Complex Changes Research Articles

A corpus-based dependency study of the syntactic complexity in the connected speech of Alzheimer’s disease

ABSTRACT Background While numerous studies observed notable changes in syntactic complexity among individuals with Alzheimer’s disease (AD), these studies predominantly concentrated on isolated internal structures of language but rarely defined the syntactic complexity variation in AD on a continuum. Given that working memory load exhibits a continuous rather than binary pattern in populations and plays a crucial role in syntactic processing and generation, research on syntactic complexity changes in AD could benefit from expanding to include this perspective. Aims To examine the probabilistic distribution of syntactic complexity in AD under controlled sentence length conditions from the perspective of working memory, and to develop a comprehensive linguistic profile of syntactic complexity variation in AD by analyzing fine-grained syntactic features. Methods & procedures The corpus materials consist of descriptions based on the Cookie-Theft picture component provided by 70 individuals with AD (mean MMSE = 20.97) and 70 cognitively intact elderly individuals (mean MMSE = 29.21) from the DementiaBank clinical language transcript dataset. We employed dependency distance to quantify working memory load and syntactic complexity. Additionally, three finer-grained dependency metrics, namely adjacent dependency distribution (1dd%), mean dependency distance (MDD) and dependency direction distribution, enable a deeper interpretation of the syntactic complexity variations in AD from different perspectives. Results (1) The distribution of dependency distances in AD was similar to that in the healthy control (HC) group, both following the Zipf-Alekseev model, and the variations of the parameters within the model were analogous between the two treebanks; (2) The performance of AD patients in adjacent dependency, MDD and dependency direction differed from that of the HC group across varying sentence lengths; (3) “Simplified” and “ungrammatical” structures were the primary syntactic features in AD. Conclusions These findings confirmed the presence of syntactic impairments in AD. The decline of syntactic complexity can be attributed to the failure of the “End Weight” principle due to working memory deficits in AD patients. The study contributes to the easier identification of AD patients and more effective working memory interventions, thereby improving language production in AD patients.

Imaging analytical technique to assess gastrointestinal motility in vivo using zebrafish larvae with diabetes mellitus-like traits

In diabetes mellitus (DM), the prevalence of gastrointestinal (GI) complications, including constipation, diarrhoea, gastroparesis, and/or enteropathy, can be up to ~75%. In this study, we compared three zebrafish larvae models of DM and established an analytical protocol for GI motility. Larvae were fed with either a standard diet (SD; control), or one of three diets to induce a DM-like phenotype: excessive feeding of SD food (ED), a high-fat diet (HFD), or exposing SD-fed larvae to 30 mmol/L glucose (SDG). DM was confirmed using a body-mass index, assessment of adipose deposit areas, two glucose assays, and one insulin assay. An analytical technique, whereby GI motility was quantified using pixel differences to track displacement along the centreline of the anterior, middle, and posterior intestine (AI, MI, and PI, respectively), was developed. Our results indicated that clear DM-like traits were observed in the HFD and SGD models, but not the ED model. In the SD controls, the AI showed similar anterograde and retrograde contractions indicating normal GI mixing; the MI exhibited more prominent forward contractions, and the PI showed distinct rectal waves. Compared to the SD, the HFD and SDG models exhibited significantly increased and decreased contraction velocities and could be used as models of diarrhoea and constipation in DM, respectively, while the ED model showed comparatively little change in motility. Together, these data indicate that complex changes in GI motility are associated with diet and therapeutics used to alleviate GI complications in DM should take these into account. Ultimately, the HFD and SDG models can be used to investigate different aspects of GI motility in association to DM. Hence, zebrafish are a useful model for studying GI dysfunctions due to DM and/or DM medication side-effects.

