Precise Model Research Articles

Data‐Driven Modeling and High‐Precision Tracking Control of a Soft Continuum Manipulator: Enabling Robotic Sorting of Multiwire Cables

As a new class of robots, soft continuum manipulators have attracted attention due to their flexibility and compliance. However, these characteristics create challenges for precise modeling and control. This study proposes a hybrid offline and online data‐driven scheme to achieve high‐precision tracking control of a soft continuum manipulator. First, a novel multiscale deep neural network learns the manipulator model offline. Specifically, the feature fusion module extracts highly discriminative features and captures long‐term dependencies from the temporal trajectory data. The self‐attention module strengthens the ability to represent fusion features and enhances the model prediction accuracy. Then, the learnt model is updated using multisensor data online, and the proposed controller further compensates for the updated model and enhances the tracking accuracy in the movement stage. Finally, the experimental results demonstrate a significant improvement in motion accuracy under different trajectory‐tracking scenarios (i.e., deviations of <1 mm in position and <0.8° in orientation). The example of the multiwire cable sorting proves the feasibility of the proposed scheme in high‐precision industrial applications.

Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks

Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions—the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)—demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency.

Eddy Currents Probe Design for NDT Applications: A Review.

Eddy current testing (ECT) is a crucial non-destructive testing (NDT) technique extensively used across various industries to detect surface and sub-surface defects in conductive materials. This review explores the latest advancements and methodologies in the design of eddy current probes, emphasizing their application in diverse industrial contexts such as aerospace, automotive, energy, and electronics. It explores the fundamental principles of ECT, examining how eddy currents interact with material defects to provide valuable insights into material integrity. The integration of numerical simulations, particularly through the Finite Element Method (FEM), has emerged as a transformative approach, enabling the precise modeling of electromagnetic interactions and optimizing probe configurations. Innovative probe designs, including multiple coil configurations, have significantly enhanced defect detection capabilities. Despite these advancements, challenges remain, particularly in calibration and sensitivity to environmental conditions. This comprehensive overview highlights the evolving landscape of ECT probe design, aiming to provide researchers and practitioners with a detailed understanding of current trends in this dynamic field.

Perfect Monomial Prediction for Modular Addition

Modular addition is often the most complex component of typical Addition- Rotation-XOR (ARX) ciphers, and the division property is the most effective tool for detecting integral distinguishers. Thus, having a precise division property model for modular addition is crucial in the search for integral distinguishers in ARX ciphers. Current division property models for modular addition either (a) express the operation as a Boolean circuit and apply standard propagation rules for basic operations (COPY, XOR, AND), or (b) treat it as a sequence of smaller functions with carry bits, modeling each function individually. Both approaches were originally proposed for the twosubset bit-based division property (2BDP), which is theoretically imprecise and may overlook some balanced bits.Recently, more precise versions of the division property, such as parity sets, threesubset bit-based division property without unknown subsets (3BDPwoU) or monomial prediction (MP), and algebraic transition matrices have been proposed. However, little attention has been given to modular addition within these precise models.The propagation rule for the precise division property of a vectorial Boolean function f requires that u can propagate to v if and only if the monomial πu(x) appears in πv(f). Braeken and Semaev (FSE 2005) studied the algebraic structure of modular addition and showed that for x ⊞ y = z, the monomial πu(x)πv(y) appears in πw(z) if and only if u + v = w. Their theorem directly leads to a precise division property model for modular addition. Surprisingly, this model has not been applied in division property searches, to the best of our knowledge.In this paper, we apply Braeken and Semaev’s theorem to search for integral distinguishers in ARX ciphers, leading to several new results. First, we improve the state-of-the-art integral distinguishers for all variants of the Speck family, significantly enhancing search efficiency for Speck-32/48/64/96 and detecting new integral distinguishers for Speck-48/64/96/128. Second, we determine the exact degrees of output bits for 7-round Speck-32 and all/16/2 output bits for 2/3/4-round Alzette for the first time. Third, we revisit the choice of rotation parameters in Speck instances, providing a criterion that enhances resistance against integral distinguishers. Additionally, we offer a simpler proof for Braeken and Semaev’s theorem using monomial prediction, demonstrating the potential of division property methods in the study of Boolean functions.We hope that the proposed methods will be valuable in the future design of ARX ciphers.

DEVELOPMENT OF MATHEMATICAL MODELS FOR EVALUATING DEMAND PARAMETER ELASTICITY IN RETAIL NETWORKS UNDER CONSUMER-DRIVEN LOGISTICS

The retail market is characterised by its dynamic nature, requiring sophisticated models for accurately assessing demand elasticity, especially within consumer-driven logistics. This study aims to develop and validate comprehensive mathematical models to evaluate demand parameters within retail networks. The study focuses on the main factors that significantly influence demand, including the number of participants within a retail network, the average number of end consumers, and the ratio of the freight flow cost in a particular retail network compared to the average price in other networks. The authors conducted extensive data collection across various retail networks, capturing essential parameters such as demand volume, the number of participants, consumer numbers, and pricing strategies. The analysis led to the development of regression models that provide valuable insights into demand dynamics. The results indicate that increasing network participants and the average number of end consumers positively correlates with higher demand volumes. On the other hand, a higher ratio of freight flow cost within a retail network negatively impacts demand, highlighting consumers’ sensitivity to price changes. This inverse relationship between cost and demand underlines the importance of pricing strategies in influencing consumer behaviour. The statistical validation of the developed models demonstrated their reliability, with high correlation coefficients and low approximation errors, confirming their high predictive capabilities. These models are not only theoretically sound but also offer substantial practical applications. Retail networks can leverage these models to optimise their marketing strategies, plan their product assortments more effectively, and manage inventory more precisely. By integrating multiple factors influencing demand, this study provides a more nuanced understanding of consumer behaviour, enabling retail networks to make well-informed, data-driven decisions. The unique contribution of this research lies in its holistic approach to demand modelling, where multiple variables are considered in conjunction rather than in isolation. This integration allows for a deeper comprehension of how different elements interact to influence consumer demand. Moreover, the models developed in this study are versatile and can be adapted to various retail settings, offering a valuable tool for academic researchers and industry practitioners. Future research could extend these models by incorporating additional variables such as seasonality, shifts in consumer preferences, and the impact of technological advancements on retail logistics. Doing so makes it possible to continuously refine the models to maintain relevance and accuracy in an ever-changing market landscape. This ongoing evolution of demand modelling is crucial for retail networks aiming to stay competitive and responsive to consumer needs in a highly dynamic environment. The findings from this study underscore the importance of a data-driven approach in retail logistics, where precise modelling and analysis can lead to significant improvements in operational efficiency and market responsiveness. Keywords: demand elasticity, retail network, logistics, mathematical modelling, demand parameters, consumer behaviour.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Mathematical Model of the Electronic Cam in Terms of Application in a Dosing Machine

The article analyses the use of servomotors in the control systems of industrial equipment, focusing on the alternative offered by position and speed synchronization in relation to classical mechanical mechanisms. A complete methodology is presented to determine the dynamic parameters of the adopted kinematic system using electronic motion profiles. The results obtained constitute a mathematical model of the execution chain and an analysis of the basic quantities for linear motion, supported by actual measurements of the drive parameters. The merit of the article is to show that the servomotors can significantly simplify the design of the device, make it more flexible in adaptation to different assortments, and allow integration with systems predicting the technical condition of the device. The analysis of the results revealed significant differences in the constant rotational speed of the servomotor, which do not align with previous findings. The results suggest that changing the angular working range of the assembly to the range (205°;270°) could significantly affect the generated linear acceleration, reducing the risk of stalling. The calculations and graphs conducted allowed for the accurate representation of the actual mechanical system, considering its dynamic characteristics. The key conclusion is that precise mathematical modelling is essential to ensure the stability and durability of engineering components.

On Negative Conglomerability

We focus on the notion of negative conglomerability. This is far less known than its counterpart, conglomerability. Both relate to the combination of conditional and unconditional information, with the latter taking in particular a foundational role in the special case of infinite partitions of the possibility space. The two notions look superficially very similar and are even equivalent in the case of precise probabilistic models. In the present paper, we do a thorough technical study of their relations with other main concepts in the literature, such as marginal extension and dilation, both in the precise and imprecise case. Moreover, we discuss why they are somewhat surprisingly different from the prescriptive point of view, in that conglomerability has a rationality stance that its negative counterpart has not.

