Modeling Experiments Research Articles

Hydrodynamic performance assessment of emerged and sub-merged semicircular breakwaters under random waves: An experimental and empirical study.

Mangrove ecosystems and other coastal protection structures are essential barriers protecting coastal populations from the damaging effects of wave energy and increasing sea levels. This study uses a semicircular breakwater (SBW) model in an effort to develop coastal protection measures. The hydrodynamic characteristics of the SBW under random wave conditions, including the transmission coefficient, reflection coefficient, and energy loss coefficient, were thoroughly investigated using physical model experimentation. The main objectives encompass understanding the behavior of the SBW model, developing empirical equations to estimate hydraulic characteristics, and enhancing coastal protection structures to facilitate the preservation and expansion of mangrove ecosystems. Hydrodynamic features of the SBW model were assessed across a spectrum of wave conditions. Experimental testing in a wave flume encompassed a range of relative water depths (d/h), including d/h = 0.667 for an emerged SBW, d/h = 1.000 for an alternatively submerged SBW, and fully submerged conditions for d/h = 1.333 and 1.667. Wave steepness (Hi/L) varied from 0.02 to 0.06, and wave periods ranged from 0.8 to 2.5 seconds. Notably, analysis of an emerged SBW with d/h = 0.667 revealed superior wave attenuation compared to d/h = 1.000, 1.333, and 1.667 configurations.

Climate change rivals fertilizer use in driving soil nitrous oxide emissions in the northern high latitudes: Insights from terrestrial biosphere models.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their exposure to rapid rates of regional warming and changing hydrology due to climate change. In this study, we used an ensemble of six process-based terrestrial biosphere models (TBMs) from the Global Nitrogen/Nitrous Oxide Model Intercomparison Project (NMIP) to quantify soil N2​O emissions across the NHL during 1861-2016. Factorial simulations were conducted to disentangle the contributions of key driving factors, including climate change, nitrogen inputs, land use change, and rising atmospheric CO2 concentration​, to the trends in emissions. The NMIP models suggests NHL soil N2O emissions doubled from 1861 to 2016, increasing on average by 2.0±1.0 Gg N/yr (p<0.01). Over the entire study period, while N fertilizer application (42 ± 20 %) contributed the largest share to the increase in NHL soil emissions, climate change effect was comparable (37 ± 25 %), underscoring its significant role. In the recent decade (2007-2016), anthropogenic sources contributed 47±17% (279±156 Gg N/yr) of the total N2O emissions from the NHL, while unmanaged soils contributed a comparable amount (290±142 Gg N/yr). The trend of increasing emissions from nitrogen fertilizer reversed after the 1980s because of reduced applications in non-permafrost regions. In addition, increased plant growth due to CO2 fertilization suppressed simulated emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions. The rigorous interplay between process modeling and field experimentation will be essential for improving model representations of the mechanisms controlling N2O fluxes in the Northern High Latitudes and for reducing associated uncertainties.

Quantify the Compound Effects caused by the interactions between inland river system and coastal processes in Hurricane Coastal Flooding through Controlled Hydrodynamic Modeling Experiments

Quantify the Compound Effects caused by the interactions between inland river system and coastal processes in Hurricane Coastal Flooding through Controlled Hydrodynamic Modeling Experiments

Metal Ion and Antibiotic Co-loaded Nanoparticles for Combating Methicillin-Rresistant Staphylococcus aureus-Induced Osteomyelitis.

Methicillin-resistant Staphylococcus aureus (MRSA) causes osteomyelitis (OM), which seriously threatens public health due to its antimicrobial resistance. To increase the sensitivity of antibiotics and eradicate intracellular bacteria, a Zn2+ and vancomycin (Van) codelivered nanotherapeutic (named Man-Zn2+/Van NPs) was fabricated and characterized via mannose (Man) modification. Man-Zn2+/Van NPs exhibit significant inhibitory activity against extra- and intracellular MRSA and obviously decrease the minimum inhibitory concentration of Van. Man-Zn2+/Van NPs can be easily internalized by MRSA-infected macrophages and significantly accumulated in infected bone via Man-mediated targeting. In vivo experiments in a mouse OM model verified that Man-Zn2+/Van NPs significantly reduce the extra- and intracellular MRSA burden, improve gait patterns, increase bone mass, and decrease inflammatory cytokine expression. The antibacterial mechanism of Man-Zn2+/Van NPs includes destruction of the MRSA membrane, degeneration of intracellular proteins and DNA, inhibition of MRSA glycolysis, and intervention in the energy metabolism of bacteria. Overall, this metal-antibiotic nanotherapeutics strategy provides new insight for combating extra- and intracellular infections caused by MRSA-induced OM.

