Explore the vast range of titles available.

Lactylation has been recently identified as a new type of posttranslational modification occurring widely on lysine residues of both histone and nonhistone proteins. The acetyltransferase p300 is thought to mediate protein lactylation, yet the cellular concentration of the proposed lactyl-donor, lactyl-coenzyme A, is about 1,000 times lower than that of acetyl-CoA, raising the question of whether p300 is a genuine lactyltransferase. Here, we report that alanyl-tRNA synthetase 1 (AARS1) moonlights as a bona fide lactyltransferase that directly uses lactate and ATP to catalyze protein lactylation. Among the candidate substrates, we focused on the Hippo pathway, which has a well-established role in tumorigenesis. Specifically, AARS1 was found to sense intracellular lactate and translocate into the nucleus to lactylate and activate the YAP-TEAD complex; and AARS1 itself was identified as a Hippo target gene that forms a positive-feedback loop with YAP-TEAD to promote gastric cancer (GC) cell proliferation. Consistently, the expression of AARS1 was found to be upregulated in GC, and elevated AARS1 expression was found to be associated with poor prognosis for patients with GC. Collectively, this work found AARS1 with lactyltransferase activity in vitro and in vivo and revealed how the metabolite lactate is translated into a signal of cell proliferation.

Urbanization and global warming have led to more frequent extreme heat events, highlighting the importance of Park Cooling Islands. This study analyzes the cooling effect (PCE) of 50 urban parks in Fuzhou to explore the relationship between park area and cooling effect. The results indicate that there is no simple positive correlation between park area and cooling effect. Specifically, while larger parks may have greater cooling potential, a larger area does not necessarily lead to better cooling effects. The optimal park area for cooling effect ranges from 0.594 to 56 hm 2 ; beyond this range, an increase in park area does not significantly enhance the cooling effect. A low proportion of impervious surfaces, a high proportion of water bodies and vegetation, as well as complex patch patterns can enhance PCE, while excessive edge density and landscape fragmentation can weaken PCE. Based on importance analysis, the external morphological characteristics and internal patch characteristics of parks significantly influence cooling effects. Furthermore, the cooling effect of parks is jointly determined by internal and external conditions, with internal conditions having a more significant impact. Therefore, merely pursuing a “large” park area does not guarantee a “good” cooling effect; instead, greater emphasis should be placed on optimizing park design and layout, simplifying boundary shapes, reducing impervious surface ratios, and increasing vegetation diversity to maximize cooling effects.

In this work, an analytical modeling method is proposed for the prediction of molten pool height, contact angle, and balling occurrence in laser powder bed fusion (LPBF) metal additive manufacturing. A closed-form temperature prediction model is employed to calculate the temperature distribution during melting process. The width and length of molten pool, and width of powder consumed band are then determined by comparing the temperature profile with the melting point of the material. The shape of the solidified cap of the molten pool is assumed to be a segmental cylinder. Per this assumption and mass conservation, the molten pool height, contact angle, and diameter of the cylindrical cap are then determined through geometrical relationships. The occurrences of balling defect are then predicted by checking the stability condition of scan tracks under different process conditions. The predicted results of molten pool width, height, and contact angle are compared with experimental results of Ti6Al4V, Inconel 625 in LPBF, and show acceptable accuracy. The predictions of balling occurrence are consistent with most experimental observations of SS316L. The sensitivities of contact angle to process conditions are discussed. In light of the fact that the temperature profiles are calculated based upon solutions in closed form, the presented computations of molten pool geometric characteristics and balling occurrence do not use any numerical iterations, which makes the proposed analytical modeling method computationally efficient. Thus, the proposed modeling method can be a fast and acceptable tool for the study of molten pool geometry and stability of single tracks in LPBF.

This paper proposes an analytical modeling method for the prediction of lack-of-fusion porosity of parts fabricated by laser powder-bed fusion (LPBF), with the consideration of boundary heat transfer and sensitivity to laser power absorption. The temperature distribution of the part was first predicted by an analytical thermal model, which consists of a linear heat source solution and a heat sink solution. The temperature increase due to laser power input was calculated by the point moving heat source solution. The temperature drop due to thermal conduction, convection, and radiation at part boundaries was calculated by the heat sink solution. The coefficient of laser power absorption was inversely obtained by comparing predicted molten pool widths with experimental measurements. The lack-of-fusion area was then calculated by plotting molten pool shapes of multi-tracks and multi-layers on a transverse cross-sectional area of the part. The powder bed porosity was calculated by an advancing front method with the consideration of powder size distribution and packing pattern. Finally, the lack-of-fusion porosity was obtained by multiplying the lack-of-fusion area with powder bed porosity. The predicted results were close to the measurements of Ti6Al4V in LPBF. The maximum deviation for porosity prediction is 5.91%. The presented model shows high computational efficiency without relying on iteration-based numerical method. The computational time for five consecutive layers is less than 100 s. The acceptable accuracy, short computational time, and the ability to consider complex boundary effects make the proposed method a good basis for future research and a promising tool for optimization of process parameters in LPBF of complex parts.

