Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and ...globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo-balancing demands and supplies in a many-to-many mode-relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.
Context
United Nations outlined 17 Sustainable Development Goals (SDGs), but at the current rate of progress most will not be achieved within the desired timeframe. Since a third of SDGs are directly ...related to land resources, it is crucial to improve the effectiveness and efficiency of land-use planning. In that regard, there is particular value in algorithmically optimizing land-use planning to better support sustainability. An ideal tool for such optimizations is the nondominated sorting genetic algorithm II (NSGA-II).
Objectives
Improved versions of NSGA-II have been actively developed for land-use problems, but no thorough evaluations and very few comparative studies have been performed. Thus, the objective is to conduct a thorough evaluation of and a systematic comparison between improved NSGA-II algorithms for sustainable land-use optimization.
Methods
We identified both the most popular and the latest improved algorithms. A theoretical comparison was first made between them in terms of initialization, crossover, mutation, and archiving strategy. Then, a framework consisting of four hierarchal levels (principle, macro-criteria, micro-criteria, and indicators) was developed and applied to make a comprehensive comparison through experiments.
Results
The most popular algorithm was demonstrated to produce high-quality results and be computationally efficient, whereas the other performs better in the diversity of results, space efficiency, and the degree of optimization. Both algorithms exhibited excellent performance in handling constraints.
Conclusions
Possible approaches to further improve the algorithms include borrowing ideas of scale optimization and gene flow. The proposed framework is capable of guiding further improvement by developers and algorithm selection by users.
Henan, China, is likely the most populous agricultural province worldwide. It is China’s major grain-producing area, with a continuously increasing population (96 million), which is greater than 93% ...of countries worldwide. However, this province has been experiencing unprecedented urbanization recently due to national policies and measures, such as a plan to build the capital city of Henan into a national center, resulting in severe conflicts in land use that endanger food security regionally and globally. To facilitate decision-making on this problem, we explored the detailed urban-rural development of Henan by modeling these land-use conflicts. Conventional modeling of a region’s urban-rural development is to navigate trade-offs (a) solely between different land-use types (b) by assuming that each type provides a single service (e.g., croplands produce all the food), and (c) under a polynomial regression-based projection of population. In contrast, we considered both land-use type and intensity, resulting in a detailed land system for Henan. By introducing the concept of land system services (e.g., food production), we established a many-to-many relationship between land system classes and services. These allowed us to carry out the most comprehensive modeling of Henan’s urban-rural development under eighteen combined scenarios of population growth and land-use policies on food production. The modeling results of these scenarios provide a solid basis for making decisions regarding Henan’s urban-rural development. We also revealed the influence mechanism of population growth, land-use policies, and their combinations, highlighting the benefits of securing food production by agricultural intensification rather than merely expanding the area of cropland.
Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection ...methods. However, existing structural similarity methods calculate all the structural similarity between bands, which leads to excessively long runtimes for these methods. Aiming to address this problem, this paper proposes a band subspace partition method and combines it with the SR-SSIM band selection method to obtain an improved band selection method: E-SR-SSIM. E-SR-SSIM consists of two parts: band subspace partition and band subspace band selection. In the first part, the hyperspectral dataset is divided into subdatasets corresponding to a number of subspaces. In the second part, a modified SR-SSIM method is used for all subdatasets to select the most representative band in each subdataset. The Indian Pines, Salinas Kennedy Space Center and Wuhan unmanned aerial vehicle-borne hyperspectral image LongKou public datasets are used to implement the experiment. The experiment uses random forest as the supervised classifier: 10% of each category sample is randomly selected as training data, and the remaining 90% is used as test data. The evaluation indicators selected in the experiment are overall accuracy, average accuracy, kappa and recall. The experimental results show that E-SR-SSIM can effectively reduce the runtime while ensuring classification compared with SR-SSIM, and quantitative proof that the band subspace partition reduces the calculated amount of structural similarity is obtained through a mathematical analysis. The improved band subspace partition method could partition a dataset more reasonably than the original band subspace partition method.
•A methodological effort toward weightless index constructions.•A novel weightless algorithm for constructing composite sustainability indexes.•Applicable framework of comparative experiments to ...optimize algorithm parameters.•Assessing global sustainability with novel weightless composite index algorithm.
A composite index based on selected indicators is a widely used tool for guiding, monitoring, and evaluating a society’s level of sustainability. However, determining the weight of each indicator is typically a methodologically problematic and highly controversial process. This paper proposes a weightless strategy for constructing composite sustainability indices based on the mathematical optimization concept of Pareto fronts. The core idea is to model each indicator as an individual objective and explore Pareto fronts within the resulting multi-objective solution space. In practice, a total of 24 typical implementations of the strategy were realized to represent four categories with varying parameter settings, i.e., straightforward/hierarchical implementations with/without avoiding basic indicator accuracy issues. Comparative experiments demonstrated that a hierarchical approach utilizing the goodness of variance fit-based (GVF = 0.80) natural breaks to nullify accuracy problems is the most effective implementation. To demonstrate its usefulness, the strategy implemented using this approach was applied to analyze the world’s sustainability by revising the well-known sustainable society index. This study provides a novel paradigm of composite sustainability indices and represents the first assessment of world sustainability using multiple criteria (indicators) without weights.
