Correlation Effects Research Articles

Frozen Natural Orbitals for the State-Averaged Driven Similarity Renormalization Group.

We present a reduced-cost implementation of the state-averaged driven similarity renormalization group (SA-DSRG) based on the frozen natural orbital (FNO) approach. The natural orbitals (NOs) are obtained by diagonalizing the one-body reduced density matrix from SA-DSRG second-order perturbation theory (SA-DSRG-PT2). We consider three criteria to truncate the virtual NOs for the subsequent electron correlation treatment beyond SA-DSRG-PT2. An additive second-order correction is applied to the SA-DSRG Hamiltonian to reintroduce correlation effects from the discarded orbitals. The FNO SA-DSRG method is benchmarked on 35 small organic molecules in the QUEST database. When keeping 98-99% of the cumulative occupation numbers, the mean absolute error in the vertical transition energies due to FNO is less than 0.01 eV. Using the same FNO threshold, we observe a speedup of 9 times compared to the conventional SA-DSRG implementation for nickel carbonyl with a quadruple-ζ basis set. The FNO approach enables nonperturbative SA-DSRG computations on chloroiron corrole [FeCl(C19H11N4)] with more than 1000 basis functions, surpassing the current limit of a conventional implementation.

Investigating the structural, electronic, and magnetic properties of Cd1-xVxTe: Insights from First-Principles calculations

In our study, we aim to investigate the structural, electronic, and magnetic properties of CdVTe, a diluted magnetic semiconductor with a Cadmium telluride (CdTe) structure. To achieve this, we employ the full potential linear augmented plane wave method implemented in the CASTEP code, which allows us to accurately simulate the behavior of the system. To describe the electronic exchange and correlation effects, we adopt the generalized gradient approximation (GGA) within the density functional theory (DFT) framework. This choice of methodology ensures reliable and accurate calculations of the electronic and magnetic properties of Cd1-xVxTe. Our results reveal that the substitution of Vanadium (V) into the Cadmium (Cd) site of the CdTe lattice does not alter the zinc blende crystal structure, demonstrating the stability and structural integrity of the system. Furthermore, our calculations demonstrate that the introduction of Vanadium impurities leads to spin polarization within the material, resulting in the emergence of a magnetic moment. Notably, we find that a Vanadium concentration of approximately x≈0.12 exhibits the strongest magnetic properties, characterized by a significant magnetic moment. These findings provide valuable insights into the behavior and potential applications of CdVTe as a diluted magnetic semiconductor, shedding light on the interplay between structural, electronic, and magnetic properties in this material.

Gene - maltreatment interplay in adult ADHD symptoms: main role of a gene-environment correlation effect in a Brazilian population longitudinal study.

Childhood maltreatment correlates with attention-deficit/hyperactivity disorder (ADHD) in previous research. The interaction between ADHD genetic predisposition and maltreatment's impact on ADHD symptom risk remains unclear. We aimed to elucidate this relationship by examining the interplay between a polygenic score for ADHD (ADHD-PGS) and childhood maltreatment in predicting ADHD symptoms during young adulthood. Using data from the 2004 Pelotas (Brazil) birth cohort comprising 4231 participants, we analyzed gene-environment interaction (GxE) and correlation (rGE). We further explored rGE mechanisms through mediation models. ADHD symptoms were assessed at age 18 via self-report (Adult Self Report Scale - ASRS) and mother-reports (Strength and Difficulties Questionnaire - SDQ). The ADHD-PGS was derived from published ADHD GWAS meta-analysis. Physical and psychological child maltreatment was gauged using the Parent-Child Conflict Tactics Scale (CTSPC) at ages 6 and 11, with a mean score utilized as a variable. The ADHD-PGS exhibited associations with ADHD symptoms on both ASRS (β = 0.53; 95% CI: 0.03; 1.03, p = 0.036), and SDQ (β = 0.20; 95% CI: 0.08; 0.32, p = 0.001) scales. The total mean maltreatment score was associated with ADHD symptoms using both scales [(βASRS = 0.51; 95% CI: 0.26;0.77) and (βSDQ = 0.24; 95% CI: 0.18;0.29)]. The ADHD-PGS was associated with total mean maltreatment scores (β = 0.09; 95% CI: 0.01; 0.17; p = 0.030). Approximately 47% of the total effect of ADHD-PGS on maltreatment was mediated by ADHD symptoms at age 6. No evidence supported gene-environment interaction in predicting ADHD symptoms. Our findings underscore the significant roles of genetics and childhood maltreatment as predictors for ADHD symptoms in adulthood, while also indicating a potential evocative mechanism through gene-environment correlation.

