Supplementary Datasets Research Articles

Stability of hot-finished and cold-formed steel hollow section columns: A comparative study

A comparative study into the buckling behaviour of hot-finished and cold-formed steel hollow section members subjected to axial compression is presented in this paper. The main differences between hot-finished and cold-formed hollow sections are initially discussed. An experimental programme conducted to investigate the local, global and local-global interactive buckling of hollow section columns is then described. Three S355 normalised hot-finished and three S355/S420 (dual certified) cold-formed profiles, covering circular, square and rectangular hollow sections (CHS, SHS and RHS respectively), were studied. The stress-strain characteristics of the examined materials were obtained through tensile coupon tests. 3D laser scanning was utilised to determine the distribution of geometric imperfections in selected SHS test specimens and a consistent approach to characterise different forms of geometric imperfections from the scan data is put forward. A stub column test was carried out on each cross-section to examine its local buckling behaviour, and a series of long column tests were performed on all test profiles. In total, 21 tensile coupon tests, six stub column tests and 40 long column tests were conducted in this programme. Finite element models were also developed, validated and used for parametric studies to generate supplementary numerical datasets. The obtained test and numerical data, along with the existing experimental results collected from the literature, were used to evaluate the accuracy and reliability of the Eurocode 3 (EC3) stability design provisions for hot-finished and cold-formed hollow section columns; aspects requiring improvements are highlighted to guide future studies.

COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.

POLOR: Leveraging Contrastive Learning to Detect Political Orientation of Opinion in News Media

News articles are naturally influenced by the values, beliefs, and biases of the reporters preparing the stories and the policies of the publishing outlets. Numerous studies and datasets have been proposed to detect the political orientation of news articles. However, most of these studies ignore real textual clues and learn the textual signature of the source (commonly the publisher and rarely the writer) of the article instead. Moreover, a good volume of opinion pieces published by major news outlets do not reflect the political orientation of the publisher but rather reflect the political orientation of a non-professional writer. Existing methods are not built to correct this difference in the training data and, hence, perform poorly on human-annotated data. We propose, POLOR, a fine-tuned BERT model that employs contrastive learning to detect the political orientation of news articles even when the training data is labeled by the source (i.e. the publisher of the news article). Unlike previous work in the literature, the model learns features by employing different contrastive learning objectives where each sentence is contrasted with sentences from various sources simultaneously. POLOR achieves a 15\% increase on our dataset compared to previously proposed baselines. Finally, we release two datasets of opinion news: source-annotated and human-annotated datasets. The full paper including supplementary materials, code, and datasets can be found at https://www.cs.unm.edu/~ajararweh/.

Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.

Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists. Using clinical markers, a graph is generated where each feature is represented by a node, and the connection between them is represented by an edge which is derived through Pearson's correlation method. A graph convolutional network (GCN) model is proposed to classify breast tumors into benign and malignant, using the graph data. Several statistical tests are performed to assess the importance of the proposed features. The performance of the proposed GCN model is improved by experimenting with different layer configurations and hyper-parameter settings. Results show that the proposed model has a 98.73% test accuracy. The performance of the model is compared with a graph attention network, a one-dimensional convolutional neural network, and five transfer learning models, ten machine learning models, and three ensemble learning models. The performance of the model was further assessed with three supplementary breast cancer ultrasound image datasets, where the accuracies are 91.03%, 94.37%, and 89.62% for Dataset A, Dataset B, and Dataset C (combining Dataset A and Dataset B) respectively. Overfitting issues are assessed through k-fold cross-validation. Several variants are utilized to present a more rigorous and fair evaluation of our work, especially the importance of extracting clinically relevant features. Moreover, a GCN model using graph data can be a promising solution for an automated feature-based breast image classification system.

Cohort profile: Born in Wales—a birth cohort with maternity, parental and child data linkage for life course research in Wales, UK

PurposeUsing Wales’s national dataset for maternity and births as a core dataset, we have linked related datasets to create a more complete and comprehensive entire country birth cohort. Data of...

Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data.

Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. This paper proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification.

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks.

Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function's data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model's robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters, 2.37×108 floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things.

Exploring the role of macrophages in the progression from atypical hyperplasia to endometrial carcinoma through single-cell transcriptomics and bulk transcriptomics analysis.

In this study, we aimed to identify key genes in endometrial cancer by conducting single-cell analysis of macrophages. We sourced clinical data from the TCGA database as well as supplementary datasets GSE201926 and GSE173682. Using bulk-seq data of atypical endometrial hyperplasia and endometrial cancer, we pinpointed key differentially expressed genes. Single-cell RNA sequencing was utilized for further gene expression analysis. Cluster analysis was conducted on TCGA tumor data, identifying two distinct subtypes. Statistical methods employed included LASSO regression for diagnostic modeling and various clustering algorithms for subtype identification. We found that subtype B was closely related to cellular metabolism. A diagnostic model was established using LASSO regression and was based on the genes CDH18, H19, PAGE2B, PXDN, and THRB. This model effectively differentiated the prognosis of cervical cancer. We also constructed a prognosis model and a column chart based on these key genes. Through CIBERSORT analysis, CDH18 and PAGE2B were found to be strongly associated with macrophage M0. We propose that these genes influence the transformation from atypical endometrial hyperplasia to endometrial cancer by affecting macrophage M0. In conclusion, these key genes may serve as therapeutic targets for endometrial cancer. A new endometrial cancer risk prognosis model and column chart have been constructed based on these genes, offering a reliable direction for future cervical cancer treatment.

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

Abstract P386: Rat Remap Initiative At The Rat Genome Database (RGD): A Resource Of Integrated Transcriptomic And Epigenomic Data For Laboratory Rats

Complex traits including the genetic control of blood pressure have been associated with both coding and non-coding single nucleotide polymorphisms (SNPs) at more than a thousand loci across the human genome. Many of these haplotypes are found between and distant from genes, and their mechanisms leading to blood pressure variation are largely unknown. Attempts to compare and translate these non-coding mechanisms to animal model systems such as the laboratory rat are difficult, but even further complicated by the difficulty of accessing already existing large-scale genome-wide datasets including transcriptomic and epigenomic (e.g. chromatin accessibility, transcription factor binding, and DNA methylation) data that are currently distributed across a variety of public data repositories and supplementary datasets. These datasets are often annotated to previous and less complete reference genome assemblies, further hampering their broad use without time consuming reanalysis. Collectively, these challenges impede discovery and leveraging of these datasets across the broader research community toward understanding mechanisms controlling complex traits such as blood pressure. To address these challenges, the Rat ReMap Initiative seeks to establish a framework for a publicly accessible resource for rat genomic, transcriptomic and epigenomic data to housed within RGD, with analysis updated to the current reference genome assembly. Users will be able to explore and find these datasets, link to the original raw data, and access analyzed summary data files. Remapped datasets will be linked to existing genomic features, strain, and genetic variation annotations to link with disease, pathways, phenotypes, and models portals already annotated and maintained in the Rat Genome Database (RGD). Here we report our initial efforts to standardize metadata and remap a targeted set of publicly available rat transcriptome, chromatin accessibility and transcription factor binding datasets, some across multiple ages and both sexes, onto the newest rat reference assembly (mRatBN7.2), and to integrate them into an updated genome browser (JBrowse2) and into a comparative genome map browser (VCMap) at RGD.