Nanozymes were the novel research field to replace natural enzymes because of stability and low cost. However, the research on nanozymes was mainly focused on peroxidase, and there was little ...research about nanozymes with oxidase-like activity, especially mimic oxidase of small molecules related to human physiology. High levels of uric acid (UA) in the body can cause hyperuricemia and gout. And natural uricase cured this disease because it could oxidize UA. The oxidase-like activity of mixed valence state metal organic frameworks with cerium (MVSM) had been studied, but MVSM was found to have uricase-like activity in this article. The catalytic process of UA with MVSM was studied by a variety of analytical methods, which was similar to the natural uricase except for further oxidation of H
2
O
2
. The catalytic activity constants of MVSM were acquired by the Michaelis-Menten equation. MVSM had a better ability to catalyze UA in in vivo and in vitro experiments. An enzyme-free analysis–based mimic uricase for UA was established. All the experimental results proved that MVSM had a good prospect to replace the natural uricase.
Graphical abstract
A nanomaterial, mixed valence state Ce-MOF (MVSM), with uricase-like activity has been found in vivo and in vitro. This material has potential to be a fluorescent analysis for detecting uric acid without uricase.
Locusts are agricultural pests found in many parts of the world. Developing efficient and accurate locust information acquisition techniques helps in understanding the relation between locust ...distribution density and structural changes in locust communities. It also helps in understanding the hydrothermal and vegetation growth conditions that affect locusts in their habitats in various parts of the world as well as in providing rapid and accurate warnings on locust plague outbreak. This study is a preliminary attempt to explore whether the batch normalization-based convolutional neural network (CNN) model can be applied used to perform automatic classification of East Asian migratory locust (AM locust), Oxya chinensis (rice locusts), and cotton locusts. In this paper, we present a way of applying the CNN technique to identify species and instars of locusts using the proposed ResNet-Locust-BN model. This model is based on the ResNet architecture and involves introduction of a BatchNorm function before each convolution layer to improve the network’s stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as input to train the model. By performing comparison experiments of the activation function, initial learning rate, and batch size, we selected ReLU as the preferred activation function. The initial learning rate and batch size were set to 0.1 and 32, respectively. Experiments performed to evaluate the accuracy of the proposed ResNet-Locust-BN model show that the model can effectively distinguish AM locust from rice locusts (93.60% accuracy) and cotton locusts (97.80% accuracy). The model also performed well in identifying the growth status information of AM locusts (third-instar (77.20% accuracy), fifth-instar (88.40% accuracy), and adult (93.80% accuracy)) with an overall accuracy of 90.16%. This is higher than the accuracy scores obtained by using other typical models: AlexNet (73.68%), GoogLeNet (69.12%), ResNet 18 (67.60%), ResNet 50 (80.84%), and VggNet (81.70%). Further, the model has good robustness and fast convergence rate.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The rapid transformation from UTM (Universal Transverse Mecator) projection to Lambert projection helps to realize timely merging, inversion, and analysis of high-frequency partitioned remote sensing ...images. In this study, the transformation error and the efficiency of the linear rule approximation method, the improved linear rule approximation method, the hyperbolic transformation method, and the conformal transformation method were compared in transforming the coordinates of sample points on WGS84 (The World Geodetic System 1984)-UTM zonal projections to WGS84-Lambert projection coordinates. The effect of the grid aspect ratio on the coordinate transformation error of the conformal transformation method was examined. In addition, the conformal transformation method-based error spatial pattern of the sample points was analyzed. The results show that the conformal transformation method can better balance error and efficiency than other numerical methods. The error of the conformal transformation method is less affected by grid size. The maximum x-error is less than 0.36 m and the maximum y-error is less than 1.22 m when the grid size reaches 300 km × 300 km. The x- and y-error values decrease when square grids are used; namely, setting the grid aspect ratio close to 1 helps to weaken the effect of increasing grid area on the error. The dispersion of the error distribution and the maximum error of sample points both decrease relative to their minimum distance to the grid edge and stabilize at a minimum distance equal to 70 km. This study can support the rapid integration of massive remote sensing data over large areas.
Sustainable development is the theme of the 21st century. To monitor the progress of sustainable development, the United Nations launched Sustainable Development Goals (SDGs) in 2015. Subsequently, ...nations of the world have drawn up a list of localized indicators regarding the United Nations SDGs as a paradigm. We established a database including SDGs indicator systems of 11 economies by collecting and determining a large number of materials. Based on this database, we analyzed SDGs indicators by designing a conceptual framework of comparative analysis that included three views. We found that the SDGs indicator systems of 11 economies are different between the number of indicators, the proportion of different categories, and the connotation of indicators. Although the SDGs indicator systems among economies regarded the United Nations SDGs as a framework and included the major social problems related to sustainability, the inconsistency between SDGs indicator systems is large. It is a major reason why scholars lack the systematic method for developing indicators. There are challenges faced in data accessibility. The framework for comparative analysis could be applied to different economies.
Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the ...complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed.
An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems.•New method for SI estimation is presented.•The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows.•Analysis of coupling relationships between driving and dependent variables in complex systems.
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