Dynamical Evolution Research Articles

Relationship between stratigraphic overlap and sedimentary facies evolution of the Junggar Basin, Northwest China

The interaction between the topography of the slope zone on the edge of the basin and the distribution of sediments is crucial for accurately predicting sediment distribution, but few studies emphasize the impact of stratigraphic overlap on the spatial evolution of sedimentary facies. The tectonic movement and sedimentary environment of Hala’alat Mountain in the northwest margin of the Junggar Basin are complex, and the sedimentary model of the whole Cretaceous system is still unclear. This article uses lithology, logging, and seismic data to explain the evolution process and sedimentary model of the Cretaceous system. The significant overlap of Cretaceous strata in the research area has a significant impact on the distribution of sedimentary facies zones and the development of sedimentary systems. Furthermore, this article explores the genesis mechanism of the Cretaceous stratigraphic overlap phenomenon, clearly defines the boundary range of stratigraphic overlap, and deeply analyzes how sedimentary facies zones are distributed and their subsequent evolution trends after the formation of overlap. The results demonstrate that there are apparent stratigraphic overlap phenomena in the second member (K1q2) and the third member (K1q3) of the Cretaceous Qingshuihe Formation in this area. The lithology at the bottom of the formation is grey sand conglomerate with finer grain size in the upper part, and the sedimentary facies are transitioned from fan delta facies to shore shallow lake beach bar facies. In member 1 (K1h1), member 2 (K1h2) and member 3 (K1h3) of the Hutubi Formation, due to the gradual slope and relatively stable sedimentary environment, the distance of stratigraphic overlap becomes shorter, the stratigraphic overlap phenomenon is no longer apparent and the sedimentary facies zone does not change. The study not only reveals the intrinsic relationship between stratigraphic overlap and the distribution of sedimentary facies zones, but also deepens the understanding of the dynamic evolution laws of sedimentary systems. The study on the overlap sedimentary mechanism of the Hala’alat Mountain in the northwest margin of Junggar Basin has reference significance.

Current status and prospects of congenital adrenal hyperplasia: A bibliometric and visualization study.

Congenital adrenal hyperplasia (CAH) is increasingly prevalent, leading to a surge in related research. To pinpoint emerging trends and recommend future directions, a bibliometric analysis of relevant CAH literature was performed. From January 1, 2000, to October 1, 2023, we searched the Web of Science Core Collection for CAH literature. For the bibliometric analysis, tools such as VOSviewer, CiteSpace, and the R package "bibliometrix" were employed. The United States and England are at the forefront among 113 countries, contributing 5034 papers to CAH research. However, there is a need for more extensive global collaboration across institutions in this field. The number of publications on CAH is increasing annually. Leading research institutions include the University of Michigan, University of California, San Francisco, Karolinska Institutet, and Karolinska University Hospital. The Journal of Clinical Endocrinology & Metabolism is the most cited in this area. The most prolific author is Falhammar H, with 88 publications, 2568 co-citations, and a significant overall contribution. Key research areas include diagnostic methods and therapeutic strategies for CAH. Emerging research hotspots are identified by keywords such as "disorders of sex development," "21-hydroxylase deficiency," "ambiguous genitalia," "testosterone" and "adrenal insufficiency." Research on CAH is expected to expand globally. Future studies will primarily focus on exploring CAH's diagnostic aspects and developing new therapies. This paper will help scholars better understand the dynamic evolution of the CAH and point out the direction for future research.

Lacustrine Wetlands Landscape Simulation and Multi-Scenario Prediction Based on the Patch-Generating Land-Use Simulation Model: A Case Study on Shengjin Lake Reserve, China

Landscape simulation and prediction are crucial for understanding the dynamic evolution and future trends of wetlands. However, only a few existing studies have focused on the applicability and limitations of commonly used land-use/cover change (LUCC) simulation models in lake wetland landscapes. Taking Shengjin Lake Reserve in China as the study area, we firstly analyzed landscape variations during 2010–2020 using multisource remote sensing images. Then, the patch-generating land-use simulation (PLUS) model was employed to simulate wetland landscapes in 2020, the accuracy and limitation of which in simulating lacustrine wetlands were also explored. Lastly, the changing trends of wetland landscapes in 2030 under different development scenarios were predicted. The results show that the landscape of Shengjin Lake Reserve has changed significantly during 2010–2020, with increases in mudflats, reservoirs/ponds, woodlands, and built-up land, and there has been decreases in lakes, grass beaches, and croplands. The PLUS model demonstrated an ideal simulation accuracy for Shengjin Lake Reserve, with the overall accuracy exceeding 80%, kappa coefficient greater than 0.75, and figure of merit (FOM) coefficient of 0.35, indicating that the model can capture the dynamic changes in wetland landscapes accurately. The simulation accuracy can be effectively improved with the adjacent initial year, shorter time interval, and the primary driver factors. Under the natural development scenario, the number of patches in the Shengjin Lake Reserve increased sharply, and landscape fragmentation intensified. Under the urban development scenario, the expansion of built-up land increased, and the average patch area increased. In the ecological protection scenario, the Shannon diversity index and Shannon evenness index of the landscape improved significantly, and the natural wetlands such as grass beaches and lakes can be protected effectively. Our study confirms the applicability of the PLUS model in simulating and predicting lacustrine wetlands landscapes, and the conclusions provide a scientific basis for formulating reasonable development strategies to realize wetland resource conservation and management.

Differential flood insurance participation and housing market trajectories under future coastal flooding in the United States

Communities respond to flooding events based upon risk perceptions and available adaptive behaviors (e.g., emigrating, purchasing insurance, constructing levees). Across the United States, sea level rise, intensifying storm-surges, and extreme rainfall may alter human-flood dynamics. Here, we use calibrated Socio-Environmental models of contiguous US coastal census tracts and two shared socio-economic pathways (SSP245 and SSP585). We project that by 2100, total flood insurance claims will increase by +25% to +130% under low (SSP245) and high (SSP585) emissions scenarios, respectively. The increase in flood insurance claims will impact mainly socially vulnerable communities. Further, we project that active NFIP policies will increase from +30% under low emission scenario to +60% under high emission scenario. Our finding also suggests the growing debt of the National Flood Insurance Program under higher emissions. Raising the water elevation threshold for coastal flooding by +1 meter via levees may reduce future surge-related losses by 95% and 40% under low and high emission scenarios and stabilize housing markets. Our future projections of flood insurance claims, policy coverage, and the impact of water elevation serve as credible hypotheses for the evolution of human flood dynamics under climate change. They can inform national flood policy and future research.

Pore Microstructure and Dynamic Evolution Characteristics of Unfrozen Water during the Freezing Process of Coal with Different Moisture Contents

Pore Microstructure and Dynamic Evolution Characteristics of Unfrozen Water during the Freezing Process of Coal with Different Moisture Contents

The Prevalence of Resonance Among Young, Close-in Planets

Multiple planets undergoing disk migration may be captured into a chain of mean-motion resonances with the innermost planet parked near the disk’s inner edge. Subsequent dynamical evolution may disrupt these resonances, leading to the nonresonant configurations typically observed among Kepler planets that are Gyr old. In this scenario, resonant configurations are expected to be more common in younger systems. This prediction can now be tested, thanks to recent discoveries of young planets, in particular those in stellar clusters, by NASA’s TESS mission. We divided the known planetary systems into three age groups: young (<100 Myr old), adolescent (0.1–1 Gyr old), and mature (>1 Gyr old). The fraction of neighboring planet pairs having period ratios within a few percent of a first-order commensurability (e.g., 4:3, 3:2, or 2:1) is 70% ± 15% for young pairs, 24% ± 8% for adolescent pairs, and 15% ± 2% for mature pairs. The fraction of systems with at least one nearly commensurable pair (either first- or second-order) is 86% ± 13% among young systems, 38% ± 12% for adolescent systems, and 23% ± 3% for mature systems. First-order commensurabilities prevail across all age groups, with an admixture of second-order commensurabilities. Commensurabilities are more common in systems with high planet multiplicity and low mutual inclinations. Observed period ratios often deviate from perfect commensurability by ∼1% even among young planets, too large to be explained by resonant repulsion with equilibrium eccentricity tides. We also find that super-Earths in the radius gap (1.5–1.9R ⊕) are less likely to be near-resonant (11.9% ± 2.0%) compared to Earth-sized planets (R p < 1R ⊕; 25.3% ± 4.4%) or mini-Neptunes (1.9R ⊕ ≤ R p < 2.5R ⊕; 14.4% ± 1.8%).