Wired for change: Sustainable business models in the transition towards electrified road freight transport

The transition to battery electric vehicles (BEVs) challenges current logistics systems and actors’ business models, especially logistics service providers (LSP). In response, the purpose of this study is to increase the understanding of the characteristics of critical factors in business models for LSPs when adapting electrified freight. A structured interview study targeting LSPs that are operating BEVs has been carried out. Eleven factors deemed as critical were identified in business model components, pointing to a need to take a holistic view when designing business models for electrified freight. Furthermore, four themes are identified that address how characteristics of logistics systems change when BEVs are adopted. This includes changes in uncertainty and complexity. To comply with the increased dynamic, the paper proposes a conceptual framework which captures the evolvement of the critical factors when the market matures by classifying them as constant, fading, adopting, and emerging. The critical factors and their evolvement can be used as guidelines for practitioners and researchers in their development of sustainable business models in the transfer towards electrified freight.

Exploring changes in epibenthic food web structure after implementation of a water-sediment regulation scheme

The water-sediment regulation scheme (WSRS) in the Yellow River is a large-scale initiative to artificially regulate the flow of sediment to the sea, thereby increasing the flood-carrying capacity of the riverbed and reservoirs. Currently, systematic studies on ecological impacts of WSRS at ecosystem-level are still insufficient. This limitation hampers the pursuit of a ‘green’, healthy, ecosystem and sustainable fisheries. This study constructed the topological structure of food webs in the Yellow River Estuary (YRE) before, during, and after implementation of the WSRS, analyzing changes in food web complexity and key species based on fishery independent data collected in June, July, and August 2023. The results showed decreases from 59 to 52 in the number of trophic species, and from 539 to 395 in the number of feeding relationships after WSRS implementation. Increased node density, decreased link density, and decreased structural complexity index also indicated a simplification of the YRE food web structure after WSRS implementation. The relatively low value of the characteristic path length indicated that the YRE food web has high connectivity with short path lengths of trophic interaction. Based on the ranking of various topological indices, Japanese seabass (Lateolabrax japonicas) and mantis shrimp (Oratosquilla oratoria) persisted as the key species. Our research revealed limited potential ecological effects that WSRS may have on the YRE food web over a short period. The effects did not persist, and omnivorous key species were identified as being critical in contributing to overall system resilience. These omnivores with high complexity, connectivity and low path lengths allowed the food web to quickly dissipate the exogenous disruption from the WSRS. This provides a theoretical basis for assessing the future ecological health and scientific management of YRE fisheries and similar large estuaries for which sediment transport mitigation is under consideration.

Oxidative and endoplasmic reticulum stress in diabetes-related hearing loss: Protective effects of thioredoxin

Diabetes mellitus (DM) induces complex physiological changes in the inner ear environment. This study investigates the roles of oxidative stress (OS) and endoplasmic reticulum stress (ERS) in diabetic hearing loss and explores the potential of thioredoxin (Trx) in regulating OS, ERS, and apoptosis-related factors to mitigate the progression of hearing impairment. We conducted auditory and serological assessments in 63 patients with type 2 diabetes and 30 healthy controls. Type 2 diabetes models were induced in wild-type and Trx transgenic (Tg) mice, with auditory brainstem response (ABR) used to evaluate hearing changes. Cochlear tissues were isolated to analyse markers of apoptosis, OS, and ERS. Both patients with diabetes and mouse models exhibited hearing loss, alongside increased serum levels of Trx1, TXNIP, and AOPP, indicating oxidative damage. H&E and succinate dehydrogenase (SDH) staining revealed varying degrees of hair cell loss from the base to the apex of the cochlea in diabetic mice, with decreased expression of the hair cell protein prestin gene. Notably, Tg mice showed significant delay in hearing loss progression. In vitro, advanced glycation end-products (AGEs) induced OS and ERS in cochlear-like HEI-OC1 cells, while Trx overexpression enhanced Nrf2 activity, alleviating AGE-induced cellular stress. In conclusion, Trx exhibits protective effects against diabetes-related hearing loss, potentially by enhancing Nrf2/HO-1/SOD2 function to reduce OS and ERS.