Rigorous mathematical modeling and physiological experimentation reveal that Ralstonia wilt pathogens consume an in planta diet of amino acids with a dash of sugar.

Rigorous mathematical modeling and physiological experimentation reveal that Ralstonia wilt pathogens consume an in planta diet of amino acids with a dash of sugar.

Up IGF-I via High-toughness Adaptive Hydrogels for Remodeling Growth Plate of Children

Abstract The growth plate is crucial for skeletal growth in children, but research on repairing growth plate damage and restoring growth is limited. Here, a high-toughness adaptive dual-crosslinked hydrogel designed to mimic the growth plate's structure, supporting regeneration and bone growth. Composed of aldehyde-modified bacterial cellulose (DBNC), methacrylated gelatin (GelMA), and sodium alginate (Alg), the hydrogel is engineered through ionic bonding and Schiff base reactions, creating a macroporous structure. This structure can transform into a denser form by binding with calcium ions. In vitro, the loose macroporous structure of the hydrogels can promote chondrogenic differentiation, and when it forms a dense structure by binding with calcium ions, it also can activate relevant chondrogenic signaling pathways under the influence of IGF-1, further inhibiting osteogenesis. In vivo experiments in a rat model of growth plate injury demonstrated that the hydrogel promoted growth plate cartilage regeneration and minimized bone bridge formation by creating a hypoxic microenvironment that activates IGF-1-related pathways. This environment encourages chondrogenic differentiation while preventing the undesired formation of bone tissue within the growth plate area. Overall, the dual-crosslinked hydrogel not only mimics the growth plate's structure but also facilitates localized IGF-1 expression, effectively reshaping the growth plate's function. This approach represents a promising therapeutic strategy for treating growth plate injuries, potentially addressing challenges associated with skeletal growth restoration in pediatric patients.

Unstable relationship between tropical Indian Ocean SST and spring drought on the Loess Plateau modulated by interdecadal variability of ENSO

Abstract The Loess Plateau (LP) is recognized as a region highly vulnerable to climate change, particularly susceptible to spring droughts. Spring drought intensity is crucial as it significantly impacts crop growth and the occurrence of dust storms. However, the underlying mechanisms of spring drought in LP remain unclear. This study investigates the relationship between spring drought intensity in the LP and interannual sea surface temperature anomalies (SSTAs) in the tropical Indian Ocean (SST_TIO). We found that this relationship is negatively correlated but exhibits instability. It was significant during 1963–2001, a time characterized by homogeneous SST_TIO changes, and became insignificant during 2002–2021, during which SST_TIO changes were inhomogeneous. Homogeneous SST_TIO changes appear to stimulate atmospheric wave trains emanating from North Africa and the Arabian Peninsula, influencing precipitation patterns in the LP via the anomalous height in Northeast Asia. In contrast, inhomogeneous SST_TIO changes do not have this effect. Additionally, the impact of homogeneous SST_TIO changes on LP's spring precipitation during 1963–2001 was slightly enhanced by synergistic SSTAs in the tropical eastern Pacific-Atlantic oceans. sensitivity experiments of numerical model confirm the above physical processes. Moreover, these synergistic changes in the TIO and tropical Pacific-Atlantic are modulated by the interdecadal variability of the preceding winter El Niño-Southern Oscillation (ENSO). SSTAs in Indian Ocean exhibit more homogeneous distributions and a closer linkage with tropical Pacific-Atlantic during the periods with stronger ENSO variability. Conversely, when ENSO variability is weak, inhomogeneous SSTAs of Indian Ocean have a weaker relation with tropical Pacific-Atlantic regions.

Toll-like receptor 4 pathway evolutionary trajectory and functional emergence.