Challenges and Strategies in Protecting Single-Colour Trademarks within the Fashion Industry

This paper examines the complexities of registering and protecting single-colour trademarks within the fashion industry, with a special focus on high-profile cases such as Christian Louboutin's iconic red soles. It investigates the unique legal challenges faced by brands in establishing trademark protection for colours, which lack inherent distinctiveness but can acquire significant brand identity through secondary meaning. Through a detailed examination of various international court cases, the study reveals how judicial systems manage to uphold the integrity of distinctive brand elements while fostering a fair and competitive market environment. Additionally, this research discusses strategic approaches that brands can adopt to effectively secure and defend their unique colour trademarks in diverse legal jurisdictions. The findings shed light on the dynamic evolution of trademark law, underscore the significant impact of judicial decisions on brand protection strategies, and explore the broader implications these have on global market competition. By integrating theoretical frameworks with case study analysis, this paper provides valuable insights into the intersection of law, commerce, and brand identity in the global fashion industry.

Operando Reconstruction of CuO/SnO2 Nanosheets Promotes the Syngas Production from the Electrochemical CO2 Reduction Reaction.

Electrochemically reducing CO2 (CO2RR) to high-value chemical products is recognized as a promising route to simultaneously reduce the consumption of fossil fuels and carbon emission. The fabrication of syngas with an appropriate H2/CO ratio by CO2RR has been widely studied, but the dynamic evolution of the catalyst is still ambiguous. Herein, the reconstruction of CuO/SnO2 nanosheets (NSs) with a heterojunction structure in the CO2RR process was reported by an in situ X-ray diffractometer technique. The products of the CO2RR on the optimized CuO/SnO2 NSs are CO and H2, achieving a nearly 100% total Faraday efficiency. Moreover, the resulting syngas is maintained at a constant 2:1 ratio of H2/CO over a wide potential of -0.8 to -1.2 V versus the reversible hydrogen electrode. Importantly, the detailed characterizations show that Cu2+ is reduced to Cu during the CO2RR process, while the valence state of Sn remains stable. This study presents strong experimental support for the dynamic evolution process of electrocatalysts in other energy conversion fields.

Holocene Floodplain Transformation Through Catchment‐Scale Human‐Environment Interactions: An Interdisciplinary Case Study of the Gete Catchment (Belgium)

ABSTRACTFloodplains across the European loess region transformed from nature‐ to human‐dominated environments during the Holocene. A general framework of this evolution is well established, but it is less clear how differences in timing—observed within and between catchments—can be explained. Although human impact is an important driver, little attention has thus far been paid to the actual human activities involved, their intensities, and spatiotemporal patterns. By adopting an interdisciplinary approach, this study provides insight into the evolution of both environmental (e.g., local floodplain geoecohydrology and regional land cover) and human dynamics (e.g., demography and land use) in the Belgian Gete catchment. Results show that the observed changes in land cover and floodplain geoecology can indeed be attributed to spatiotemporal variations in human impact. Human‐induced vegetation change, driven by population growth and associated agrarian production, resulted in hydrological changes and localized colluviation. Catchment‐scale improvement of hillslope‐channel connectivity initiated alluviation in the valleys and completed the transformation from forested marsh to open floodplain with overbank sedimentation. In turn, this allowed people to settle in the floodplains in the Early Medieval period, laying the foundations for our present‐day landscape.