Reduced-order hybrid modelling for powder compaction: Predicting density and classifying diametrical hardness

Drawing on machine learning (ML) techniques and physics-based modelling, two feature-based reduced-order models are presented: one for the quantitative prediction of density and another for the classification of the diametrical hardness of pellets from a powder compaction process (pelleting). For interpretability, the models use as input only the parameters from a modified Drucker–Prager Cap (DPC) model calculated from process data monitoring and the applied maximal compression force. For quantitative density prediction, 8 features linked to first-principles models of powder compaction are generated, and the final model uses only 2. A critical part of the modelling, and also one of the main contributions, is a data augmentation step for the primary data set of this study by leveraging much smaller supplementary data sets that have measurements not present in the primary data set.The final results imply a significant reduction in the quantity of data needed for model input and cut down the cost of data acquisition, storage, and computational time. Additionally provided is a detailed analysis of the impact and relevance of the generated features on the model performance.The density prediction model, using only 2 features, reaches a mean absolute scaled error (MASE) of 12.9% and a mean absolute error (MAE) of 0.10 (where r2=0.975). The scaled (diametrical) hardness classifier has an F1 score of 0.915 using 4 features.

A Recommender Approach to Enable Effective and Efficient Self-Service Analytics in Data Lakes

As a result of the paradigm shift away from rather rigid data warehouses to general-purpose data lakes, fully flexible self-service analytics is made possible. However, this also increases the complexity for domain experts who perform these analyses, since comprehensive data preparation tasks have to be implemented for each data access. For this reason, we developed BARENTS, a toolset that enables domain experts to specify data preparation tasks as ontology rules, which are then applied to the data involved. Although our evaluation of BARENTS showed that it is a valuable contribution to self-service analytics, a major drawback is that domain experts do not receive any semantic support when specifying the rules. In this paper, we therefore address how a recommender approach can provide additional support to domain experts by identifying supplementary datasets that might be relevant for their analyses or additional data processing steps to improve data refinement. This recommender operates on the set of data preparation rules specified in BARENTS—i.e., the accumulated knowledge of all domain experts is factored into the data preparation for each new analysis. Evaluation results indicate that such a recommender approach further contributes to the practicality of BARENTS and thus represents a step towards effective and efficient self-service analytics in data lakes.

On the political right, the customer is always right: Political ideology, entitlement, and complaining

AbstractAcross three preregistered studies and five supplementary datasets, we predicted and found that conservatives were more inclined to complain than liberals due to conservative consumers feeling a greater sense of entitlement. This research contributes to the literature by introducing consumer entitlement as a novel explanation for ideological differences in consumer behavior, and by building on previous work suggesting that conservative consumers complain less than liberals (Journal of Consumer Research, 2017, 44, 477). Evidence is provided across several service contexts and types of complaining behaviors. Study 1 and 4 supplementary datasets supported the basic process. Next, theory‐relevant boundary conditions provided converging process evidence. In Study 2, complaining intentions decreased among conservatives when they felt less (vs. more) entitled than the target of social comparison. In Study 3, complaining intentions decreased among conservatives when a service recovery was framed as providing special treatment. Implications and future research directions are discussed.

Deep Insight on Land Use/Land Cover Geospatial Assessment through Internet-Based Validation Tool in Upper Karkheh River Basin (KRB), South-West Iran

Recently, the demand for high-quality land use/land cover (LULC) information for near-real-time crop type mapping, in particular for multi-relief landscapes, has increased. While the LULC classes are inherently imbalanced, the statistics generally overestimate the majority classes and underestimate the minority ones. Therefore, the aim of this study was to assess the classes of the 10 m European Satellite Agency (ESA) WorldCover 2020 land use/land cover product with the support of the Google Earth Engine (GEE) in the Honam sub-basin, south-west Iran, using the LACOVAL (validation tool for regional-scale land cover and land cover change) online platform. The effect of imbalanced ground truth has also been explored. Four sampling schemes were employed on a total of 720 collected ground truth points over approximately 14,100 ha. The grassland and cropland totally canopied 94% of the study area, while barren land, shrubland, trees and built-up covered the rest. The results of the validation accuracy showed that the equalized sampling scheme was more realistically successful than the others in terms of roughly the same overall accuracy (91.6%), mean user’s accuracy (91.6%), mean producers’ accuracy (91.9%), mean partial portmanteau (91.9%) and kappa (0.9). The product was statistically improved to 93.5% ± 0.04 by the assembling approach and segmented with the help of supplementary datasets and visual interpretation. The findings confirmed that, in mapping LULC, data of classes should be balanced before accuracy assessment. It is concluded that the product is a reliable dataset for environmental modeling at the regional scale but needs some modifications for barren land and grassland classes in mountainous semi-arid regions of the globe.