Toll-like receptors 4 (TLR4) recognize lipopolysaccharides (LPS) from bacteria as their conventional ligands and undergo downstream signaling to produce cytokines. They mediate the signaling either by the TIRAP-MyD88 complex or by the TRAM-TRIF complex. The MyD88 pathway is common to all other TLRs, whereas the TRAM-TRIF complex is largely exclusive to TLR4. Here we study the TIR domain of TRAM and TRIF ortholog proteins that are crucial for downstream signaling. Our previous work on pan-genome-wide survey, indicates Callorhincus milli to be the ancestral organism with both TRAM and TRIF proteins. To gain a deeper insight into the protein function and to compare them with Homo sapiens adaptor proteins, we modeled the docking of the TRAM-TRIF complex of representative organisms across various taxa. These modeling experiments provide insights to ascertain a possible interaction surface and calculate the energetics and electrostatic potential of the complex. Furthermore, this enables us to employ normal mode analysis (NMA) to examine fluctuating, interacting, and other specific residue clusters that could have a role in protein functioning in both C. milli and H. sapiens. We also performed molecular dynamics simulations of these complexes and cross-validated the functionally important residues using network parameters. We compared the stoichiometry of TRAM-TRIF complexes and found that the tetrameric models (TRAM and TRIF dimer) were more stable than the trimeric model (TRAM dimer and TRIF monomer). While the critical residues of TIRAP, TRIF, and MyD88 were preserved, we also found that the important residues of TRAM signaling were not conserved in C. milli. This suggests the presence of functional TIRAP-MyD88-mediated TLR4 signaling and TRIF-mediated TLR3 signaling in the ancestral species. The overall biological function of this signaling domain appears to be gradually acquired through the orchestration of several motifs through an evolutionary scale.

The Optimization of the Rear Guide Vane of a Bulb Tubular Pump Based on Orthogonal Tests

Bulb tubular pumps have been widely used in hydraulic engineering because of their compact structure, easy maintenance, high adaptability, and other characteristics. In this paper, the performance optimization of the bulb tubular pump in the South-to-North water diversion project is studied, as well as the influence of the design of the rear guide vane structure on the hydraulic efficiency of the pump. This study takes a certain type of bulb tubular pump as its research object, optimizing the rear guide vane. Firstly, the accuracy of the numerical simulation method is verified using grid convergence analysis and model experimentation. The orthogonal experimental design method is used to optimize the design, and the range analysis results show that the blade wrap angle has the most significant influence on the hydraulic efficiency and head. Finally, the optimization results under a 0° impeller setting angle were verified by numerical analysis, and the hydraulic efficiency of the optimized pump was increased by 0.7%, 0.88%, and 1.1% under low flow, design flow, and high flow, respectively. By introducing entropy generation theory for inflow analysis, the reduction in energy loss in the pump is proven, thus verifying the effectiveness of the optimization. Through the optimization, the separation fluid phenomenon on the guide vane surface is improved, the vortex scale is reduced, and the flow field in the pump is improved to a certain extent.

Mouse models for metabolic health research: molecular mechanism of exercise effects on health improvement through adipose tissue remodelling.

Exercise provides health benefits to multiple metabolic tissues through complex biological pathways and interactions between organs. However, investigating these complex mechanisms in humans is still limited, making mouse models extremely useful for exploring exercise-induced changes in whole-body metabolism and health. In this review, we focus on gaining a broader understanding of the metabolic phenotypes and molecular mechanisms induced by exercise in mouse models. We first discuss the differences in adaptations induced by aerobic and resistance exercise, and compare voluntary wheel running and forced treadmill exercise, the two main methods of aerobic exercise research in mice, to show the similarities and differences between the same aerobic exercise but different methods, and their impact on experimental outcomes. The effects of exercise on metabolic phenotypes, including alleviation of obesity and metabolic disorders, and the mechanisms involved in adipose tissue remodelling and browning are explored, as well as the role of the gut microbiota in mediating the physiological responses and metabolic effects of exercise. Understanding these molecular mechanisms and methodological aspects of exercise experiments in mouse models can serve as a valuable template for the design of future basic research in exercise physiology and will provide a strong scientific evidence base for optimizing the design of exercise intervention programmes for metabolic health.

An Integrated Stacking Ensemble Model for Natural Gas Purchase Prediction Incorporating Multiple Features

Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models—Informer, multiple linear regression (MLR), and support vector regression (SVR)—for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including R2, MRE, and RMSE. Notably, the R2 values improved by 4–15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model’s predictive strength in the energy sector.

A Method for Imaging the Ischemic Penumbra with MRI using IVIM.

