Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Pharmacotherapeutics and molecular docking studies of alpha-synuclein modulators as promising therapeutics for Parkinson’s disease

Parkinson’s disease (PD) is an age-related neurodegenerative ailment that affects dopamine-producing neurons in a specific area of the brain called the substantia nigra of the ventral midbrain. It is clinically characterized by movement disorder and marked with unusual synaptic protein alpha-synuclein accumulation in the brain. To date, only a few Food and Drug Administration (FDA) approved drugs are available on the market for the treatment of PD. Nonetheless, these drugs show parasympathomimetic related adverse events and remarkably higher toxicity; hence, it is important to find more efficacious molecules to treat PD. In our study, We chosen 22 natural compounds as inhibitors that potentially block the alpha-synuclein clump—the pathological hallmark of PD—and provide new avenues for its treatment. Most of these molecules exhibited good pharmacokinetic behaviors, making them decisively favorable drug candidates to cure PD. Molecular docking studies were performed to investigate the binding interactions between natural compounds and alpha-synuclein as anti-Parkinson drug targets. Among the examined compounds, curcumin and piperine emerged as promising phytochemicals with the highest binding affinity, key residual stable bindings and showed a good inhibitory features. Thus, the present study indicates that curcumin and piperine hold the potential to be developed as treatment options against PD. Experimental validations are needed for insights into their mechanism of action and potential clinical application.     

miR-181b promotes the oncogenesis of renal cell carcinoma by targeting TIMP3

Renal cell carcinoma (RCC) has a poor prognosis due to limited diagnosis and treatment. Thus, it is necessary to find novel prognostic biomarkers and therapeutic targets. The aberrant expression of microRNAs plays an important role in RCC oncogenesis. Tissue inhibitors of metalloproteinase 3 (TIMP3) acts as a downstream target of miR-181b. The aim of this study was to understand the role and molecular mechanism of miR-181b in RCC oncogenesis. The results showed that miR-181b expression was significantly higher in RCC tumour tissues, especially in those with significant invasion or metastasis. miR-181b overexpression promoted proliferation and migration of the RCC cell line 786-O, while miR-181b knockdown had the opposite effect. In addition, miR-181b was inversely correlated with TIMP3 expression in RCC tumour tissues. miR-181b overexpression reduced TIMP3 expression in RCC cell line 786-O or OS-RC-2, while miR-181b knockdown had the inverse effect. Mechanistically, a luciferase reporter assay confirmed the binding sites of miR-181b on the 3’-UTR of TIMP3, confirming the targeting effect of miR-181b on TIMP3. Overall, miR-181b promotes the development and progression of RCC by targeting TIMP3 expression, indicating the potential use of miR-181b in the diagnosis and treatment of RCC.     

富平银沟遗址青瓷标本的类型学分析

富平银沟遗址位于陕西省渭南市富平县华朱乡东北部银沟村。经过为期两年的考古调查与勘探,在该地区发现和搜集了大量的精美陶瓷产品。其中青瓷产品占比最大,具有很高的研究价值。本文主要通过选取银沟地区青瓷器形较为完整,并具有典型代表性的青瓷标本进行类型学的整理分析。根据器形,青瓷标本可分为碗、盘、盒、钵、香炉、瓶、罐、壶等。通过与耀州窑典型青瓷器的器形与纹饰对比,从器物的类型学解析银沟遗址青瓷器的烧造年代时期。     

汽车用钢弹性模量测试探讨

采用静态拉伸试验方法对系列汽车用钢材料进行测试，得到了汽车用钢材料的弹性模量，分析了强化方式、轧制方向、塑性变形等对材料弹性模量的影响。结果表明：强化方式影响材料的弹性模量，固溶强化的汽车用钢材料弹性模量最大；材料的弹性模量表现出各向异性特点，垂直于轧制方向的弹性模量最大；塑性变形降低材料的弹性模量。研究结果为工程设计、冲压模拟、碰撞模拟、回弹模拟等提供计算依据。     

Poisson’s ratio of porous and cellular materials with randomly distributed isometric pores or cells

The porosity dependence of Poisson's ratio of materials with random microstructure is investigated via analytical and numerical modeling. It is shown that all analytical models predict porosity independence if the solid Poisson ratio is 0.2 and for low porosities a converging trend toward this value with increasing porosity. From all theory-based relations, only power-law and exponential relations allow for auxetic behavior. Numerical calculations on computer-generated digital microstructures (overlapping and isolated spherical pores, pores between overlapping spherical grains, wall-based cellular materials/closed-cell foams, and strut-based cellular materials/open-cell foams) confirm the general qualitative trends of the analytical models, although a closer look reveals significant quantitative differences. Cellular materials and foams exhibit similar features as porous materials in general, but lack their converging trend toward values around 0.2. Comparison of our results with the classical Roberts-Garboczi results shows good agreement, with subtle differences due to the different microstructures generated.     

基于P波模量的原位胶结充填体强度预测模型研究

原位胶结充填体质量评价通常采用单轴抗压强度作为表征指标,由于在现场工程中无法快速、准确获取充填体的强度值,因此需要建立一种参数易于获取且准确的原位胶结充填体强度预测模型。基于胶结充填体物理力学参数的内在联系,从量纲平衡的角度探索了胶结充填体单轴抗压强度与P波模量的关系。根据C矿和I矿两种原位胶结充填体的密度、P波速度及取芯强度的测试数据,采用线性拟合的方法获得了基于P波模量的原位胶结充填体强度预测模型。通过对C矿和I矿的强度预测模型拟合效果分析可知,P波模量与取芯强度呈正相关性,且拟合效果较好,证明原位胶结充填体强度预测模型精度高,具有很强的实用性。     

Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods

Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions. The field tests for determination of deformation modulus are cumbersome, expensive and time-consuming. This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications, with rock mass rating (RMR) being one of the frequently used classifications. The regression equations are of different types ranging from linear to nonlinear functions like power and exponential. Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties. The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open. Therefore, a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites. The study also investigated the performance of different types of regression equations in estimation of the deformation modulus. Statistics, probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations. It was found that power and exponential types of regression equations provide a better estimate than linear regression equations. In addition, it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation.     

矿井水库煤柱坝体力学性能分析

为研究矿井采空区地下水库煤柱坝体在井下作业环境中的力学性能变化以及变形情况,以李家壕煤矿为研究背景,采用实验室相似模拟和FLAC3D数值模拟的方法,首先,将取自李家壕煤矿3-1煤层的煤样按照ISRM加工成标准试样,试样按照一定时间周期浸水处理,对其进行力学性能分析实验;随后,采用数值模拟技术模拟煤柱坝体的实际作业环境,分析煤柱坝体力学性能和变形情况。研究得出:水对煤样有软化作用,随着浸水时间的增加煤样单向抗压强度、单向抗拉强度均有所下降,但并未出现规律性递减趋势;在软化作用和上覆岩层的压力的共同作用下,煤柱浸水面容易发生水平变形,变形随着时间的增长逐渐增大,靠近底板处的变形量最大,从而现场煤柱更容易遭到破坏。