Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen Concentrations in North Temperate Lakes

Predicting dissolved oxygen (DO) concentrations in north temperate lakes requires a comprehensive study of phenological patterns across various ecosystems, which highlights the significance of selecting phenological features and feature interactions. Process-based models are limited by partial process knowledge or oversimplified feature representations, while machine learning models face challenges in efficiently selecting relevant feature interactions for different lake types and tasks, especially under the infrequent nature of DO data collection. In this paper, we propose a Nature-Guided Cognitive Evolution (NGCE) strategy, which represents a multi-level fusion of adaptive learning with natural processes. Specifically, we utilize metabolic process-based models to generate simulated DO labels. Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks. These models are not only capable of undergoing crossover and mutation mechanisms within intra-populations but also, albeit infrequently, engage in inter-population crossover. The second stage involves refining these models by retraining them with real observed labels. We have tested the performance of our NGCE strategy in predicting daily DO concentrations across a wide range of lakes in the Midwest, USA. These lakes, varying in size, depth, and trophic status, represent a broad spectrum of north temperate lakes. Our findings demonstrate that NGCE not only produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lakes

Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations

To quantify uncertainties of the inverse problems governed by partial differential equations (PDEs), the inverse problems are transformed into statistical inference problems based on Bayes' formula. Recently, infinite-dimensional Bayesian analysis methods are introduced to give a rigorous characterization and construct dimension-independent algorithms. However, there are three major challenges for infinite-dimensional Bayesian methods: prior measures usually only behaves like regularizers (can hardly incorporate prior information efficiently); complex noises (e.g., more practical non-identically distributed noises) are rarely considered; many computationally expensive forward PDEs need to be solved in order to estimate posterior statistical quantities. To address these issues, we propose a general infinite-dimensional inference framework based on a detailed analysis on the infinite-dimensional variational inference method and the ideas of deep generative models that are popular in the machine learning community. Specifically, by introducing some measure equivalence assumptions, we derive the evidence lower bound in the infinite-dimensional setting and provide possible parametric strategies that yield a general inference framework named variational inverting network (VINet). This inference framework has the ability to encode prior and noise information from learning examples. In addition, relying on the power of deep neural networks, the posterior mean and variance can be efficiently generated in the inference stage in an explicit manner. In numerical experiments, we design specific network structures that yield a computable VINet from the general inference framework.Numerical examples of linear inverse problems governed by an elliptic equation and the Helmholtz equation are given to illustrate the effectiveness of the proposed inference framework.Comment: 46 page

Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced

Identifying the Active Site on ZnxCryOz for HC-O Bond Cleavage in Syn-gas Conversion

The excellent performance of ZnxCryOz catalysts, used in the process of converting CO/H2 to methanol and light olefins, is highly attractive, but the microstructure of ZnxCryOz structure under the syngas conversion conditions remains elusive experimentally and theoretically because of the limitation of the detecting facilities/methods. By using the genetic-algorithm-based global structural search accelerated by machine learning in combination with a local cluster sampling strategy in the active learning scheme, we reveal the structure/composition evolution of ZnxCryOz structures and uncover that the catalytic activities of these catalysts strongly depend on the Zn/Cr ratios under the syngas conversion conditions. The possible active phase at the thermodynamically stable condition is identified and the critical active site influencing the catalytic property is unraveled. We show that the catalyst Zn2Cr2O5, which consists of a thin ZnO layer and the ZnCr2O4 structures, achieves a high catalytic activity for syngas conversion and its X-ray diffraction patterns are in agreement with the experimental result. Importantly, the presence of a hexahedral configuration ([ZnCrO2]hex) is found to affect the catalyst activity significantly, and this result is further supported by the analysis based on the structure-activity relationship

SSIA: a sensitivity-supervised interlock algorithm for high-performance microkinetic solving

Microkinetic modeling has drawn increasing attention for quantitatively analyzing catalytic networks in recent decades, in which the speed and stability of the solver play a crucial role. However, for the multi-step complex systems with a wide variation of rate constants, the often encountered stiff problem leads to the low success rate and high computational cost in the numerical solution. Here, we report a new efficient sensitivity-supervised interlock algorithm (SSIA), which enables us to solve the steady state of heterogeneous catalytic systems in the microkinetic modeling with a 100% success rate. In SSIA, we introduce the coverage sensitivity of surface intermediates to monitor the low-precision time-integration of ordinary differential equations, through which a quasi-steady-state is located. Further optimized by the high-precision damped Newton’s method, this quasi-steady-state can converge with a low computational cost. Besides, to simulate the large differences (usually by orders of magnitude) among the practical coverages of different intermediates, we propose the initial coverages in SSIA to be generated in exponential space, which allows a larger and more realistic search scope. On examining three representative catalytic models, we demonstrate that SSIA is superior in both speed and robustness compared with its traditional counterparts. This efficient algorithm can be promisingly applied in existing microkinetic solvers to achieve large-scale modeling of stiff catalytic networks.<br/

Geographically and Ontologically Oriented Scoping of a Dry Valley and Its Spatial Characteristics Analysis: The Case of the Three Parallel Rivers Region

A dry valley is a special landscape type that is formed by the combined effect of climate and topography. Accurately defining the scope of a dry valley and knowledge of its spatial distribution characteristics can provide data support for relevant studies in the region. Starting from natural ontological characteristics and formation mechanisms, we constructed a geographical ontological model of dry valleys through an analysis of concepts related to the dry valley and combined GIS technology and methods to accurately define the scope and analyze the spatial characteristics of the dry valleys in the Three Parallel Rivers Region (DVT). Our results show that: (1) The geographically and ontologically oriented method developed to define the scope of the dry valley has a high accuracy, with an overall accuracy of 92.3% and a kappa coefficient of 0.84, therefore it can provide a better mechanism for defining the scope of a dry valley on a large scale. (2) The total area and total length of the DVT are 6147.1 km2 and 2125.3 km, respectively. The dry valleys in this region are mainly located in the Tibet Autonomous Region and in the Sichuan and Yunnan provinces in China. (3) The terrain in the DVT is precipitous, and areas with slopes greater than 25° account for 70% of the total area of the dry valleys. The DVT area of sunny aspects (north, northeast, and northwest aspects) is larger than that of shady aspects (south, southeast, and southwest aspects), and the land cover is mainly grassland with a desert substrate. The result of our study can provide data support for further in-depth research in related fields of dry valleys

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning