The reception of M. Yu. Lermontov’s creativity in China: the newest literary criticism (article second)

В статье рассматривается история изучения творческого наследия М. Ю. Лермонтова в Китае на современном этапе, демонстрируются научные достижения китайского лермонтоведения как результат его предшествующего развития. Особое внимание в статье уделяется юбилею Лермонтова, отмеченного в Китае публикацией целого ряда работ, проведением научных конференций. Делается вывод о том, что китайское лермонтоведение находится на новом подъеме, что выражается во все более глубоком осмыслении творчества великого русского поэта.The article discusses the history of the study of the creative heritage of M. Yu. Lermontov in China at the present stage, demonstrates the scientific achievements of the Chinese literary study of Lermontov as a result of his previous development. Special attention is paid to the anniversary of Lermontov, marked in China publishing a number of works, holding of scientific conferences. The conclusion is that the study of Lermontov in China is on the new lift, which is expressed in an ever deeper understanding of the literary creation of the great russian poet

Trade-Offs between Economic Benefits and Ecosystem Services Value under Three Cropland Protection Scenarios for Wuhan City in China

Over the past few decades urbanization and population growth have been the main trend all over the world, which brings the increase of economic benefits (EB) and the decrease of cropland. Cropland protection policies play an important role in the urbanization progress. In this study, we assess the trade-offs between EB and ecosystem services value (ESV) under three cropland protection policy scenarios using the LAND System Cellular Automata for Potential Effects (LANDSCAPE) model. The empirical results reveal that trade-offs between EB and ESV in urbanizing areas are dynamic, and that they considerably vary under different cropland protection policy scenarios. Especially, the results identify certain "turning points" for each policy scenario at which a small to moderate growth in EB would result in greater ESV losses. Among the three scenarios, we found that the cropland protection policy has the most adverse effect on trade-offs between EB and ESV and the results in the business as usual scenario have the least effect on the trade-offs. Furthermore, the results show that a strict balance between requisition and compensation of cropland is an inappropriate policy option in areas where built-up areas are increasing rapidly from the perspective of mitigating conflict between EB and ESV and the numbers of cropland protection that restrained by land use planning policy of Wuhan is a better choice

Projecting future impacts of cropland reclamation policies on carbon storage

Cropland reclamation policies result in carbon storage loss by the conversion of natural land. However, the future impacts of cropland reclamation policies (CRP) on carbon storage have seldom been explored. Taking Hubei, China as study area, this study assesses the impacts of cropland reclamation policies before and after optimization on carbon storage from 2010 to 2030. The LAND System Cellular Automata model for Potential Effects (LANDSCAPE) was used to simulate the land use patterns in 2030, while the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Carbon Storage and Sequestration model was applied to calculate the changes in carbon storage. Results indicate that carbon storage loss due to cropland reclamation policies is expected to increase from 0.48 Tg·C to 4.34 Tg·C between 2010 and 2030 in Hubei. This increase is related to the loss of wetland and forest. Carbon storage loss can be reduced by 52%–73% by protecting carbon-rich lands. This study highlights the importance of considering the carbon storage loss when implementing cropland reclamation policies

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe

Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs.Comment: 9 pages, 11 figure