Framework For Quantitative Analysis Research Articles

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Multidimensional evolutionary analysis of China’s BIM technology policy based on quantitative mapping

ABSTRACT In the wave of the digital era, Building Information Modelling (BIM) has emerged as the primary tool for intelligent construction and digital transformation in the Architecture, Engineering, and Construction (AEC) industry. China's focus on policy guidance for the dissemination and application of BIM technology has promoted high-quality development and technological innovation in the AEC industry. However, few studies have described the multi-dimensional evolution picture of BIM technology policy in China, and lack of effective grasp and prediction of policy evolution law and future direction. An improved policy quantitative analysis framework is built using natural language processing (NLP) based on deep learning in this paper. The dynamic characteristics of 242 Chinese BIM technology policy documents from 1998 to 2022 in the PKULAW database are quantitatively analyzed by the framework, and the four-stage integrated evolution of Chinese BIM technology policy is quantitatively mapped in a visual form through network analysis and dynamic topic model (DTM). Furthermore, this research conducts a comprehensive discussion from three dimensions, providing valuable insights for policy makers and industry participants in China. The findings contribute to the deep integration of policy optimization and the construction industry, while also aiming to share China's experience in BIM technology governance with other countries and regions. Ultimately, this study seeks to support the global AEC industry in its digital transformation and development by offering valuable knowledge and perspectives.

Does the sticky relationships of global value chains help stabilize employment? Evidence from China

Global production beyond borders is becoming an invisible force for stabilizing employment, and the behaviour of firms featuring transnational corporations has become critical to the development of production. In this paper, we construct a systematic quantitative analytical framework of measuring global value chain (GVC) sticky relationships and analyze the steady growth of employment from the perspective of inter-firm interactions firstly, furthermore, provide the latest evidence from China. Our findings show that: multiple interactions between firms for that participate GVC contribute to the development of the GVC sticky relationships and promotion of employment growth, the GVC sticky relationship accounts for approximately 17.32 % of the total employment in China, and domestic GVC sticky relationship activities have become an important force in stabilizing and promoting employment growth in China, it surpasses cross-border GVC employment since 2008. Furthermore, we characterize the shape of GVC sticky relationships and find that the circle-structure relationship effect has greater potential to stabilize employment in China, particularly in the sectors of ICT, wholesale and retailor. Therefore, mitigating the transaction costs of participating in GVC by reducing institutional barriers in the process of firms' interactions is significant for maintaining the employment stability.

The selection of reform models for provincial rural credit cooperatives unions in China using an extended CPT-TODIM method based on novel type-2 fuzzy numbers

The selection of a suitable reform model for provincial rural credit cooperatives unions (PRCCUs) is a pivotal aspect of China’s ongoing financial reform endeavors. This involves optimizing internal processes, enhancing efficiency, minimizing operational costs, elevating service quality, strengthening financial innovation capabilities, and promoting the healthy growth of rural financial markets. However, current research on this matter primarily concentrates on qualitative analysis. As of yet, a scientifically grounded decision-making framework from a quantitative standpoint has not been developed. This absence of quantitative analysis results in inaccuracies in research findings, thereby hindering the practical applicability of outcomes. To tackle this formidable challenge, we propose an innovative quantitative analysis framework employing the multi-criteria group decision-making method. This framework, which builds upon the cumulative prospect theory (CPT), the TODIM method, and the most recent advances in type-2 fuzzy numbers (T2FNs), is referred to as the T2FNs-CPT-TODIM. Initially, we define a novel score function, followed by the introduction of a fresh method for computing the distance between the T2FNs. Subsequently, our model pioneers an exclusive weight allocation strategy for decision-makers, grounded in the principles of Dice Similarity. Furthermore, the objective weights of criteria can be computed utilizing the Distance Correlation-based CRITIC approach. We then optimize the rankings by applying the advantage of gains and losses preference in the CPT-TODIM. Ultimately, we apply our proposed model to the practical task of selecting the optimal reform model for a specific PRCCU. Thorough comprehensive sensitivity and comparative analyses, the results validate the theoretical robustness and practical efficacy of our proposed method.

