optimal scale
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2021 ◽  
pp. 259-270
Author(s):  
Izmail Kantarzhi ◽  
Alexander Gogin

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1192
Author(s):  
Gang Fu ◽  
Wei Wang ◽  
Junsheng Li ◽  
Nengwen Xiao ◽  
Yue Qi

Landscape metrics are widely used in landscape planning and land use management. Understanding how landscape metrics respond with scales can provide more accurate prediction information; however, ignoring the interference of multi-scale interaction may lead to a severe systemic bias. In this study, we quantitatively analyzed the scaling sensitivity of metrics based on multi-scale interaction and predict their optimal scale ranges. Using a big data method, the multivariate adaptive regression splines model (MARS), and the partial dependence model (PHP), we studied the scaling relationships of metrics to changing scales. The results show that multi-scale interaction commonly exists in most landscape metric scaling responses, making a significant contribution. In general, the scaling effects of the three scales (i.e., spatial extent, spatial resolution, and classification of land use) are often in a different direction, and spatial resolution is the primary driving scale in isolation. The findings show that only a few metrics are highly sensitive to the three scales throughout the whole scale spectrum, while the other metrics are limited within a certain threshold range. This study confirms that the scaling-sensitive scalograms can be used as an application guideline for selecting appropriate landscape metrics and optimal scale ranges.


2021 ◽  
Author(s):  
Yingjie Zhu ◽  
Bin Yang

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.


2021 ◽  
Vol 5 (4) ◽  
pp. 1280-1289
Author(s):  
Arief Saputro ◽  
◽  
Nuhfil Hanani ◽  
Fahriyah Fahriyah

The low productivity of sugarcane in various regions, especially in production centers, has caused domestic sugar production to fluctuate and unable to meet national sugar needs. That is thought to be due to the inefficient use of inputs by sugarcane farmers, lack of access to capital and information, resulting in farmers being unable to provide the latest technology for sugarcane cultivation. The research objective was to analyze the performance of the sugar cane business in Malang Regency by measuring its technical analysis using the Data Envelopment Analysis (DEA) approach. This research was conducted using a survey method of 50 respondent sugarcane farmers in Malang Regency with the multistage random sampling method. The average total technical efficiency (TE CRS) of sugarcane farmers in Malang Regency is 0.766, the average value of pure technical assessment (TE VRS) is 0.829, and the average efficiency scale is 0.926. Farmers who are technically efficient at optimal scale (CRS) of 18% and 82% are not yet at optimal scale. Farmers who are not at an optimal scale are in an IRS condition of 50% and DRS condition of 32%. The result of correlation analysis shows that land areas as a control variable significantly affect the relationship between technical analysis and income, which shows a solid and positive value is 0.415.


2021 ◽  
Vol 8 ◽  
Author(s):  
Sandra Calcat-i-Cervera ◽  
Clara Sanz-Nogués ◽  
Timothy O'Brien

Advanced therapy medicinal products (ATMPs) offer new prospects to improve the treatment of conditions with unmet medical needs. Kidney diseases are a current major health concern with an increasing global prevalence. Chronic renal failure appears after many years of impairment, which opens a temporary window to apply novel therapeutic approaches to delay or halt disease progression. The immunomodulatory, anti-inflammatory, and pro-regenerative properties of mesenchymal stromal cells (MSCs) have sparked interest for their use in cell-based regenerative therapies. Currently, several early-phase clinical trials have been completed and many are ongoing to explore MSC safety and efficacy in a wide range of nephropathies. However, one of the current roadblocks to the clinical translation of MSC therapies relates to the lack of standardization and harmonization of MSC manufacturing protocols, which currently hinders inter-study comparability. Studies have shown that cell culture processing variables can have significant effects on MSC phenotype and functionality, and these are highly variable across laboratories. In addition, heterogeneity within MSC populations is another obstacle. Furthermore, MSCs may be isolated from several sources which adds another variable to the comparative assessment of outcomes. There is now a growing body of literature highlighting unique and distinctive properties of MSCs according to the tissue origin, and that characteristics such as donor, age, sex and underlying medical conditions may alter the therapeutic effect of MSCs. These variables must be taken into consideration when developing a cell therapy product. Having an optimal scale-up strategy for MSC manufacturing is critical for ensuring product quality while minimizing costs and time of production, as well as avoiding potential risks. Ideally, optimal scale-up strategies must be carefully considered and identified during the early stages of development, as making changes later in the bioprocess workflow will require re-optimization and validation, which may have a significant long-term impact on the cost of the therapy. This article provides a summary of important cell culture processing variables to consider in the scale-up of MSC manufacturing as well as giving a comprehensive review of tissue of origin-specific biological characteristics of MSCs and their use in current clinical trials in a range of renal pathologies.


2021 ◽  
Author(s):  
Eugenia Andreasen ◽  
Sofía Bauducco ◽  
Evangelina Dardati

This paper studies the effect of capital controls on misallocation and welfare in an economy with financial constraints. We build a general equilibrium model with heterogeneous firms, financial constraints and international trade and calibrate it to the Chilean economy. Since high-productivity and exporting firms need to borrow more to reach their optimal scale, capital controls that tax international borrowing hit them harder. As a result, misallocation increases relatively more for this group of firms, and for young firms that are still trying to reach their optimal scale. In terms of welfare, the model predicts a sizable aggregate loss of 2.39 percent when capital controls are introduced, with welfare decreasing twice as much for high-productivity firms. We empirically corroborate the main insights in terms of misallocation obtained from the model using Chilean manufacturing firm data from 1990 to 2007.


2021 ◽  
Vol 92 ◽  
pp. 107107
Author(s):  
Haoran Wang ◽  
Wentao Li ◽  
Tao Zhan ◽  
Kehua Yuan ◽  
Xingchen Hu

Author(s):  
Zaid Al-Huda ◽  
Donghai Zhai ◽  
Yan Yang ◽  
Riyadh Nazar Ali Algburi

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.


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