A biotechnological agenda for the third world

Third World countries should exploit the genetic information stored in their flora and fauna to develop independent and highly competitive biotechnological and pharmaceutical industries. The necessary condition for this policy to succeed is the reshaping of their universities and hospitals—to turn them into high-caliber research institutions dedicated to the creation of original knowledge and biomedical invention. Part of the service of the Third World foreign debt should be co-invested with the lending banks in high technology enterprises. This should be complemented with an active program of investments in First World biotech companies and university research departments which could contribute to the solving of problems connected with the First World. These strategic alliances would allow effective training of molecular biologists, improvement of South American universities, and education of biotechnologists, managers, and lawyers in the complexities of high-technology business. The establishment of real joint ventures between developed and underdeveloped countries might contribute to change the present strained relations between the North and the South, and science and technology could become real forces of social and economic development.     

企业安全生产主因素辨析的模糊数学模型及应用——来自电网企业的实证研究

应用模糊数学的思想建立了企业的安全主因素辨析的模糊数学模型。运用在电网企业调研的实际数据,分析和证实了影响电网经营企业安全的主要因素,它们依次是管理、员工素质、电网结构、环境。对电网企业而言,员工素质和电网结构在短期内具有不易变的特点,管理是影响安全效果的最活跃的因素。在对“管理”要素作为二级分析时,辨析出8个安全管理方面的“要素”,其分别影响着电网安全生产。研究结果对电网安全管理实践有一定的指导意义。     

综合污染指数计算公式的改进

针对水环境质量的算术均值污染指数和几何均值污染指数评价方法的缺点,提出采用新模式计算综合污染指数,用此方法对南通市的内河水质进行分析。并与均值污染指数法和几何均值综合污染指数法进行了比较,效果显著。     

The attractiveness fragment—AFLP analysis of local adaptation and sexual selection in a caeliferan grasshopper, <Emphasis Type="Italic">Chorthippus biguttulus</Emphasis>

Genetic variability among males is a necessary precondition for the evolution of female choice based on indirect genetic benefits. In addition to mutations and host–parasite cycles, migration of locally adapted individuals offers an explanation for the maintenance of genetic variability. In a previous study, conducting a reciprocal transplant experiment on a grasshopper, Chorthippus biguttulus, we found that environmental conditions significantly influenced not only body condition but also an important trait of male calling song, the amplitude of song. Although not significant, all other analysed physical and courtship song traits and attractiveness were superior in native than in transferred males. Thus, we concluded that local adaptation has a slight but consistent influence on a range of traits in our study populations, including male acoustic attractiveness. In our present study, we scanned male grasshoppers from the same two populations for amplification fragment length polymorphism (AFLP) loci connected with acoustic attractiveness to conspecific females. We found greater differences in allele frequencies between the two populations, for some loci, than are expected from a balance between drift and gene flow. These loci are potentially connected with locally adapted traits. We examined whether these alleles show the proposed genotype environment interaction by having different associations with attractiveness in the two populations. One locus was significantly related to sexual attractiveness; however, this was independent of the males’ population affiliation. Future research on the evolution of female choice will benefit from knowledge of the underlying genetic architecture of male traits under intraspecific sexual selection, and the ‘population genomics’ approach can be a powerful tool for revealing this structure.     

Safety-based process plant layout using genetic algorithm

This paper presents a method based on a genetic algorithm for optimizing process plant layout. The relative location of main process units is determined to minimize an annual cost function including the cost of material transfer between process units (piping and pumping costs), land cost, and the expected annual loss resulting from damage to each secondary unit caused by primary accidents occurring in nearby process units. This method is an improvement over previous attempts using genetic algorithms or mathematical programming techniques to optimize plant layout, which neglected pumping costs and included safety issues by evaluating the infringement of predefined safety distances only. In this approach the operating cost of material transfer is included and the likelihood of accidents is taken into account thus providing good practical solutions to the plant layout problem incorporating more realistic cost functions and constraints. In the paper, after discussing the structure of the annual cost function and describing the working logic of the layout generating algorithm, a case study is described to demonstrate the effectiveness of the proposed methodology.     

