At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage. Full article

During the underwater sensor networks (UWSNs) operation, node drift with water environment causes network topology changes. Periodic node location examination and adjustment are needed to maintain good network monitoring quality as long as possible. In this paper, a node redeployment algorithm based on stratified connected tree for UWSNs is proposed. At every network adjustment moment, self-examination and adjustment on node locations are performed firstly. If a node is outside the monitored space, it returns to the last location recorded in its memory along straight line. Later, the network topology is stratified into a connected tree that takes the sink node as the root node by broadcasting ready information level by level, which can improve the network connectivity rate. Finally, with synthetically considering network coverage and connectivity rates, and node movement distance, the sink node performs centralized optimization on locations of leaf nodes in the stratified connected tree. Simulation results show that the proposed redeployment algorithm can not only keep the number of nodes in the monitored space as much as possible and maintain good network coverage and connectivity rates during network operation, but also reduce node movement distance during node redeployment and prolong the network lifetime. Full article

This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance. Full article

Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. Full article

Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime. Full article

Most of the existing node depth-adjustment deployment algorithms for underwater wireless sensor networks (UWSNs) just consider how to optimize network coverage and connectivity rate. However, these literatures don’t discuss full network connectivity, while optimization of network energy efficiency and network reliability are vital topics for UWSN deployment. Therefore, in this study, a depth-adjustment deployment algorithm based on two-dimensional (2D) convex hull and spanning tree (NDACS) for UWSNs is proposed. First, the proposed algorithm uses the geometric characteristics of a 2D convex hull and empty circle to find the optimal location of a sleep node and activate it, minimizes the network coverage overlaps of the 2D plane, and then increases the coverage rate until the first layer coverage threshold is reached. Second, the sink node acts as a root node of all active nodes on the 2D convex hull and then forms a small spanning tree gradually. Finally, the depth-adjustment strategy based on time marker is used to achieve the three-dimensional overall network deployment. Compared with existing depth-adjustment deployment algorithms, the simulation results show that the NDACS algorithm can maintain full network connectivity with high network coverage rate, as well as improved network average node degree, thus increasing network reliability. Full article

According to the monitoring of the wide area measurement system, inter-area oscillations happen more and more frequently in a real power grid of China, which are close to the forced oscillation. Applying the conventional forced oscillation theory, the mechanism of these oscillations cannot be explained well, because the oscillations vary with random amplitude and a narrow frequency band. To explain the mechanism of such oscillations, the general forced oscillation (GFO) mechanism is taken into consideration. The GFO is the power system oscillation excited by the random excitations, such as power fluctuations from renewable power generation. Firstly, properties of the oscillations observed in the real power grid are analyzed. Using the GFO mechanism, the observed oscillations seem to be the GFO caused by some random excitation. Then the variation of the wind power measured in this power gird is found to be the random excitation which may cause the GFO phenomenon. Finally, simulations are carried out and the power spectral density of the simulated oscillation is compared to that of the observed oscillation, and they are similar with each other. The observed oscillation is thus explained well using the GFO mechanism and the GFO phenomenon has now been observed for the first time in real power grids. Full article

Ecosystem services are tightly coupled with availability of solar energy and its partition into energy fluxes, and biomass accumulation, which represents the energy flux in ecosystems, is a key aspect of ecosystem services. This study analyzed the effects of climate change and land use and land cover change (LUCC) on the biomass accumulation change in the Lower Heihe River Basin during 2001–2010. Biomass accumulation was represented with net primary productivity (NPP), which was estimated with the C-Fix model, and scenario analysis was carried out to investigate effects of climate change and LUCC on biomass accumulation change in a spatially explicit way. Results suggested climate change had an overall positive effect on biomass accumulation, mainly owning to changes in CO₂ concentration and temperature. LUCC accounted for 70.61% of biomass accumulation change, but primarily owning to fractional vegetation change (FVCC) rather than land conversion, and there is a negative interactive effect of FVCC and climate change on biomass accumulation, indicating FVCC resulting from water diversion played a dominant in influencing biomass accumulation. These results can provide valuable decision support information for the local ecosystem managers and decision makers to guarantee sustainable provision of essential ecosystem services. Full article

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks. Full article

Existing node deployment algorithms for underwater sensor networks are nearly unable to improve the network coverage rate under the premise of ensuring the full network connectivity and do not optimize the communication and move energy consumption during the deployment. Hence, a node deployment algorithm based on connected dominating set (CDS) is proposed. After randomly sowing the nodes in 3D monitoring underwater space, disconnected nodes move to the sink node until the network achieves full connectivity. The sink node then performs centralized optimization to determine the CDS and adjusts the locations of dominated nodes. Simulation results show that the proposed algorithm can achieve a high coverage rate while ensuring full connectivity and decreases the communication and movement energy consumption during deployment. Full article

Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption. Full article

Considering that deployment strategies for underwater sensor networks should contribute to fully connecting the networks, a Guaranteed Full Connectivity Node Deployment (GFCND) algorithm is proposed in this study. The GFCND algorithm attempts to deploy the coverage nodes according to the greedy iterative strategy, after which the connectivity nodes are used to improve network connectivity and fully connect the whole network. Furthermore, a Location Dispatch Based on Command Nodes (LDBCN) algorithm is proposed, which accomplishes the location adjustment of the common nodes with the help of the SINK node and the command nodes. The command nodes then dispatch the common nodes. Simulation results show that the GFCND algorithm achieves a comparatively large coverage percentage and a fully connected network; furthermore, the LDBCN algorithm helps the common nodes preserve more total energy when they reach their destination locations. Full article

