic), sugars, nutritional status of the plant, organic acid exudation pattern As accumulation from the different parts of the plants in spinach (Spinacia oleracea L.) under the different As concentrations i.e., 0 (no As), 60 and 120 μM] which were primed with combined application of two levels of FeO−NPs (10 and 20 mg L−1) and ZnO−NPs (20 and 40 mg L−1). Results from the present study showed that the increasing levels of As in the soil significantly (P \u3c 0.05) decreased plant growth and biomass, photosynthetic pigments, gas exchange attributes, sugars, and nutritional contents from the roots and shoots of the plants. In contrast, increasing levels of As in the soil significantly (P \u3c 0.05) increased oxidative stress indicators in term of malondialdehyde, hydrogen peroxide, and electrolyte leakage, and also increased organic acid exudation patter in the roots of S. oleracea. The negative impact of As toxicity can overcome the combined application of ZnO−NPs and FeO-NPs, which ultimately increased plant growth and biomass by capturing the reactive oxygen species, and decreased oxidative stress in S. oleracea by decreasing the As contents in the roots and shoots of the plants. Research findings, therefore, suggest that the combined application of ZnO−NPs and FeO-NPs can ameliorate As toxicity in S. oleracea, resulting in improved plant growth and composition under As stress, as depicted by balanced exudation of organic acids

Expanding Language-Image Pretrained Models for General Video Recognition

Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable "zero-shot" generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem. In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios. In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using 12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited. Code and models are available at https://aka.ms/X-CLIPComment: Accepted by ECCV2022, Ora

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%

Recalling, Sharing and Participating in a Social Media Intervention Promoting HIV Testing: A Longitudinal Analysis of HIV Testing Among MSM in China.

Social media interventions may enhance HIV services among key populations, including men who have sex with men (MSM). This longitudinal analysis examined the effect of recalling, sharing, and participating in different components of a social media intervention on HIV testing among MSM. The social media intervention included six images/texts and information about an online local community contest to promote testing. Of the 1033 men, they recalled a mean of 2.7 out of six images and shared an average of one image online. 34.5% of men recalled information on the online local community contest and engaged in a mean of 1.3 contest. Recalling images/texts (aOR = 1.13, 95% CI 1.02-1.25) and recalling a local contest (aOR = 1.59, 95% CI 1.13-1.24) were associated with facility-based HIV testing. This study has implications for the development and evaluation of social media interventions to promote HIV testing

MSM Behavior Disclosure Networks and HIV Testing: An Egocentric Network Analysis Among MSM in China.

Men who have sex with men (MSM) disclose same-sex behaviors with others, creating disclosure networks. This study examined the characteristics of disclosure networks that are associated with HIV testing among MSM in China through an online nationwide survey. Name-generator questions were used to ask each participant ("ego") to nominate up to five social network members ("alters") with whom he had disclosed same-sex behaviors. Among the 806 men, the average disclosure network size was 4.05. MSM who reported larger disclosure networks were more likely to have been tested for HIV (aOR 1.21, 95% CI 1.08-1.34). The most common disclosure network alters were friends (45.1%), followed by sex partners (18.7%) and healthcare professionals (2.5%). Men who disclosed to healthcare professionals were more likely to test for HIV compared to men who disclosed to family members (aOR 5.43, 95% CI 2.11-14.04). Our findings can inform disclosure network-based interventions to promote MSM HIV testing

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R2) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%