Optimizing Sodium Ion Adsorption Through Robust d–d Orbital Modulation for Efficient Capacitive Deionization

AbstractUnraveling the fundamental mechanisms of sodium ion adsorption behavior is crucial for guiding the design of electrode materials and enhancing the performance of capacitive deionization systems. Herein, the optimization of sodium ion adsorption is systematically investigated through the robust d–d orbital interactions within zinc‐doped iron carbide, facilitated by a novel liquid nitrogen quenching treatment. Liquid nitrogen quenching treatment can enhance the coordination number, strengthen d–d orbital interactions, promote electron transfer, and shift the d‐band center of Fe closer to the Fermi level, thereby enhancing sodium ions adsorption energy. Consequently, the obtained electrode material achieves a superior gravimetric adsorption capacity of 121.1 mg g−1 and attractive cyclic durability. The adsorption capacity is highly competitive compared to the vast majority of related research works in the field of capacitive deionization. Furthermore, sodium ion adsorption/desorption mechanisms are substantiated through ex situ techniques, revealing dynamic atomic and electronic structure evolutions under operational conditions. This work demonstrates that optimizing sodium ion adsorption via robust d–d orbital modulation enabled by liquid nitrogen quenching treatment is an effective approach for developing efficient capacitive deionization electrode materials.

Dynamic Response and Microstructure Evolution of Ta-2.5W and Cu Under Detonation Loading

Dynamic Response and Microstructure Evolution of Ta-2.5W and Cu Under Detonation Loading

Trends in the dynamic evolution of blockchain and circular economy: A literature review and bibliometric analysis

AbstractThe intersection of blockchain technology and circular economy (CE) practices holds significant potential for enhancing sustainability, transparency, and efficiency. This study conducts a comprehensive bibliometric analysis to explore the current research landscape in this domain. Key findings indicate a rapid growth in publications, highlighting a burgeoning interest in leveraging blockchain for CE initiatives. The analysis identifies critical approaches such as enhancing supply chain transparency, improving resource management, and facilitating product lifecycle tracking. However, the integration of blockchain into CE practices presents challenges, including technical complexities, scalability issues, and regulatory hurdles. Addressing these challenges requires interdisciplinary and cross‐sectoral collaboration, as well as continuous research and innovation. Future research should focus on the integration of emerging technologies with blockchain, longitudinal studies on long‐term impacts, socio‐economic implications, and the development of regulatory frameworks. This study contributes to the existing body of knowledge by providing unique insights and outlining potential research directions at the intersection of blockchain and CE.

Safety of and chorioretinal circulation during repeated low-level red-light therapy for myopic children.

To evaluate the safety of repeated low-level red-light (RLRL) therapy in children, and the dynamic evolution of choroidal and retinal blood flow. This is a single-centre, randomised, single-blind, parallel-group clinical trial. Seventy myopic children were randomly assigned to either the intervention group [receiving RLRL therapy plus single-vision spectacle (SVS)] or the control group (wearing SVS). Participants underwent comprehensive ophthalmic examinations following their first irradiation, 9 months continuous RLRL therapy and stop of treatment. Quantitative analyses of choroidal and retinal microcirculation were analysed via optical coherence tomography angiography. Over 9 months of treatment, while the RLRL treatment demonstrated significantly less increases in refractive error and axial length compared with the SVS treatment (ps < 0.05), no abnormalities in fundus structure or visual function (mfERG, VEP and microperimetry) were detected (ps > 0.05). A single red-light exposure did not exert a significant influence on choroidal thickness (ps > 0.05). Upon continuous treatment, the RLRL group achieved peak values in these circulations at 9 months (ps < 0.05). Following cessation of exposure, all circulations exhibited a declining trend, reaching similar levels in both groups (ps > 0.05). As the frequency of red-light exposures intensified, there was a consistent surge in these circulations (ps < 0.05). Nine months of continuous RLRL exposure does not cause toxic side effects on retinal or optic nerve functions, and there is a time-dependent cumulative response in choroidal and retinal circulation.