Back-reaction and correlation effects on prethermalization in Mott-Hubbard systems

Back-reaction and correlation effects on prethermalization in Mott-Hubbard systems

A novel reconstruction method for displacement missing data of arch dam via hierarchical clustering and deep learning

The absence of displacement monitoring data presents a challenge to real-time dam safety monitoring. This article introduces a novel method for reconstructing missing displacements, comprehensively taking into account the spatiotemporal correlation and causal effect mechanism of displacement. Firstly, adaptive weighted derivative dynamic time warping (AWDDTW) and adaptive weighted dynamic time warping (AWDTW), in conjunction with clustering algorithms, are proposed to extract spatiotemporal correlation among dam displacements. Secondly, by stacking residual blocks within the residual network and densifying the shortcut connections between residual blocks, the deep stacked residual network (DS-ResNet) is established to effectively capture the intricate mappings between displacements and various factors. Finally, using two arch dams, as examples, we simulated scenarios of continuous long-term and multiple short-term missing data to validate the new method proposed in this study. The results indicate that the proposed clustering algorithm can accurately compute the similarity between displacement sequences of different lengths and monitoring frequencies, thereby providing more precise displacement point partitioning results. Compared to models such as random forests, relevance vector machines, multilayer perceptron, ResNet and Transformer, the DS-ResNet demonstrates superior capability in identifying and extracting effective information from displacement sequences under both continuous long-term and multiple short-term missing data scenarios. Compared to methods that solely consider spatiotemporal correlation or causal effect mechanism of displacement, the proposed method can more comprehensively reflect the operating patterns of arch dams and more accurately reconstruct missing displacement values. The research presented herein offers new technical means and solution approaches for dam safety monitoring and operational management.

Exploring the impact of green finance and technological innovation on green economic growth: Evidence from emerging market economies

AbstractThe sustainable future of emerging market economies (EMEs) lies within achieving sustainable development goals (SDGs), specifically SDG 8, which is linked with green economic growth (GEG) and its determinants, such as green finance (GF) and technological innovation (TI), along with some control variables. However, the current literature has insufficiently addressed these determinants in the context of emerging and developing economies. In this regard, this study deployed the most effective and advanced techniques like cross‐sectional auto‐regressive distributed lags, augmented mean group, and common correlated effects mean group to handle the issues of endogeneity, cross‐sectional dependence, and heterogeneity, which is not an easy task for conventional methods used in existing studies. This study has compiled a panel data set spanning from 2000 to 2019, across 13 EMEs. The main outcomes revealed that GF, TI, and human capital (HC) help in fostering significant GEG, whereas financial development (FD) and economic globalization (ECOGLB) fail. The study underscores the need for policymakers to focus on the promotion of diffusion of GF and enhancement of TI and HC while regulating the FD and ECOGLB process to steer emerging nations toward sustainable development.

Electric Field Gradient in Chiral and Tetrahedral Molecules within High-Order LRESC Formalism.

In this work, we present the electric field gradient (EFG) given by the linear response elimination of the small component (LRESC) scheme up to the 1/c4 order (c is the speed of light in vacuum) in CHFClX (X = Br, I, At) chiral molecules, together with CHF2Br and CH2FX (X = Br, I, At) tetrahedral systems. The former could be good candidates for further parity violation studies, especially when heavy atoms are surrounding. In this context, the LRESC scheme demonstrates effective applicability to large tetrahedral and chiral molecules that incorporate heavy elements, with relativistic effects playing a crucial role. The LRESC results of EFG exhibit an excellent agreement with those calculated at the four-component level, giving differences of only hundredths order in a.u. (atomic units) for the bromine nucleus and less than 0.1 a.u. for the iodine nucleus. Regarding the other nuclei, for the chiral molecules, there is a heavy atom effect on the light atom (HALA) for chlorine and fluorine atoms as the substituent halogen atom becomes heavier. Furthermore, the electronic part of the EFG for the central carbon and the fluorine nuclei presents an important dependence with the environment in the molecules under study. With accurate calculations of the EFG and tabulated nuclear quadrupole moment, the nuclear quadrupole coupling constant is obtained within the LRESC scheme, including for the first time correlation effects on the spin-dependent corrections with this methodology, providing results close to the experimental ones for Cl, Br, and I atoms. At the Hartree-Fock level, the differences are around 6% for Br and I nuclei, and at the density functional theory level with the LDA and PBE0 functionals, the differences are no more than 2%.