In acute ischemic stroke, the amount of "local" CBF distal to the occlusion, i.e. all blood flow within a region whether supplied antegrade or delayed and dispersed through the collateral network, may contain valuable information regarding infarct growth rate and treatment response. DSC CBF using a local arterial input function (AIF) is one method of quantifying local CBF (local-qCBF) and correlates with collaterals. Similarly, intravoxel incoherent motion MRI (IVIM) is "local", with excitation and readout in the same plane, and a potential alternative way to measure local-qCBF. The purpose of this work was to compare IVIM local-qCBF against DSC local-qCBF in the ischemic penumbra, compare measurement of perfusion-diffusion mismatch (PWI/DWI), and examine if local-qCBF may improve prediction of final infarct. Eight experiments in a pre-clinical canine model of middle cerebral artery occlusion were performed; native collateral circulation was quantified via x-ray DSA 30 minutes post-occlusion, and collateralization was subsequently enhanced in a subset of experiments with simultaneous pressor and vasodilator. IVIM and DSC MRI were acquired 2.5hr post-occlusion. IVIM was post-processed to return local-qCBF from fD*, water transport time (WTT) from D*, diffusion from D, and the PWI/DWI mismatch. These were compared with DSC parameters processed first with a standard global-AIF and then with a local-AIF. These DSC parameters included time-to-maximum, local MTT, standard-qCBF, local-qCBF and PWI/DWI mismatch. Infarct volume was measured with DWI at 2.5hrs and 4hrs post-occlusion. 2.5hr post-occlusion, IVIM local-qCBF in the non-infarcted ipsilateral territory strongly correlated with DSC local-qCBF (slope=1.00, R2=0.69, Lin's CCC=0.71). Correlation was weaker between IVIM local-qCBF and DSC standard-qCBF (R2=0.13). DSC localqCBF and IVIM local-qCBF in the non-infarcted ipsilateral territory both returned strong prediction of final infarct volume (R2=0.78, R2=0.61 respectively). DSC standard-qCBF was a weaker predictor (R2=0.12). The hypoperfused lesion from DSC local-qCBF and from IVIM local-qCBF both predicted final infarct volume with good sensitivity and correlation (slope=2.08, R2=0.67, slope=2.50, R2=0.68 respectively). The IVIM PWI/DWI ratio was correlated with infarct growth (R2=0.70) and WTT correlated with DSC MTT (R2=0.60). Non-contrast IVIM measurement of local-qCBF and PWI/DWI mismatch may include collateral circulation and improve prediction of infarct growth. AIF: arterial input function, IVIM: intravoxel incoherent motion, qCBF: quantitative cerebral blood flow, WTT: water transport time, MCAO: middle cerebral artery occlusion, MD: mean diffusivity.

The Role of Atmospheric Composition in Defining the Habitable Zone Limits and Supporting E. coli Growth.

Studying exoplanet atmospheres is essential for assessing their potential to host liquid water and their capacity to support life (their habitability). Each atmosphere uniquely influences the likelihood of surface liquid water, defining the habitable zone (HZ)-the region around a star where liquid water can exist. However, being within the HZ does not guarantee habitability, as life requires more than just liquid water. In this study, we adopted a two-pronged approach. First, we estimated the surface conditions of planets near the HZ's inner edge under various atmospheric compositions. By utilizing a 3D climate model, we refined the inner boundaries of the HZ for planets with atmospheres dominated by H2 and CO2 for the first time. Second, we investigated microbial survival in these environments, conducting laboratory experiments on the growth and survival of E. coli K-12, focusing on the impact of different gas compositions. This innovative combination of climate modeling and biological experiments bridges theoretical climate predictions with biological outcomes. Our findings indicate that atmospheric composition significantly affects bacterial growth patterns, highlighting the importance of considering diverse atmospheres in evaluating exoplanet habitability and advancing the search for life beyond Earth.