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Prediction of sludge bulking is a matter of growing importance around the world. Sludge volume index (SVI) should be monitored to predict sludge bulking for a wastewater treatment plant. This study was an effort to develop hybrid artificial neural network-genetic algorithm models (MLPANN-GA and RBFANN-GA) to accurately predict SVI. Operating parameters, including MLVSS, pH, DO, temperature, TSS, COD and total nitrogen were the inputs of neural networks. Genetic algorithm was utilized in order to optimize weights and thresholds of the MLPANN and RFBANN models. Training procedures for SVI estimation were successful for both the MLPANN-GA and RBFANN-GA models. The training and validation models showed an almost perfect match between experimental and predicted values of SVI. The results indicated that with low experimental values of input data to train ANNs, the MLPANN-GA compared with the RBFANN-GA is more accurate due to higher coefficient of determination (R²) and lower root mean squared error (RMSE) values. The values of RMSE and R² for the optimal models approached 0 and 1, respectively. The mean average error for the ANN models did not exceed 3% of the input values of the measured SVI. The GA increased the accuracy of all the MLPANN and RBFANN models.     

The Integration of a Genetic Programming-Based Feature Optimizer With Fisher Criterion and Pattern Recognition Techniques to Non-Intrusive Load Monitoring for Load Identification

Identification of electricity energy consumption on individual household appliances used in a smart house is the first important step for making the use and conservation of electricity energy more efficient. In the past, Non-Intrusive Load Monitoring (NILM) techniques, which are part of smart grid techniques realized to improve electricity energy usage efficiency, have been developed to identify individual appliances with avoiding installing many smart meters for appliances in a field. In this paper, a new NILM technique that integrates an efficient Genetic Programming (GP)-based feature optimizer with pattern recognition techniques is proposed to identify which appliance is being turned on or off. The proposed GP-based feature optimizer with Fisher criterion is used to generate a more efficient feature than original potential transient features extracted from captured transient response of household appliances through analysis of NILM. The new feature generated by GP is used by pattern recognition techniques as load identifiers for load identification. The load identifiers used and compared in this paper include k-Nearest-Neighbor Rule, Back-Propagation Artificial Neural Network, and Learning Vector Quantization. Experiments are conducted under different single-load and multiple-load operation circumstances at different actual experimental environments with small disturbances. As shown from the experimental results, the proposed is confirmed to be feasible and usable.     

基于遗传算法的洪水灾情评估神经网络模型探讨

洪水灾情评估实际上是一种模式识别问题,而人工神经网络模型具有逼近有界闭子集上任意非线性映射的特性,且模型更新方便。本文邮基于遗传算法的洪水灾情评估神经网络模型,阐述了其基本原理和算法,实例研究表明其实用性、客观性和通用性。     

灾民撤退网络流模型及其GIS模拟技术

灾民撤退问题实质上是一个多源点、多收点的网络流问题。本文建立了用于灾民撤退的网络流分析模型,介绍了基于地理信息系统网络分析的灾民撤退模拟技术。通过对某蓄洪区的实例分析表明,网络分析是模拟灾民撤退的有效方法,地理信息系统技术是辅助制定灾民撤退方案的崭新途径。     

Kin associations and direct vs indirect fitness benefits in colonial cooperatively breeding sociable weavers Philetairus socius

Indirect fitness benefits are believed to be an important force behind the evolution of cooperative breeding. However, helpers may associate with their relatives as a result of delayed dispersal, hence, kin associations might be a consequence of demographic viscosity rather than active choice. In addition, recent studies showed that helpers may have access to reproduction therefore direct benefits might also play an important role. Here, we investigate the possible roles of direct genetic benefits and kin associations on helping behavior in the sociable weaver Philetairus socius, a colonial and cooperatively breeding passerine. We used a microsatellite-based genotyping method to describe the genetic structure within nests and colonies. Within a colony, we found considerable genetic structure between males but not females. Sociable weaver colonies have several nests that are simultaneously active, giving individuals a choice of associating with a range of first-order kin to unrelated individuals. Helpers were significantly more related to the young in the helped nests than in other nests of the colony, suggesting an active choice for associating with kin. The helpers were generally offspring or first-order relatives of one (50%) or both (43%) breeders, although more infrequently, seemingly unrelated individuals also helped (7%). We found no supporting evidence of extrapair parentage and hence no direct genetic gains from helping in our population. This strong reproductive skew is contrary to theoretical models predicting conflicts over reproduction in stepfamilies. We discuss whether female decisions and/or other direct benefits of remaining in kin associations or helping might explain the high skew observed.