Seasonal dynamics of suspended solids in a giant subtropical reservoir (China) in relation to internal processes and hydrological features

To explore the factors regulating seasonal variation of total suspended solids (TSS) and its two fractions in a giant dendritic reservoir (the Three-Gorges Reservoir of China, TGR) in the subtropical monsoon region, suspended solids, chlorophyll a (a surrogate for lake internal processes) and water residence time (an index of hydrologic flushing) were examined monthly from August 2005 to July 2006. TSS ranged from 0.6 to 200.3 mg/L and from 0.6 to 78 mg/L respectively in the mainstream and in a typical reservoir-bay (the Xiangxi Bay) of the TGR,. TSS exhibited a typical seasonal pattern in the mainstream rather than in the Xiangxi Bay of the TGR. The fraction of non-volatile suspended solids (NVSS) was often more dominant in the mainstream than in the Xiangxi Bay, especially during the flood season. Regressions analysis showed that 87.6% and 89.8% of seasonal variation in TSS and NVSS of the mainstream, respectively, are explained by water residence time. In contrast, suspended solids (particularly volatile suspended solids, VSS) of the Xiangxi Bay displayed significant correlation with algal biomass, and no correlation with hydrological parameters. It implies that the Xiangxi Bay was a more autochthonous system than the mainstream of the TGR where exogenous influences were the more determinant factors. (C) 2009 Elsevier Ltd and INQUA. All rights reserved

Temporal Asynchrony of Trophic Status Between Mainstream and Tributary Bay Within a Giant Dendritic Reservoir: The Role of Local-Scale Regulators

Limnologists have regarded temporal coherence (synchrony) as a powerful tool for identifying the relative importance of local-scale regulators and regional climatic drivers on lake ecosystems. Limnological studies on Asian reservoirs have emphasized that climate and hydrology under the influences of monsoon are dominant factors regulating seasonal patterns of lake trophic status; yet, little is known of synchrony or asynchrony of trophic status in the single reservoir ecosystem. Based on monthly monitoring data of chlorophyll a, transparency, nutrients, and nonvolatile suspended solids (NVSS) during 1-year period, the present study evaluated temporal coherence to test whether local-scale regulators disturb the seasonal dynamics of trophic state indices (TSI) in a giant dendritic reservoir, China (Three Gorges Reservoir, TGR). Reservoir-wide coherences for TSICHL, TSISD, and TSITP showed dramatic variations over spatial scale, indicating temporal asynchrony of trophic status. Following the concept of TSI differences, algal productivity in the mainstream of TGR and Xiangxi Bay except the upstream of the bay were always limited by nonalgal turbidity (TSICHL−TSISD <0) rather than nitrogen and phosphorus (TSICHL−TSITN <0 and TSICHL−TSITP <0). The coherence analysis for TSI differences showed that local processes of Xiangxi Bay were the main responsible for local asynchrony of nonalgal turbidity limitation levels. Regression analysis further proved that local temporal asynchrony for TSISD and nonalgal turbidity limitation levels were regulated by local dynamics of NVSS, rather than geographical distance. The implications of the present study are to emphasize that the results of trophic status obtained from a single environment (reservoir mainstream) cannot be extrapolated to other environments (tributary bay) in a way that would allow its use as a sentinel site

Acquisition Method of User Requirements for Complex Products Based on Data Mining

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility

Module division method of complex products for responding to user’s requirements

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved Fuzzy C-means Algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements

Seasonal dynamics of suspended solids in a giant subtropical reservoir (China) in relation to internal processes and hydrological features

To explore the factors regulating seasonal variation of total suspended solids (TSS) and its two fractions in a giant dendritic reservoir (the Three-Gorges Reservoir of China, TGR) in the subtropical monsoon region, suspended solids, chlorophyll a (a surrogate for lake internal processes) and water residence time (an index of hydrologic flushing) were examined monthly from August 2005 to July 2006. TSS ranged from 0.6 to 200.3 mg/L and from 0.6 to 78 mg/L respectively in the mainstream and in a typical reservoir-bay (the Xiangxi Bay) of the TGR,. TSS exhibited a typical seasonal pattern in the mainstream rather than in the Xiangxi Bay of the TGR. The fraction of non-volatile suspended solids (NVSS) was often more dominant in the mainstream than in the Xiangxi Bay, especially during the flood season. Regressions analysis showed that 87.6% and 89.8% of seasonal variation in TSS and NVSS of the mainstream, respectively, are explained by water residence time. In contrast, suspended solids (particularly volatile suspended solids, VSS) of the Xiangxi Bay displayed significant correlation with algal biomass, and no correlation with hydrological parameters. It implies that the Xiangxi Bay was a more autochthonous system than the mainstream of the TGR where exogenous influences were the more determinant factors. (C) 2009 Elsevier Ltd and INQUA. All rights reserved

A Decision-Making Method for Design Schemes Based on Intuitionistic Fuzzy Sets and Prospect Theory

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems

A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction

Eucommia ulmoides leaf extract alters gut microbiota composition, enhances short-chain fatty acids production, and ameliorates osteoporosis in the senescence-accelerated mouse P6 (SAMP6) model

The bark and the leaf of Eucommia ulmoides Oliv. content similar bioactive components, but the leaf of this medically important plant is mostly abandoned. In this study, we revealed that the aqueous extract of E. ulmoides leaf (EUL) can promote the growth of the probiotic Lactobacillus bulgaricus (LB) and inhibit the formation of osteoclast in vitro. This extract was next administrated to senescence-accelerated mice P6 to evaluate examine its influence on the composition of gut microbiota (GM), short-chain fatty acids (SCFAs), and osteoporosis (OP). The results showed that supplementation of the EUL aqueous extract to the mouse model: (a) increased bacterial diversity and Firmicutes/Bacteroidetes ratio in the gut, (b) increased SCFAs concentration in the feces and serum, and (c) ameliorated OP based on the results of bone mineral density (BMD), Dual-energy X-ray bone scan, and HE staining of distal femur

A Mathematical Model and Self-Adaptive NSGA-II for a Multiobjective IPPS Problem Subject to Delivery Time

Process planning and scheduling are two important components of manufacturing systems. This paper deals with a multiobjective just-in-time integrated process planning and scheduling (MOJIT-IPPS) problem. Delivery time and machine workload are considered to make IPPS problem more suitable for manufacturing environments. The earliness/tardiness penalty, maximum machine workload, and total machine workload are objectives that are minimized. The decoding method is a crucial part that significantly influences the scheduling results. We develop a self-adaptive decoding method to obtain better results. A nondominated sorting genetic algorithm with self-adaptive decoding (SD-NSGA-II) is proposed for solving MOJIT-IPPS. Finally, the model and algorithm are proven through an example. Furthermore, different scale examples are tested to prove the good performance of the proposed method