三个膀胱癌细胞系侵袭相关分子的研究

目的　探讨膀胱癌细胞侵袭能力不同的机制。方法　取来源于同一膀胱癌组织的三个细胞系 ,检测其软琼脂克隆形成率及基底膜黏附能力。采用荧光偏振法测定细胞膜脂流动性 ,免疫组化法和流式细胞术分析E cadherin、基质金属蛋白酶 2 (matrixmetalloproteinases 2 ,MMP 2 )和CD44的表达。 结果　BLX和BLZ细胞的软琼脂克隆形成率高于BLS细胞 ,同时基底膜黏附能力均大于BLS细胞 ,BLX和BLZ细胞的膜脂流动性小于BLS细胞 ,BLX、BLZ和BLS细胞在MMP 2表达上无差异 ,BLS和BLZ的E cadherin表达强于BLX细胞。流式细胞仪分析BLS细胞CD44阳性率高于BLX和BLZ细胞。结论　膀胱癌细胞系在侵袭能力上的差异可能与其膜脂流动性及CD44的表达不同有关     

自动引导车辆(AGV)地图生成方法研究

提出了在人工导引方式下生成AGV数字地图的方法.利用立体视觉提取障碍物边界点,将不同视点处得到的观测集成到数字地图中,进一步提高数字地图的精度.由于AGV运动的不确定性,在将最新的观测集成到地图中之前,通过匹配新的观测数据与地图数据计算AGV位置.实验表明了该方法的有效性.     

基于Petri网的仓储物流系统建模与仿真

Petri网作为一种离散事件动态系统的建模和分析方法，提供了在逻辑时序下研究系统特性和性能的有效手段，并有图形方法的直观性和逻辑方法的概括性．运用Petri网对一个简化了的仓库物流系统进行建模，运用基于Petri网的仿真软件对其仓储系统进行了仿真，并简单地分析了仿真的运行结果，由此提出了一种物流系统实际运作的仿真方法．     

小儿细菌性脑膜炎脑脊液β_2－微球蛋白含量的动态观察

应用放射免疫法测定了２５例细菌性脑膜炎（ＢＭ）患儿脑脊液（ＣＳＦ）β２－微球蛋白的含量并进行动态观察。结果发现，ＣＳＦβ２－微球蛋白对ＢＭ的正确诊断、病情估计具有一定的临床意义；通过动态观察，可观察治疗效果，监测病情变化。     

接触网检测车振动补偿研究

针对接触检测车实际工况,分析了检测车车体振动的动力学特性,建立了基于运动规律的数学模型,论述了训练样本得出检测车拉出值振动补偿函数的原理和方法。     

人宫颈癌FHIT基因的表达改变与HPV16感染的关系

目的探讨FHIT基因表达改变和HPV16感染与人宫颈癌发生的关系。方法采用反转录-巢式聚合酶链反应方法测定5种人宫颈癌细胞株(SiHa、HeLa、RJC-1、CS1213、C4-1)和58例宫颈癌组织与18例正常宫颈对照中FHIT mRNA的表达;回收7例FHIT基因不同的转录扩增产物,纯化后进行DNA测序;PCR技术检测组织中HPV16型的感染状况。结果SiHa、HeLa和C4-1宫颈癌细胞中有FHIT基因转录异常;宫颈癌组织中39例(67.2%)存在FHIT基因异常表达,显著高于正常对照组0例(0%)(P<0.05);37例(63.8%)有HPV16感染,显著高于正常对照组1例(5.0%)(P<0.05)。宫颈癌组织中有HPV16感染患者的FHIT基因表达异常数(30/37)显著高于HPV16未感染的患者(9/21),二者之间存在相关性(P<0.01);FHIT基因的异常表达和HPV16的感染与患者的年龄、临床分期、肿瘤直径、病理分级及是否伴淋巴结转移无相关性(P>0.05)。序列分析发现FHIT基因转录本主要存在不同程度的外显子的缺失,以第5位和第6位外显子的缺失为主,未见未知序列的插入和点突变。结论FHIT基因在人宫颈癌组织中的异常表达率明显增高,且与HPV16的感染有关,这些改变可能在人宫颈鳞癌的发生中起着重要作用。     

提前右转组织方式及其影响分析

以可插车间隙理论和概率论为基础,建立了固定与可变交织区长度的提前右转组织方式下右转机动车通行能力计算模型与延长交织区所能够增加的通行能力的计算模型,提出了提前右转组织方式的适用条件。以平均延误为指标,对比分析了两种组织方式下右转机动车的运行效果,基于Webster信号配时方法,对实行提前右转方式的交叉口内直行和左转车流的通行能力进行了分析。分析结果表明:采用提前右转组织方式后交叉口直行和左转通行能力增加;在右转机动车提前右转能够满足通行能力时,提前右转可减小冲突区的面积,否则,可以通过延长交织区长度来增加通行能力,以减少右转车的平均延误。     

城市轨道交通网络化客流特征及成长规律——基于京沪穗深城市轨道交通网络客流数据分析

中国各大城市轨道交通将陆续进入网络化运营阶段,然而对于网络化进程中客流特征及成长规律尚缺乏深入细致的分析与研究。对北京、上海、广州、深圳4个城市轨道交通网络化进程中的客流特征及成长规律进行系统总结,涉及客运量及客运强度、断面客流量分布、线网换乘系数、乘距与运距、客流时间分布等。同时,针对轨道交通沿线土地利用、线网规模、服务水平、票制票价等影响客流特征变化的主要因素进行分析。在此基础上,对当前中国城市轨道交通规划、建设、运营组织的若干关键问题提出建议。     

Employability skills of maritime business graduates: industry perspectives

The maritime industry underpins international business and world trade. As to be expected, business management is critical for the maritime industry, requiring highly trained individuals and teams to lead the development, implementation and control of sound contemporary management practices. Maritime business degrees are developed by universities to meet such demand by providing graduates with sufficient skills for the onshore business-related roles. This empirical study conducted in Australia, USA and Canada, investigates current and future industry employability skills for maritime business graduates through focus groups, individual interviews and an online survey with senior managers in maritime organisations. This study found the important employability skills for maritime business graduates which include communication, problem solving, adaptability, self-management, team work, and digital literacy and technology. Demand for digital literacy and technology knowledge and skills have increased due to the maritime industry having a trend of moving towards digitalisation and automation. However, the survey findings revealed that a skills focus for maritime business graduates will not be the technology itself but the use and management of technology. In relation to future skills/knowledge required from maritime business degree graduates in 10 years’ time, communication and adaptability are recognised as being the most important. Employers expect that maritime business graduates should be able to adopt new technology and be competent in communication, and be more adaptable given the highly dynamic nature of the maritime industry. Moreover, they require graduates to be equipped with a higher level of computer skills, have a strong work ethic and multilingual skills.     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.