Understanding the psychological benefits in organic consumerism: An empirical exploration

This research builds on the Theory of Planned Behavior (TPB) by proposing two additional constructs: warm glow and self-expressive benefits which could further drive consumer attitude and purchase intentions in the context of organic food consumption. We employed structural equation modelling (SEM) technique with Smart PLS 3.0 (Partial Least Squares) for the analysis of the hypothesised relationships between these psychological factors and the constructs of the Theory of Planned Behavior (TPB). The theoretical model was tested with samples from India (n = 471) and the USA (n = 440) collected using Amazon’s Mechanical Turk (M Turk). Results confirmed the importance of warm glow and self-expressive benefits in organic consumerism in both markets. The study makes an important contribution in adding these two constructs to the TPB and then suggests practical tips to policy makers.     

Physics-informed deep learning for one-dimensional consolidation

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research.In this context,a review of related research is first presented and discussed.The potential offered by such physics-informed deep learning models for computations in geomechanics is demonstrated by application to one-dimensional(1D)consolidation.The governing equation for 1D problems is applied as a constraint in the deep learning model.The deep learning model relies on automatic differentiation for applying the governing equation as a constraint,based on the mathematical approximations established by the neural network.The total loss is measured as a combination of the training loss(based on analytical and model predicted solutions)and the constraint loss(a requirement to satisfy the governing equation).Two classes of problems are considered:forward and inverse problems.The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for 1D consolidation problems.Inverse problems show prediction of the coefficient of consolidation.Terzaghi’s problem,with varying boundary conditions,is used as a numerical example and the deep learning model shows a remarkable performance in both the forward and inverse problems.While the application demonstrated here is a simple 1D consolidation problem,such a deep learning model integrated with a physical law has significant implications for use in,such as,faster realtime numerical prediction for digital twins,numerical model reproducibility and constitutive model parameter optimization.     

A stochastic approach to model material variation determining tow impregnation in out-of-autoclave prepreg consolidation

Material variations are always present even though out-of-autoclave prepregs are machine-made. They strongly determine the consolidation and may eventually lead to voids within the final part, depending on applied process conditions. To capture any contingencies, stochastic differential equations are derived to describe various interacting phenomena in OoA consolidation. In a second step the probabilistic space is discretized using the Karhunen–Loève truncation and the Probabilistic Collocation method is applied in order to use deterministic solvers for flow and compaction problems. The initial degree of impregnation is represented by an Ornstein–Uhlenbeck process and calibrated with CT-images.     

江苏油田H区固井质量影响因素分析及对策

为了解决江苏油田H区固井质量优质率偏低的问题,通过统计2013年上半年35口井固井质量、固井前钻井液性能、前置液性能、水泥浆性能以及其他影响因数,分析各单因素对水泥浆封固质量的影响,得到了影响固井质量的主要因素和优化值。但无法判断影响因数影响力大小,通过引入灰色系统理论,编写关联度计算程序,得到各影响因素对水泥浆封固质量的关联度。现场应用表明,根据影响因数关联度排序,控制关键影响因数,可有效提高固井质量。     

Electro-osmosis treatment techniques and their effect on dewatering of soils,sediments, and sludge: A review

Electro-osmosis is an established method of dewatering fine soils, sediments, and sludge (SSS). The efficiency of electro-osmotic treatment is controlled by the electrical resistance of the system. Due to an increase in SSS resistance during treatment, its cost efficiency is reduced, limiting the widespread use of this technique. The aim of this paper is to discuss the main reasons for the increase in SSS resistance during treatment and then to analyze the most recent and widely spread modifications to classical electro-osmotic treatment that attempt to combat these issues and improve the efficiency of the technique. These modifications to electro-osmotic treatment are polarity reversal, an intermittent current, the injection of chemical solutions at the electrodes, and the use of geo-synthetics. The paper discusses the relevant research on the above adaptations, and the advantages and disadvantages of each are evaluated and compared using the available laboratory and field tests in the literature on electro-osmotic dewatering research. All four methods of modification are shown to provide significant improvements and can be successfully translated to the field for greater use. However, the improvements they bring about may not be sufficient to warrant their general use in geotechnical applications.     

Prediction of nonlinear vertical settlement of a pile group consisting of new and existing displacement piles in clay strata

Reusing existing displacement piles is economical, time-saving, and environment-friendly. This paper presents an analytical approach for predicting the load carrying behavior of pile groups consisting of new and existing displacement piles. The evolutions of the undrained shear strength and shear modulus of clay adjacent to the piles from installation through consolidation to long-term ageing are investigated to determine the load carrying behavior of displacement piles. The nonlinear load-settlement behavior of an individual pile is modelled by load-transfer method, where the exponential function-based load-transfer models integrating the two developed soil parameters are employed to represent the nonlinear behavior at the pile-soil interface. The pile-pile interaction in the pile group is explored based on the shear displacement method. Combining the load-transfer method and the shear displacement method, an analytical framework is proposed for predicting the load-settlement behavior of pile groups consisting of new and existing piles. The proposed framework is validated by predicting the vertical settlement of a high-rise apartment built on a pile group consisting of 74 new displacement piles and 22 existing displacement piles during construction. Good agreement is achieved between the predicted and measured results. A parametric study is performed to explore the stiffness efficiency and the load-settlement behavior of pile groups with different layouts of new and existing piles. The results indicate that the pile group with larger ratio of the number of existing piles to the number of total piles shows both stronger stiffness and a higher load carrying capacity.     

城市绿色空间健康效益的社会生态调节因素研究

城市绿色空间是城市生态系统服 务的重要载体。既往人—境交互视角下的 大量研究基本证实了绿色空间的健康效 益,但对影响二者间效益转化的调节因素 缺乏深入研究。根据社会生态理论与相关 研究,从社会人口和物质环境两方面定性 分析了调节因素及其作用机制,即社会生态 调节因素通过影响使用动机、使用机会或 易用性来影响绿色空间使用并由此调节健 康效益。进一步,基于城市游园研究案例对 此予以实证。结果显示,游园使用具有一定 的情绪健康改善作用,但此过程受到使用 者年龄、教育程度、收入水平、家庭结构以 及游园使用距离等因素的调节。面对不断 凸显的公共健康问题和愈发紧缺的绿色空 间,未来相关研究的焦点有必要从“绿色空 间是否影响健康”的基础研究转向包括调 节过程在内的“绿色空间如何影响健康”的 应用研究,以有效提升应用实践效率。     

Methods for approximate reliability analysis

Approximate solutions are developed for reliability problems involving constant-in-time and time-dependent random parameters. They are based on estimates of the distribution of scalar control variables obtained from approximations of the safety condition (control variable approach) or on a transformation of the input space of parameters into a space in which the safety condition is nearly linear (linearization approach). The distribution of the control variable or of the random parameters in the transformed space can be estimated from moments of these variables. The control variable approach is most effective when the random parameters are constant in time while the linearization approach is useful for time-dependent reliability studies since it permits application of various rules (e.g., Turkstra's rule) for approximate load combination analysis.     

多元智能理论及其对教育的启示

多元智能理论是一种全新的有关人类智能结构的理论,为多元学习的开发提供了一个全新的视角。实施多元学习,是有效开发多项智能,开发学生潜能,促进学生全面发展的有效途径。多元智能理论对教学改革具有重要的借鉴意义。