A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph

The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context. There are two reasons for this. Firstly, regions have spatial heterogeneity, and the (ECPRS)needs to consider factors like climate, topography, vegetation, etc., to recommend civilization patterns adapted to specific ecological environments, ensuring the feasibility and practicality of the recommendations. Secondly, the abstract features of the ecological civilization patterns in the real world have not been fully utilized., resulting in poor richness in their embedding representations and consequently, lower performance of the recommendation system. Considering these limitations, we propose the ECPR-MML method. Initially, based on the novel method UGPIG, we construct a knowledge graph to extract regional representations incorporating spatial heterogeneity features. Following that, inspired by the significant progress made by Large Language Models (LLMs) in the field of Natural Language Processing (NLP), we employ Large LLMs to generate multimodal features for ecological civilization patterns in the form of text and images. We extract and integrate these multimodal features to obtain semantically rich representations of ecological civilization. Through extensive experiments, we validate the performance of our ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG. Furthermore, multimodal data can indeed enhance recommendation performance. However, the data generated by LLM is not as effective as real data to a certain extent

Conflating point of interest (POI) data: A systematic review of matching methods

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research

Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China

BackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA.MethodsData for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated.ResultsEight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (BMI) > 25.2, surgery on Monday, age > 67.5, postoperative complications, blood transfusion, and operation time > 120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850]] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective.ConclusionThis study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients

Ecoenzymatic stoichiometry reveals widespread soil phosphorus limitation to microbial metabolism across Chinese forests

8 páginas.- 4 figuras.- 57 referencias.- Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s43247-022-00523-5Forest soils contain a large amount of organic carbon and contribute to terrestrial carbon sequestration. However, we still have a poor understanding of what nutrients limit soil microbial metabolism that drives soil carbon release across the range of boreal to tropical forests. Here we used ecoenzymatic stoichiometry methods to investigate the patterns of microbial nutrient limitations within soil profiles (organic, eluvial and parent material horizons) across 181 forest sites throughout China. Results show that, in 80% of these forests, soil microbes were limited by phosphorus availability. Microbial phosphorus limitation increased with soil depth and from boreal to tropical forests as ecosystems become wetter, warmer, more productive, and is affected by anthropogenic nitrogen deposition. We also observed an unexpected shift in the latitudinal pattern of microbial phosphorus limitation with the lowest phosphorus limitation in the warm temperate zone (41-42 degrees N). Our study highlights the importance of soil phosphorus limitation to restoring forests and predicting their carbon sinks. Phosphorus limitation of soil microbial communities in forests is widespread, increases with soil depth, and is enhanced under wetter and warmer climates and elevated anthropogenic nitrogen deposition, according to ecoenzymatic stoichiometric analyses across 181 forests in China.This study was financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB40000000), Funds for International Cooperation and Exchange of National Natural Science Foundation of China (32061123007), National Natural Science Foundation of China (41977031), Program of State Key Laboratory of Loess and Quaternary Geology CAS (SKLLQGZR1803). Contributions from Dr. Chen are funded by H2020 Marie Skłodowska-Curie Actions (No. 839806). M.D.-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I+D+i project PID2020-115813RA-I00 funded by CIN/AEI/10.13039/501100011033. M.D.-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático “01–Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA).Peer reviewe

Estimating soil water content from surface digital image gray level measurements under visible spectrum

Zhu, Y., Wang, Y., Shao, M. and Horton, R. 2011. Estimating soil water content from surface digital image gray level measurements under visible spectrum. Can. J. Soil Sci. 91: 69-76. Determining soil water content (SWC) is fundamental for soil science, ecology and hydrology. Many methods are put forward to measure SWC, such as drying soil samples, neutron probes, time domain reflectrometry (TDR) and remote sensing. Sampling and drying soil is time-consuming. A neutron probe cannot determine SWC of surface soil accurately because neutrons escape when they are emitted near soil surface and TDR is, to some extent, influenced by soil salinity and temperature. Remote sensing can obtain SWC over a large area across a range of temporal and spatial scales. Complicated terrain and atmospheric conditions often make remote sensing data unreliable. Determining SWC from surface gray level (GL) measurements in the visible spectrum may have advantages over other remote sensing techniques, because surface soil images can be easily acquired by digital cameras, even with complicated landforms and meteorological conditions. However, few studies use this method, and further work is required to develop the ability of visible spectrum digital images to accurately estimate SWC. In this study, 42 soil samples were collected to investigate the relationship between surface GL and SWC using computer processing of soil surface images acquired by a digital camera. After establishing an equation to describe this relationship, a simple calibrated model was developed. The calibrated model was validated by an independent set of 48 soil samples. The results indicate that surface GL was sensitive to SWC. There was a negative linear relationship between surface GL and the square of SWC for the 42 calibration soil samples (correlation coefficients > 0.91). Based on this negative relationship, a model was established to estimate SWC from surface GL. The results of model validation showed the estimated SWCs by surface GL were very close to the measured SWCs (correlation coefficient =0.99 at a significant level of 0.01). Generally, SWC could be estimated from surface GL for a given soil, and the model could be used to quickly and accurately determineg SWC from surface GL measurements

Using soil surface gray level to determine surface soil water content

How to determine surface soil water content (SWC) quickly and accurately is fundamental in studying eco-hydrological processes and their modeling. Here we use laboratory experiments to determine surface SWC using soil surface gray level (SGL) values. A negatively exponential relationship exists between SGL and SWC, i.e., SGL increases with the decrease of SWC. SGL can be estimated based on initial SGL value (surface gray level when SWC=0), SWC, and a surface roughness coefficient characterized by mean soil particle size. The variation range of SGL was larger than that of SWC, indicating that changes in SWC were enhanced in SGL, and that SGL would thus be sensitive to changes in soil water. At the 95% confidence level, SWC can be determined by using the relationship between SWC and SGL established by the experiments. The determination of SWC has a high precision when SWC was between dry and saturated

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges