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Geographic Named Entity Matching and Evaluation Recommendation Using Multi-Objective Tasks: A Study Integrating a Large Language Model
Geographical named entity matching, a crucial step in address encoding, aims to enhance address resolution accuracy through the precise identification and linkage of geographical named entity data. However, existing approaches tend to ignore the spatial information of entities, leading to misclassification. Drawing on the human process of searching for addresses, this study proposes a multi-objective learning model named GNEMM that integrates the semantic and spatial information of geographical named entities. To further mimic the human cognitive process during address search, it incorporates the Retrieval-Augmented Generation (RAG) technique. By integrating newly added external address data with an advanced large language model (LLM) like GPT-4, it achieves precise address evaluation and recommendation. The model was tested using a standard geographical named entity dataset from Shandong Province, focusing on three sub-tasks: element segmentation, matching, and spatial similarity score prediction. The experimental results indicate that the method achieves a geographical named entity matching accuracy of up to 99%, with improvements of 10% and 5% in the segmentation and prediction sub-tasks. GNEMM performs best in address-matching tasks of various scales, and the vectors extracted by GNEMM perform best in the downstream retrieval and matching of various address types, which verifies its applicability in geographical named entity recommendation applications.
Journal Article
Phenotypic, Geological, and Climatic Spatio-Temporal Analyses of an Exotic IGrevillea robusta/I in the Northwestern Himalayas
The last five decades (since 1980) have witnessed the introduction of exotic trees as a popular practice in India to fulfill the demand of forest-based products for utilization in afforestation programmes. This study examines the distribution and habitat suitability of exotic Grevillea robusta trees in the northwestern Himalayas (state: Uttarakhand), focusing on the interaction between G. robusta and abiotic factors, such as climate, soil, and habitat suitability. This multipurpose agroforestry species is mainly grown by farmers as a boundary tree, windbreak, or shelterbelt and among intercrops on small farms in agroforestry systems worldwide. The results indicate that phenotypic plasticity is determined by tree height and diameter, indicating a higher frequency of young and adult trees. The study also highlights spatio-temporal modeling coupled with geological analysis to address the current distribution pattern and future habitat suitability range through MaxEnt modeling. The AUC ranged from 0.793 ± 3.6 (RCP 6.0_70) to 0.836 ± 0.008 (current) with statistical measures, such as K (0.216), NMI (0.240), and TSS (0.686), revealing the high accuracy of the model output. The variables, which include the minimum temperature of the coldest month (Bio 6), the slope (Slo), the mean temperature of the driest quarter (Bio 9), and the precipitation of the driest quarter (Bio 17), contribute significantly to the prediction of the distribution of the species in the Himalayan state. The model predicts a significant habitat suitability range for G. robusta based on bio-climatic variables, covering an area of approximately ~1641 km[sup.2] with maximal occurrence in Pauri (~321 km[sup.2]) and Almora (~317 km[sup.2]). Notably, the future prediction scenario corroborates with the regions of Tons (Upper Yamuna, Uttarkashi), Kalsi (Mussoorie, Dehradun), the Kedarnath Wildlife Sanctuary, and the Badrinath Forest Division for the potentially suitable areas. The climate was found to have a strong influence on the species’ distribution, as evidenced by its correlation with the Köppen–Geiger climate classification (KGCC) map. While the species demonstrated adaptability, its occurrence showed a high correlation with bedrocks containing an elevated iron content. Furthermore, the study also provides the first trees outside forests (TOF) map of G. robusta in the region, as well as insight into its future habitat suitability.
Journal Article
QGIS in remote sensing set. Volume 1, QGIS and generic tools
These four volumes present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The volumes detail applications of remote sensing over continental surfaces, with a first one discussing applications for agriculture. A second one presents applications for forest, a third presents applications for the continental hydrology, and finally the last volume details applications for environment and risk issues.
BOOK
An early-diverging iguanodontian
Intensifying macrovertebrate reconnaissance together with refined age-dating of mid-Cretaceous assemblages in recent decades is producing a more nuanced understanding of the impact of the Cretaceous Thermal Maximum on terrestrial ecosystems. Here we report discovery of a new early-diverging ornithopod, Iani smithi gen. et sp. nov., from the Cenomanian-age lower Mussentuchit Member, Cedar Mountain Formation of Utah, USA. The single known specimen of this species (NCSM 29373) includes a well-preserved, disarticulated skull, partial axial column, and portions of the appendicular skeleton. Apomorphic traits are concentrated on the frontal, squamosal, braincase, and premaxilla, including the presence of three premaxillary teeth. Phylogenetic analyses using parsimony and Bayesian inference posit Iani as a North American rhabdodontomorph based on the presence of enlarged, spatulate teeth bearing up to 12 secondary ridges, maxillary teeth lacking a primary ridge, a laterally depressed maxillary process of the jugal, and a posttemporal foramen restricted to the squamosal, among other features. Prior to this discovery, neornithischian paleobiodiversity in the Mussentuchit Member was based primarily on isolated teeth, with only the hadrosauroid Eolambia caroljonesa named from macrovertebrate remains. Documentation of a possible rhabdodontomorph in this assemblage, along with published reports of an as-of-yet undescribed thescelosaurid, and fragmentary remains of ankylosaurians and ceratopsians confirms a minimum of five, cohabiting neornithischian clades in earliest Late Cretaceous terrestrial ecosystems of North America. Due to poor preservation and exploration of Turonian-Santonian assemblages, the timing of rhabdodontomorph extirpation in the Western Interior Basin is, as of yet, unclear. However, Iani documents survival of all three major clades of Early Cretaceous neornithischians (Thescelosauridae, Rhabdodontomorpha, and Ankylopollexia) into the dawn of the Late Cretaceous of North America.
Journal Article
Vertical versus horizontal Spatial-Numerical Associations
Humans have associations between numbers and physical space on both horizontal and vertical dimensions, called Spatial-Numerical Associations (SNAs). Several studies have considered the hypothesis of there being a dominant orientation by examining on which dimension people are more accurate and efficient at responding during various directional SNA tasks. However, these studies have difficulty differentiating between a person's efficiency at accessing mental representations of numbers in space, and the efficiency at which they exercise motor control functions, particularly bilateral ones, when manifesting a response during an explicit directional SNA task. In this study we use a conflict test employing combined explicit magnitude and spatial directional processing in which pairs of numbers are placed along the diagonal axes and response accuracy/efficiency are considered across the horizontal and vertical dimensions simultaneously. Participants indicated which number in each pair was largest using a joystick that only required unilateral input. The experiment was run in English using Arabic numerals. Results showed that directional SNAs have a vertical rather than horizontal dominance. A moderating factor was also found during post-hoc analysis, where response efficiency, but not accuracy, is conditional on a person's native language being oriented the same as the language of the experiment, left to right. The dominance of the vertical orientation suggests adopting more vertical display formats for numbers may provide situational advantages, particularly for explicit magnitude comparisons, with some domains like flight controls and the stock market already using these in some cases.
Journal Article
Estimation of PMsub.2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite
The use of satellite data to estimate PM[sub.2.5] is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM[sub.2.5], the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM[sub.2.5] using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM[sub.2.5] retrieval. We identified the optimal conditions for the effective estimation of PM[sub.2.5]. The quantitative results indicated that, under these optimal conditions, the PM[sub.2.5] concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R[sup.2] of 0.9272 and an RMSE of 7.38 µg·m[sup.−3]. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R[sup.2] of 0.9099 and RMSE of 7.42 µg·m[sup.−3], indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R[sup.2] of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM[sub.2.5] retrieval and providing support for PM[sub.2.5] estimation through multi-angle polarized data.
Journal Article