Characterization of mechanical behavior of nanocrystalline layer induced by SMAT using micro-pillar compression technique coupled with finite element analysis

Micro-pillar compression tests were used to study the mechanical behavior of a stainless steel that has undergone SMAT (Surface Mechanical Attrition Treatment). Micro-pillars were machined using a Focused Ion Beam (FIB) on the cross-section of a SMATed specimen at different distances from the treated surface. These micro-pillars were thus located in different areas more or less affected by the SMAT. They were then compressed with a flat head mounted on a nanoindenter to obtain loading-displacement curves. These compression tests can give information on the mechanical gradient present from the top surface down to the bulk material after SMAT: a superficial nanocrystalline layer (from 10 to 50 micrometers thick and composed of grains with a diameter ranging from 10 to 50 nm) is indeed generated as well as a transition layer (between 200 and 300 micrometers thick and characterized by a grain size gradient from the nanometer to the micrometer scale as the distance from the surface increases) just below the nanocrystalline layer. These compression tests coupled with finite element analysis (FEA) can provide precious information at the mesoscopic scale on the mechanical behavior of the different layers present in the SMATed steel. FEA was used to study the effect of experimental parameters including taper angle (the angle between the tangent of wall and the axis of pillar), aspect ratio (the ratio of height and diameter of the pillar), and misalignment between the pillar axis and the compression direction. Based on the results of FEA, the constitutive behavior in the form of stress-strain curve was identified for the different layers beneath the treated surface including the nanocrystalline layer. According to the obtained stress-strain curves, the mechanical strength of the stainless steel is significantly improved after SMAT

A Cold-Inducible Coactivator of Nuclear Receptors Linked to Adaptive Thermogenesis

AbstractAdaptive thermogenesis is an important component of energy homeostasis and a metabolic defense against obesity. We have cloned a novel transcriptional coactivator of nuclear receptors, termed PGC-1, from a brown fat cDNA library. PGC-1 mRNA expression is dramatically elevated upon cold exposure of mice in both brown fat and skeletal muscle, key thermogenic tissues. PGC-1 greatly increases the transcriptional activity of PPARγ and the thyroid hormone receptor on the uncoupling protein (UCP-1) promoter. Ectopic expression of PGC-1 in white adipose cells activates expression of UCP-1 and key mitochondrial enzymes of the respiratory chain, and increases the cellular content of mitochondrial DNA. These results indicate that PGC-1 plays a key role in linking nuclear receptors to the transcriptional program of adaptive thermogenesis

Constrained Multiobjective Biogeography Optimization Algorithm

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA

The quality difference in five oolong tea accessions under different planting management patterns in south Fujian of China

IntroductionOolong tea, celebrated for its significance in Chinese tea culture, was the subject of investigation in this study.MethodsFive varieties of Minnan oolong tea were sampled, each cultivated under two distinct management approaches: conventional management and natural growth methods. The study aimed to discern variations in sensory attributes, encompassing appearance and liquor color, alongside the analysis of chemical composition.Results and discussionThe results indicated that oolong tea cultivated through conventional manual management generally exhibited qualities in terms of shape and foliage appearance, in contrast to those grown naturally. However, naturally grown oolong tea tended to exhibit more favorable aroma and taste profiles compared to conventionally managed counterparts. Furthermore, the content of water extract, amino acids, polyphenols, caffeine, and other pivotal chemical constituents were typically higher in naturally grown tea varieties compared to conventionally managed ones. Conversely, catechin content was found to be more abundant in traditionally managed bushes than in those grown naturally. These findings emphasize the significance of implementing appropriate natural growth management practices to enhance the quality of Minnan oolong tea and maintain ecological sustainability

GDF15 Provides an Endocrine Signal of Nutritional Stress in Mice and Humans.

GDF15 is an established biomarker of cellular stress. The fact that it signals via a specific hindbrain receptor, GFRAL, and that mice lacking GDF15 manifest diet-induced obesity suggest that GDF15 may play a physiological role in energy balance. We performed experiments in humans, mice, and cells to determine if and how nutritional perturbations modify GDF15 expression. Circulating GDF15 levels manifest very modest changes in response to moderate caloric surpluses or deficits in mice or humans, differentiating it from classical intestinally derived satiety hormones and leptin. However, GDF15 levels do increase following sustained high-fat feeding or dietary amino acid imbalance in mice. We demonstrate that GDF15 expression is regulated by the integrated stress response and is induced in selected tissues in mice in these settings. Finally, we show that pharmacological GDF15 administration to mice can trigger conditioned taste aversion, suggesting that GDF15 may induce an aversive response to nutritional stress.This work and authors were funded by the NIHR Cambridge Biomedical Research Centre; NIHR Rare Disease Translational Research Collaboration; Medical Research Council [MC_UU_12012/2 and MRC_MC_UU_12012/3]; MRC Metabolic Diseases Unit [MRC_MC_UU_12012/5 and MRC_MC_UU_12012.1]; Wellcome Trust Strategic Award [100574/Z/12/Z and 100140]; Wellcome Trust [107064 , 095515/Z/11/Z , 098497/Z/12/Z, 106262/Z/14/Z and 106263/Z/14/Z]; British Heart Foundation [RG/12/13/29853]; Addenbrooke’s Charitable Trust / Evelyn Trust Cambridge Clinical Research Fellowship [16-69] US Department of Agriculture: 2010-34323-21052; EFSD project grant and a Royal College of Surgeons Research Fellowship, Diabetes UK Harry Keen intermediate clinical fellowship (17/0005712). European Research Council, Bernard Wolfe Health Neuroscience Endowment, Experimental Medicine Training Initiative/AstraZeneca and Medimmune

Targeting PGC-1 alpha to control energy homeostasis.

The prevalence of Type 2 diabetes is increasing at an alarming rate in most parts of the world. Effective therapeutic drugs are urgently needed, not only to control the disease but also to prevent or delay its progression. Therapies that target the underlying pathogenesis could, in theory, hold such potential. Recent evidence strongly suggests that impaired mitochondrial function is part of the underlying pathogenesis of insulin resistance and Type 2 diabetes. Peroxisome proliferator-activated receptor gamma co-activator-1 alpha (PGC-1alpha) is a transcription co-activator that plays a key role in regulating mitochondrial biogenesis and energy metabolism in multiple tissues. Thus, improvement and restoration of mitochondrial function and oxidative capacity through activation of PGC-1alpha could provide new treatments for metabolic diseases. A diverse array of proteins has been shown to regulate PGC-1alpha transcription and/or activity, some of which represent promising targets for pharmaceutical intervention

Transfer Learning for Multi-Premise Entailment with Relationship Processing Module

Using the single premise entailment (SPE) model to accomplish the multi-premise entailment (MPE) task can alleviate the problem that the neural network cannot be effectively trained due to the lack of labeled multi-premise training data. Moreover, the abundant judgment methods for the relationship between sentence pairs can also be applied in this task. However, the single-premise pre-trained model does not have a structure for processing multi-premise relationships, and this structure is a crucial technique for solving MPE problems. This paper proposes adding a multi-premise relationship processing module based on not changing the structure of the pre-trained model to compensate for this deficiency. Moreover, we proposed a three-step training method combining this module, which ensures that the module focuses on dealing with the multi-premise relationship during matching, thus applying the single-premise model to multi-premise tasks. Besides, this paper also proposes a specific structure of the relationship processing module, i.e., we call it the attention-backtracking mechanism. Experiments show that this structure can fully consider the context of multi-premise, and the structure combined with the three-step training can achieve better accuracy on the MPE test set than other transfer methods

Influence of Anisotropy and Nonhomogeneity on Stability Analysis of Fissured Slopes Subjected to Seismic Action

Based on the upper bound theorem of limit analysis, this paper presents a procedure for assessment of the influence of the soil anisotropy and nonhomogeneity on the stability of fissured slopes subjected to seismic action. By means of a mathematical optimization procedure written in Matlab software codes, the stability factors NS and λcφ are derived with respect to the best upper bound solutions. A series of stability charts are obtained in this paper, and then the critical locations of cracks are determined for cracks of known depth. The results demonstrate a significant influence of the soil anisotropy and nonhomogeneity on the stability of the fissured slopes and the location distribution of the cracks. In addition, the procedures for getting the factor of safety are put forward. It is shown that a decrease in the nonhomogeneity coefficient n0 and an increase in the anisotropy coefficient k could lead to the fissured slopes becoming unsafe. Finally, this article also illustrates the variation in the safety factor of fissured slopes under the impact of three factors (Kh, H1/H, and λ)

Urban traffic prediction from mobility data using deep learning

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.MOE (Min. of Education, S’pore