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Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces. [ABSTRACT FROM AUTHOR]

Frequent flooding seriously affects people's safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the "7·20" Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation. [ABSTRACT FROM AUTHOR]

Over the past decades, accumulating evidence suggests that the gut microbiome exerts a key role in Alzheimer's disease (AD). The Alzheimer's Association Workgroup is updating the diagnostic criteria for AD, which changed the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS." Previously, most of studies focus on the correlation between the gut microbiome and amyloid beta deposition ("A"), the initial AD pathological feature triggering the "downstream" tauopathy and neurodegeneration. However, limited research investigated the interactions between the gut microbiome and other AD pathogenesis ("TNIVS"). In this review, we summarize current findings of the gut microbial characteristics in the whole spectrum of AD. Then, we describe the association of the gut microbiome with updated biomarker categories of AD pathogenesis. In addition, we outline the gut microbiome‐related therapeutic strategies for AD. Finally, we discuss current key issues of the gut microbiome research in the AD field and future research directions. Highlights: The new revised criteria for Alzheimer's disease (AD) proposed by the Alzheimer's Association Workgroup have updated the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS."The associations of the gut microbiome with updated biomarker categories of AD pathogenesis are described.Current findings of the gut microbial characteristics in the whole spectrum of AD are summarized.Therapeutic strategies for AD based on the gut microbiome are proposed. [ABSTRACT FROM AUTHOR]

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method's technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction. [ABSTRACT FROM AUTHOR]

Land use/land cover change (LUCC) constitutes a significant contributor to variations in the storage of carbon within ecosystems and holds substantial significance within the context of the carbon cycling process. This study analyzed land use data from the Nanchang urban agglomeration in 2000 and 2020 to investigate changes in land use and carbon storage using the PLUS model and GIS. The results show the following: (1) From 2000 to 2020, the Nanchang urban agglomeration experienced reductions in the extents of croplands, woodlands, grasslands, and unused lands. The predominant trend in land transformation involved the conversion of cropland into built-up land. (2) Between 2000 and 2020, there was a declining trajectory observed in carbon storage for the Nanchang urban agglomeration, with an overall decrease of 1.13 × 107 t. The space is characterized by a high-altitude perimeter and a low-altitude center. Urbanization's encroachment on cropland is the main reason for declining carbon storage. (3) The predictive outcomes reveal that, in 2040, carbon storage in the Nanchang urban agglomeration will be reduced by 1.00 × 107 t under the natural development scenario, and reduced by 3.90 × 106 t and increased by 2.29 × 105 t, respectively, under the cropland protection and ecological protection scenarios. The risk of carbon loss is significantly reduced by ecological protection policy interventions. Our analysis of the land use patterns and carbon storage distribution in the Nanchang urban agglomeration over the past 20 years and our exploration of the land use change trend over the next 20 years under the conservation policy provide a reference basis for increasing the carbon sink in the core area of the ecological city cluster of Poyang Lake and realizing the sustainable development of the city. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas. [ABSTRACT FROM AUTHOR]

Volunteered geographic information (VGI) plays an increasingly crucial role in flash floods. However, topic classification and spatiotemporal analysis are complicated by the various expressions and lengths of social media textual data. This paper conducted applicability analysis on bidirectional encoder representation from transformers (BERT) and four traditional methods, TextRank, term frequency–inverse document frequency (TF-IDF), maximal marginal relevance (MMR), and linear discriminant analysis (LDA), and the results show that for user type, BERT performs best on the Government Affairs Microblog, whereas LDA-BERT performs best on the We Media Microblog. As for text length, TF-IDF-BERT works better for texts with a length of <70 and length >140 words, and LDA-BERT performs best with a text length of 70–140 words. For the spatiotemporal evolution pattern, the study suggests that in a Henan rainstorm, the textual topics follow the general pattern of "situation-tips-rescue". Moreover, this paper detected the hotspot of "Metro Line 5" related to a Henan rainstorm and discovered that the topical focus of the Henan rainstorm spatially shifts from Zhengzhou, first to Xinxiang, and then to Hebi, showing a remarkable tendency from south to north, which was the same as the report issued by the authorities. We integrated multi-methods to improve the overall topic classification accuracy of Sina microblogs, facilitating the spatiotemporal analysis of flooding. [ABSTRACT FROM AUTHOR]