The excitation energies and hyperfine structures for 2l, 3l states in lithiumlike ions

Combining the multi-configuration Dirac–Hartree–Fock method and the model-quantum electrodynamics (QED) approach, the wave functions, transition energies and hyperfine structure constants for 2l, 3l states in Li-like Ne7+, Mg9+, Al10+, P12+, Ar15+ and Ca17+ ions are calculated. The effects of electron correlation, Breit interactions, nuclear recoil and QED effects are analyzed in detail. We find that the contribution of the non-diagonal elements of the self-energy is significant for the 2p1/2→2s1/2 and 2p3/2→2s1/2 transitions. However, for the 3l→2s1/2 transitions, the contribution of non-diagonal elements is small. The accuracy of the currently calculated hyperfine structure constants is expected to be within 1%.

Successive quasienergy collapse and breakdown of photon blockade in the few-emitter limit

The emergent behavior that arises in many-body systems of increasing size follows universal laws that become apparent in order-to-disorder transitions. While this behavior has been traditionally studied for large numbers of emitters, recent progress allows for the exploration of the few-emitter limit, where correlations can be measured and connected to microscopic models to gain further insight into order-to-disorder transitions. We explore this few-body limit in the driven and damped Tavis–Cummings model, which describes a collection of atoms interacting with a driven and damped cavity mode. Our exploration revolves around the dressed states of the atomic ensemble and field, whose energies are shown to collapse as the driving field is increased to mark the onset of a dissipative quantum phase transition. The collapse occurs in stages and is an effect of light-matter correlations that are overlooked for single atoms and neglected in mean-field models. The implications of these correlations over the macroscopic observables of the system are presented. We encounter a shift in the expected transition point and an increased number of parity-broken states to choose from once the ordered phase is reached.

Abstract PO5-21-02: Breast cancer screening based on physical breast examination: ITABERAÍ randomized trial

Abstract Introduction: Approximately 70% of the Brazilian population depends on the public healthcare system (Sistema Único de Saúde - SUS). However, only 16% of SUS users have access to mammography for breast cancer screening, which necessitates the evaluation of new strategies to reduce mortality from the disease, including expanding physical breast examination (PBE) by non-medical professionals. Within the primary healthcare network, the Community Health Agent (CHA) is a technical-level professional who performs activities through home visits. The primary objective of this study is to evaluate the effectiveness of screening actions based on PBE performed by CHA in reducing advanced tumors (Stage III and IV) and breast cancer mortality rates. Secondary objectives include comparing the number of breast cancer diagnoses between the groups, establishing the overdiagnosis rate, comparing cancer-specific survival, comparing overall survival, comparing the number of diagnostic procedures, assessing the interval between the first symptom and diagnosis, evaluating the interval between diagnosis and the start of treatment, and comparing the financial cost of diagnosis and treatment for breast cancer patients. Trial design: Multicenter, phase III, prospective, randomized clinical trial. The target population comprises women aged 40 years or older and users of SUS. The randomization process will be carried out based on the spatial analysis of each participating center, using sociodemographic information on women from each macroregion to verify the homogeneity of data between groups. Intervention: After randomization, CHA in each group underwent training at different times. CHA in the CG received guidance on breast cancer screening and prevention according to Ministry of Health recommendations, while CHA in the IG received the same guidance and participated in theoretical and practical training for PBE (only female professionals). Variables and data collection: An application was developed to be installed on mobile devices, which the CHA used during home visits. The graphical interface of the application was developed in Dart/Flutter. For the backend, Python programming language was used along with the Flask framework to create the Application Programming Interface (API) responsible for the communication between the database and the application. The MySQL software was used for the relational database. The variables used for building the application included sociodemographic data, lifestyle habits, medical history, information on previous examinations, and physical breast examination. Statistical analysis: For the analyses, the database will be exported to the REDCap (Research Electronic Data Capture) platform. The calculations were defined to detect a reduction in severe cases and mortality (10% and 20%, respectively). For this purpose, a sample power of 80% and a Type I error of 5% were adopted after adjusting for intraclass correlation and sampling effect (0.032 and 1.892, respectively). The calculations were estimated using nQuery software (version 9.1). For the city of Itaberaí, a population of 1,894 women was estimated in each group, resulting in a total of 3,788. Present and target accrual: The trial was activated in December 2022, and as of June 30th, 2023, 2,467 patients had been accrued. The study will last for 16 years, and the first data analyses will be conducted in 2024. Registration and ethical aspects: The trial is registered in ReBEC (Brazilian register): RBR-39vm2nd. Contact information: Prof. Dr. Ruffo Freitas-Junior, Federal University of Goiás, ruffojr@terra.com.br. Citation Format: Ruffo Freitas-Junior, Danielle Rodrigues, Rosangela Corrêa, Leonardo Soares. Breast cancer screening based on physical breast examination: ITABERAÍ randomized trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-21-02.