Structural evolution of the reactivated Møre–Trøndelag Fault Complex, Fosen Peninsula, Norway

The ENE–WSW-trending Møre–Trøndelag Fault Complex (MTFC) in Central Norway is a 10–50 km-wide, steeply dipping reactivated fault zone. Onshore, it transects Devonian sedimentary rocks and a series of east to SE transported metamorphic nappes, which were emplaced during the Scandian (Silurian–Devonian) Orogeny. Offshore, the MTFC defines the southern margin of the Møre Basin and the northern margin of the Viking Graben, meaning that the fault complex played a major role in controlling the architecture of these Mesozoic basins. Onshore, the MTFC has had a prolonged and heterogeneous kinematic history. The complex comprises two major fault strands: the Hitra–Snåsa Fault (HSF) and the Verran Fault (VF). These two faults seem to have broadly initiated as part of a single system of sinistral ductile shear zones during the early Devonian ( c. 410 Ma). Sinistral transtensional reactivation (Permo-Carboniferous; 290 Ma) of the ENE–WSW-trending HSF and VF led to the development of cataclasites and pseudotachylites together with the formation of north–south-trending faults establishing the present-day brittle fault geometry of the MTFC. Later phases of Mesozoic reactivation focused along the Verran Fault Zone (VFZ) and north–south-linking structures were probably related to mid- to late Jurassic to early Cretaceous rifting and late Cretaceous to early Cenozoic opening of the North Atlantic. Supplementary material: Supplementary datasets are available at https://doi.org/10.6084/m9.figshare.c.6390518 Thematic collection: This article is part of the Caledonian Wilson cycle collection available at: https://www.lyellcollection.org/topic/collections/the-caledonian-wilson-cycle

Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning

Abstract. This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes, and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over 50 % or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

Estimation of rock mass deformability based on empirical relations for Ghezel Ozan dam site in the north of Iran

Rock mass deformability modulus is one of the most important parameters for underground structures and dam site design. The current methods for determining this parameter are in situ tests and empirical equations. In situ tests are the best and most accurate methods and include plate bearing, flat jack and dilatometer tests. However, these in situ tests are difficult, time-consuming, expensive and sometimes even impossible. Therefore, many empirical equations have been developed to determine the rock mass deformability indirectly based on various parameters. In this study, the correlation between the deformation modulus values, rock mass rating (RMR) and tunnelling quality index ( Q ) classification has been studied for the Ghezel Ozan dam site in NW Iran. Then, empirical equations are used to calculate the rock mass deformation modulus ( E m ) based on dilatometer in situ tests and rock mass classifications as mentioned above are defined. Finally, the correlation between the equations given in this study and the empirical equations presented by other researchers based on RMR and TQI ( Q ) are also investigated using statistical indicators. According to this analysis, the equation estimated based on RMR has a good compliance with the equations presented by earlier researchers. Supplementary material: Supplementary datasets and figures are available at https://doi.org/10.6084/m9.figshare.c.6139108

Global Distributions of Tropospheric and Stratospheric Gravity Wave Momentum Fluxes Resolved by the 9-km ECMWF Experiments

Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future.

Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model

Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics.

Nitrogen and iron availability drive metabolic remodeling and natural selection of diverse phytoplankton during experimental upwelling: Supplementary Datasets

Nitrogen and iron availability drive metabolic remodeling and natural selection of diverse phytoplankton during experimental upwelling: Supplementary Datasets