Implementation of mechanistic-empirical pavement analysis in the State of Qatar

The State of Qatar is experiencing tremendous growth in infrastructure including road network and highways. The current methods used in design of asphalt pavements in the State of Qatar are empirical and might not be suitable for the design of long-lasting pavements. Given the significant increase in traffic, road authorities in the State of Qatar have been considering the use of mechanistic-empirical methods in the design and analysis of asphalt pavements. This study documents the results of a study in which the mechanistic-empirical pavement design guide (M-E PDG) software was used in the design of asphalt pavements with input parameters that were carefully selected to reresent local materials and climatic conditions. The selection of material properties was based primarily on specifications and design guides in the State of Qatar and on published literature about these materials. The mechanistic-empirical method was also used to assess the benefits of adopting the concepts of perpetual pavement design and also to compare the performance of pavement structures in which various bitumen grades, granular bases and chemically stabilised sub-base were used. A life-cycle cost analysis was carried out to determine the design with the highest net present value among the various options investigated. It is expected that the outcomes of this study would promote the use of mechanistic-empirical methods in the State of Qatar and the region. Inevitably, this will require significant efforts to calibrate material and damage prediction models used in the M-E PDG for more accurate representation of material properties and measured pavements performance.     

Frankenrigs: building character rigs from multiple sources

We present a new rigging and skinning method which uses a database of partial rigs extracted from a set of source characters. Given a target mesh and a set of joint locations, our system can automatically scan through the database to find the best-fitting body parts, tailor them to match the target mesh, and transfer their skinning information onto the new character. For the cases where our automatic procedure fails, we provide an intuitive set of tools to fix the problems. When used fully automatically, the system can generate results of much higher quality than a standard smooth bind, and with some user interaction, it can create rigs approaching the quality of artist-created manual rigs in a small fraction of the time.     

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water–cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete.     

Drilling performance of functionally graded composite: Comparison with glass and carbon/epoxy composites

Functionally graded composite (FGC) materials are categorized as advanced materials that display different thermal and mechanical responses compared with well-known composites, such as carbon fiber or glass fiber-reinforced composites. This paper presents the experimental results for the drilling of three materials, namely glass/epoxy, carbon/epoxy, and FGC material. FGC was compared with carbon and glass/epoxy composites in terms of thrust force, delamination factor, diameter of hole, and roundness during drilling. This study illustrated that the drilling performance of FGC is considerably more complicated than that of more common composite materials, such as glass/epoxy and carbon/epoxy. Delamination factor at the exit of hole during drilling of FGC was mainly affected by the material placed at the exit of the hole. The proposed cutting parameters and drill geometries to minimize the occurrence of delamination during drilling of carbon/epoxy and glass/epoxy apparently does not meet the expectation in drilling FGC.     

Ensemble canonical correlation analysis

Canonical Correlation Analysis (CCA) aims at identifying linear dependencies between two different but related multivariate views of the same underlying semantics. Ignoring its various extensions to more than two views, CCA uses these two views as complex labels to guide the search of maximally correlated projection vectors (covariates). Therefore, CCA can overfit the training data, meaning that different correlated projections can be found when the two-view training dataset is resampled. Although, to avoid such overfitting, ensemble approaches that utilize resampling techniques have been effectively used for improving generalization of many machine learning methods, an ensemble approach has not yet been formulated for CCA. In this paper, we propose an ensemble method for obtaining a final set of covariates by combining multiple sets of covariates extracted from subsamples. In comparison to those obtained by the application of the classical CCA on the whole set of training data, combining covariates with weaker correlations extracted from a number of subsamples of the training data produces stronger correlations that generalize to unseen test examples. Experimental results on emotion recognition, digit recognition, content-based retrieval, and multiple view object recognition have shown that ensemble CCA has better generalization for both the test set correlations of the covariates and the test set accuracy of classification performed on these covariates.     

