(1)Background: Early-age concrete shrinkage induces stress that impact the cost and service life of concrete pavements. (2)Methods: In this study, strain measurements of field slabs were conducted and a methodology was presented that independently derived autogenous, drying, and thermal shrinkages in the initial stages of concrete placement. Total strain was measured according to five different environmental conditions and shrinkage strain was calculated for each condition. (3)Results: By measuring the strain of the slab and the specimen, the drying shrinkage strain was measured to be approximately 54% better than that by the conventional non-stressed cylinder method because it was possible to measure the drying shrinkage strain at the surface rather than in the middle part of the slab along its depth direction. When the water-to-cement ratio increased (35→40%), there was a considerable reduction (317με→82με) of autogenous shrinkage strain for the concrete at 28 days of age. Furthermore, calculation of stress-dependent strain allowed the presentation of more intuitive and accurate results. (4)Conclusion: As the measurement of independent shrinkage occurrence is possible, the consequent calculated result of the stress-dependent strain acting on real slabs will facilitate improvement in the construction quality, reduction in the development of defects in the concrete structure, and increase in the service life.

Fitting parameters on spline curves produces more parsimonious models, maintaining fit quality. Smoothing the splines reduces predictive variance. Individual splines are often fit by type of variable, e.g., in age-period-cohort models. Linear and cubic splines are most common. Several smoothing criteria have been used for parameter curves, with cubic splines fit by constraining the integral of splines’ squared-second derivatives popular recently. Constraining the sum of squared second differences for linear splines is analogous. Generally the degree of smoothing is selected using cross-validation. Known spline dummy-variable matrices allow regression estimation of splines, with smoothing done via constrained regression. Smoothing criteria based on sums of squares or absolute values of parameters, as in ridge regression or LASSO, improves predictive accuracy and produces splines similar to smoothing by second-derivative constraints. Variables with very low t-statistics represent points where curve-shapes barely change. Eliminating those variables leaves knots concentrated where spline shapes change. A Bayesian version of this puts shrinkage priors on spline parameters. This yields realistic joint parameter distributions, avoids problems associated with using cross-validation for parameter estimation, and readily expands to non-linear modeling, such as interactions among variable types. Regularized regression and Bayesian spline methods are compared for two example datasets.

Splenic micronodular T-cell/histiocyte-rich large B-cell lymphoma is possibly derived from nodal T-cell/histiocyte-rich large B-cell lymphoma; however, a transition between the nodal and splenic micronodular forms has not been described to date. Of note, the only lymph nodes to be involved in association with the splenic micronodular pattern of the disease are the splenic hilar lymph nodes, and that, with partial involvement only. Kan et al, in their series of articles, have raised the possibility that corticosteroids, when prescribed prior to splenectomy, cause histopathological and functional modulations (apoptosis, necrosis, tissue shrinkage), which modify or even obscure the diagnostic morphological features. The indications for glucocorticoid therapy are either related to the suspected lymphoma, or else to other comorbidities, like asthma and autoimmune disorders. We propose that patients with the splenic, rather than nodal subset of the disease are likely to have been prescribed corticosteroids prior to histopathologic examination of the involved tissue, causing disparate morphologies in the spleen. Apoptosis, as induced by corticosteroids, is hypothesized as the major mechanism initiating the histopa-thological and functional changes in the splenic micronodular variant of our patients.

Semisolid casting can provide excellent castings, but the nature of the pore-forming mechanisms has not been properly clarified. In the current communication, it was suggested that hydrogen precipitated during slurry making might have a decisive role in the formation of both gas and shrinkage porosity. Intensive stirring at the end of the slurry making process may act as a degassing step. Without the intense stirring, structures of primary slurry particles form around the hydrogen pores, strongly affecting pore formation and feeding during the intensification stage.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