The problem of the relative position coordinated control for spacecraft formation flying with a leader spacecraft under the obstacle environment is the focus of this paper. To avoid obstacle/collision and maintain the formation configuration, the Null-Space-Based behavioral control architecture is built by defining the priorities of the basic tasks and computing the corresponding velocity vectors. Through the null-space projection, the desired velocity of each follower spacecraft can be calculated by merging the basic tasks. Moreover, due to the partial access to the dynamic leader spacecraft’s states, the distributed estimators are presented for each follower spacecraft. Then, based on the desired velocity, the adaptive coordinated tracking control algorithm incorporated with the barrier Lyapunov function is designed such that the states satisfy the time-varying constraints, even subject to uncertainties and unknown disturbances. Finally, numerical simulations are performed to illustrate the main results.

In this work, a three-dimensional (3D) analytical modeling method is proposed for the prediction of keyhole porosity in laser powder bed fusion (LPBF) metal additive manufacturing. The proposed method consists of a physics-based analytical thermal model for keyhole melting mode and a pore formation model. The thermal model is used to calculate the molten pool size and vapor depression depth, with given process conditions and material properties. It consists of a moving point heat source on the part surface and a moving finite line heat source penetrating into the part. The pore formation model considers the process of bubble generation and trapping. It is used to calculate the volume fraction of pores in solidified molten pool, with the molten pool dimensions, vapor depression depth, velocity of fluid flow, frequency of bubble emission, and average bubble size as inputs. To verify the proposed method, the predictions of keyhole porosity are compared with documented experimental data of Ti6Al4V and display acceptable agreement. No finite element analyses are included in the proposed method, which can save computational resources. Thus, the proposed method is useful for the rapid prediction of keyhole porosity and can help understand the physics and optimize the process conditions in LPBF. The sensitivity of keyhole porosity to process conditions is also discussed.

In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining.In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly.

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization–artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R 2 , mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

We adopted a spectral clustering algorithm to divide the document co-citation network of 1,776 papers in the field of Lancang-Mekong water, and 14 clusters were identified. For each cluster, the top-cited references construct the knowledge base, and the most-coverage cities are taken as the research frontier. Three indicators, namely betweenness centrality, citation burstness strength, and Sigma, were used to identify the research outputs with pioneering and transformative value. The changes in the research topics and hotspots are closely related to the planning, construction, and operation progress of hydropower engineering, that affected by the gaming results of all parties. The 2009–2010 is an important time boundary, with the original research hotspots including the impact of upstream reservoirs on the hydrological regime and sediment (Clu#3) and arsenic contamination of groundwater in the Lower Mekong (Clu#4) that obtained periodical achievements and reached consensus to some extent around 2008, and the new research boom turns to the Tonle Sap Lake and flood pulse (Clu#2) in short-term characterized literatures with the highest burstness strength mainly concentrated around 2012.

Porosity is a common process-induced defect in laser powder bed fusion (LPBF) metal additive manufacturing, which will have detrimental effects on the mechanical performance of the fabricated products. In this study, an analytical modeling method with closed-form solutions is developed for the prediction of keyhole-induced porosity in LPBF. A two-dimensional model which considers the keyhole pores formation and trapping is employed to calculate the keyhole porosity, with the molten pool geometries, average pore size, velocity of melt flow, and frequency of pore formation as inputs. An analytical temperature prediction model is used to compute the temperature distribution in LPBF. The molten pool shapes and dimensions are determined by comparing the predicted temperature profiles with melting temperature. The relationship between average pore size and laser power energy density is obtained by regression analysis. The velocity of melt flow and frequency of keyhole pore formation are adapted from the literature. To validate the model, the predictions of keyhole porosity under various process conditions are compared with experimental measurements of Ti6Al4V in LPBF. The predicted results are in good agreement with experimental data, which demonstrates the acceptable predictive accuracy of the proposed model. Also, the analytical modeling method does not include any iteration-based numerical calculations, which makes it computationally efficient. Thus, the proposed model can be an acceptable tool for the fast prediction of keyhole porosity and can also help the researchers understand the physics behind the formation of part porosity.