客车定位节拍生产工艺流程体系的构建

结合传统的客车生产管理模式及现代的精益生产管理思想,构建“客车定位节拍生产工艺流程体系”,并简述其实施过程及效果预测。     

人脐血CD_3AK细胞生物活性及抗瘤作用的实验研究

目的　应用抗CD3单克隆抗体(CD3McAb)和重组人白细胞介素- 2(rhIL- 2)共同诱导人脐血单个核细胞以制备CD3AK细胞,动态观察其体外增殖能力、杀伤活性、免疫表型变化及分泌细胞因子水平。方法　用台盼蓝活细胞计数计算细胞扩增倍数,MTT法测定细胞杀伤活性,间接免疫荧光法分析细胞免疫表型,ELISA双抗夹心法检测肿瘤坏死因子- α(TNF- α)、干扰素 -γ(IFN- γ)、白细胞介素 -6(IL- 6)的水平。结果　脐血CD3AK细胞在第2周增殖能力最强,增殖倍数为78.56;培养至第12天的脐血CD3AK细胞杀伤活性最高,并对多种靶细胞的杀伤作用显著;表型分析脐血CD3AK细胞属异质性细胞群,培养第7、14 天,群体中 CD3+、CD8+、CD25+、CD38+、CD16+及 CD56+ 细胞较培养前显著增加(P<0.01); CD3 AK细胞培养至第 3 天的上清中 TNF -α、IFN -γ、IL -6 水平明显升高。结论　脐血CD3AK细胞是一种具有广谱杀瘤活力且增殖活性强的免疫细胞。本研究为脐血 CD3 AK细胞的免疫治疗提供了实验和理论依据。     

"诗可以群"的诗学理论与先秦时期的文学活动

孔子对诗的"兴观群怨"的理论,是先秦时期重要的诗歌创作理论,也是历代诗歌批评史讨论的对象,并且贯穿于历代诗歌的创作实际中.但长期以来,人们往往把"诗可以群"从"兴"、"观"、"怨"中割裂开来,导致人们忽视了"诗可以群"的诗学理论与先秦时代文学活动的关系."诗可以群"的诗学理论反映了先秦时期文学活动的集体性.这一理论为我们研究文学觉醒的时间问题提供了一个窗口.     

船闸日到船概率分布参数估计

讨论了船闸日到船分布参数的确定方法，分析了样本容量和方差估计精度的关系及方差估计相对误差对日到船量计算的敏感性，提出了合理的数据容量和取样方法。应用结果表明，样本容量取90时，日到船量计算的相对误差主要控制在5％以内，对于工程实际是偏于安全的，这说明该方法可行。     

混凝土复合掺合料火山灰活性与形貌研究

用锂盐渣、粉煤灰按比例复合作为一种新型的混凝土活性掺合料，对该复合掺合料的试验研究表明：锂盐渣掺量对复合掺合料的火山灰活性影响差别较大；对混凝土7d，28d和60d各龄期强度影响也较大。研究中采用了扫描电镜及X衍射等现代测试技术，并对该复合掺合料火山灰活性及复合掺合料水泥浆体水化初期和后期形貌特征进行了深入研究，得出复合掺合料中锂盐渣存在较佳掺量。     

基于作业成本管理的企业内部经济责任制研究

阐述了建立基于作业成本管理的企业内部经济责任制的必要性及此经济责任制的特点、具体内容。     

乳腺癌中端粒酶活性与细胞凋亡的研究

目的　探讨端粒酶活性及细胞凋亡与乳腺癌生物学行为的关系。方法　分别采用端粒序列重复扩增法(TRAP法 )和末端脱氧核苷酸转移酶介导缺口末端标记 (TUNEL)法对 6 8例乳腺癌标本进行端粒酶活性和细胞凋亡的检测 ,并结合临床资料进行分析。结果　 6 8例乳腺癌标本端粒酶活性阳性率为 88.2 % (6 0 / 6 8) ,细胞凋亡指数为 (5 6 .7± 15 .3) % ;端粒酶活性强度及细胞凋亡指数与患者的年龄无关 (P >0 .0 5 ) ,与肿瘤的大小、分级、分期、腋淋巴结转移数、激素受体状况有关 (P <0 .0 1) ,即端粒酶活性越高和 (或 )细胞凋亡越少 ,则肿瘤分化程度越低 ,提示预后越差。端粒酶活性强度与细胞凋亡指数呈明显的负相关 (r=- 0 .6 7,P <0 .0 1)。结论　乳腺癌端粒酶活性及细胞凋亡与肿瘤大小、分级、分期、淋巴结转移数和激素受体状况有关 ,端粒酶活性及细胞凋亡的检测有助于乳腺癌的临床分析 ,并有望成为预后的观察指标     

基于地震活动性参数b值的地应力评估方法研究

在铁路选线的可行性研究阶段,当线路方案尚未确定时,不宜通过大规模的钻探查明地应力数值,而希望借鉴现有资料或经验公式对地应力状况有所把握。国内外已有研究表明,在反映地震震级与发生频率的幂律关系中,幂指数b值与地应力水平具有反相关关系,据此提出增加参数b,对水平主应力随埋深分布规律统计回归方程进行修正的方法。以川藏交通廊道地应力分析为例,绘制地震活动性参数b值的空间分布云图;基于研究区已有地应力实测数据,以埋深H和b值作为自变量,拟合得到地应力量值的评估模型,经检验,与目前仅采用埋深作为自变量的方法相比,评估精度明显提高。     

Analysis of within-household effects and between-household differences in maintenance activity allocation

This paper investigates the allocation of household individuals to out-of-home maintenance activities using the rich activity-travel diary data from the San Francisco Bay Area. Two inter-related decisions are considered in this context: (i) whether the given activity episode is performed individually (solo) or jointly, and (ii) the person who participates in the activity, if it is a solo activity. To account for the conditional nature of the solo activity person selection, a nested mixed logit modeling framework is proposed and implemented to jointly analyze person allocation for all maintenance activities performed by a household on a given day. The model is used to investigate within-household effects and between-household differences. The proposed model relaxes some important restrictions in person allocation models by accounting for various sources of correlations and relaxing the assumption of constant variance across households. The proposed model is used to analyze the differences in person allocation between different types of households. The results indicate that life-cycle and household role, income, gender, employment status, and several types of constraints (activities including cost, time-availability, vehicle-availability, coordination constraints, and child-care obligations) affect person allocation decisions in the context of maintenance activities. The empirical results indicate the presence of various sources of correlations across persons, over activities, and within-household that are significant. In addition, the data also provides evidence that the unobserved variances in person selection utilities are not constant across households. A better understanding of these within-household interactions and between-household differences may be used in activity-based simulation models and to develop more effective and focused demand management measures.     

Generating complete all-day activity plans with genetic algorithms

Activity-based demand generation contructs complete all-day activity plans for each member of a population, and derives transportation demand from the fact that consecutive activities at different locations need to be connected by travel. Besides many other advantages, activity-based demand generation also fits well into the paradigm of multi-agent simulation, where each traveler is kept as an individual throughout the whole modeling process. In this paper, we present a new approach to the problem, which uses genetic algorithms (GA). Our GA keeps, for each member of the population, several instances of possible all-day activity plans in memory. Those plans are modified by mutation and crossover, while bad instances are eventually discarded. Any GA needs a fitness function to evaluate the performance of each instance. For all-day activity plans, it makes sense to use a utility function to obtain such a fitness. In consequence, a significant part of the paper is spent discussing such a utility function. In addition, the paper shows the performance of the algorithm to a few selected problems, including very busy and rather non-busy days.