Microstructure and mechanical properties of NZ30K alloy by semi-continuous direct chill and sand mould casting processes

The Mg-3.0Nd-0.2Zn-0.4Zr (NZ30K) alloys were prepared by direct-chill casting (DCC) and sand mould casting (SMC) processes, respectively and their microstructures and mechanical properties were investigated. The results indicate that casting method plays a remarkable influence on the microstructure and mechanical properties of as-cast NZ30K alloy. The grain size increases from 35-40 μm in the billets made by the DCC to about 100-120 μm in the billets by the SMC. The aggregation of Mg12Nd usually found at the triple joints of grain boundaries in the billets prepared by SMC while is not observable from the billets by DCC. The tensile strengths and elongations of the billets are 195.2 MPa and 15.5% by DCC, and 162.5 MPa and 3.2% by SMC, respectively. The tensile strength of the alloy by DCC is remarkably enhanced by T6 heat treatment, which reached 308.5 MPa. Fracture surfaces of NZ30K alloy have been characterized as intergranular fracture by SMC and quasi-cleavage fracture by DCC, respectively

Surface Characteristics and High Cycle Fatigue Performance of Shot Peened Magnesium Alloy ZK60

The current work investigated the effect of shot peening (SP) on high cycle fatigue (HCF) behavior of the hot-extruded ZK60 magnesium alloy. SP can significantly improve the fatigue life of the ZK60 alloy. After SP at the optimum Almen intensities, the fatigue strength at 107 cycles in the as-extruded (referred to as ZK60) and the T5 aging-treated (referred to as ZK60-T5) alloys increased from 140 and 150 MPa to 180 and 195 MPa, respectively. SP led to a subsurface fatigue crack nucleation in both ZK60 and ZK60-T5 alloys. The mechanism by which the compressive residual stress induced by shot peening results in the improvement of fatigue performance for ZK60 and ZK60-T5 alloys was discussed

Origin of the Domesticated Horticultural Species and Molecular Bases of Fruit Shape and Size Changes during the Domestication, Taking Tomato as an Example

Domestication of crop plants is the foundation of modern agriculture, which brings forth desirable changes in cultivated species that distinguish them from their wild relatives. This resulted in the origin of crop species at known geographical locations coinciding with the transition of human societies from hunter-gather to agrarian civilizations. Fruit size and shape are very important traits for horticulture industry, as well as for studying the domestication of the horticultural species. In this review, we have summarized the origin of some widely-grown horticultural crops and also the molecular bases of the fruit size and shape changes of the horticultural crops during the domestication, taking tomato as an example

The effect of lizardite on talc flotation using carboxymethyl cellulose as a depressant

The effect of lizardite on talc flotation when using carboxymethyl cellulose (CMC) as a depressant was studied by micro-flotation experiments and adsorption measurements, zeta-potential measurements, magnesium ion dissolution analysis, and solution chemistry calculation. The results for the micro-flotation experiments showed that the addition of lizardite further decreased the floatability of talc at pH 8.5 when using CMC as the depressant. The mechanism was that magnesium ions dissolved from lizardite lattice, then formed hydrolyzed species of magnesium cations and interacted with talc surfaces, which promoted CMC adsorption, and thus decreasing talc floatability

Keypoint-Aware Single-Stage 3D Object Detector for Autonomous Driving

Current single-stage 3D object detectors often use predefined single points of feature maps to generate confidence scores. However, the point feature not only lacks the boundaries and inner features but also does not establish an explicit association between regression box and confidence scores. In this paper, we present a novel single-stage object detector called keypoint-aware single-stage 3D object detector (KASSD). First, we design a lightweight location attention module (LLM), including feature reuse strategy (FRS) and location attention module (LAM). The FRS can facilitate the flow of spatial information. By considering the location, the LAM adopts weighted feature fusion to obtain efficient multi-level feature representation. To alleviate the inconsistencies mentioned above, we introduce a keypoint-aware module (KAM). The KAM can model spatial relationships and learn rich semantic information by representing the predicted object as a set of keypoints. We conduct experiments on the KITTI dataset. The experimental results show that our method has a competitive performance with 79.74% AP on a moderate difficulty level while maintaining 21.8 FPS inference speed

PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and [email protected]. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% [email protected] in TT100K, which exceeds the benchmark network by 4.9%

GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information

Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/

Identification of Dual-Target Inhibitors for Epidermal Growth Factor Receptor and AKT: Virtual Screening Based on Structure and Molecular Dynamics Study

Epidermal growth factor EGFR is an important target for non-small cell lung (NSCL) cancer, and inhibitors of the AKT protein have been used in many cancer treatments, including those for NSCL cancer. Therefore, searching small molecular inhibitors which can target both EGFR and AKT may help cancer treatment. In this study, we applied a ligand-based pharmacophore model, molecular docking, and MD simulation methods to search for potential inhibitors of EGFR and then studied dual-target inhibitors of EGFR and AKT by screening the immune-oncology Chinese medicine (TCMIO) database and the human endogenous database (HMDB). It was found that TCMIO89212, TCMIO90156, and TCMIO98874 had large binding free energies with EGFR and AKT, and HMDB0012243 also has the ability to bind to EGFR and AKT. These results may provide valuable information for further experimental study