Numerical prediction of the rheological properties of fresh self-compacting concrete

Self-Compacting Concrete (SCC) is a high-performance construction material that can simplify classical handling on concrete construction by avoiding the need for additional vibrational compaction. Challenges in the use of SCC lie in ensuring optimal operation of the material in terms of properly filled castings in presence of complex reinforcement arrangements, reduction of entrained gas bubbles and limitation of aggregate separation. A major factor influencing the aforementioned aspects is the rheological properties of SCC mixtures under varying conditions (e.g. content composition, mechanical impact, temperature, moisture). This contribution aims at unified constitutive modelling of SCC in the setting stage. Concrete setting describes the transition from fluid-like fresh concrete, which -in presence of time- dependent transport-reaction processes- develops a porous cementitious structure, to hardened concrete showing solid-like behaviour. The constitutive model is implemented using the open-source finite element framework FENICS and applied to a number of benchmark problems

Synergistic strategy with hyperthermia therapy based immunotherapy and engineered exosomes−liposomes targeted chemotherapy prevents tumor recurrence and metastasis in advanced breast cancer

Advanced breast cancer with recurrent and distal organ metastasis is aggressive and incurable. The current existing treatment strategies for advanced breast cancer are difficult to achieve synergistic treatment of recurrent tumors and distant metastasis, resulting in poor clinical outcomes. Herein, a synergistic therapy strategy composed of biomimetic tumor-derived exosomes (TEX)-Liposome-paclitaxel (PTX) with lung homing properties and gold nanorods (GNR)-PEG, was designed, respectively. GNR-PEG, with well biocompatibility, cured recurrent tumors effectively by thermal ablation under the in situ NIR irradiation. Meanwhile, GNR-mediated thermal ablation activated the adaptive antitumor immune response, significantly increased the level of CD8+ T cells in lungs and the concentration of serum cytokines (tumor necrosis factor-α, interlekin-6, and interferon-γ). Subsequently, TEX-Liposome-PTX preferentially accumulated in lung tissues due to autologous tumor-derived TEX with inherent specific affinity to lung, resulting in a better therapeutic effect on lung metastasis tumors with the assistance of adaptive immunotherapy triggered by GNR in vivo. The enhanced therapeutic efficacy in advanced breast cancer was a combination of thermal ablation, adaptive antitumor immunotherapy, and targeted PTX chemotherapy. Hence, the synergistic strategy based on GNR and TEX-Liposome provides selectivity to clinical treatment of advanced breast cancer with recurrent and metastasis

Rapid diagnosis of duck Tembusu virus and goose astrovirus with TaqMan-based duplex real-time PCR

The mixed infection of duck Tembusu virus (DTMUV) and goose astrovirus (GoAstV) is an important problem that endangers the goose industry. Although quantitative PCR has been widely used in monitoring these two viruses, there is no reliable method to detect them at the same time. In this study, by analyzing the published genomes of DTMUV and goose astrovirus genotype 2 (GoAstV-2) isolated in China, we found that both viruses have high conservation, showing 96.5 to 99.5% identities within different strains of DTMUV and GoAstV, respectively. Subsequently, PCR primers and TaqMan probes were designed to identify DTMUV and GoAstV-2, and different fluorescent reporters were given to two probes for differential diagnosis. Through the optimization and verification, this study finally developed a duplex TaqMan qPCR method that can simultaneously detect the above two viruses. The lower limits of detection were 100 copies/μL and 10 copies/μL for DTMUV and GoAstV-2 under optimal condition. The assay was also highly specific in detecting one or two viruses in various combinations in specimens, and provide tool for clinical diagnosis of mixed infections of viruses in goose

Phase-field predictive model for setting of fresh self-compacting concrete

The initial setting of fresh concrete is mainly caused by the dissolution of cement grains and the precipitation of calcium-silicate-hydrates during cement hydration. Progressing hydration drives the transition from a dense suspension to a porous solid phase. Fresh mixture of self-compacting concrete (SCC) can be considered as a phase-changing multi-component material and can be described as a continuum at the macro scale, interacting with a set of transport-reaction-diffusion processes which in turn are driven by phenomena at the level of the microstructure. This contribution focuses on a predictive model for the setting of fresh SCC where the liquid-solid phase transition is captured by a phase-field variable using the Ginzburg-Landau type free energy function. Hydration-related chemical reactions together with heat and mass transfer are volume coupled with the mechanical behaviour and determined by the environmental conditions. The weak form of the predictive model is discretised using the finite element method and implemented with the FEniCS computational framework

Phase-field predictive model for setting of fresh self-compacting concrete

The initial setting of fresh concrete is mainly caused by the dissolution of cement grains and the precipitation of calcium-silicate-hydrates during cement hydration. Progressing hydration drives the transition from a dense suspension to a porous solid phase. Fresh mixture of self-compacting concrete (SCC) can be considered as a phase-changing multi-component material and can be described as a continuum at the macro scale, interacting with a set of transport-reaction-diffusion processes which in turn are driven by phenomena at the level of the microstructure. This contribution focuses on a predictive model for the setting of fresh SCC where the liquid-solid phase transition is captured by a phase-field variable using the Ginzburg-Landau type free energy function. Hydration-related chemical reactions together with heat and mass transfer are volume coupled with the mechanical behaviour and determined by the environmental conditions. The weak form of the predictive model is discretised using the finite element method and implemented with the FEniCS computational framework

Learning Large Margin Classifiers Locally and Globally

A new large margin classifier, named MaxiMin Margin Machine (M⁴) is proposed in this paper. This new classifier is constructed based on both a "local" and a "global" view of data, while the most popular large margin classifier, Support Vector Machine (SVM) and the recently-proposed important model, Minimax Probability Machine (MPM) consider data only either locally or globally. This new model is theoretically important in the sense that SVM and MPM can both be considered as its special case. Furthermore, the optimization of M⁴ can be cast as a sequential conic programming problem, which can be solved efficiently. We describe the M⁴ model definition, provide a clear geometrical interpretation, present theoretical justifications, propose efficient solving methods, and perform a series of evaluations on both synthetic data sets and real world benchmark data sets. Its comparison with SVM and MPM also demonstrates the advantages of our new model