Identification of Water Scarcity and Providing Solutions for Adapting to Climate Changes in the Heihe River Basin of China

In ecologically fragile areas with arid climate, such as the Heihe River Basin in northwestern China, sustainable social and economic development depends largely on the availability and sustainable uses of water resource. However, there is more and more serious water resource shortage and decrease of water productivity in Heihe River Basin under the influence of climate change and human activities. This paper attempts to identify the severe water scarcity under climate change and presents possible solutions for sustainable development in Heihe River Basin. Three problems that intervened land use changes, water resource, the relevant policies and institutions in Heihe River basin were identified, including (1) water scarcity along with serious contradiction between water supply and demand, (2) irrational water consumption structure along with low efficiency, and (3) deficient systems and institutions of water resource management along with unreasonable water allocation scheme. In this sense, we focused on reviewing the state of knowledge, institutions, and successful practices to cope with water scarcity at a regional extent. Possible solutions for dealing with water scarcity are explored and presented from three perspectives: (1) scientific researches needed by scientists, (2) management and institution formulation needed by governments, and (3) water resource optimal allocation by the manager at all administrative levels

Projected Urbanization Impacts on Surface Climate and Energy Budgets in the Pearl River Delta of China

The climate impacts of future urbanization in the Pearl River Delta (PRD) region in China were simulated with the Dynamics of Land Systems (DLS) model and the Weather Research and Forecasting (WRF) model in this study. The land use and land cover data in 2000 and 2020 were simulated with the DLS model based on the regional development planning. Then the spatial and temporal changes of surface air temperature, ground heat flux, and regional precipitation in 2020 were quantified and analyzed through comparing simulation results by WRF. Results show that the built-up land will become the dominant land use type in the PRD in 2020. Besides, the near-surface air temperature shows an increasing trend on the whole region in both summer and winter, but with some seasonal variation. The urban temperature rise is more apparent in summer than it is in winter. In addition, there is some difference between the spatial pattern of precipitation in summer and winter in 2020; the spatial variation of precipitation is a bit greater in summer than it is in winter. Results can provide significant reference for the land use management to alleviate the climate change

Multi-source Education Knowledge Graph Construction and Fusion for College Curricula

The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multifaceted information. In the context of university education, the availability of numerous specialized courses and complicated learning resources often leads to inferior learning outcomes for students. In this paper, we propose an automated framework for knowledge extraction, visual KG construction, and graph fusion, tailored for the major of Electronic Information. Furthermore, we perform data analysis to investigate the correlation degree and relationship between courses, rank hot knowledge concepts, and explore the intersection of courses. Our objective is to enhance the learning efficiency of students and to explore new educational paradigms enabled by AI. The proposed framework is expected to enable students to better understand and appreciate the intricacies of their field of study by providing them with a comprehensive understanding of the relationships between the various concepts and courses.Comment: accepted by ICALT202

Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student's distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table

Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table

No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).Comment: Accepted by CIKM 2023, 10 pages, 8 figures, 5 table

Mesoscale aerosol numerical system developed in NMC, China

National Meteorogical Center(China)AQRM, Meterological Service of CanadaProceeding : International Symposium of Kanazawa University 21st-Century COE Program Vol.2(2004),Schedule: February 29(SUN)-March 3(WED), Venue: 29 FEB, Ishikawa Life-Long Learning Center(Former Prefectural Government Building) / 1-3 MAR Kanazawa Art Hall, Organized by: Kanazawa University 21st-Century COE Program / Ishikawa International Cooperation Research Centre / United Nations University-Institute of Advanced Studies, Supported by: Ishikawa Prefectural Government / City of Kanazawa, Eds : Hayakawa, Kazuichi / Kizu, Ryoichi / Kamata, Naok

ABCC3 as a marker for multidrug resistance in non-small cell lung cancer

Multidrug resistance (MDR) contributes to the failure of chemotherapy and high mortality in non-small cell lung cancer (NSCLC). We aim to identify MDR genes that predict tumor response to chemotherapy. 199 NSCLC fresh tissue samples were tested for chemosensitivity by MTT assay. cDNA microarray was done with 5 samples with highest resistance and 6 samples with highest sensitivity. Expression of ABCC3 mRNA and protein was detected by real-time PCR and immunohistochemisty, respectively. The association between gene expression and overall survival (OS) was examined using Cox proportional hazard regression. 44 genes were upregulated and 168 downregulated in the chemotherapy-resistant group. ABCC3 was one of the most up-regulated genes in the resistant group. ABCC3-positive expression correlated with lymph node involvement, advanced TNM stage, more malignant histological type, multiple-resistance to anti-cancer drugs, and reduced OS. ABCC3 expression may serve as a marker for MDR and predictor for poor clinical outcome of NSCLC

Short-term physiological responses to drought stress in seedling of tropical and temperate maize (Zea mays L.) cultivars

Understanding of the response of tropical and temperate maize (Zea mays L.) to drought is the first step for tolerant temperate maize improvement. Eight maize hybrids were used to investigate physiology responses under drought stress, four of them were tropical maize and the others were temperate maize. Results showed that there were different drought tolerances but similar trends in both tropical maize and temperate maize. Gas exchange parameters revealed different strategies of maize under the stress. In our study, most of the temperate hybrids maintained open stomata to keep a higher photosynthesis rate at the beginning of stress, while the other hybrids decreased stomatal conductance. Compared to temperate maize, the tropical maize had higher antioxidase activity and greater physiological parameter variation among hybrids. KS5731 and ZD309 had stronger drought resistance among tropical and temperate maize hybrids separately. Tolerant hybrids maintained active photosynthesis, have higher osmotic adjustment ability and antioxidase activities but lower malonaldehyde content than the sensitive ones. Our results led to a better understanding of the physiological responses of tropical and temperate maize plants to drought stress and may provide an insight of breeding for drought resistance in maize