Modeling and Evaluation of Attention Mechanism Neural Network Based on Industrial Time Series Data

Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause safety accidents. Therefore, this paper proposes an improved transformer model based on a self-attention mechanism for modeling industrial processes. Then, an evaluation mechanism based on root mean square error (RMSE) and Kullback–Leibler divergence (KLD) metrics is designed to obtain more appropriate model parameters. The Variational Auto-Encoder (VAE) network is used to compute the associated KLD. Finally, a real nonlinear dynamic process in the petrochemical industry is modeled and evaluated using the proposed methodology to predict the time series data of the process. This study demonstrates the validity of the proposed transformer model and illustrates the versatility of using an integrated modeling, evaluation, and prediction scheme for nonlinear dynamic processes in process industries. The scheme is of great importance for the field of industrial soft measurements as well as for deep learning-based time series prediction. In addition, the issue of a suitable time domain for the prediction is discussed.

Western disturbances and climate variability: a review of recent developments

Abstract. Western disturbances (WDs) are synoptic-scale weather systems embedded within the subtropical westerly jet. Manifesting as upper-level troughs often associated with a lower-tropospheric low over western or northern India, they share some dynamical features with extratropical cyclones. WDs are most common during the boreal winter (December to March), during which they bring the majority of precipitation – both rain and snow – to the western Himalaya, as well as to surrounding areas of north India, Pakistan, and the Tibetan Plateau. WDs are also associated with weather hazards such as heavy snowfall, hailstorms, fog, cloudbursts, avalanches, frost, and cold waves. In this paper, we review recent developments in understanding WDs and their impacts. Over the last decade, recent studies have collectively made use of novel data, novel analysis techniques such as tracking algorithms, and the increasing availability of high-resolution weather and climate models. This review is separated into six main sections – structure and thermodynamics, precipitation and impacts, teleconnections, modelling experiments, forecasting at a range of scales, and paleoclimate and climate change – each motivated with a brief discussion of the accomplishments and limitations of previous research. A number of step changes in understanding are synthesised. Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. Improved observation systems have helped quantification of water security over the western Himalaya. Convection-permitting models have improved our understanding of how WDs interact with the Himalaya to trigger natural hazards. Improvements in paleoclimate and future climate modelling experiments have helped to explain how WDs and their impacts over the Himalaya respond to large-scale natural and anthropogenic forcings. We end by summarising unresolved questions and outlining key future WD research topics.

Learning Outcomes: How is the Experimentation of the AIR Learning Model?

This study examines how the experimentation of the Auditory, Intellectually, Repetition (AIR) learning model impacts students' learning outcomes in the Islamic Cultural History (SKI) subject at MTs Negeri 1 Bandar Lampung. The research is motivated by students' learning outcomes that have not met the Minimum Competency Criteria (KKM), which may be due to limitations in teaching methods and the influence of other external factors. This study employs a quantitative approach, with the population consisting of all eighth-grade students. The sample includes class VIII(A) as the experimental group and class VIII(B) as the control group. The data were analyzed using an independent t-test, yielding a final significance value of 0.070. This indicates that there is no statistically significant difference between the experimental and control groups. These results suggest that the AIR learning model does not produce significantly different learning outcomes compared to conventional teaching methods in this context. Therefore, further research is needed to optimize the application of the AIR learning model in the Islamic Cultural History (SKI) subject.

Biomimetic bioreactor for potentiated uricase replacement therapy in hyperuricemia and gout.

Uricase replacement therapy is a promising approach for managing hyperuricemia and gout but is hindered by challenges such as short blood circulation time, reduced catalytic activity, and excessive hydrogen peroxide (H2O2) production. These limitations necessitate innovative strategies to enhance therapeutic efficacy and safety. We designed and synthesized RBC@SeMSN@Uri, a red blood cell-coated biomimetic self-cascade bioreactor, which encapsulates uricase (Uri) and a selenium-based nano-scavenger (SeMSN) within RBC membranes. This design aims to reduce immunogenicity, extend systemic circulation, and maintain enzymatic activity. In vitro assays were conducted to evaluate biocompatibility, anti-inflammatory effects, and oxidative stress protection. In vivo experiments in hyperuricemia and gout models assessed therapeutic efficacy, biodistribution, and biosafety. RBC@SeMSN@Uri effectively degraded uric acid (UA) into allantoin and converted H2O2 into water, preventing oxidative damage and inflammation. In vitro assays demonstrated excellent biocompatibility and reduced H2O2-induced inflammatory responses compared to free uricase. In vivo, the bioreactor prolonged circulation time, significantly reduced uric acid levels, alleviated kidney damage, and mitigated symptoms of hyperuricemia and gout. It also targeted inflamed joints, reducing swelling and inflammation in gouty arthritis models. This study presents RBC@SeMSN@Uri as a novel biomimetic strategy for enzyme replacement therapy in hyperuricemia and gout. By integrating uricase and selenium-based nano-scavenger within RBC membranes, the bioreactor addresses key limitations of traditional therapies, offering enhanced stability, reduced immunogenicity, and superior therapeutic efficacy. This platform holds potential for broader applications in protein or antibody delivery for enzyme replacement therapies in other diseases.

