Spatial Variation of Surface Residual Stress in Metallic Materials

Shot peening is commonly used to reduce fatigue failures in industrial parts by introducing compressive residual stress into the surface of a material. However, it is challenging to assess the performance of the parts without destroying them. Solving this problem requires a combined model that predicts both recrystallization and residual stress using experimental measurements and predictive computational modelling. Experiments were performed to prove that the surface properties of materials after thermal treatments can be accessed, and the spatial variation of residual stress in metallic materials, including the relationship between surface and subsurface behavior can be evaluated. This process involves investigating the surface residual stress profile using a spatially sensitive X-ray diffraction technique, followed by other procedures such as cutting and investigation of microstructure and subsurface residual stress. With a model like this, the performance of industrial parts can be assessed in a non-destructive way. It is crucial that the parts can still serve the original purpose after being tested

Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems: A comparative study

Purpose: – In real world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, it is very time-consuming in use of finite element methods. The purpose of this paper is to study the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization, and compares it with the performance of Genetic Algorithms (GA). Design/methodology/approach: – In this paper, the enhanced multipoint approximation method (MAM) is utilized to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the Sequential Quadratic Programming (SQP) technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems. Findings: – The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem and the superiority of the Hooke-Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded. Originality/value: – The authors propose three efficient hybrid algorithms: the rounding-off, the coordinate search, and the Hooke-Jeeves search assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy, and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors φ defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects

ChatTraffic: Text-to-Traffic Generation via Diffusion Model

Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation, and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic

Otterbein Aegis May 1909

https://digitalcommons.otterbein.edu/aegis/1182/thumbnail.jp

BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation, and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic

Computational inference of mRNA stability from histone modification and transcriptome profiles

Histone modifications play important roles in regulating eukaryotic gene expression and have been used to model expression levels. Here, we present a regression model to systematically infer mRNA stability by comparing transcriptome profiles with ChIP-seq of H3K4me3, H3K27me3 and H3K36me3. The results from multiple human and mouse cell lines show that the inferred unstable mRNAs have significantly longer 3′Untranslated Regions (UTRs) and more microRNA binding sites within 3′UTR than the inferred stable mRNAs. Regression residuals derived from RNA-seq, but not from GRO-seq, are highly correlated with the half-lives measured by pulse-labeling experiments, supporting the rationale of our inference. Whereas, the functions enriched in the inferred stable and unstable mRNAs are consistent with those from pulse-labeling experiments, we found the unstable mRNAs have higher cell-type specificity under functional constraint. We conclude that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNAs, and distinguish stable mRNAs from highly expressed ones. In summary, we represent the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements

Productivity and Quality of Alpine Grassland Vary With Soil Water Availability Under Experimental Warming

The plant productivity of alpine meadow is predicted to generally increase under a warming climate, but it remains unclear whether the positive response rates will vary with soil water availability. Without consideration of the response of community composition and plant quality, livestock grazing under the current stocking rate might still lead to grassland degradation, even in meadows with high plant biomass. We have conducted a warming experiment from 2010 to 2017 to examine the interactive effects of warming and soil water availability on plant growth and forage quality at individual and functional group levels in an alpine meadow located in the permafrost region of the Qinghai–Tibetan Plateau. Warming-induced changes in community composition, biomass, and forage quality varied with soil water availability. Under dry conditions, experimental warming reduced the relative importance of grasses and the aboveground biomass by 32.37 g m−2 but increased the importance value of forbs. It also increased the crude fat by 0.68% and the crude protein by 3.19% at the end of summer but decreased the acid detergent fiber by 5.59% at the end of spring. The increase in crude fat and protein and the decrease in acid detergent fiber, but the decrease in aboveground biomass and increase the importance value of forbs, which may imply a deterioration of the grassland. Under wet conditions, warming increased aboveground biomass by 29.49 g m−2 at the end of spring and reduced acid detergent fiber by 8.09% at the end of summer. The importance value of grasses and forbs positively correlated with the acid detergent fiber and crude protein, respectively. Our results suggest that precipitation changes will determine whether climate warming will benefit rangelands on the Qinghai–Tibetan Plateau, with drier conditions suppressing grassland productivity, but wetter conditions increasing production while preserving forage quality

Curcumin-Loaded Mixed Micelles: Preparation, Characterization, and In Vitro

The objective of this study was to prepare curcumin-loaded mixed Soluplus/TPGS micelles (Cur-TPGS-PMs) for oral administration. The Cur-TPGS-PMs showed a mean size of 65.54 ± 2.57 nm, drug encapsulation efficiency over 85%, and drug loading of 8.17%. The Cur-TPGS-PMs were found to be stable in various pH media (pH 1.2 for 2 h, pH 6.8 for 2 h, and pH 7.4 for 6 h). The X-ray diffraction (XRD) patterns illustrated that curcumin was in the amorphous or molecular state within PMs. The In vitro release test indicated that Cur-TPGS-PMs possessed a significant sustained-release property. The cell viability in MCF-7 cells was found to be relatively lower in Cur-TPGS-PM-treated cells as compared to free Cur-treated cells. CLSM imaging revealed that mixed micelles were efficiently absorbed into the cytoplasm region of MCF-7 cells. Therefore, Cur-TPGS-PMs could have the significant value for the chronic breast cancer therapy