Analysis of Small Traders' Preferences When Deciding to Owe Loans Using Behavioral Planning Theory

Objective: The aim of this research is to analyze the factors that influence small traders' decisions in borrowing money from loan sharks. Method: This research uses Behavioral Planning Theory (TPB) as a quantitative analysis framework. Results and Discussion: The results of data processing show that the financial behavior of small traders is influenced by factors such as ease of loans, disbursement without time constraints, social communication, payment flexibility, and other social dimensions. The research results provide insight into the complex dynamics of small traders' financial behavior and provide a basis for finding relevant and sustainable solutions in increasing fairer financial access at the community level. Research Implications: This research investigates the financial behavior of small traders who interact with moneylenders in the context of communal finance. Amid intense market competition, small traders face financial differentiation, creating complexity in their financial behavior. Limited access to finance and the complex role of moneylenders makes it difficult for small traders to manage debt. Originality/Value: This research can help design better policies to support the economic sustainability of small traders and overcome the financial challenges they face.

Exploratory factor analysis of sustainable craft theory among Finnish craft hobbyists

This study explores how Finnish craft hobbyists view their craft practice in a sustainable craft theory context. We collected the data for this study through an online survey in February and March 2021. We analyse the survey questionnaire data (N = 929) using exploratory factor analysis. The findings of the study show detailed information about craft hobbyists’ perceptions towards sustainable craft and reveal how craft hobbyists find sustainable crafts to be well-being-related activities that enhance meaning-making in their leisure time. This study also provides a sustainable craft measure framework for quantitative examination and analysis. The findings of the study can be utilized to understand sustainable behaviour through hobby crafts in the green transition.

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences

Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

Visualization of translation reorganization upon persistent ribosome collision stress in mammalian cells

Aberrantly slow ribosomes incur collisions, a sentinel of stress that triggers quality control, signaling, and translation attenuation. Although each collision response has been studied in isolation, the net consequences of their collective actions in reshaping translation in cells is poorly understood. Here, we apply cryoelectron tomography to visualize the translation machinery in mammalian cells during persistent collision stress. We find that polysomes are compressed, with up to 30% of ribosomes in helical polysomes or collided disomes, some of which are bound to the stress effector GCN1. The native collision interface extends beyond the invitro-characterized 40S and includes the L1 stalk and eEF2, possibly contributing to translocation inhibition. The accumulation of unresolved tRNA-bound 80S and 60S and aberrant 40S configurations identifies potentially limiting steps in collision responses. Our work provides a global view of the translation machinery in response to persistent collisions and a framework for quantitative analysis of translation dynamics in situ.

Fluidity in Reporting Gender Identity Labels in a Sample of Transgender and Gender Diverse Adolescents and Young Adults, Los Angeles, California, and New Orleans, Louisiana, 2017-2019.

Treating gender identity as a fixed characteristic may contribute to considerable misclassification and hinder accurate characterization of health inequities and the design of effective preventive interventions for transgender and gender diverse (TGD) adolescents and young adults. We examined changes in how an ethnically and racially diverse sample of TGD adolescents and young adults reported their gender identity over time, the implications of this fluidity on public health, and the potential effects of misclassification of gender identity. We recruited 235 TGD adolescents and young adults (aged 15-24 y) in Los Angeles, California, and New Orleans, Louisiana, from May 2017 through August 2019 to participate in an HIV intervention study. We asked participants to self-report their gender identity and sex assigned at birth every 4 months for 24 months. We used a quantitative content analysis framework to catalog changes in responses over time and classified the changes into 3 main patterns: consistent, fluctuating, and moving in 1 direction. We then calculated the distribution of gender identity labels at baseline (initial assessment) and 12 and 24 months and described the overall sample by age, race, ethnicity, and study site. Of 235 TGD participants, 162 (69%) were from Los Angeles, 89 (38%) were Latinx, and 80 (34%) were non-Latinx Black or African American. Changes in self-reported gender identity were common (n = 181; 77%); in fact, 39 (17%) changed gender identities more than twice. More than 50% (n = 131; 56%) showed a fluctuating pattern. Gender identity labels varied over time, suggesting that misclassification may occur if data from a single time point are used to define gender identity. Our study lays the foundation for launching studies to elucidate the associations between shifting gender identities and health outcomes.

Continuous-Time Reinforcement Learning Control: A Review of Theoretical Results, Insights on Performance, and Needs for New Designs.

