Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Contemporary design process requires the development of a new computational intelligence or soft computing methodology that involves intelligence integration and hybrid intelligent systems for design, analysis and evaluation, and optimization. This paper first presents a discussion of the need to incorporate intelligence into an automated design process and the various constraints that designers face when embarking on industrial design projects. Then, it presents the design problem as optimizing the design output against constraints and the use of soft computing and hybrid intelligent systems techniques. In this paper, a soft-computing-integrated intelligent design framework is developed. A hybrid dual cross-mapping neural network (HDCMNN) model is proposed using the hybrid soft computing technique based on cross-mapping between a back-propagation network (BPNN) and a recurrent Hopfield network (HNN) for supporting modeling, analysis and evaluation, and optimization tasks in the design process. The two networks perform different but complementary tasks—the BPNN decides if the design problem is a type 0 (rational) or type 1 (non-rational) problem, and the output layer weights are then used as the energy function for the HNN. The BPNN is used for representing design patterns, training classification boundaries, and outputting network weight values to the HNN, and then the HNN uses the calculated network weight values to evaluate and modify or re-design the design patterns. The developed system provides a unified soft-computing-integrated intelligent design framework with both symbolic and computational intelligence. The system has self-modifying and self-learning functions. Within the system, only one network training is needed for accomplishing the evaluation, rectification/modification, and optimization tasks in the design process. Finally, two case studies are provided to illustrate and validate the developed model and system.     

GIS支持下的物探数据的管理与分析——以地质灾害勘查地球物理信息管理系统的建设为例

利用GIS强大的数据管理能力和空间分析能力,以ArcView为平台,基于VB、VC和二次开发函数Avenue,实现了地质灾害勘查物探数据的管理与分析.该系统除具备GIS的基本功能外,针对物探数据的特点,还具备物探成果一维、二维、三维显示和人机交互地质解释功能.     

汽车电子商务发展与企业ERP的整合

通过对我国汽车工业电子商务和企业ERP实施的现状分析，分析了电子商务和ERP之间的密切关系。在电子商务时代，作为企业信息系统之主体的制造资源计划MRP-II/ERP系统应该与电子商务有机结合起来，即是在制造资源计划MRP-II/ERP的基础上，充分利用互联网络技术，将供需链管理、客户关系管理、电子商务、办公业务等功能全面集成，形成资源共享、数据共享，适应网络经济的充分柔性的企业管理信息系统。     

81-02对放射性核素~(234)Th、~(144)Ce 的促排效果

81-02为1,2-邻苯二酚3,6-双甲基亚胺二乙酸(代号7601)的衍生物。对~(234)Th,~(144)Ce 有较好的促排效果。可使大鼠~(234)Th 排出量为注入量的73.9%,为对照组的18倍,肝、骨组织中~(234)Th 的蓄积量亦有明显下降。促排效果优于811。可使大鼠尿中~(144)Cc 的排出量达到对照组的20倍,优于7601。81-02的有效剂量甚低,当剂量为12mg/kg 时,尚能使尿中~(234)Th 排出40%。     

基于光谱学结合分子模拟研究咖啡酸和对香豆酸与脲酶的相互作用

目的 脲酶可以特异性地催化尿素水解释放出氨和二氧化碳, 从而对幽门螺旋杆菌起到保护作用, 引发胃炎和胃溃疡等。本文为探究同属于肉桂酸衍生物, 且分子结构上有所相似的咖啡酸(caffeic acid, CA)和对香豆酸(p-coumaric acid, PCA)与脲酶的相互作用。方法 以具有细微分子结构差异的CA和PCA为研究对象, 通过荧光光谱研究了其对脲酶的荧光淬灭常数以及结合常数, 使用圆二色谱结合变温技术考察了不同温度条件下对脲酶二级结构的影响, 利用分子模拟方法探讨了CA和PCA与脲酶的结合位点与空间构象。结果 CA和PCA对脲酶均属于静态淬灭, 与脲酶的结合是自发形成的(ΔG°<0), 且结合摩尔比为1:1。CA与脲酶的亲和力和对脲酶二级结构的影响都高于PCA, 而它们与脲酶的结合稳定性随温度的升高而降低。分子对接进一步证明了CA与脲酶的结合能力强于PCA, 并且在结合过程中氢键为主要相互作用力, CA和PCA与脲酶结合在双镍活性位点周边。结论 CA和PCA可以与脲酶自发结合, 其稳定性随着温度的升高而下降, 研究结果为基于构效关系的天然小分子的功能性应用提供新思路。     