Identifications of Complex Fracture Geometry and Changing Drainage Radius in Tight Gas Reservoirs

Identifications of Complex Fracture Geometry and Changing Drainage Radius in Tight Gas Reservoirs

Enhanced Stability, Quality and Flavor of Bread through Sourdough Fermentation with Nisin-Secreting Lactococcus lactis NZ9700

Sourdough bread is traditionally made fermenting a starter with high-gluten flour and water among other ingredients. However, the stability and safety of naturally fermented sourdough cannot always be fully guaranteed. In this study, Lactococcus lactis NZ9700, which actively secretes the bacteriocin nisin, was employed for sourdough fermentation and bread production. Three control groups were established to explore the complex and diverse changes occurring during sourdough fermentation, including a non-acidified dough group, a chemically acidified dough group, and a sourdough group fermented with L. lactis NZ9000 (which does not secrete nisin). The results demonstrate that fermentation with L. lactis leads to a rapid increase in colony-forming units (CFU) , stabilizing the pH of the sourdough at approximately 4.2 within 12 h. The in situ production of acids, sugars, and other metabolites during sourdough fermentation has a significant impact on the dough's microstructure. Microstructural analysis reveals that sourdough fermentation results in a more organized gluten network, with the L. lactis NZ9700 group exhibiting a more refined microstructure and fewer prominent starch granules. Furthermore, L. lactis NZ9700 produces stable nisin during fermentation, which persists even after baking, providing continuous antimicrobial protection to the final product. This reduced fungal contamination and extended the shelf life of the bread. Last, the growth of L. lactis NZ9700 during the early stages of fermentation provides more alcohols and ketones to the breads, endowing the sourdough breads with richer and more complex flavor profiles.

Responses of vegetation dynamics to complex environmental changes in the Runoff Producing area of the World’s Sixth Longest River: Evolution, Identification, and prediction

Vegetation plays a crucial role as a resilient safeguard for both natural ecosystems and human livelihoods in the source area of great rivers. Analyzing the variability of vegetation cover (VC) within the context of various complex drivers holds significant practical importance. In this study, an Evolution-Identification-Prediction (EIP) framework was developed to achieve the integrated assessment of vegetation dynamics and to identify its response to environmental changes. It is designed not only to display spatio-temporal features of vegetation change but also to emphasize determining the linkages between climate change (i.e., meteorological elements, climatic extremes, atmospheric circulation), human activities, anthropogenic factors (i.e., population, gross domestic product, land-use type), and vegetation dynamics. Furthermore, future patterns of vegetation were estimated by extracting information on historical changes in VC. In the above process, various methods and novel ideas with strong applicability have been proposed or introduced to achieve the stated objective. Then, the driving mechanisms of environmental changes on vegetation dynamics in the Upper Yellow River basin (UYRB) of China were examined across multiple dimensions, scales, and aspects. Results show that: a) complex correlation between NDVI and climate change appears, which is reflected in the differences of regions, factors, and correlation intensity; b) R10, PRCP, SDII and CWD are the main influences among extreme climate indices with explanatory power above 0.3; c) climate change and human activity jointly dominate vegetation dynamics in the UYRB, with the impact of human activity obviously enhanced; d) human activity has an overall positive effect on VC, and higher NDVI is generally found in forested, low population density and gross domestic product regions; e) the VC of the UYRB is projected to further improve under the combined influence of environmental factors, with the rate of high VC increase from 21.87% (2019) to 30.44% (2030). The findings can support the implementation of vegetation conservation and restoration initiatives to address the risks posed by environmental change.

Haplotype-Based Approach Represents Locus Specificity in the Genomic Diversification Process in Humans (Homo sapiens)

Background/Objectives: Recent progress in evolutionary genomics on human (Homo sapiens) populations has revealed complex demographic events and genomic changes. These include population expansion with complicated migration, substantial population structure, and ancient introgression from other hominins, as well as human characteristics selections. Nevertheless, the genomic regions in which such evolutionary events took place have remained unclear. Methods: Here, we focused on eight loci containing the haplotypes that were previously presented as atypical for the mutation pattern in sequence and/or geographic distribution pattern with the model of recent African origin, which constitute two major clusters: African only, and global. This was the consensus model before information regarding introgression from Neanderthal (Homo neanderthalensis) was available. We compared diversity in identical datasets of the modern human population genome, with the 1000 Genomes project among them. Results/Conclusions: This study identified representative genomic regions that show traces of various demographic events and genomic changes that modern humans have undergone by categorizing the relationships in sequence similarity and in worldwide geographic distribution among haplotypes.

