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In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future. [ABSTRACT FROM AUTHOR]

Kaolinite is one of the principal rare earth element (REE) ion-adsorption clays that hosts a wide range of elements, including Dy(III) as a representative example. Ammonium sulfate is a typical salt used to leach REEs. Due to the carbon dioxide emissions which occur during ammonia production, it is urgently necessary to develop low environmental pollution leaching agents that can replace (NH4)2SO4. MgSO4 is regarded as the most promising eco-friendly leaching agent. Herein, the first-principles plane-wave pseudopotential method based on the density functional theory (DFT) was used to investigate the stable adsorption structures of Dy(III) and its hydrated ions, MgSO4 leaching agent ions and the corresponding hydrated ions on the surface of kaolinite, which revealed the adsorption mechanism of Dy(III), Mg(II), and SO42− on the silico–oxygen plane and the aluminum–hydroxyl plane of kaolinite. Based on the research results of the steric hindrance effect of Dy(III) on the silico–oxygen plane and the aluminum–hydroxyl plane of kaolinite, the adsorption of Dy(H2O)103+ was more stable on the silico–oxygen plane. It was easier to leach out Dy(III) with MgSO4, while SO42− tended to interact with the rare earth ions in an aqueous solution. The results provide theoretical guidance for efficient rare earth extraction and obtaining novel efficient leaching agents. [ABSTRACT FROM AUTHOR]

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leverages the temporal information of two consecutive LBSN check-ins. Specifically, these methods only focus on adjacent visit sequences but ignore non-contiguous visits, while these visits can be important in understanding the spatio-temporal correlation within the trajectory. In order to fully mine this non-contiguous visit information, we propose a multi-layer Spatio-Temporal deep learning attention model for POI recommendation, Spatio-Temporal Transformer Recommender (STTF-Recommender). To incorporate the spatio-temporal patterns, we encode the information in the user's trajectory as latent representations into their embeddings before feeding them. To mine the spatio-temporal relationship between any two visited locations, we utilize the Transformer aggregation layer. To match the most plausible candidates from all locations, we develop on an attention matcher based on the attention mechanism. The STTF-Recommender was evaluated with two real-world datasets, and the findings showed that STTF improves at least 13.75% in the mean value of the Recall index at different scales compared with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and 'cold start', since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the 'cold start' problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method. [ABSTRACT FROM AUTHOR]

Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%. [ABSTRACT FROM AUTHOR]

Vitamin E is easily oxidized by light, air, oxidizing agents and heat, limiting its application in many ways. Compared to vitamin E, vitamin E ester derivatives exhibit improved stability and a stronger antioxidant capacity, and even gain new biological functions. In recent years, enzymatic synthesis of vitamin E ester derivatives has received increasing attention due to its environmental friendliness, high catalytic efficiency, and inherent selectivity. This paper reviews the related progress of lipase-mediated preparation of vitamin E ester derivatives. The function of different vitamin E ester derivatives, and the main factors influencing the enzymatic acylation process, including enzyme species, acyl donor and acceptor, reaction media and water activity, are summarized in this paper. Finally, the perspective of lipase-catalyzed synthesis of vitamin E ester derivatives is also discussed. [ABSTRACT FROM AUTHOR]

Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone's method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories. [ABSTRACT FROM AUTHOR]

We address the problem of data acquisition in large distributed wireless sensor networks (WSNs). We propose a method for data acquisition using the hierarchical routing method and compressive sensing for WSNs. Only a few samples are needed to recover the original signal with high probability since sparse representation technology is exploited to capture the similarities and differences of the original signal. To collect samples effectively in WSNs, a framework for the use of the hierarchical routing method and compressive sensing is proposed, using a randomized rotation of cluster-heads to evenly distribute the energy load among the sensors in the network. Furthermore, L1-minimization and Bayesian compressed sensing are used to approximate the recovery of the original signal from the smaller number of samples with a lower signal reconstruction error. We also give an extensive validation regarding coherence, compression rate, and lifetime, based on an analysis of the theory and experiments in the environment with real world signals. The results show that our solution is effective in a large distributed network, especially for energy constrained WSNs.