Simulating X-ray Absorption Spectroscopy in Challenging Environments: Methodological Insights from Water-Solvated Ammonia and Ammonium Systems.

Core-electron excitations in solvated systems, influenced by solvent geometry and hydrogen bonding, make X-ray absorption spectroscopy (XAS) a valuable tool for assessing solvent-solute interactions. However, calculating XAS spectra with electronic-structure methods has proven challenging due to a delicate interplay between correlation and solvation effects. This study provides a computational procedure for XAS modeling in solvated systems, with water-solvated ammonia and ammonium systems serving as probes. Exploring methodological challenges, we investigate explicit embedding models, specifically the polarizable embedding family, including polarizable density embedding and extended polarizable density embedding. Our linear-response time-dependent density functional theory (LR-TDDFT) XAS calculations reveal the efficiency of this approach, with extended polarizable density embedding emerging as a robust improvement over polarizable density embedding. Contrary to some recent literature, our study challenges the belief that LR-TDDFT cannot accurately describe XAS spectra of ammonia and ammonium solvated in water.

Grinding wheel specification cybernetic recommendation with multi-task multi-imbalanced learning in smart manufacturing system

Over the years, the grinding wheels industry has played a crucial role in mechanical engineering. Grinding wheel specification is composed of various factors such as abrasive type, wheel grade, bond type, and more, which are derived from customer workpiece operation information. Traditionally, the determination process of grinding wheel specifications is complicated. Experienced engineers need to check out the limitation and consider the interaction of each factor. When the new specification order comes, engineers have to spend a lot of time on historical data search and discussion. Recently, the application of artificial intelligence attracts more and more attention in the manufacturing industry. Through collecting historical data, the model has the ability to extract the domain knowledge, predict the specification that does not exist, and reduce the decision time. However, when training the model, this grinding wheel specification faced multiple imbalanced targets and multi-task issues. In the past, research efforts primarily focused on studying single-task learning (STL) algorithms. Seldom of them has a systematic way to discuss the model determination of multiple targets and correlation influence, especially on the tabular-type dataset. In this study, we proposed a multiple resampling chain multi-task learning framework to address the issue of data science. To tackle the multi-task imbalance resampling problem, we also proposed a resampling strategy called Multiple Resampling Chain. Moreover, we conducted a sensitivity analysis to examine the correlation effect on model improvement. Finally, we proposed a cyber-recommendation system that integrates the training model and interface to provide a more intuitive way of specification prediction. This system not only can help engineers to improve the time spent on making decisions, but also make the whole manufacturing process to be more intelligent.

In the Eye of the Storm: Hurricanes, Climate Migration, and Climate Attitudes

Climate disasters raise the salience of climate change’s negative consequences, including climate-induced migration. Policy action to address climate displacement is especially contentious in the United States, where weak support for tackling climate change intersects with high opposition to migration. Do climate disasters foster receptivity toward climate migrants and broader willingness to combat climate change? To study this question, we leverage the occurrence of Hurricane Ian during fielding of a preregistered survey in autumn 2022. Hurricane exposure increased concern about and support for policies to address climate migration. Hurricane exposure also increased support for climate action and belief in anthropogenic climate change. Effects of hurricane exposure cross-cut partisanship, education, age, and other important correlates of climate attitudes but decay within 6 months. Together, these results suggest that climate disasters may briefly increase favorability toward climate migrants and climate policy action but are unlikely to durably mobilize support even in severely impacted areas.