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of affine nonlinear systems. We review four seminal methods that are the centerpieces of the most recent results on CT-RL control. We survey the theoretical results of the four methods, highlighting their fundamental importance and successes by including discussions on problem formulation, key assumptions, algorithm procedures, and theoretical guarantees. Subsequently, we evaluate the performance of the control designs to provide analyses and insights on the feasibility of these design methods for applications from a control designer's point of view. Through systematic evaluations, we point out when theory diverges from practical controller synthesis. We, furthermore, introduce a new quantitative analytical framework to diagnose the observed discrepancies. Based on the analyses and the insights gained through quantitative evaluations, we point out potential future research directions to unleash the potential of CT-RL control algorithms in addressing the identified challenges.

DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-Cell Transcriptomics Data

Abstract Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for studying cellular heterogeneity in various fields, particularly in virological research. By studying the viral and cellular transcriptomes, the dynamics of viral infection can be investigated at a single-cell resolution. However, limited studies have been conducted to investigate whether RNA transcripts from clinical samples contain substantial amounts of viral RNAs, and a specific computational framework for efficiently detecting viral reads based on scRNA-seq data has not been developed. Hence, we introduce DVsc, an open-source framework for precise quantitative analysis of viral infection from single-cell transcriptomics data. When applied to approximately 200 diverse clinical samples that were infected by more than 10 different viruses, DVsc demonstrated high accuracy in systematically detecting viral infection across a wide array of cell types. This innovative bioinformatics pipeline could be crucial for addressing the potential effects of surreptitiously invading viruses on certain illnesses, as well as for designing novel medicines to target viruses in specific host cell subsets and evaluating the efficacy of treatment. DVsc supports the FASTQ format as an input and is compatible with multiple single-cell sequencing platforms. Moreover, it could also be applied to sequences from bulk RNA-sequencing data. DVsc is available at http://62.234.32.33:5000/DVsc.

A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network

This study introduces an innovative probabilistic network designed to integrate workflow, operations, and safety barriers involved in operational risk propagation, based on which a generic risk model is proposed to quantify the impact of safety barriers on operational risk. For this purpose, a structural barrier module is defined to describe the interaction mechanisms between operations and risk. This module encompasses scenarios such as risk mitigation based on barriers, risk escalation facilitated due to barrier performance degradation, and natural risk attenuation. The barrier modules are integrated into the probabilistic network, which incorporates both workflow and risk propagation, thereby providing detailed insights into risk propagation under the impact of barriers and operations. To evaluate the impact of barriers, a quantitative analysis framework is presented based on probability theory, including the network Green's function and path search algorithms. Furthermore, a sensitivity analysis is employed based on Green's function to illustrate the optimization of possible intervention strategies. Finally, the impact and sensitivity of the barriers are combined in a two-dimensional space to classify the barriers, leading to recommendations or countermeasures for safety barrier management. The methodologies proposed in this study shed light on the cooperative mechanisms of barriers for operational risk control.

Quantitative Analysis Village Spatial Morphology Using “SPSS + GIS” Approach: A Case Study of Linxia Hui Autonomous Prefecture

This research comprehensively analyzes the spatial morphology of 177 traditional villages within Linxia Hui Autonomous Prefecture, Gansu Province. The study delineates these characteristics utilizing a combination of five quantitative measured indices—ratio, boundary, saturation, building density, and dispersion coefficients. Leveraging sophisticated analytical techniques facilitated by “SPSS + GIS” integration, the investigation systematically explores the intricate details of village spatial form. Their overarching distribution patterns, and the determinant factors influencing them, provide insights across both granular and broad-scale dimensions. The aim is to establish a robust quantitative data analysis framework, facilitating a precise description of traditional villages’ spatial dynamics. The findings categorize the spatial morphology of Linxia’s traditional villages into three distinct types: linear multi-point concentration, dense clustering, and irregular dispersion. Common traits among these categories include widespread dispersal, small settlements, and a mix of dwellings. Spatial distribution patterns vary, with dense clusters forming an “olive-shaped” trend in the southeast–northwest direction, while irregularly dispersed villages develop along mountains and valleys, exhibiting multi-core structures. Additionally, linear multi-point concentrated villages display a random, multi-point distribution interspersed with dense clusters. The survival strategies of these commercial, subsistence, and resource-based villages are shaped by a confluence of factors such as elevation, river proximity, ancient road networks, and the interplay between Han Chinese and Tibetan cultural influences. The implications of this study are significant for understanding traditional village dynamics, promoting sustainable development, and refining quantitative methods for rural studies.

