重庆主城区城市空间结构演变与优化——基于公共服务功能组织视角

城市空间结构与城市公共服务功能组织密切相关。重庆主城区作为典型的"多中心、组团式"城市,在城市公共服务功能的就近组织、公共服务需求的均衡化方面具有先天的优势。本文梳理重庆主城区空间结构的演进历程以及与公共服务需求变化的互动关系,辨识随着城市功能由生产向消费转型,空间面临的结构问题及其内在动因,借鉴马斯洛理论,提出顺应当前及未来公共服务需求层次的分异,城市空间结构应从"多中心、组团式"进一步向"分层多中心、组团式"优化,引导形成相对集中、功能综合的基层"小、微中心"体系,以有序组织公共服务功能,最大限度地减少非必要交通出行,降低居民出行的时耗,提升城市效率,并借鉴恩格尔系数构建衡量城市效率的评价框架,为破解山地城市空间结构问题,推动城市空间转型提供新的思考视角。     

Three-dimensional porous SnO2@NC framework for excellent energy conversion and storage

SnO₂-based materials are deemed to be attractive electrodes for lithium/sodium ion batteries (LIBs and SIBs) and electrocatalytic CO₂ reduction reaction (CRR) because of high energy density and large abundance. However, the practical application of the SnO₂-based materials is prevented by low electrical conductivity and large volume change. Herein, we construct a three-dimensional (3D) porous network with SnO₂ nanoparticles into N-doped carbon (namely P–SnO₂@NC) synthesized by freeze drying followed by a pyrolyzation process. In the composite, the 3D hierarchical framework can facilitate the ion penetration and gas diffusion. In addition, the NC network can optimize the conductivity of the material and suppress the electrode material to fall off from the electrode. Therefore, the electrode delivers excellent electrochemical properties with high capacities of 510 mA h g⁻¹ after 1000 cycles for LIBs and 497 mA h g⁻¹ after 500 cycles for SIBs. Furthermore, the electrode shows high selectivity for CRR with a large coulombic efficiency (CE) of 52.7% for HCOOH at 0.6 V.     

机械结构分层优化技术研究进展

复杂机械结构优化设计一般表现为多目标、多约束、多参数的优化问题,所以复杂结构优化设计过程通常存在计算复杂、不易收敛等困难。分层优化技术是复杂结构优化设计的一种有效途径,通过将优化问题中复杂的约束、设计变量以及功能目标合理分解为若干子层问题进行求解,然后通过协调得到复杂机械结构整体优化问题的结果。文章对机械结构分层优化技术的理论、应用研究现状进行了总结并探讨了其关键技术环节及发展趋势。     

多级孔HZSM-5分子筛催化快速热解生物质制芳烃

采用酸和/或碱处理法制备了一系列多级孔HZSM-5分子筛，采用XRD、N₂吸附、XRF、TEM、²⁷Al MAS NMR和NH₃-TPD等表征手段对其孔道结构和酸性进行表征。表征结果表明，采用碱处理方法，可获得孔径集中于3~6 nm的介孔结构，通过改变酸、碱处理次序，可调变酸中心数量和强酸/总酸中心比例。在Py-GC/MS装置上，以纤维素和水稻秸秆为原料，研究多级孔分子筛结构对催化快速热解（CFP）制芳烃反应的影响。反应评价结果表明，同商品级HZSM-5相比，采用先碱后酸处理获得的多级孔HZSM-5分子筛（HZ-OH/H），可将纤维素CFP芳烃碳产率由32.3%提高至43.6%，可将水稻秸秆CFP芳烃碳产率由23.0%提高至30.8%。多级孔HZ-OH/H分子筛的孔道结构和酸中心分布特征，对开发应用于生物质制芳烃的高效工业催化剂具有借鉴意义。     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.