A node non-uniform deployment based on clustering algorithm for underwater sensor networks (UWSNs) is proposed in this study. This algorithm is proposed because optimizing network connectivity rate and network lifetime is difficult for the existing node non-uniform deployment algorithms under the premise of improving the network coverage rate for UWSNs. A high network connectivity rate is achieved by determining the heterogeneous communication ranges of nodes during node clustering. Moreover, the concept of aggregate contribution degree is defined, and the nodes with lower aggregate contribution degrees are used to substitute the dying nodes to decrease the total movement distance of nodes and prolong the network lifetime. Simulation results show that the proposed algorithm can achieve a better network coverage rate and network connectivity rate, as well as decrease the total movement distance of nodes and prolong the network lifetime. Full article

Water availability is at the core of sustainable socioeconomic development and ecological conservation along with global climate and land use changes, especially in the areas that experience water problems. This study investigated the impacts of land use change on surface runoff and water yield with scenario-based land use change in the upper and middle reaches of the Heihe River Basin, the second largest inland river basin in the arid region of northwestern China. Firstly, three land use structure scenarios were established, with different water utilization ratio levels (low-level, middle-level and high-level water utilization ratios). Then the spatial pattern of land uses was simulated with the Dynamic of Land System (DLS). Thereafter, the simulated land use data were used as the input data to drive the Soil and Water Assessment Tool (SWAT) model, keeping other input data unchanged to isolate the land use change impacts on surface runoff and water yield. The results showed that the forestland and grassland will expand along with the increase in water utilization ratio. The quick-response surface runoff would decrease significantly due to forest and grassland expansion, which may cause an overall decreasing trend of the water yield. This indicated the unreasonable allocation of water resources may exert negative impacts on the water yield even if the water utilization ratio is increased; therefore, water resources should be reasonably allocated for different land use demand, which is critical for sustainable development. The results of this study will be informative to decision makers for sustainable water resource and land management when facing land use change and an increasing demand for water resources in the Heihe River Basin. Full article

Growing water scarcity is one of the central challenges for sustainability in China, given its burgeoning industry and huge population, especially in the arid and semi-arid inland river basin where precipitation is very limited. Industrial transformation is an important engine of economic growth, which is required to be implemented by governments at all levels in China. Economic models have generally been applied to evaluate the effects of economic policy change (e.g., industrial transformation or adjustment of price) on the allocation of production factors. The computable general equilibrium (CGE) model is an effective tool to reallocate the primary factors across sectors for different industrial transformation scenarios. In this research, we first briefly introduced the principles and structure of the CGE model, which embeds water resources as a primary factor of production. Then we chose Zhangye as an example to evaluate the impacts of industrial transformation on water consumption under three designed scenarios with the water-embedded CGE model. Simulation results showed that there will be considerable water saving benefit from industrial transformation when the output value of secondary industry and tertiary industry increases and the contribution of the planting sector to the total output value decreases. Finally, we put forward a scheme that can improve water utilization efficiency in policy options. Full article

Pressure on existing water resources in China is expected to increase with undergoing rapid demographic transformation, economic development, and global climate changes. We investigate the economy-wide impacts of projected urban population growth and economic structural change on water use and allocation in China. Using a multi-regional CGE (Computable General Equilibrium) model, TERM (The Enormous Regional Model), we explore the implications of selected future water scenarios for China’s nine watershed regions. Our results indicate that urbanization and industrial transformation in China will raise the opportunity cost of water use and increase the competition for water between non-agricultural users and irrigation water users. The growth in water demand for domestic and industrial uses reduces the amount of water allocated to agriculture, particularly lower-value and water-intensive field crops. As a response, farmers have the incentive to shift their agricultural operations from traditional field crop production to higher-value livestock or intensive crop production. In addition, our results suggest that growing water demand due to urbanization and industrial transformation will raise the shadow price of water in all nine river basins. Finally, we find that national economic growth is largely attributable to urbanization and non-agricultural productivity growth. Full article

This study focuses on the potential impacts of large-scale land use and land cover changes (LUCC) on surface temperature from a global perspective. As important types of LUCC, urbanization, deforestation, cultivated land reclamation, and grassland degradation have effects on the climate, the potential changes of the surface temperature caused by these four types of large-scale LUCC from 2010 to 2050 are downscaled, and this issue analyzed worldwide along with Representative Concentration Pathways (RCPs) of the Intergovernmental Panel on Climate Change (IPCC). The first case study presents some evidence of the effects of future urbanization on surface temperature in the Northeast megalopolis of the United States of America (USA). In order to understand the potential climatological variability caused by future forest deforestation and vulnerability, we chose Brazilian Amazon region as the second case study. The third selected region in India as a typical region of cultivated land reclamation where the possible climatic impacts are explored. In the fourth case study, we simulate the surface temperature changes caused by future grassland degradation in Mongolia. Results show that the temperature in built-up area would increase obviously throughout the four land types. In addition, the effects of all four large-scale LUCC on monthly average temperature change would vary from month to month with obviously spatial heterogeneity. Full article

Heat transfer between a thermoacoustic engine and its surrounding heat reservoirs can be out of phase with oscillating working gas temperature. The paper presents a generalized heat transfer model using a complex heat transfer exponent. Both the real part and the imaginary part of the heat transfer exponent change the power versus efficiency relationship quantitatively. When the real part of the heat transfer exponent is fixed, the power output P decreases and the efficiency η increases along with increasing of the imaginary part. The Optimization zone on the performance of the thermoacoustic heat engine is obtained. The results obtained will be helpful for the further understanding and the selection of the optimal operating mode of the thermoacoustic heat engine. Full article