IMPACTS OF GEOSPATIAL FACTORS ON VILLAGE SITE SELECTION ALONG THE GREAT WALL WATCHTOWER BASED ON LOGISTIC REGRESSION: A CASE STUDY IN GUBEIKOU SECTION, BEIJING, CHINA

Abstract. The Great Wall was listed as a World Heritage Site in 1987. In recent years, the Chinese government placed significant emphasis on the preservation of the Great Wall and its invaluable resources. However, it remains challenging to quantitatively analyze the impact of multiple factors and further identify the Great Wall resource sites. In this paper, a spatial quantitative analysis framework with binary logistic regression is implemented for the impact factors of village site selection along the Great Wall watchtowers. Elevation, slope, orientation, water distance, road distance, distance-to-watchtowers were selected as candidates. 177 villages were analyzed, and the model calculation was assessed with the confusion matrix and ROC curve. Youden index is used to determine the optimal threshold for the uncertain villages, subsequently. And we classified villages into three categories: villages with strong, medium and weak relevance with Great Wall resource sites. Experimental results show that the strong and medium relevance villages accounted for 68.36%. These villages are primarily located in the central part of the study area, characterized by flat terrain and closely aligned with the road network. Village density decreases with distance from the watchtowers. Additionally, out of the 40 uncertain villages, 5 were confirmed as villages while the remaining were classified as non-villages. The conclusions of this study can serve as a reference for evaluating and predicting suitable conservation and planning initiatives for the Great Wall and its surrounding villages in the future.

A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems

In urban infrastructure systems, resilience is crucial for maintaining functionality, minimizing losses, and expediting recovery during disruptive incidents. Effective allocating resources across various phases of emerging disturbances can generally enhance the system's capacity to cope with disasters. However, it is imperative to recognize that distinct resource allocation strategies may lead to divergent outcomes in terms of resilience performance. Therefore, this study develops a framework for optimizing resource allocation based on multiple resilience objectives by understanding the interplay between resilience performance and dynamic decision-making. The resilience processes are first formalized into distinct stages, considering the technical and organizational resilience of the infrastructure system in the event of disruption. Building upon this foundation, five decision scenarios are proposed, contingent on the allocation or non-allocation of resources to each resilience stage. A multi-resilience-objective mixed-integer linear programming (MROMILP) model is formulated to optimize the resource allocation scheme for each resilience stage within the constraints of internal resources. Finally, the model and framework are tested using a power system as a tangible example. The integrated multi-stage quantitative resilience assessment and optimization method proposed in this study can assist decision-makers in making dynamic and continuous trade-offs between resources and resilience targets.

The role of land use landscape patterns in the carbon emission reduction: Empirical evidence from China

The regulation of land use landscape patterns to achieve carbon reduction in pursuit of carbon neutrality has not received sufficient attention. Existing studies have largely focused on specific land use types, such as urban built-up areas. As a result, such studies lack comprehensive regional research, hindering the formulation of integrated carbon reduction policies and sustainable land use planning. Taking the Yangtze River Economic Belt as the study object, this study constructs a quantitative analysis framework that conducts regional land use carbon emission evaluation, landscape pattern analysis, dynamic correlation examination, and measures their respective mechanisms. The results indicate that cropland in the Yangtze River Economic Belt has progressively decreased over the last 20 years (−3.32 %), whilst wetland areas have experienced slow growth (1.81 %) and built-up land has expanded significantly (90.14 %) over the same period. Except for the Interspersion and juxtaposition index (IJI) and Modified Simpson’s evenness index (MEISI), other indices exhibit a decreasing trend with distinct temporal characteristics. By designing and deploying an annual panel regression model, this study quantifies the impact of land use and landscape pattern changes on carbon emissions from a regional perspective. The results reveal that lower levels of landscape heterogeneity and fragmentation and a lower concentration of urban built-up land are conducive to carbon reduction; contrastingly, a decrease in landscape complexity and connectivity increases carbon emissions. Finally, recommendations are put forward regarding land use landscape patterns under the framework of carbon neutrality.

A quantitative analysis framework of placenta accreta spectrum: placenta subtype, intraoperative bleeding, and hysterectomy risk evaluation based on magnetic resonance imaging-anatomical-clinical features.