A probabilistic integration of LSTM and Gaussian process regression for uncertainty-aware reservoir water level predictions

ABSTRACT Reservoir-level forecasting, while being crucial for optimal operation, is challenged by complex physical processes and changing climate conditions. Machine learning approaches offer deterministic predictions but often neglect system physics and uncertainty. This article presents a probabilistic data-driven approach combining long short-term memory (LSTM) and Gaussian process regression (GPR) to provide both point forecasts and uncertainty estimates. The hybrid model leverages LSTM’s fitting capabilities with GPR’s robust Bayesian frameworks for uncertainty estimation in non-linear problems, offering accurate predictions without extensive high-fidelity modelling, and avoiding frequent training and parameter optimization. Evaluation with real reservoir data from India shows the model’s superiority over the vanilla LSTM for both univariate and multivariate scenarios. The proposed model achieved a Nash-Sutcliffe efficiency of 0.97 to 0.98, a mean biased error of −0.5634 to −1.0314 for 10-day forecasts, and a continuous ranked probability score of 5.80 and 1.87 for the Bhakra and Pong reservoirs, respectively.

Effects of an Innovative High-Fat Diet on Intestinal Structure, Barrier Integrity, and Inflammation in a Zebrafish Model of Visceral Obesity

High-fat diet (HFD)-induced obesity is a global health concern associated with gastrointestinal disorders. While mammalian models have elucidated the effects of a HFD on intestinal structure and function, its impact on zebrafish, a crucial model for studying diet-induced obesity and gastrointestinal dysfunction, remains inadequately characterized. This study investigated the influence of a HFD on zebrafish intestinal morphology, tight junction (TJ) protein expression, and inflammatory markers. Zebrafish fed a control diet or HFD with 40% or 60% fat exhibited significant alterations in intestinal morphology, with increased villi number but reduced villi width and length, suggesting compensatory responses to dietary stress. TJ protein expression (Claudin 2, Claudin 3, and Claudin 10) showed complex changes, particularly in the HFD60 juvenile group, indicating a multifaceted response in barrier integrity. Pro-inflammatory cytokine IL-6 and TNF-α levels were lower in both the juvenile and adult HFD60 groups than in the HFD40 and control groups, while elevated anti-inflammatory IL-10 levels in HFD60 adult zebrafish suggested activation of compensatory mechanisms. These findings highlight zebrafish as a valuable model for studying the effects of HFD on intestinal health and provide insights into the relationship between dietary fat, gut dysfunction, and inflammation.

Dynamical changes in seismic properties prior to, during, and after the 2014–2015 Holuhraun eruption, Iceland

Abstract. When a volcano is monitored using only a single discipline or a single seismic station, it becomes important to harvest information from the limited data set. Changes in the seismic complexity could reveal a dynamic change due to magma propagation. We evaluated permutation entropy (PE) and phase permutation entropy (PPE) to monitor the 2014–2015 Holuhraun eruption in Iceland. These methods provide fast and robust quantification of time series complexity. We additionally calculated the instantaneous frequency (IF), commonly used to monitor the frequency changes in a non-stationary signal; the root-mean square (RMS); and the root-median square (RMeS) of the seismic amplitude. We observed distinct changes in the temporal variation in PE, PPE, and IF, which are consistent with the changing state from quiescence to magma propagation and then to eruption. During the eruption, PE and PPE fit the lava discharge rate, showing their potential to forecast the duration of the eruption. While one parameter may be more sensitive to one stage, the other may respond better to another stage. Therefore, combining them may provide more reliable information. Cluster analysis of these combined parameters shows clusters consistent with the expert interpretation, confirming the power of these parameters to distinguish different eruption stages.