Application of Industry 4.0 and digital twin in the field of smart healthcare

There is an increasing need for preventive health care as well as precise diagnosis and tailored treatment of different diseases in recent years. Providing customized treatment for each patient and maximizing accuracy and efficiency are main goals of a good healthcare system. This thesis explores the integration of digital twin technology with Industry 4.0 in healthcare. Digital twins create virtual representations of physical systems, enabling real-time monitoring and tailored treatments. Key elements include the living human body, IoT, digital twin, cloud computing, and simulation. The architecture comprises data acquisition, data munging, data storage, data simulation with analytics, and user access layers. Creating a digital twin involves precise 3D modeling, representing the entire body or specific organs. A case study demonstrates real-time monitoring using wearable sensors for blood pressure, blood sugar, and heart rate. Data is transmitted, integrated with the digital twin, and accessible via a website with alarms for abnormal readings. While Industry 4.0 and digital twin adoption in healthcare is evolving, the architecture serves as a reference. It offers real-time data for diagnosis and treatment, with potential for advanced simulations. Challenges include automated data systems and privacy concerns. Despite limitations, this integration holds promise for precision medicine and personalized healthcare.

Precise modeling and stress response analysis of the aero-engine clamp: A simulation and experimental study

The clamp plays a vital role in supporting and suppressing vibration in pipeline systems. The vibration stress analysis of clamps is an essential basis for evaluating the strength of clamps, which is crucial for ensuring the safety and stability of the aero-engine pipeline system. However, there is a lack of relevant research to analyze the vibration response of the clamp. To make up for this defect, the precise modeling and vibration stress analysis of the clamp are carried out in this article. First, simulation and experimental testing are conducted on a bracket-clamp-additional mass system under the base excitation. The maximum stress errors of simulation and experiment under the excitation of sweep-frequency and fixed-frequency are 6.64% and 23.16%, respectively. Then, the influence of different excitation amplitude and additional masses on the surface stress of the clamp is analyzed. It is found that the surface stress of the clamp increases with the increase of the additional mass, and the effect of excitation amplitude on the surface stress of the clamp has a linear law. The simulation and experiment work in this article provides a deeper insight into the physical understanding for the design of clamp-pipeline system, and the precise clamp modeling idea proposed can also be extended to other specifications of engine clamp modeling.

Tracing the sources of paleotsunamis using Bayesian frameworks

Paleotsunami deposits provide a compelling record of these events, including valuable insights into their recurrence and associated magnitudes. However, precisely determining the sources of these sedimentary evidence remains challenging due to the complex interplay between hydrodynamic and geological phenomena and the intricacy of the processes responsible for forming and preserving tsunami deposits. Here, we introduce a novel approach that employs Bayesian inference methods to divide the complex tsunami process into segments and independently handle uncertainties, thereby enabling more precise and comprehensive modelling of the sources. We provide a list of potential earthquake scenarios with different likelihoods instead of a single best fit. Based on this method, we calculated that the source of the 869 Jogan earthquake had a magnitude ranging from Moment Magnitude 8.84 to 9.1 (within one standard deviation) with different slip distributions along the Japan Trench. Our results reaffirm that the Jogan event had a similar order of magnitude to the 2011 Tohoku-oki tsunami and enhanced the applicability of paleotsunami deposits to hazard assessment.

Positive Youth Development at Sea: A Case Study of the Shenandoah Model

Background: Tall ship sail training, a form of outdoor adventure education, has historically been used with youth to build competency in seamanship and social and emotional skills. However, there is a void in the literature documenting precise program models connected to specific goals. Purpose: This paper presents a case study of the Shenandoah Model of sail training. It details its Positive Youth Development (PYD) framework and mean changes in PYD assets reported by participants. Methodology/Approach: Participants were surveyed using a one-group retrospective pretest-posttest pre-experimental design to assess the impact of participation. Findings/Conclusions: The participants reported significant mean increases across all PYD assets (Caring, Connection, Contribution, Competence, Character, Confidence, and Happiness), including moderate effect sizes for all measures except Happiness. In addition, over 70% of the participants would recommend the program and/or do it again, suggesting program satisfaction. Implications: Connecting different PYD assets to various program activities allows future program designs to intentionally develop sail training voyages to build competencies. Future follow-up research is needed, including qualitative methods to capture the impact of these programs from the participants’ viewpoint.

Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models

The escalating threat of congestion in wireless networks on a global scale prompts the need for effective detection and management techniques. This study investigates the tracking and detection of congestion in wireless networks, particularly within the banking industry, where digital transactions are rapidly increasing. It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. This research utilizes a dataset sourced from MainOne Limited, which covered August 18th, 20th, 22nd, 23rd, and 24th, 2023, and included banking operation hours from 7 AM to 4 PM each day. Preprocessing of data is conducted to optimize model training. Following training, various performance metrics including accuracy, precision, recall, F1 score, response time, and confusion matrix are assessed. Results demonstrate that Random Forest outperforms other models in accuracy, precision, recall, F1 score, and response time, with an accuracy of 98.90%. This research discusses the importance of continuous innovation in banking network analytics to tackle evolving congestion challenges. Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further.

Development and deployment of data-driven turbulence model for three-dimensional complex configurations

Abstract In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling frameworks, often overlooking the practical deployment and application of trained models. This paper examines the entire process from model construction to real-world deployment, using data-driven turbulence modeling for high Reynolds number flows over complex three-dimensional configurations as a case study. Key stages include data generation, input-output feature construction, model training, model compilation and optimization, deployment, and validation. We successfully implemented the entire workflow in a heterogeneous supercomputing environment and, through mixed programming techniques, integrated the resulting turbulence model into the Platform for Hybrid Engineering Simulation of Flows (PHengLEI) open-source software framework. This allowed for mixed-precision simulations, with the main equations solved in double precision and the turbulence model in half precision. The new computational framework was validated through large-scale parallel numerical simulations on grids with tens of millions of elements for three-dimensional complex configurations. The results highlight the efficiency of our model deployment, with overall computational efficiency improving by 13.35% and the turbulence model’s solution speed increasing by approximately 3.9 times. The accuracy of the computations was also confirmed, with the average relative error in the lift and drag coefficients calculated by the data-driven turbulence model within 3%. Across various computing nodes, the relative error in the computed aerodynamic coefficients remained within 1%, demonstrating the framework’s scalability. Notably, our contributions have been incorporated as a case study in the latest PHengLEI open-source project5 5 https://forge.osredm.com/PHengLEI/PHengLEI-TestCases/tree/master/Y02_ThreeD_M6_Unstruct_Branch_Ascend. .

Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism

Introduction and objectiveDue to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates. Materials and methodsThe data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients. ResultsIn this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment. ConclusionsIn summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.

Model predictive complex system control from observational and interventional data

Complex systems, characterized by intricate interactions among numerous entities, give rise to emergent behaviors whose data-driven modeling and control are of utmost significance, especially when there is abundant observational data but the intervention cost is high. Traditional methods rely on precise dynamical models or require extensive intervention data, often falling short in real-world applications. To bridge this gap, we consider a specific setting of the complex systems control problem: how to control complex systems through a few online interactions on some intervenable nodes when abundant observational data from natural evolution is available. We introduce a two-stage model predictive complex system control framework, comprising an offline pre-training phase that leverages rich observational data to capture spontaneous evolutionary dynamics and an online fine-tuning phase that uses a variant of model predictive control to implement intervention actions. To address the high-dimensional nature of the state-action space in complex systems, we propose a novel approach employing action-extended graph neural networks to model the Markov decision process of complex systems and design a hierarchical action space for learning intervention actions. This approach performs well in three complex system control environments: Boids, Kuramoto, and Susceptible-Infectious-Susceptible (SIS) metapopulation. It offers accelerated convergence, robust generalization, and reduced intervention costs compared to the baseline algorithm. This work provides valuable insights into controlling complex systems with high-dimensional state-action spaces and limited intervention data, presenting promising applications for real-world challenges.