2015—2020年宜宾市肉类食品沙门菌血清型及分子分型研究

目的 研究宜宾市肉类食品中沙门菌分离株的血清型和分子分型特征,分析沙门菌污染的危险因素。方法 收集2015—2020年宜宾市辖区(县)肉类食品沙门菌分离株29株,进行血清型鉴定和脉冲场凝胶电泳(PFGE)分子分型分析,并就地域、物种来源和售卖方式对沙门菌血清群检出构成比的影响进行多因素Logistic回归分析。结果 29株沙门菌分离株共检出14种血清型(分属4个群),主要优势血清型为鼠伤寒沙门菌、吉韦沙门菌和肠炎沙门菌。聚类分析显示PFGE分子分型为18种带型。肠炎沙门菌带型相似度100%,来自同一传染链;吉韦沙门菌有2种带型,鼠伤寒沙门菌有4种带型,相似度较差;多因素Logistic分析显示,影响D群沙门菌检出的主要危险因素为地域分布(OR=1.053,P<0.05);影响E群沙门菌检出的主要危险因素为物种来源(OR=3.887,P<0.05)和地域分布(OR=1.227,P<0.05)。结论 监管部门应从家禽养殖地地域来源和肉类物种来源两个方面加强宜宾市肉类食品安全监督。     

超高效液相色谱-高分辨质谱法测定牛奶中7种有机磷酸酯类阻燃剂

目的 建立牛奶中7种有机磷酸酯类(OPEs)阻燃剂的超高效液相色谱-高分辨质谱分析方法。方法 基于乙腈-冷冻诱导液液萃取技术提取、净化和富集样品,以甲醇-水作为流动相,梯度洗脱程序,HSS T3色谱柱(2.1 mm×100 mm,1.8μm)分离,高分辨质谱靶向单一离子监测模式测定,内标法定量。结果 7种OPEs在0.2～20μg/L范围内呈良好的线性关系(R²>0.99),方法检出限为0.01～0.21μg/L,定量限为0.04～1.72μg/L,3个不同加标水平的回收率为88.4%～118%,相对标准偏差为1.15%～7.15%。结论 该方法操作简便,重复性好,灵敏度高,可应用于牛奶中OPEs的痕量检测。     

近五年电子鼻技术在食品安全的应用进展

电子鼻因快速、无损、便捷的特点,已在食品安全保障发挥积极作用。本文综述了近五年电子鼻在新鲜度监控及货架期预测、农残检测、致病微生物及毒素检测、掺假及鉴伪检测、过敏原识别的应用,对比了其他检测手段,展示了与其他技术融合的情况,分析了存在问题,并展望了发展趋势。     

铜离子对PEMFC膜电极性能影响及恢复方法

为了研究铜离子对质子交换膜燃料电池膜电极组件性能的影响并提出相应的恢复方法,采用X射线衍射分析法检测了金属极板腐蚀后附着在膜电极表面的污染物成分,并采用稀H2SO4溶液沸腾的方法去除污染物并恢复膜电极性能.结果表明,金属极板的防腐层存在缺陷时,极板在缺陷处被腐蚀,并且金属离子严重影响膜电极的性能.采用c=0.2 mol/L的稀H2SO4沸腾的方法能去除膜电极表面的金属离子并恢复其性能.对于热压三合一的膜电极组件,采用稀硫酸沸腾的方法会破坏碳纸的疏水性,不如分体式膜电极的恢复效果好.     

Identification of Phytotoxic Substances from Early Growth of Barnyard Grass (Echinochloa crusgalli) Root Exudates

Barnyard grass is a problematic weed worldwide. It competes with crops and causes reduction in crop yields. In this study, barnyard grass suppressed rice emergence, and the degree of rice inhibition was proportional to the density of barnyard grass. Root exudates of barnyard grass reduced germination and growth of lettuce, rice, and monochoria. Fifteen compounds potentially involved in the phytotoxic activities of barnyard grass were isolated and identified, including phenolics, long-chain fatty acids, lactones, diethyl phthalate, acenaphthene, and derivatives of phthalic acids, benzoic acid, and decane. Quantities of diethyl phthalate, decanoic acid, myristic acid, stearic acid, 7,8-dihydro-5,6-dehydrokavain, and 7,8-dihydrokavain were 2.7, 11.1, 19.6, 35.5, 10.3, and 15.5 μg/ml of barnyard grass root exudates, respectively. The two lactones exhibited the greatest inhibition, followed by the phenolics and the derivatives of phthalic acids. Fatty acids had stronger suppression than diethyl phthalate and ethyl ester-4-ethoxy-benzoic acid. The acenaphthene and decane derivatives were the least phytotoxic. The phytotoxins released by barnyard grass roots showed strong inhibition on growth of broadleaf indicator plants and paddy weeds, but were less effective on barnyard grass itself and rice. Our study revealed that in addition to competition, barnyard grass also interferes with rice and other plants in its surroundings by chemical means.T. D. Khanh and I. M. Chung are members of the research team of Friendly Environmental Low Input Natural Herbicide New Material Study of the Konkuk University.