Research on the Construction of Music Recognition Model Based on Improved CNN Convolutional Neural Network

Abstract. In the context of today's informationized society, music recognition technology within the field of intelligent audio processing has received extensive attention and become a research focus. However, traditional music recognition techniques are difficult to adapt to the complex changes of music signals due to the inherent defects in feature extraction and model construction. To address these difficulties, this study constructs an innovative music recognition algorithm, which is based on an optimized convolutional neural network (CNN). The efficacy of feature extraction is enhanced by fusing the hash convolutional neural network (Hash-CNN), and the temporal data is processed using the long short-term memory network (LSTM) to improve the accuracy of recognition. In the preprocessing stage of music signals, we applied various noise reduction and normalization means to enhance the data quality to a high standard. According to the experimental data, the model performs well in recognizing complex music segments, especially when analyzing multi-level and multi-dimensional music features, it shows strong robustness. Meanwhile, compared with traditional techniques, the model proposed in this study shows significant improvement in both recognition efficiency and accuracy, confirming its great potential and broad prospect in practical applications.

Pengembangan Sistem Informasi Akuntansi untuk Meningkatkan Efisiensi dan Akurasi: Studi Kasus pada Implementasi Perubahan Program Akuntansi

This article discusses the development of accounting information systems to improve efficiency and accuracy, focusing on a case study of the implementation of accounting program changes. Previous studies have shown that effective accounting information systems can significantly improve the quality of accounting and managerial information. The implementation of accounting program changes often involves the integration of information technology to improve the process of financial reporting, internal control, and analysis of company performance. Previous studies have shown that companies that adopt information technology in their accounting systems have a competitive advantage in managing and analyzing financial information more efficiently. Important factors in the development of accounting information systems are the need to ensure data accuracy, reporting speed, and real-time availability of information for internal and external stakeholders. This study also explores the positive impact of the use of accounting information systems on the company's operational efficiency and strategic decision making. Successful implementations show that modern accounting information systems are able to integrate various business functions, such as finance, inventory, and manufacturing, to improve the coordination and effectiveness of the organization as a whole. Thus, the development of accounting information systems not only improves the company's internal processes but also enhances the company's adaptability to rapid and complex changes in the business environment. This study provides a strong theoretical foundation for understanding the importance of information technology integration in the context of accounting program changes to achieve higher efficiency and accuracy goals.

Deep learning‐based inversion with discrete cosine transform discretization for two‐dimensional basement relief imaging of sedimentary basins from observed gravity anomalies

AbstractSedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.

Research on Irrigation Grade Discrimination Method Based on Semantic Segmentation

As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased.

Production and properties of powder and pigments from bacuri (Platonia insignis Mart) rinds

Bacuri (Platonia insignis Mart) pulp content does not reach 20 % of the fruit's weight, rind corresponds to 64–70 % of the raw material, and presents phenolic compounds which suggests the possibility of obtaining colored compounds by complexation with metals. This work studied the bacuri rind powder without treatment, as we as with different metals (calcium, iron, zinc, selenium, copper, magnesium and aluminum) to evaluating which ones had potential for application after the complexation process. The best results in complexation, FT-IR spectroscopy, and color change were obtained with iron and aluminum salts. Temperature and pH had a strong influence on the color of the pigments. At room temperature, complexed with iron and aluminum salts in the range of pH 5 and 6 showed a significant color difference (p < 0.05) from the control (without treatment). The results of colors in the terracotta range (yellow to reddish brown) can be especially recommended for future studies for cosmetics, as well as plant-based doughs, or other chemical applications, with prospects for replacing yellow tartrazine and caramel, in specific application, according its properties.

Fibrillarin homologs regulate translation in divergent cell lineages during planarian homeostasis and regeneration.

