沙地人工植物群落生物量动态及其更新途径

小叶锦鸡儿群落是科尔沁沙地固定、半固定沙丘具有代表性的人工固沙植物群落之一，其群落种类组成，群落特征均趋于不断稳定和完善。根据小叶锦鸡儿生长发育过程中的两个不同阶段，探讨了不同龄级小叶锦鸡儿人工群落生物量的季节性动态，提出了生物量动态曲线模型。确定萌芽更新是该群落的主要更新途径，采取平茬措施可促进萌蘖，提高地上部生物量。平茬更新的适宜年龄级为６～８ａ。     

中华路立交桥的爆破拆除

详细论述了齐齐哈尔市中华路立交桥爆破拆除施工中所遇到的各种技术问题及解决办法。     

黑火药控制爆破在花岗岩石材开采中的应用

介绍了黑火药控制爆破法在河南省商城县花岗岩开采中的试验与应用，给出了工作面开采要素和合理的爆破参数。     

核电秦山二期坝址开山工程爆破技术综述

结合大型开山工程的实际，总结了设备选型和施工，管理技术，从合理布设穿孔间距，防滚石下海，地平面保护等方面控制台阶爆破质量，并介绍了台阶爆破的安全控制措施。     

太平洋牡蛎高产育苗技术的研究

本文主要从强化亲贝蓄养，加强洗卵和分池孵化工作入手，提高其孵化率，及时选幼；幼虫培养及时筛选不同规格幼虫，投附着基后严格管理等措施来提高其变态率，达到稳产、高产，取得单位水体出苗量平均为３８万多个／ｍ３的好效果和明显的经济效益。     

泡沫分离技术在扇贝育苗上的应用研究

本文从泡沫分离后有机物颗粒的大小及含量入手，进行泡沫分离后有机物及胶体物质含量和物理特性的分析，研究了泡沫分离后的水质变化和对幼虫生长发育的影响，取得比较好的效果，可明显改善育苗水质，从而提高幼虫成活率和育苗成功率．     

城市旅游开发规划研究进展评述

在总结国内旅游开发规划工作经验的基础上,从理论和实践两方面探讨了综合与整体规划、配套建设、滚动开发的操作模式以及当前旅游开发重点等,强调要突出旅游文化背景和旅游形象策划,并归纳了旅游开发规划需要研究的一系列问题。     

Evaluation of the ability of an artificial neural network model to simulate the input-output responses of a large karstic aquifer: the La Rochefoucauld aquifer (Charente,France)

The ability of artificial neural networks (ANN) to model the rainfall-discharge relationships of karstic aquifers has been studied in the La Rochefoucauld karst system, south-west France, which supplies water to the city of Angoulême. A neural networks model was developed based on MLP (multi-layer perceptron) networks and the Levenberg-Marquardt optimization algorithm. Raw rainfall data were used without transformation into effective rainfall. This allowed for the elimination of certain non-verifiable simplifying assumptions and their subsequent introduction into the modeling procedure. The raw rainfall and discharge data were divided into three groups for the training, the validation and the prediction test of the ANN model. The training and validation phases led to a very satisfactory calibration of the ANN model. The attempt to predict discharges showed that the ANN model is able to simulate the karstic aquifer discharges. The shape of the simulated hydrographs was found to be similar to that of the actual hydrographs. These encouraging results make it possible to consider interesting and new prospects for the modeling of karstic aquifers, which are highly non-linear systems.

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Resumen Se ha estudiado la capacidad de las redes artificiales neurales (ANN) para modelizar las relaciones de lluvia-descarga de acuíferos kársticos en el sistema kárstico La Rochefocauld, al suroeste de Francia, el cual abastece de agua a la ciudad de Angoulême. Se desarrolló un modelo de redes neurales en base a redes MLP (Perceptron Multi-Capas) y el algoritmo de optimización Levenberg-Marquardt. Se utilizaron datos de lluvia sin la transformación hacia lluvia efectiva. Esto permitió la eliminación de ciertos supuestos simplificadores no verificables y su subsiguiente introducción en el procedimiento de modelizado. Los datos brutos de descarga y lluvia se dividieron en 3 grupos para la preparación, validación y la prueba de predicción del modelo ANN. Las fases de preparación y validación llevaron a una calibración muy satisfactoria del modelo ANN. El intento por predecir descargas mostró que el modelo ANN es capaz de simular las descargas del acuífero kárstico. Se encontró que la forma de los hidrogramas sintéticos es similar a la de los hidrogramas reales. Estos resultados alentadores hacen posible considerar prospectos nuevos e interesantes para el modelizado de acuíferos kársticos los cuales son sistemas altamente no-lineares.

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Résumé L’aptitude des réseaux de neurones artificiels (RNA) à modéliser les relations pluie-débit des aquifères karstiques a été évaluée sur le karst de La Rochefoucauld (Sud-Ouest de la France), qui fournit l’eau potable à la capitale régionale Angoulême. Un modèle RNA a été développé à cet effet, basé sur les réseaux PMC (Perceptron Multicouche) et l’algorithme d’optimisation de Levenberg-Marquardt. Les données de pluie utilisées concernent la pluie brute, sans transformation en pluie efficace, ce qui permet de s’affranchir de certaines hypothèses simplificatrices non vérifiables. Les données de pluie brute et de débit ont été réparties en 3 groupes pour l’apprentissage, la validation et le test de prédiction du RNA. Les phases d’apprentissage et de validation ont permis d’aboutir à une calibration très satisfaisante du modèle RNA. La tentative de prédiction a montré que le RNA est apte à simuler les débits de l’aquifère karstique à partir de la pluie brute. La forme des hydrogrammes simulés est semblable à celle des hydrogrammes réels. Les résultats obtenus sont très encourageants et permettent d’envisager des perspectives intéressantes et nouvelles de modélisation des aquifères karstiques, qui sont des systèmes hautement non-linéaires.

     

Risk degree of debris flow applying neural networks

A number of methods for prediction of debris flows have been studied. However, the successful prediction ratios of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for risk degree predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 171 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 99.12% demonstrates that the presented ANN model with seven significant factors can provide a highly stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Creation of an artificial carbonate sand

This paper deals with the efficacy of creating artificial carbonate sand from crushed chalk to model the natural carbonate sand. Direct drained shear using 100 mm shear box and compressibility tests using 150 mm Rowe cell have been performed on artificial carbonate sand in order to determine the shear stress-strain and compressibility characteristics of the artificial material. These are compared with data from natural carbonate sands given in the literature. The results obtained demonstrate that shear stress-strain and compressibility characteristics of the artificial material closely mirror that of natural carbonate soils. The artificial carbonate sand shows generally similar characteristics, which are in the range of the natural carbonate material.