Potential Diagnostic Biomarkers in Heart Failure: Suppressed Immune-Associated Genes Identified by Bioinformatic Analysis and Machine Learning

Heart failure (HF) threatens tens of millions of people’s health worldwide, which is the terminal stage in the development of majority cardiovascular diseases. Recently, an increasing number of studies have demonstrated that bioinformatics and machine learning (ML) algorithms can offer new insights into the diagnosing and treating HF. To further discover HF diagnostic genes, we utilized least absolute shrinkage and selection operator (LASSO) and Support Vector Machine (SVM) to identify novel immune-related genes. The HF dataset was obtained from the gene expression omnibus (GEO) database and three candidate genes (LCN6, MUC4, and TNFRSF13C) were finally screened. In addition, the myocardial infarction (MI) modeling experiments on mice were performed to validate the expression of LCN6, MUC4, and TNFRSF13C on experimental HF mice. Altogether, these three candidate genes are promising targets for the prediction of HF with immunological perspective.

Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change

Global warming is expected to exacerbate extreme rainfall events, potentially increasing the risk of landslides. While landslides have been extensively studied, much of the focus has been on developing static frameworks for landslide susceptibility, with relatively little exploration of spatiotemporal modeling. Furthermore, previous studies have often overlooked the spatiotemporal impacts of climate change and dynamic socio-economic factors on landslide susceptibility and risk. This has resulted in a lack of understanding of how landslide risk will evolve in the future. Consequently, this study proposes a modeling approach to simulate the dynamics of landslide susceptibility and exposure population over the next 80 years. The approach involves a series of novel modeling experiments using rainfall-induced landslide data collected over the past decade in Jiangxi Province, using a GAMs that considers the effects of spatial relationships and spatio-temporal cross-validation, with an AUC of 0.885 and an error rate of 15.32%, and combining with the CMIP6 precipitation data to the direct effect of climate change on landslide susceptibility. In addition, a dynamic risk prediction model combining static and dynamic populations was developed to provide a more comprehensive understanding of future landslide impacts. This research framework serves as a scientific foundation and valuable reference for comprehending the effects of climate change on landslide hazards and their management in urban planning and risk mitigation. It aims to inform the development of more effective strategies to mitigate potential losses from future landslide risks.

Some empirical studies for the applications of fractional $ G $-Brownian motion in finance

&lt;p&gt;Since the fractional $ G $-Brownian motion (fGBm) generalizes the concepts of the standard Brownian motion, fractional Brownian motion, and $ G $-Brownian motion, while it can exhibit long-range dependence or antipersistence and feature the volatility uncertainty simultaneously, it can be a better alternative stochastic process in the financial applications. Thus, in this paper, some empirical studies for the financial applications of the fGBm were carried out, where the recent high-frequency data for some selected assets in the financial market are from the Oxford-Man Institute of Quantitative Finance Realized Library. There are two main empirical findings. One was that the H-$ G $-normal distributions associated with the fGBm are more suitable in describing the dynamics of daily returns and increments of log-volatility for these assets than the usual distributions, since they not only characterize the properties of skewness, excess kurtosis, and long-range dependence $ \left(\frac{1}{2} &amp;lt; H &amp;lt; 1\right) $ or antipersistence $ \left(0 &amp;lt; H &amp;lt; \frac{1}{2}\right) $, but also feature the volatility uncertainty. The other one was that the daily return and log-volatility both behave essentially as fGBm with different $ \underline{\sigma}^2 $ and $ \overline{\sigma}^2 $, but Hurst parameters $ H &amp;lt; \frac{1}{2} $, at any reasonable time scale. Then a generalized stochastic model for the dynamics of the assets called rough fractional stochastic volatility model driven by fGBm (RFSV-fGBm) was developed. Finally, some parameter estimates and numerical experiments for the RFSV-fGBm model were investigated and carried out.&lt;/p&gt;