Ecological tradeoffs lead to complex evolutionary trajectories and sustained diversity on dynamic fitness landscapes

The course and outcome of evolution are critically determined by the fitness landscape, which maps genotype to fitness. Most theory has considered static fitness landscapes or fitness landscapes that fluctuate according to abiotic environmental changes. In the presence of biotic interactions between coexisting genotypes, the fitness landscape becomes dynamic and frequency-dependent.Here, we introduce a fitness landscape model that incorporates ecological interactions between individuals in a population. In the model, fitness is determined by individuals competing for resources according to a set of traits they possess. An individual’s genotype determines the trait values through a Rough Mount Fuji fitness landscape model, allowing for tunable epistasis (i.e. non-additive gene interaction) and trait correlations (i.e. whether there are tradeoffs or synergies in the ability to use resources). Focusing on the effects of epistasis and trait correlations, we quantify the resulting eco-evolutionary dynamics under simulated Wright–Fisher dynamics (i.e. including genetic drift, mutation, and selection under the assumption of a constant population size) on the dynamics fitness landscape in comparison with a similar, static, fitness landscape model without ecological interactions.Whereas the non-ecological model ultimately leads to the maintenance of one main genotype in the population, evolution in the ecological model can lead to the long-term coexistence of several genotypes at intermediate frequencies across much of the parameter range. Including ecological interactions increases steady-state diversity whenever the trait correlations are not too strong. However, strong epistasis can hinder coexistence, and additive genotype–phenotype maps yield the highest haplotype diversity at the steady state. Interestingly, we frequently observe long-term coexistence also in the absence of induced trade-offs in the ability to consume resources.In summary, our simulation study presents a new dynamic fitness landscape model that highlights the complex eco-evolutionary consequences of a (finite) genotype–phenotype-fitness map in the presence of biotic interactions.

Rovibrational calculations without model Hamiltonians: The infrared and microwave spectra of thiopropynal

AbstractRovibrational configuration interaction theory has been used to study the rotational spectrum and low lying rovibrational states of thiopropynal, a molecule, which has recently been detected in an interstellar cloud. Geometrical parameters, fundamental vibrational transitions and spectroscopic constants are also provided. All calculations rely on an ‐mode representation of the multidimensional potential energy surface, which has been obtained from explicitly correlated coupled‐cluster approaches including core correlation effects and an approximate treatment of high‐order coupled‐cluster terms. Comparison with experimental data is provided for several properties and the agreement was found to be excellent in most cases.

Labor linkages and flow paths of industry in China

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm. By decomposing the path structure, the average propagation distance of the labor force population's demand for each industry can be determined. By employing labor force population data from the corresponding years and China's input-output tables published by the National Bureau of Statistics of China (NBS) from 2005 to 2020, this study conducts a quantitative analysis of the correlation effect between labor force population and the trend of its transfer across 19 industries. The findings indicate that the correlation effect between labor force and population is most pronounced in the manufacturing sector. Furthermore, the construction sector faces an especially critical requirement for labor force personnel from other industries. The article culminates with a recommendation that the government enhance its macro-control endeavors to address labor market risk shocks and take an active stance in response to labor market fluctuations.

Toward Direct Exploration of the Few-Femtosecond Dynamics of Electronic Coherence and Correlation in Quantum Materials Using Time- and Angle-Resolved Photoemission Spectroscopy

Over the last two decades, time- and angle-resolved photoemission spectroscopy (trARPES) has become a mature and established experimental technique for the study of ultrafast electronic and structural dynamics in materials. To date, most trARPES investigations have focused on the investigation of processes occurring on time scales of ≳30 fs, in particular, relaxation and thermalization, and have therefore been blind to the initial sub-10 fs dynamics related to electronic coherence and correlation effects. In this article, we illustrate how current trARPES setups reach their limits when it comes to addressing such extraordinarily short time scales and present an experimental configuration that provides the time, energy, and momentum resolutions required to monitor few-femtosecond dynamics on the relevant energy and momentum scales. We discuss the potential capabilities of such an experiment to study the electronic response of materials in the strong-field interaction regime at PHz frequencies and finally review a theoretical concept that may in the future even overcome the competing resolution limitations of trARPES experiments, as imposed by the time–bandwidth product of the probing laser pulse. Our roadmap for ultrafast trARPES indicates a path to break new experimental ground in quantum nonequilibrium electronic dynamics, from which new possibilities for ultrafast control of optical and electronic signals in quantum materials can be explored.

Determination of speed and assessment of conditioning in horses submitted to a lactate minimum test-alternative approaches.

