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The article deals with the problem of calculating the frequency of network motifs with a help of Rand-ESU algorithm. We have established that while using a Rand-ESU algorithm, it is necessary to cut off (to thin out) the network motifs only on the last level of ESU-tree (and therefore, an implementation of the algorithm requires the construction of almost entire ESU-tree). Examples of calculations are given, they demonstrate, that other strategies to cut-off sampling lead to larger distance errors in calculation.

This article features online supplementary material. In this article, we propose a computationally efficient approach-space (Sparse PArtial Correlation Estimation)-for selecting nonzero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of space by extensive simulation studies. It is shown that space performs well in both nonzero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. We then apply space to a microarray breast cancer dataset and identify a set of hub genes that may provide important insights on genetic regulatory networks. Finally, we prove that, under a set of suitable assumptions, the proposed procedure is asymptotically consistent in terms of model selection and parameter estimation.

An efficient and service-oriented transportation network is a necessary resource for successful less-than-truckload operations. The design and evaluation of transportation networks are mainly driven by quantitative particularly cost-oriented measures, such as transport and transshipment costs. This type of measurement, however, simply cannot represent the manifold performance of a transportation network. In particular, incorporating network concentration into network design decisions overcomes the shortcomings of purely cost-oriented decisions because spatial network concentration is at the root of many aspects of network performance (e.g., congestion and network vulnerability). This paper suggests modifications to the network concentration index and the hubbing concentration index from the passenger airline context for less-than-truckload road transportation. The modified indices enable information to be conveyed by network concentration into less-than-truckload network design decisions and provide a suitable perspective to include service-oriented aspects into network design.

We have been developing a wireless sensor network system to monitor the quality of lake water in real time. It consists of a sensor module and a system module, which includes communication and power modules. We have focused on pH, turbidity and chlorophyll a concentration as the criteria for qualifying lake water quality. These parameters will be detected by a microfluidic device based sensor module embedded in the wireless sensor network system. In order to detect the turbidity and the chlorophyll a concentration simultaneously, we propose a simple optical measurement method using LED and photodiode in this paper. Before integrating a turbidity and chlorophyll a concentration sensor into the microfluidic device based pH sensor, we performed feasibility studies such as confirmation of the working principle and experiments using environmental water samples. As a result, we successfully verified our simultaneous sensing method by using a simple optical setup of the turbidity and the chlorophyll a concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (N ES ), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Q t-n ) and suspended sediment concentration (S t-n ) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (S t-1 , S t-2 , S t-3 , S t-4 ) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest N ES  = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest N ES  = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

This study investigates the origins of variation in the structures of interorganizational networks across industries. We combine empirical analyses of existing interorganizational networks in six industries with an agent-based simulation model of network emergence. Using data on technology partnerships from 1983 to 1999 between firms in the automotive, biotechnology and pharmaceuticals, chemicals, microelectronics, new materials, and telecommunications industries, we find that differences in technological dynamism across industries and the concomitant demands for value creation engender variations in firms' collaborative behaviors. On average, firms in technologically dynamic industries pursue more-open ego networks, which fosters access to new and diverse resources that help sustain continuous innovation. In contrast, firms in technologically stable industries on average pursue more-closed ego networks, which fosters reliable collaboration and helps preserve existing resources. We show that because of the observed cross-industry differences in firms' collaborative behaviors, the emergent industry-wide networks take on distinct structural forms. Technologically stable industries feature clan networks, characterized by low network connectedness and rather strong community structures. Technologically dynamic industries feature community networks, characterized by high network connectedness and medium-to-strong community structures. Convention networks, which feature high network connectedness and weak community structures, were not evident among the empirical networks we examined. Taken together, our findings advance an environmental contingency theory of network formation, which proposes a close association between the characteristics of actors' environment and the processes of network formation among actors.

We represent the economy as a network of industries connected through customer and supplier trade flows. Using this network topology, we find that stronger product market connections lead to a greater incidence of cross-industry mergers. Furthermore, mergers propagate in waves across the network through customer-supplier links. Merger activity transmits to close industries quickly and to distant industries with a delay. Finally, economy-wide merger waves are driven by merger activity in industries that are centrally located in the product market network. Overall, we show that the network of real economic transactions helps to explain the formation and propagation of merger waves.

Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related to models used and data input. In this study, random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) models are developed with three sampling frequency datasets (i.e., 4-hourly, daily, and weekly) and five conventional indicators (i.e., water temperature (WT), hydrogen ion concentration (pH), electrical conductivity (EC), dissolved oxygen (DO), and turbidity (TUR)) as surrogates to individually estimate riverine total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH 4 + -N) in a small-scale coastal watershed. The results show that the RF model outperforms the SVM and BPNN machine learning models in terms of estimative performance, which explains much of the variation in TP (79 ± 1.3%), TN (84 ± 0.9%), and NH 4 + -N (75 ± 1.3%), when using the 4-hourly sampling frequency dataset. The higher sampling frequency would help the RF obtain a significantly better performance for the three nutrient estimation measures (4-hourly > daily > weekly) for R 2 and NSE values. WT, EC, and TUR were the three key input indicators for nutrient estimations in RF. Our study highlights the importance of high-frequency data as input to machine learning model development. The RF model is shown to be viable for riverine nutrient estimation in small-scale watersheds of important local water security.

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model is compared with support vector machine (SVM) and artificial neural network (ANN) models, formerly shown to be robust AI algorithms. The Fanno Creek in Oregon (USA) is selected as a case study and daily values of water temperature, specific conductance, streamflow discharge, pH, and DO concentration are used as input variables to predict DO concentration for three different lead times (“t + 1,” “t + 3,” and “t + 7”). Based on Pearson’s correlation coefficient, several input variable combinations are formed and used for prediction. The model prediction performance is evaluated using various indices such as correlation coefficient, Nash–Sutcliffe efficiency, root mean square error, and mean absolute error. The results identify the DRNN model ( CC Testing = 0.97 , N S E Testing = 0.948 , RMSE Testing = 0.43 and MAE Testing = 0.25 ) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.