Placenta accreta spectrum (PAS) is a significant contributor to maternal morbidity and mortality. Our objective was to develop a quantitative analysis framework utilizing magnetic resonance imaging (MRI)-anatomical-clinical features to predict 3 clinically significant parameters in patients with PAS: placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy). A total of 125 pregnant women with PAS from 2 medical centers were enrolled into an internal training set and an external testing set. Some 21 MRI-anatomical-clinical features were integrated as input into the framework. The proposed quantitative analytic framework contains mainly 3 classifiers built by extreme gradient boosting (XGBoost) and their testing in external datasets. We also further compared the accuracy of placenta subtype prediction between the proposed model and 4 radiologists. A quantitative model interpretation method called SHapley Additive exPlanations (SHAP) was conducted to explore the contribution of each feature. The placenta subtype (invasive vs. non-invasive), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) demonstrated impressive area under the receiver operating characteristic curve (AUROC) values of 0.93, 0.88, and 0.90, respectively, in the internal validation set. Even in the external testing set, these metrics maintained their strength, achieving AUROC values of 0.91, 0.82, and 0.82, respectively. Comparing our proposed framework to the 4 radiologists, our model exhibited superior accuracy, specificity, and sensitivity in predicting placental subtypes within the external testing cohort. The features associated with intraplacental dark T2 bands played a crucial role in the decision-making process of all 3 prediction models. The quantitative analysis framework can provide a robust method for classification of placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) based on MRI-anatomical-clinical features in PAS.

Connections Between Personality Adaptations and Self-Presentation on Dating Sites

This study was carried out as a part of the master's thesis at National Research University Higher School of Economics under the supervision of Doctor of Psychological Sciences, Professor V.A. Petrovsky. The aim of the study was to analyze the relationship between personality adaptations and self-presentation on dating sites. In a comparative aspect, the relationship between personality adaptations and self-presentation strategies on dating sites has not been previously studied. Based on the analyzed theoretical and empirical materials, an author's model for studying the relationship between personality adaptations and self-presentation on dating sites was developed, which formed the basis of this study. The study was conducted in several stages and contains qualitative and quantitative analysis. Quantitative analysis was used for statistical processing of the data on the types of personality adaptations obtained using the survey by V. Joines to identify patterns and significant characteristics of self-presentation. Qualitative analysis made it possible to identify significant additional patterns that could not be traced in the framework of quantitative analysis, and to reflect in what words, phrases and in what context the self-presentation of representatives of the most common adaptations on dating sites manifests itself. The respondents were men, aged 25 to 40, living in Moscow and the Moscow region. Tinder, the most popular dating app in Russia, and the fastest growing and developing dating site in Russia VK Dating were chosen as the dating sites. The obtained results are of practical importance and can serve as a basis for further research.

Machine learning framework for Hazard Extraction and Analysis of Trends (HEAT) in wildfire response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction and Analysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident response forms. The HEAT framework produces safety- and risk-relevant analyses, consisting of: (1) a set of hazards extracted from text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-style table and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potential predictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards documented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEAT can be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply the framework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire response arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrix identifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

Establishment risk of invasive golden mussel in a water diversion project: An assessment framework

Inter-basin water diversion projects have led to accelerated colonization of aquatic organisms, including the freshwater golden mussel (Limnoperna fortunei), exacerbating global biofouling concerns. While the influence of environmental factors on the mussel's invasion and biofouling impact has been studied, quantitative correlations and underlying mechanisms remain unclear, particularly in large-scale inter-basin water diversion projects with diverse hydrodynamic and environmental conditions. Here, we examine the comprehensive impact of environmental variables on the establishment risk of the golden mussel in China's 1432-km-long Middle Route of the South-to-North Water Diversion Project. Logistic regression and multiclass classification models were used to investigate the environmental influence on the occurrence probability and reproductive density of the golden mussel. Total nitrogen, ammonia nitrogen, water temperature, pH, and velocity were identified as crucial environmental variables affecting the biofouling risk in the project. Logistic regression analysis revealed a negative correlation between the occurrence probability of all larval stages and levels of total nitrogen and ammonia nitrogen. The multiclass classification model showed that elevated levels of total nitrogen hindered mussel reproduction, while optimal water temperature enhanced their reproductive capacity. Appropriate velocity and pH levels were crucial in maintaining moderate larval density. This research presents a quantitative analytical framework for assessing establishment risks associated with invasive mussels, and the framework is expected to enhance invasion management and mitigate biofouling issues in water diversion projects worldwide.