Frequent expression of Bcl-2 in renal-cell carcinomas carrying wild-type p53

Lunate excision alone is seldom utilized in the management of Kienbock's disease due to concerns about progressive carpal collapse following removal of this central carpal bone. We report a 32-year follow-up of a patient who underwent lunate excision only for treatment of Kienbock's disease with a successful outcome. Although lunate excision is thought to be associated with a high failure rate, a review of the literature suggests that success rates following lunate excision are comparable to those reported for other more conventional techniques such as radial shortening, ulnar lengthening, limited carpal fusions, and proximal row carpectomy. The current perception that lunate excision is associated with a high failure rate is not supported in the literature. As such, it may not be appropriate to assign this operation to the category of "historical interest only."     

Modification of Two-Dimensional Tin-Based Perovskites by Pentanoic Acid for Improved Performance of Field-Effect Transistors

Understanding and controlling the nucleation and crystallization in solution-processed perovskite thin films are critical to achieving high in-plane charge carrier transport in field-effect transistors (FETs). This work demonstrates a simple and effective additive engineering strategy using pentanoic acid (PA). Here, PA is introduced to both modulate the crystallization process and improve the charge carrier transport in 2D 2-thiopheneethylammonium tin iodide ((TEA)₂SnI₄) perovskite FETs. It is revealed that the carboxylic group of PA is strongly coordinated to the spacer cation TEAI and [SnI₆]⁴⁻ framework in the perovskite precursor solution, inducing heterogeneous nucleation and lowering undesired oxidation of Sn²⁺ during the film formation. These factors contribute to a reduced defect density and improved film morphology, including lower surface roughness and larger grain size, resulting in overall enhanced transistor performance. The reduced defect density and decreased ion migration lead to a higher p-channel charge carrier mobility of 0.7 cm² V⁻¹ s⁻¹, which is more than a threefold increase compared with the control device. Temperature-dependent charge transport studies demonstrate a mobility of 2.3 cm² V⁻¹ s⁻¹ at 100 K due to the diminished ion mobility at low temperatures. This result illustrates that the additive strategy bears great potential to realize high-performance Sn-based perovskite FETs.     

A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case

This paper investigates the forecasting accuracy of fuzzy extended group decisions in the adjustment of statistical benchmark results. DELPHI is a frequently used method for implementing accurate group consensus decisions. The concept of consensus is subject to expert characteristics and it is sometimes ensured by a facilitator’s judgment. Fuzzy set theory deals with uncertain environments and has been adapted for DELPHI, called fuzzy-DELPHI (FD). The present paper extends the recent literature via an implementation of FD for the adjustment of statistical predictions. We propose a fuzzy-DELPHI adjustment process for improvement of accuracy and introduced an empirical study to illustrate its performance in the validation of adjustments of statistical forecasts in the dry bulk shipping index.     

Development of waste tire‐derived graphene reinforced polypropylene nanocomposites with controlled polymer grade,crystallization and mechanical characteristics via melt‐mixing

In the present work, single layer graphene nanoplatelets (GNPs) derived from waste tires by recycling and upcycling approaches were integrated in homopolymer (Homo‐) and copolymer (Copo‐) polypropylene (PP) matrices by fast and efficient mixing in the melt phase. The effect of GNP content on crystallization and mechanical behaviors was investigated in detail at different loading levels. Regarding isothermal and non‐isothermal crystallization experiments, GNPs significantly accelerated the nucleation and growth of crystallites, and the crystallization degree in Homo‐PP nanocomposites was slightly higher than that of Copo‐PP based nanocomposites. Also, there was significant improvement in mechanical and thermal properties of GNP reinforced polymers compared to neat polymers. As the GNP concentration increased from 1 to 5 wt%, there was a gradual increase in flexural modulus and strength values. In tensile tests, an increase in GNP content in both polymer grades led to a slight increase in yield strength coming from the proper distribution of nano‐reinforcement by creating stress concentration sites. After the yield point, Homo‐PP based nanocomposites showed higher strain hardening than GNP reinforced Copo‐PP owing to a high crystallization degree and linear chains of Homo‐PP. This work showed that functionalized graphene can act as both nucleating and reinforcing agent in the compounding process and its exfoliation through polymer chains is much better in homopolymers at a faster and high shear rate. © 2020 Society of Chemical Industry