The study is based on the results of long-term field studies, satellite and aerial data analysis. In the maximum of the Little Ice Age (LIA) 243 glaciers with a total area of 353.4 km2 were reconstructed. By the results of interpretation of Corona images by 1968 the number of glaciers increased (236), the total area reduced to 242 km2. In 2010 glaciation was represented by 237 glaciers with a total area of 201 km2. Thus, from the maximum of the LIA, the glaciation of the Tavan-Bogd mountains decreased by 43%, which is somewhat less than the neighboring glacial centers (Ikh-Turgen, Tsambagarav, Tsengel-Khayrkhan and Mongun-Taiga mountains). The probable cause is the predominance of larger glaciers relatively resistant to warming and higher altitudes. The effect of glacier size on their stability is supported by differences in the relative shrinkage of glaciers in different parts of the Tavan-Bogdo-Ola massif: the smallest decline occurred in the basins of the Tsagan-Gol (31.7%) and Sangadir (36.4%) rivers where the largest glaciers are located. On the contrary, on the lower periphery of the massif, where small glaciers predominate, the relative reduction was large (74-79%). On the background of the general retreat trend large valleys glaciers retreated faster in 1968-1977 and after 2010. In 1990-s the retreat was slow. After 2010 the glacial retreat was extremely fast. The retreat of glaciers in the last 50-60 years was caused by a trend towards a decrease in the amount of precipitation until the mid-1970s and a sharp warming in the 1990s and early 2000s.

Binder jet printed components typically have low overall density in the green state and high shrinkage and deformation after heat treatment. It has previously been demonstrated that, by including nanoparticles of the same material in the binder, these properties can be improved as the nanoparticles can fill the interstices and pore throats between the bed particles. The beneficial effects from using these additive binder particles can be improved by maximising the binder particle size, enabling the space within the powder bed to be filled with a higher packing efficiency. The selection of maximum particle size for a binder requires detailed knowledge of the pores and pore throats between the powder bed particles. In this paper, a raindrop model is developed to determine the critical radius at which binder particles can pass between pores and penetrate the bed. The model is validated against helium pycnometry measurements and binder particle drop tests. It is found that the critical radius can be predicted, with acceptable accuracy, using a linear function of the mean and standard deviation of the particle radii. Percolation theory concepts have been employed in order to generalise the results for powder beds that have different mean particle sizes and size distributions. The results of this work can be employed to inform the selection of particle sizes required for binder formulations, to optimise density and reduce shrinkage in printed binder jet components.

Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a unified frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the finite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by different tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of model selection and model averaging estimators. From our data examples, we find that the OLS post-SCAD(BIC) estimator in finite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.

The distributed fiber optic strain measurement based on Rayleigh scattering has recently become increasingly popular in automotive or mechanical engineering for strain monitoring and in the construction industry, especially structural health monitoring. This technology enables the monitoring of strain along the entire fiber length. This article addresses integrating optical fibers of different coatings into the concrete matrix to measure the shrinkage deformations. However, previous studies do not give a clear statement about the strain transfer losses of fiber optic sensors in this application. In this context, three different coating types were investigated regarding their strain transfer. The fibers were integrated into fine-grained concrete prisms, and the shrinkage strain was compared with a precise dial gauge. The analysis shows a high correlation between the reference method and the fiber measurement, especially with the Ormocer coating. The used acrylate coating is also consistent in the middle area of the specimen but requires a certain strain introduction length to indicate the actual strain. The main result of this study is a recommendation for fiber coatings for shrinkage measurement in fine-grain concretes using the distributed fiber optic strain measurement. In addition, the advantages and disadvantages of the measurement method are presented.

Alkali Silica Reaction (ASR) is known to be a serious problem for concrete worldwide, especially in high humidity and high temperature regions. ASR is a slow process that develops over years to decades and it is influenced by changes in environmental and loading conditions of the structure. The problem becomes even more complicated if one recognizes that other phenomena like creep and shrinkage are coupled with ASR. This results in synergistic mechanisms that can not be easily understood without a comprehensive computational model. In this paper, coupling between creep, shrinkage and ASR is modeled within the Lattice Discrete Particle Model (LDPM) framework. In order to achieve this, a multi-physics formulation is used to compute the evolution of temperature, humidity, cement hydration, and ASR in both space and time, which is then used within physics-based formulations of cracking, creep and shrinkage. The overall model is calibrated and validated on the basis of experimental data available in the literature. Results show that even during free expansions (zero macroscopic stress), a significant degree of coupling exists because ASR induced expansions are relaxed by meso-scale creep driven by self-equilibriated stresses at the meso-scale. This explains and highlights the importance of considering ASR and other time dependent aging and deterioration phenomena at an appropriate length scale in coupled modeling approaches.