基于BIM的房地产企业价值网利益主体博弈分析

建筑信息模型（BIM）作为一项现代信息化技术,它的应用将使房地产产品具有更好的顾客吸引力．然而目前BIM技术尚未得到充分的推广应用,其推广缺乏足够的理论支撑．通过构建房地产企业的价值网解构模型,进行房地产价值网主体的角色定位．应用博弈理论,研究完全信息条件下的消费者与房地产企业、房地产企业之间的博弈模型,解出博弈模型不同情况下的纳什均衡解,并根据纳什均衡解得出相应结论,为BIM技术的推广奠定理论基础．     

加强应用化学专业学生实践能力培养的改革与实践

实践教学是培养应用化学专业创新型人才的一个重要途径,通过构建“一体化、三层次”的实验教学体系和“三阶段”、“三结合”的实习模式,有利于从根本上解决学校教育与社会需求不能很好衔接的问题,切实增强学生的综合实践能力,使应用化学专业更好的服务天津经济社会发展．     

天津市战略性新兴产业的培育对策研究

培育和发展战略性新兴产业是推进产业结构升级,加快经济增长方式转变,实现经济社会可持续发展的必由之路.为此,要通过合理定位、科学选择重点产业和优先领域,建立健全政策体制,完善法律法规建设,营造良好发展环境;应强化产学研合作,构建完善的科技中介服务体系和多渠道的激励机制,加大重大科技成果转化力度,推动天津战略性新兴产业的发展.     

海河、永定新河、独流减河三河口 生态需水量研究

保证河口一定规模的生态用水需求,是恢复河口生态系统健康的重要因素。依据沈焕庭的河口定义,参考有关文献水文模型界定了海河、永定新河及独流减河河口区域。据此计算了永定新河、海河、独流减河河口的适宜生态需水量。与多年入海径流量及文献数据对比,永定新河、独流减河口的生态需水量计算结果较为合理;但海河口的计算结果明显偏低,其主要原因是海河口开发利用强度大,填海造陆,原有河口海域被大量侵占,水域面积大量减少等等。     

改性纳米金刚石液压导轨油

介绍了所研制的CLH 0150型改性纳米金刚石液压导轨油。与市售的顶级国际知名品牌1409型液压导轨油相比具有显著的优越性。当Pr/Ps=0.4时，滑轨流阻系数Rh高出12-25%。当负荷为1400kg时，静压滑轨的面积系数α1约高18%。当负荷为740kg时，劲度系数Se可高出20%以上。使用该油，可使滑轨油膜承载能力提高，防爬行能力增强，从而提高加工精度。     

基于矩阵半张量积的信息物理融合系统状态不透明性分析与控制

状态不透明性作为一种重要的机密属性,能够表征入侵者窃取系统隐私信息的能力。针对带有不可观测事件的信息物理融合系统(CPSs),该文提出一种基于矩阵半张量积(STP)的代数状态空间方法,并且分析与验证CPSs的状态不透明性。首先利用矩阵STP理论对CPSs的状态演化进行建模,得到系统的动态代数表达式,然后利用STP运算的特性,给出验证系统当前状态不透明性的充分必要代数条件。最后,通过数值仿真算例验证了方法的有效性。该文提出的基于矩阵STP方法为CPSs相关隐私分析与安全控制研究提供了一个新的思路和框架。     

Non-Volatile Electrolyte-Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low-Power Neuromorphic Computing and Logic-In-Memory

Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS₂-based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS₂-based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm⁻²), and long retention characteristics (>10⁴ s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G_max/G_min ratio (10³), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the non-volatile characteristics of the GDY/MoS₂-based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS₂-based EGT for next-generation low-power electronics beyond von Neumann architecture.     

An Ultrafast Nonvolatile Memory with Low Operation Voltage for High-Speed and Low-Power Applications

Memory plays a vital role in modern information society. High-speed and low-power nonvolatile memory is urgently demanded in the era of big data. However, ultrafast nonvolatile memory with nanosecond-timescale operation speed and long-term retention is still unavailable. Herein, an ultrafast nonvolatile memory based on van der Waals heterostructure is proposed, where a charge-trapping material, graphdiyne (GDY), serves as the charge-trapping layer. With the band-engineered heterostructure and excellent charge-trapping capability of GDY, charges are directly injected into the GDY layer and are persistently captured by the trapping sites in GDY, which result in an ultrafast writing speed (8 ns), a low operation voltage (30 mV), and a long retention time (over 10⁴ s). Moreover, a high on/off ratio of 10⁶ is demonstrated by this memory, which enables the achievement of multibit storage with 6 discrete storage levels. This device fills the blank of ultrafast nonvolatile memory technology, which makes it a promising candidate for next-generation high-speed and low-power-consumption nonvolatile memory.     

Simultaneous displacement measurement method of multiple targets based on laser self-mixing interference

In order to simultaneously measure the displacements of multiple targets, the laser self-mixing interference(SMI) measurement method based on variational mode decomposition(VMD) is proposed. The SMI signal containing the motion information of the external targets is detected by a photodiode. The VMD can non-recursively decompose the mixed SMI signal into SMI signals corresponding to different external targets. The displacement signals can be reconstructed by fringe counting method and interpolation method. The experimental results show that the displacement signals of different external targets can be reconstructed with half wavelength accuracy, which verifies the correctness and feasibility of this method. This method can improve the measurement efficiency, which offers an effective method for the multi-channel displacement measurement.     

WB-KNN for emotion recognition from physiological signals

K-nearest neighbor (KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues:the majority vote only by the nearest neighbors is too simple to deal with complex (like skewed) class distribution; features with the same contribution to the similarity will degrade the classification accuracy; samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight (both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.