Observing the dynamic community structure of urban travel networks based on navigation data

Dynamic community division is crucial for comprehending the interactive structure and mechanism between residents and space. Currently, there is insufficient research on dynamic community changes in travel networks regarding travel distance. This study utilizes residents’ travel trajectories to construct a multi-layer network based on travel purposes. The study establishes methods for identifying travel network communities and analyzing community evolution influence. Dynamic characteristics of travel network communities at different distances are extracted. Results show that within 3–7 km travel distance, different travel network communities exhibit continuous spatial aggregation, whereas beyond 11 km, heterogeneity is observed. Moreover, communities within 3-7 km experience drastic dynamic evolution, whereas beyond 7 km, they tend to stabilize, forming a large-scale and fixed travel mode. This study enriches the dynamic interaction research of functional space from the perspective of distance, and the experimental results provide an effective reference for urban planning and management.

Spatio-temporal envolutional graph neural network for traffic flow prediction in UAV-based urban traffic monitoring system

With the advancement of modern UAV technology, UAVs have become integral to creating traffic management monitoring systems. Additionally, UAV-based traffic monitoring systems can predict traffic flow by integrating machine learning methods. Specifically, traffic flow data contains both spatial and temporal information, which can be effectively processed by graph neural networks (GNNs). However, GNNs often face the challenge of oversmoothing, which hinders their ability to capture complex structures in the data. The Spatio-Temporal Graph Ordinary Differential Equations (STGODE) model addresses this issue by introducing Neural Ordinary Differential Equations (NODEs) to construct deeper GNNs. Despite this, STGODE relies on initially predefined semantic neighborhood matrices, which do not adapt well to the dynamic nature of traffic information. To overcome this limitation, we propose an evolutionary graph neural network for traffic prediction, capable of continuously updating the semantic adjacency matrix throughout the training process. This dynamic evolution of the semantic adjacency matrix allows it to adapt to the features and semantic relations of the current data, enhancing its ability to capture the complexity and variability of traffic patterns. We validate our approach through experiments on several real-world datasets, demonstrating that our method outperforms state-of-the-art benchmarks.

A longitudinal study of evolving polycentric pattern in Shanghai

In the context of technological progress and economic transition, the urban spatial structure has consistently undergone dynamic evolution. However, the trajectories of polycentric patterns display significant national and regional variations. It is crucial to examine the dynamic evolution of spatial structures within Chinese metropolitan areas amidst economic transitions. Focusing on the Shanghai Metropolitan Area (SMA), this study investigates the temporal dynamics of job distribution and employment centres and considers industry differentiation to understand the evolution of polycentric patterns. Additionally, spatial regression models are utilized to probe the intrinsic mechanisms through which adjustments in industrial structure influence spatial evolution. The findings indicate that both the service and manufacturing sectors have the potential to concentrate employment and form subcentres in the SMA. Nevertheless, within the economic transition context, the downtown area, dominated by the service sector, underwent specialized development, fostering continuous prosperity and the emergence of employment subcentres in the periphery. In contrast, employment centres in suburban areas, dominated by the manufacturing sector, experienced a declining trend. Consequently, the locations of employment clusters in the SMA shifted, concurrently reshaping the relationships between the main centre and subcentres and leading to a transformation from a weaker separated polycentric pattern to a stronger continuous polycentric pattern. This paper elucidates the underlying mechanisms through which industrial transition influences urban spatial structure, offering insights into the evolution of polycentric patterns in the postindustrial era from a local Chinese perspective.

Ultrafast visible fiber laser mode-locked by frequency-shifted feedback of an α-BaTeMo2O9 crystal acousto-optic modulator.

We report the first demonstration, to the best of our knowledge, of visible mode-locked fiber laser using frequency-shifted feedback (FSF) with a visible α-BaTeMo2O9 (α-BTM) crystal acousto-optic modulator (AOM). First, an α-BTM crystal is used as the visible high-quality AOM with a high diffraction efficiency of 85%, a fast rise/fall time of 79/98 ns, and a low insertion loss of 0.2 dB at 635 nm. Then, the 635 nm FSF mode-locked fiber laser is achieved using a Pr3+:doped ZBLAN double-clad fiber as a visible gain medium and the α-BTM AOM as a frequency-shifting element. Self-starting mode-locking at 635 nm directly generates red laser pulses with a pulse duration of 45 ps and a repetition frequency of 31.8 MHz. Furthermore, we investigate the evolution of mode-locking dynamics as the α-BTW-AOM feedback-frequency, which helps further understand the FSF dynamics by directly generating visible ultrashort pulses.