The maximal lactate steady state (MLSS) is a well-known gold standard method for determining the aerobic capacity of athletic horses. Owing to its high cost and complex execution, there is a search for standardized exercise tests that can predict this value in a single session. One of the methods described for this purpose is the lactate minimum test (LMT), which could be more accurate despite being adequate to predict MLSS. This study aimed to examine the impact of training on the speed corresponding to lactate minimum speed (LMS) and to apply new mathematical methods to evaluate the fitness level of horses based on the curve obtained by the LMT. Ten Arabian horses underwent a 6-week training program based on LMS calculated by second-degree polynomial regression (LMSP). In addition, the LMS was also determined by visual inspection (LMSV), bi-segmented linear regression (LMSBI) and spline regression (LMSS). From the curve obtained during the LMT, it was possible to calculate angles α, β and ω, as well as the total area under the curve (AUCTOTAL) before (AUCPRELMS) and after (AUCPOSLMS) the LMS. The methods for determining the LMS were evaluated by ANOVA, intraclass correlation coefficient (ICC) and effect size (ES) by Cohen's d test. The Pearson correlation coefficient (r) between the proposed LMS determination methods and other mathematical methods was also calculated. Despite showing a good correlation (ICC >0.7), the LMS determination methods differed from each other (p < 0.05), albeit without a significant difference resulting from conditioning. There were reductions in α:β ratio, angle α, and AUCPOSTLMS, with the latter indicating lower lactate accumulation in the incremental phase of LMT after conditioning, in addition to an improvement in the animals' aerobic capacity. Considering that the most common methods for determining the LMS are applicable yet with low sensitivity for conditioning assessment, the approaches proposed herein can aid in analyzing the aerobic capacity of horses subjected to LMT. The mathematical models presented in this paper have the potential to be applied in human lactate-guided training program trials with a comparable study basis.

Theoretical triple differential cross sections for ionization of N 2 and CH 4 molecules by electron impact

We present the angular distribution of electron emission by calculating the triple differential cross sections for (e, 2e) process on N 2 and CH 4 by using the three-body formalism of molecular first Born approximation (MFBA), two-Coulomb wave (M2CW), and three-Coulomb wave (M3CW) models, respectively. In these models, a Coulomb distorted wave is considered for the motion of the incident electron. We have considered the continuum-continuum correlation effect by choosing the final state as the three-Coulomb and two-Coulomb wave functions in M3CW and M2CW models, whereas the ejected electron is affected by a single centre field of the target in the MFBA model. In the M2CW model, the interaction between scattered electron-residual target ion has been described as a plane wave. The distinguishing feature among the three models has been noted in the TDCS as a strong binary peak with and without a recoil peak for several electron emission energies at fixed scattering angle. In the case of N 2 molecule, the TDCS shows oscillatory behaviour with the variation of the electron emission angle. The positions of the binary peak obtained by our theoretical models are well established by the experimental findings, but a large deviation is found in the region of the recoil peak. The contributions of TDCS for different molecular orbitals of the molecules to the spectrum of angular distributions at different electron emission energies have also been analyzed. Finally, a comparison is made with the measurement in coplanar asymmetry geometry. Overall, good agreement was found between experiments and M3CW theory.

Towards sustainability: The spatiotemporal patterns and influence mechanism of urban sprawl intensity in the Yangtze River Delta urban agglomeration

Urban sprawl has been a great challenge to current sustainable development goals, especially in urban agglomeration areas. Based on the nighttime light and WorldPop population grid data from 2011 to 2020, this study established a multidimensional evaluation system of urban sprawl intensity in the Yangtze River Delta Urban Agglomeration (YRDUA) with a "scale-density-structure" framework. The Dagum Gini coefficient, spatial Markov chain, and econometric analysis were applied to analyze urban sprawl intensity. The results suggested that: (1) the urban sprawl intensities of the cities in the YRDUA became more imbalanced over the decade; (2) the mild sprawl cities were dominant in the study area, but the moderate and severe sprawl cities were mostly located in the west and the only inland province--Anhui; (3) the spatial distribution of urban sprawl intensity indicated "club convergence" and strong spatial correlation effect, but poor stability; and (4) land resource mismatch and population urbanization were the main explanatory variables for urban sprawl intensity, with large cities more sensitive to the former, and medium cities to the latter. These findings can provide important policy implications to sustainable development and compact growth.