Systematic analysis of constitutive models of brain tissue materials based on compression tests

It's crucial to understand the biomechanical properties of brain tissue to comprehend the potential mechanisms of traumatic brain injury. This study, distinct from homogeneous models, integrates axonal coupling in both axial and transverse compressive experiments within a continuum mechanics framework to capture its intricate mechanical behaviors. Fresh porcine brains underwent unconfined compression at strain rates of 0.001/s and 0.1/s to 0.3 strain, allowing for a comprehensive statistical analysis of the directional, regional, and strain-rate-dependent mechanical properties of brain tissue. The established constitutive model, fitted to experimental data, delineates material parameters providing intuitive insights into the stiffness of gray/white matter isotropic matrices and neural fibers. Additionally, it predicts the mechanical performance of white matter matrix and axonal fibers under compressive loading. Results reveal that gray matter is insensitive to loading direction, exhibiting insignificant stiffness variations within regions. White matter, however, displays no significant differences in mechanical properties under axial and transverse loading, with an overall higher average stress than gray matter and a more pronounced strain-rate effect. Stress-strain curves indicate that, under axial compression, white matter axons primarily resist the load before transitioning to a matrix-dominated response. Under transverse loading, axonal fibers exhibit weaker resistance to lateral pressure. The mechanical behavior of brain tissue is highly dependent on loading rate, region, direction, and peak strain. This study, by combining experimentation with phenomenological modeling, elucidates certain phenomena, contributing valuable insights for the development of precise computational models.

Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer

BackgroundIt has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities. MethodsIn this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo. ResultsARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARShigh group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARShigh patients. ConclusionsCollectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

Enhancing Patient Outcomes: A Novel Nomogram PredictionModel Based on Systemic Immune-Inflammation Index for Esophageal Stricture After Endoscopic SubmucosalDissection.

Endoscopic submucosal dissection (ESD) is a widely utilized treatment for early esophageal cancer. However, the rising incidence of postoperative esophageal stricture poses a significant challenge, adversely affecting patients' quality of life and treatment outcomes. Developing precise predictive models is urgently required to enhance treatment outcomes. This study retrospectively analyzed clinical data from 124 patients with early esophageal cancer who underwent ESD at Ningbo Medical Center Lihuili Hospital. Patients were followed up to assess esophageal stricture incidence. Binary logistic regression analysis was used to identify factors associated with post-ESD esophageal stricture. A novel nomogram prediction model based on Systemic Immune-inflammation Index (SII) was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ROC curve analysis showed that the optimal value of SII for predicting esophageal stricture was 312.67. Both univariate and multivariate analyses identified lesion infiltration depth (< M2 vs. ≥ M2, p = 0.002), lesion longitudinal length (< 4 cm vs. ≥ 4 cm, p = 0.008), circumferential resection range (< 0.5, 0.5-0.75, ≥ 0.75, p = 0.014), and SII (< 312.67 vs. ≥ 312.67, p = 0.040) as independent risk factors for post-ESD esophageal stricture. A novel nomogram prediction model incorporating these four risk factors was developed. Validation using ROC curve analysis demonstrated satisfactory model performance, while calibration curves indicated good agreement between model-predicted risk and observed outcomes. We successfully constructed a novel nomogram prediction model based on SII, which can accurately and intuitively predict the occurrence of esophageal stricture after ESD, providing guidance for clinicians and improving treatment outcomes.

Effects of Tool Geometry Parameters on Clinched Joint Strength Using FEM

Abstract Clinching joining is one of the predominant techniques used to join aluminium alloys in the automotive industry. Although the technique can be easily automated, cost-effective, and not affect the material’s mechanical properties during the joining process, the tool geometry parameters significantly affect the strength of the clinched joint. Therefore, a precise finite element model is critical to improve the strength and reduce the cliched joint’s production cost. This study investigated the effect of the punch fillet radius, the punch angle, the die diameter and the die depth on the strength of the clinched joint of 5754 aluminium alloys. The clinched joint was produced with different geometry parameters. The cross-section of the clinched joints was observed. In the meantime, the interlocking value, the neck thickness, and tool parameters were measured to investigate the joinability of the aluminium alloys. The static tensile shear tests were also conducted using the universal tensile machine. Numerical studies have been conducted to analyse the influence of the geometrical parameters using a 2D axisymmetric model. As a result, the numerical results agreed with the experimental results. Additionally, the result showed that the tool geometry significantly impacted the strength of the clinched joint of aluminium alloys. The study concluded that the neck thickness and the interlocking value have played a vital role in the enhancement of the cliched joint strength.