Seasonal Variations in Fatty Acid Composition of Different Tissues of Mirror Carp,Cyprinus Carpio,in Orenler Dam Lake,Afyonkarahisar, Turkey

The seasonal variations of fatty acids compositions in the muscle, liver, and ovarian tissues of mirror carp (Cyprinus carpio) living in Örenler Dam Lake were determined by using gas chromatography. The results obtained were subjected to statistical analyses by employing SPSS software and p < 0.05 was accepted as significant value. In the statistical analyses, multiple comparisons tests were performed. The results showed that monounsaturated fatty acids levels were found to be higher than that of polyunsaturated fatty acids and saturated fatty acids in all seasons. Palmitic and stearic acid levels were the highest in ovaries (19.95% in winter and 7.79% in summer, respectively). Oleic and palmitoleic levels were at the highest levels in liver tissues (34.53% in summer and 18.37% in winter, respectively). Docosahexaenoic (C22:6ω3, DHA), eicosapentaenoic (C20:5ω3, EPA), eicosatrienoic acid, and arachidonic (C20:4ω6, AA) levels were at the highest level as 17.27, 2.54, and 8.41% in ovaries in the winter and 6.37% in muscle tissues in the spring, respectively. ω3/ω6 ratios were 0.62–0.98 in muscle, 1.54–2.82 in the liver, and 2.82–3.89 in the ovaries. From the point of total fatty acid variations, the highest variations were observed as follows: muscle in spring (26.73%), liver in winter (33.85%), and ovaries in winter 36.38%.     

A Pressure Dam Bearing Analysis with Adiabatic Thermal Effects©

To improve the stability of fixed geometry journal bearings in high-speed rotating machinery, pressure dam or stepped bearings have often been used in place of plain or other bearings. Despite their long history and wide range of applications in industry, only isoviscous analyses have been reported in the literature. Since stability problems usually occur at relatively high speeds where considerable heating occurs, it is desirable to include the thermal effects in pressure dam bearing analysis. This study presents a full adiabatic thermohydrodynamic analysis for pressure dam bearings. The theoretical results show that the pressure and temperature distributions on a stepped pad are quite different from those on a smooth pad. The thermal effects have significant influence on the predicted bearing performance, such as journal eccentricity, dynamic coefficients, and rotor stability. The example of a flexible rotor indicates improved stability prediction with the thermal effects included.     

鸽子沱水库大坝除险加固

鸽子沱水库为均质土坝,建于上世纪六七十年代,是典型的"三边"工程,对于我国当时建成的水库存在的问题具有一定的代表性.本文针对鸽子沱水库大坝病险原因及大坝的稳定状况进行分析,并提出工程加固方案.整治加固后,水库运行良好,因此该整治加固方法对类似水库除险加固具有一定的参考价值.     

大坝沉降变形的灰色分析方法

用灰色理论建立了大坝沉降变形的GM(1,1)预测模型,分析了观测数据列长度、观测时间的不等步长性、数据累加方式等对建模和预测精度的影响,考虑了荷载变化对沉降过程的影响和处理方法,探讨了灰色等维预测模型维数尺度的确定原则和精度标准。     

东北某水库坝基岩体质量分级研究

应用单因素和多因素分类方法,对东北某水库坝基岩体质量进行了分级研究.研究结果表明,除新鲜岩体和微风化岩体外,该区弱风化岩体为良质岩体,经适当处理后,可以考虑作为该水库坝基岩体;而对小部分的弱风化镶嵌结构的岩体,性质较差,需要挖除.     

基于PLC的分布式大坝安全监测数据采集系统

针对大坝现场自然条件恶劣的特点,设计了基于PLC的分布式数据采集系统,相对于单片机系统,具有可扩展性好,可靠性高,抗干扰能力强等优点,使用VC＋＋6．0实现PLC和上位机的通讯,对于现场PLC供电的特殊要求,设计了后备电源解决方案,并进行了实际应用。     

地域文化元素融入都江堰旅游产品的设计策略

目的 针对目前市面上都江堰旅游产品识别性较弱、同质化严重,对具有地域文化特色的旅游产品开发较少的问题,以地域文化元素作为都江堰旅游产品设计的核心,提出旅游产品的设计策略。方法 研究都江堰旅游产品设计,应从地域化和文化内涵入手。通过分析旅游产品现状,地域文化元素及新旅游时代背景下旅游产品的发展趋势,结合情感化设计理念,围绕用户体验进行探究,总结出都江堰旅游产品设计开发模式。以李冰石像和鱼嘴段水利工程元素为例,进行产品实践。结论 提出符合都江堰区域特色的旅游产品设计策略,即树立品牌意识、打造IP形象,以地域文化元素为设计核心,加强情感互动、与科技相融合的设计策略。赋予旅游产品更深层次的内涵,打造更多具有地域文化特色的旅游产品,从而推动历史文化与商业价值的融合,也能够为同类型旅游产品提供具有应用价值和参考的设计策略和思路。     

小浪底工程粉煤灰质量波动对砼性能影响的试验论证

结合小浪底水利枢纽工程的施工,对用于小浪底工程粉煤灰的质量波动是否影响砼性能,进行了试验,结果分析表明：在细度为15～25％、烧失量4～12％之间变化,其它指标满足Ⅱ级粉煤灰要求的情况下,砼力学性能、变形和耐久性没有明显变化。该成果的采用,为保证工程进度、节约费用、确保工程质量产生了巨大的综合效益。     

丹江口大坝加高工程关键技术研究综述

丹江口大坝水利枢纽作为南水北调中线工程的水源工程,其大坝加高工程技术难度在国内外均属少见,无成熟可供借鉴的经验。大坝加高中的关键技术研究难题主要有：新老混凝土结合问题研究、大坝抗震安全问题研究、高水头大坝帷幕耐久性及补强灌浆技术研究三大类。针对关键技术研究难题,在总结前期设计研究成果的基础上,采用理论分析、数值模拟、室内实验和现场试验、原形观测相结合方法,提出具体的对策。     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.