Tissue homeostasis and regeneration involve complex cellular changes. The role of rRNA modification-dependent translational regulation in these processes remains largely unknown. Planarians, renowned for their ability to undergo remarkable tissue regeneration, provide an ideal model for the analysis of differential rRNA regulation in diverse cell types during tissue homeostasis and regeneration. We investigated the role of RNA 2'-O-methyltransferase, Fibrillarin (FBL), in the planarian Schmidtea mediterranea and identified two FBL homologs: Smed-fbl-1 (fbl-1) and Smed-fbl-2 (fbl-2). Both are essential for planarian regeneration, but play distinct roles: fbl-1 is crucial for progenitor cell differentiation, while fbl-2 is important for late-stage epidermal lineage specification. Different 2'-O-methylation patterns were observed upon fbl-1 and fbl-2 knockdown, suggesting their roles in translation of specific mRNA pools during regeneration. Ribo-seq analysis further revealed differing impacts of fbl-1 and fbl-2 knockdown on gene translation. These findings indicate divergent roles of the duplicate fbl genes in specific cell lineage development in planarians and suggest a role of rRNA modifications in translational regulation during tissue maintenance and regeneration.

Perturbational and variational energy decomposition analysis on hydrogen bonds of coordinated glycine with water molecule.

Three types of hydrogen bonds of coordinated glycine and water had been investigated: NH/O of α-amino group, O1/HO involving oxygen coordinated to the metal ion (O1), and O2/HO involving α-carbonyl oxygen (O2). Various glycine complexes were investigated: octahedral cobalt(III) and nickel(II), square pyramidal copper(II), and square planar copper(II), palladium(II), and platinum(II) complexes. Nature of these three hydrogen bond types was analysed using symmetry-adapted perturbation theory (SAPT) and variational energy decomposition analysis (EDA) method (TPSS-D3/def2-TZVPP). The results of the EDA decomposition are in good agreement with the reliable SAPT2+3/def2-TZVPP and its total interaction values with CCSD(T)/CBS energies. Electrostatic interaction is generally the dominant attractive energy term in most of the interactions, followed by orbital relaxation, and lastly dispersion as the weakest. We compared EDA results of various complexes to determine the effects of complex charge, metal oxidation, coordination, and atomic number on the energy decomposition terms. The complex charge influences the values of decomposition terms the most, followed by metal oxidation and coordination number, while atomic number effects them the least. All complex and metal changes have a more significant effect on the results of NH/O and O1/HO then O2/HO interactions, due to its location further away from the metal ion.

Binary response of runoff generation and erosion processes to karst vegetation-soil-rock structure and its hydrodynamic mechanisms

Rocky desertification has formed a unique vegetation-soil-rock structure (VSRS) on the underlying surface of karst area, resulting in complex surface processes. Despite their significance, the response of runoff generation and erosion processes at binary interfaces to VSRS is still poorly understood. In this study, artificial simulated rainfall experiments were carried out on karst micro-plots with binary structure. Driven by rainfall intensity (R) and VSRS, the runoff and sediment output processes and its hydrodynamic mechanisms at the binary interface were studied. The results showed that the surface runoff rate (SRr) decreased with the increase of moss cover (MC) or rock exposure rate (RE), and increased with the increase of soil thickness (ST), with an amplitude more than or equal to 4.98%. With the increase of R, the surface runoff yield (SR) and runoff leakage (RL) showed an increasing trend, but the SR increased even more. This indicated that SR is more susceptible to R. Surface sediment yield (SS) showed complex changes with increasing test gradients, while sediment leakage (SL) showed a single increase (r ≥ 0.46, P < 0.05). Runoff and sediment leakage occurred at different VSRS, and the initial rate was greater than 0.09 L·min−1 or 0.02 g·min−1. This suggested that soil and water leakage in karst areas may be widespread. Proportion of SR and SS increased with increasing R, and became the main loss pathway (>54.77 %) during heavy rainfall (120 mm/h). Prediction performance of hydrodynamic parameters for SRr was 12.25 % higher than that for surface sediment rate (SSr), and V was the best factor for SRr (R2 > 0.84, P < 0.01). This study further enriches the theory of hydrological processes in karst rocky desertification areas, and provides important support for decision-making on soil and water conservation and ecological restoration.