大一学生实验室安全教育实效研究*

为考查线上线下混合安全教育模式实施后的实际效果,对天津理工大学化工、环安、机械、材料学院2020级大一学生进行实验室安全知识测试。结果表明:接受线上线下混合安全教育的大一学生安全知识的掌握情况较好;不足之处在于学生普遍对应急处置知识掌握较差。因此,在后续安全教育过程中,仍需进一步优化安全教育内容与模式,全面推进全员、全程安全教育。     

基于改进SIR模型的知识密集型企业隐性知识转移研究

通过对比分析传染病传染机制与隐性知识的转移过程,引入SIR模型,根据隐性知识转移的特点对SIR模型进行改进,并在组织遗忘视角下,将员工知识遗忘细分为主动遗忘和被动遗忘,建立知识密集型企业隐性知识转移模型.而后,通过Matlab对该模型的演化函数进行模拟,分析知识接收方占比、知识转移能力、知识遗忘率及核心员工流失率对知识密集型企业隐性知识转移的影响.最后,基于模拟分析结果,为提升知识密集型企业隐性知识转移效益提出相应策略.     

LC–MS bioanalysis of intact proteins and peptides

Bioanalysis assays that reliably quantify biotherapeutics and biomarkers in biological samples play pivotal roles in drug discovery and development. Liquid chromatography coupled with mass spectrometry (LC–MS), owing to its superior specificity, faster method development and multiplex capability, has evolved as one of the most important platforms for bioanalysis of biotherapeutics, particularly new scaffolds such as half-life extension platforms for proteins and peptides, as well as antibody drug conjugates. Intact LC–MS analysis is orthogonal to bottom-up surrogate peptide approach by providing whole molecule quantitation and high-level sequence and structure information. Here we review the latest development in LC–MS bioanalysis of intact proteins and peptides by summarizing recent publications and discussing the important topics such as the comparison between top-down intact analysis and bottom-up surrogate peptide approach, as well as simultaneous quantitation and catabolite identification. Key bioanalytical issues around intact protein bioanalysis such as sensitivity, data processing strategies, specificity, sample preparation and LC condition are elaborated. For peptides, topics including quantitation of intact peptide vs. digested surrogate peptide, metabolites, sensitivity, LC condition, assay performance, internal standard and sample preparation are discussed.     

A discontinuous Galerkin immersed boundary solver for compressible flows: Adaptive local time stepping for artificial viscosity–based shock-capturing on cut cells

We present a higher-order cut cell immersed boundary method (IBM) for the simulation of high Mach number flows. As a novelty on a cut cell grid, we evaluate an adaptive local time stepping (LTS) scheme in combination with an artificial viscosity–based shock-capturing approach. The cut cell grid is optimized by a nonintrusive cell agglomeration strategy in order to avoid problems with small or ill-shaped cut cells. Our approach is based on a discontinuous Galerkin discretization of the compressible Euler equations, where the immersed boundary is implicitly defined by the zero isocontour of a level set function. In flow configurations with high Mach numbers, a numerical shock-capturing mechanism is crucial in order to prevent unphysical oscillations of the polynomial approximation in the vicinity of shocks. We achieve this by means of a viscous smoothing where the artificial viscosity follows from a modal decay sensor that has been adapted to the IBM. The problem of the severe time step restriction caused by the additional second-order diffusive term and small nonagglomerated cut cells is addressed by using an adaptive LTS algorithm. The robustness, stability, and accuracy of our approach are verified for several common test cases. Moreover, the results show that our approach lowers the computational costs drastically, especially for unsteady IBM problems with complex geometries.     

高速公路PPP项目股权结构、质量水平与VFM的关系研究

高速公路PPP项目属于具有经营收益的准经营性项目,政府制定有效的激励措施,可以鼓励项目公司发挥专业的建设和运营能力,提高项目质量和收益水平。本文以VFM作为政府绩效评价指标,通过构建完全信息静态博弈模型,研究了PPP项目股权结构、质量水平与VFM之间的关系,结果发现:虽然社会资本较多持有项目公司股份有助于提高VFM水平,但随着社会资本方股权比例的增加,项目质量水平在不断降低,所以政府应该将社会资本方的持股比例控制在一定范围内,在保证项目质量的同时达到VFM最大化,从而降低政府方风险,提高社会效益。在高速公路PPP项目中,政府应优化股权决策,激励项目公司改善项目质量和水平,为公私双方带来良好的绩效水平。     

Tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber

Optical fiber temperature sensors have been widely employed in enormous areas ranging from electric power industry, medical treatment, ocean dynamics to aerospace. Recently, graphene optical fiber temperature sensors attract tremendous attention for their merits of simple structure and direct power detecting ability. However, these sensors based on transfer techniques still have limitations in the relatively low sensitivity or distortion of the transmission characteristics, due to the unsuitable Fermi level of graphene and the destruction of fiber structure, respectively. Here, we propose a tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber (Gr-PCF) with the non-destructive integration of graphene into the holes of PCF. This hybrid structure promises the intact fiber structure and transmission mode, which efficiently enhances the temperature detection ability of graphene. From our simulation, we find that the temperature sensitivity can be electrically tuned over four orders of magnitude and achieve up to ～ 3.34×10^-3 dB/(cm·℃) when the graphene Fermi level is ～ 35 meV higher than half the incident photon energy. Additionally, this sensitivity can be further improved by ～ 10 times through optimizing the PCF structure (such as the fiber hole diameter) to enhance the light-matter interaction. Our results provide a new way for the design of the highly sensitive temperature sensors and broaden applications in all-fiber optoelectronic devices.     

Computational modelling of multi-material energetic materials and systems

ABSTRACT

In this paper, we present a systematic roadmap for developing a robust and parallel multi-material reactive hydrodynamic solver that integrates historically stable algorithms with new and current modern methods to solve explosive system design problems. The Ghost Fluid Method and Riemann solvers were used to enforce appropriate interface boundary conditions. Improved performance in terms of computational work and convergence properties was achieved by modifying a local node sorting strategy that decouples ghost nodes, allowing us to set material boundary conditions via an explicit procedure, removing the need to solve a coupled system of equations numerically. The locality and explicit nature of the node sorting concept allows for greater levels of parallelism and lower computational cost when populating ghost nodes. Non-linear numerical issues endemic to the use of real Equations of State in hydro-codes were resolved by using more thermodynamically consistent forms allowing us to accurately resolve large density gradients associated with high energy detonation problems at material interfaces. Pre-computed volume tables were implemented adding to the robustness of the solver base.     

The impact of a conceptual model-based mathematics computer tutor on multiplicative reasoning and problem-solving of students with learning disabilities

The study explored the impact of Please Go Bring Me-COnceptual Model-based Problem Solving (PGBM-COMPS) computer tutoring system on multiplicative reasoning and problem solving of students with learning disabilities. The PGBM-COMPS program focused on enhancing the multiplicative reasoning and problem solving through nurturing fundamental mathematical ideas and moving students above and beyond the concrete level of operation. This is achieved by taking advantages of the constructivist approach from mathematics education and explicit conceptual model-based problem solving approach from special education. Participants were three elementary students with learning disabilities (LD). A mixed method design was employed to investigate the effect of the PGBM-COMPS program on enhancing students’ multiplicative reasoning and problem solving. It was found that the PGBM-COMPS program significantly improved participating students’ problem solving performance not only on researcher developed criterion tests but also on a norm-referenced standardized test. Qualitative and quantities data from this study indicate that, in addition to nurturing fundamental concept of composite units, it is necessary to help students to understand underlying problem structures and move toward mathematical model-based problem representation and solving for generalized problem solving skills.     

Recent advances of organometallic complexes in emerging photovoltaics

Organometallic complexes (OMCs) consisting of organic and metal active moieties have shown immense potential for application in solar cells. The diverse structure, rich porosity, and unique charge centers of OMCs enable them to be functional in solar cells. In this review, we introduced four types of OMCs, such as crown organometallic complexes, β-diketone metal complexes, cyclometallic complexes, and main chain metal-containing polymers, providing an in-depth analysis of the structure-performance relationship. OMCs could serve as active or interlayer materials in a variety of solar cell systems such as organic solar cells, perovskite solar cells, and dye-sensitized solar cells, especially some metals to improve the photoelectric performance of the device as dopants. In the end, perspectives on the opportunities and challenges of OMCs are given.