Abstract:Reservoirs impose many negative impacts on riverine ecosystems. To balance human and ecosystem needs, we propose a reservoir operation method that combines reservoir operating rule curves with the regulated minimum water release policy to meet the environmental flow requirements of riverine ecosystems. Based on the relative positions of the reservoir and the water intakes, we consider three scenarios: water used for human needs (including industrial, domestic and agricultural) is directly withdrawn from (1) the reservoir; (2) both reservoirs and downstream river channels and (3) downstream river. The proposed method offers two advantages over traditional methods: First, it can be applied to finding the optimal reservoir operating rule curves with the consideration of environmental flow requirement, which is beneficial to the sustainable water uses. Second, it avoids a problem with traditional approaches, which prescribe the minimum environmental flow requirements as the regulated minimum environmental flow releases from reservoirs, implicitly giving lower priority to the riverine ecosystem. Our method instead determines the optimal regulated minimum releases of water to sustain environmental flows while more effectively balancing human and ecosystem needs. To demonstrate practical use of the model, we present a case study for operation of the Tanghe reservoir in China's Tang river basin for the three above-mentioned scenarios. The results demonstrate that this approach will help the reservoir's managers satisfy both human and environmental requirements.
In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.
The La(Fe,Si)13‐based compounds have been recently developed as promising negative thermal expansion (NTE) materials by elemental substitution, which show large, isotropic and nonhysteretic NTE properties as well as relatively high electrical and thermal conductivities. In this paper, the La(Fe,Si)13 hydrides are prepared by a novel electrolytic hydriding method. Furthermore, the thermal expansion and magnetic properties of La(Fe,Si)13 hydrides are investigated by the variable‐temperature X‐ray diffraction and physical property measurement system. Fascinatingly, it is found that room‐temperature NTE properties and zero thermal expansion (ZTE) properties with broad operation‐temperature window (20–275 K) have been achieved after electrolytic hydriding. The further magnetic properties combined with theoretical analysis reveal that the improvements of NTE and ZTE properties in the La(Fe,Si)13 hydrides are ascribed to the variations of magnetic exchange couplings after hydrogenation. The present results highlight the potential applications of La(Fe,Si)13 hydrides with room‐temperature NTE and broad operation‐temperature window ZTE properties.
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