High‐Pressure Modulation of NIR Luminescence in Cr3+‐Doped Cs2AgInCl6 Double Perovskites: The Role of Ultrafast Energy Transfer

AbstractLead‐free halide double perovskites (DPs) have attracted much attention due to their potential applications in the field of near‐infrared (NIR) optoelectronics. However, regulating the NIR luminescence properties of such materials remains a challenge. In this study, it is found that Cs2AgInCl6:Cr3+ DPs exhibit a broad NIR emission under ultraviolet excitation, with a peak value of 980 nm. In the range of 9 GPa, the emission intensity increases significantly. As the pressure increases, a transition from broadband NIR emission to narrow‐line emission occurs, accompanied by a decrease in the full width at half maximum (FWHM) from 260 to 40 nm, attributed to increased local structural asymmetry and enhanced crystal field around Cr3+ ions. Ultrafast transient absorption experiments reveal the changes of energy transfer under pressure and the emergence of new radiation channels, demonstrating the dynamic evolution of emission characteristics under high pressure. The research results deepen the understanding of high pressure luminescence in Cr3⁺‐doped halide DPs, providing strategies for designing highly efficient NIR emitters in metal halide DPs.

Exploring Core Knowledge in Interdisciplinary Research: Insights from Topic Modeling Analysis

Although interdisciplinary research has garnered extensive attention in academia, its core knowledge structure has yet to be systematically explored. To address this gap, this study aims to uncover the underlying core knowledge topics within interdisciplinary research, enabling researchers to gain a deeper understanding of the knowledge framework, improve research efficiency, and offer insights for future inquiries. Based on the Web of Science (WoS) database, this study collected 153 highly cited papers and employed the LDA topic model to identify latent topics and extract the knowledge structure within interdisciplinary research. The findings indicate that the core knowledge topics of interdisciplinary research can be categorized into four major areas: the knowledge framework and social impact of interdisciplinary research, multidisciplinary approaches in cancer treatment and patient care, Covid-19 multidisciplinary care and rehabilitation, and multidisciplinary AI and optimization in industrial applications. Moreover, the study reveals that AI-related interdisciplinary research topics are rapidly emerging. Through an in-depth analysis of these topics, the study discusses potential future directions for interdisciplinary research, including the cultivation and development of interdisciplinary talent, evaluation systems and policy support for interdisciplinary research, international cooperation and interdisciplinary globalization, and AI and interdisciplinary research optimization. This study not only uncovers the core knowledge structure of interdisciplinary research but also demonstrates the effectiveness of the LDA topic model as a data mining tool for revealing key topics and trends, providing practical tools for future research. However, this study has two main limitations: the time lag of highly cited papers and the dynamic evolution of interdisciplinary research. Future research should address these limitations to further enhance the understanding of interdisciplinary research.

Gramian angular field-based state-of-health estimation of lithium-ion batteries using two-dimensional convolutional neural network and bidirectional long short-term memory

Accurate assessment of the state of health (SOH) of Lithium-ion batteries (LIBs) is crucial for ensuring the stability and reliability of associated equipment. However, in practical applications, current feature extraction techniques face challenges due to process complexity and the difficulty of models in capturing the dynamic evolution of time series data. This study introduces an advanced method for predicting LIBs SOH by integrating two-dimensional (2D) convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with the Gramian angular field (GAF) technique. Health indicators (HIs) are derived from the incremental capacity and charging voltage curves, which are transformed into two-dimensional images using GAF. Bayesian optimization determines the optimal hyperparameters for the model, which uses image data of two health factors as inputs. The results demonstrate that the proposed dual-channel(CH2) image data input model, combined with voltage data during the charging phase, achieves superior performance, with lower errors and higher accuracy, evidenced by an average root mean square error (RMSE) of 0.0112 and an average mean absolute error (MAE) of 0.0087. The model's generalization capability and comparative analysis with existing methodologies affirm its practical significance.