Composite Model Research Articles

Optimization of the extrusion rate for different layer thicknesses to achieve controlled mechanical properties in MEX 3D printing of low-alloy steel

Composite extrusion modeling is an advanced material extrusion additive manufacturing process that requires fine-tuning of printing parameters for printing injection molding feedstocks. In order to achieve comparable high-quality parts at different layer thicknesses, the parameters must be optimized for the respective layer thicknesses. In this work, the printing parameters for AISI 8740 low-alloy steel injection molding material are optimized for four different layer thicknesses. The extrusion multiplier values for each layer thickness are tuned, allowing fine adjustment of the printer's screw rotation speed at every layer thickness, resulting in all final parts' maximum densities (≥ 98 %) with high accuracies. Scanning electron microscope images, surface roughness measurements, and printing precision tests proved the good and comparable high-quality of all optimized printed parts. Young's modulus, tensile and yield strengths also raised by ∼24 %, ∼16 % and ∼15 %, respectively, compared to the non-optimized printed sintered part. The achieved tensile strength range of (∼888–947) MPa and yield strength range of (∼574–586) MPa of all optimized printed sintered parts were also higher than those provided by the feedstock manufacturer. This promising approach significantly reduces printing time and manufacturing costs without compromising the printed final parts' quality and mechanical properties. The adjusting screw rotation speed methodology allows broader applicability across diverse material extrusion processes.

Formation and evolution of apple (Malus pumila Mill.) bruising based on high-speed camera verification and finite element method

To analyse the characteristics and internal mechanical changes of bruising formed on apples under different working conditions, this paper explored the formation mechanism of bruising using finite element method. A composite model of peel and flesh was established by reverse engineering, and the accuracy of the apple model was validated two aspects: drop velocity and bruise volume, with a maximum error of 2.51 %. The collision velocity, contact force, stress, and energy of apple bruising were analysed using commonly used contact materials (wood and rubber) at falling heights of 600, 900, 1200, 1500, 1800, and 2100 mm. The characteristics of apple bruising after formation were analysed and the susceptibility of apple bruising were explored. The findings suggest that as the height increased, so does the maximum stress and bruise susceptibility. At a height of 2100 mm, the apple’s bruise susceptibility was measured at 24955.87 mm3 J−1 (wood) and 24710.18 mm3 J−1 (rubber), respectively. This study offers valuable insights into understanding the mechanism behind bruising in fruits, and has a guiding role in the control of bruising and the optimal design of related machinery.

Experimental and theoretical investigation of shear lag effect in twin box‐shaped composite girders of long‐span cable‐stayed bridges

AbstractSteel‐concrete composite box girders offer notable advantages such as high stiffness, excellent torsional performance, and light self‐weight, making them prevalent in large‐span bridges, particularly in cable‐stayed configurations. However, a persistent challenge arises from the shear lag effect in the concrete slab within the flanges induced by shearing interactions between the webs and flanges. Limited attention has been devoted to investigating the experimental and theoretical aspects of shear lag in long‐span cable‐stayed bridges with twin box‐shaped composite girders. This research addresses this gap by designing a segmental scale twin box‐shaped composite model specimen to simulate the cable spacing of the main girder in a cable‐stayed bridge, specifically targeting the shear‐lag effect. The study successfully captures the non‐uniform distribution of cross‐sectional strain in the top concrete slab. Subsequently, analytical strain solutions for the composite girder under mid‐span concentrated load and axial load are derived using the energy variation method. The theoretical model's applicability is then calibrated against experimental results. The investigation extends to the in‐situ testing of the Chibi Yangtze River Bridge, the world's largest steel‐concrete composite girder cable‐stayed bridge, boasting a main span of up to 720 m. An established finite element (FE) analysis model for the Chibi Yangtze River Bridge simulates the transverse stress distribution under typical construction conditions of CC6. Comparative analyses between the simulated results, calculated values from elementary beam theory, and the theoretical model against field‐measured values reveal that the FE model provides a more accurate estimation of the shear lag effect in the concrete slab. Notably, the FE model effectively reflects the bridge's characteristics and serves as a baseline for subsequent studies. This comprehensive investigation contributes significantly to the understanding and analysis of shear lag phenomena in twin box composite girders of cable‐stayed bridges. The integration of experimental, analytical, and construction site validations establishes robust tools for engineering design and the construction of safe and serviceable cable‐stayed bridges. The research augments knowledge regarding shear lag behavior and underscores the necessity to appropriately consider its effects in bridge analysis and design.

Intelligent design of reconfigurable flexible assembly fixture for aircraft panels based on smart composite jig model and knowledge graph

The current demand for aircraft panel assembly fixtures necessitates agile manufacturing solutions. Traditional welding fixtures struggle to meet diverse panel types, high quantities, and short manufacturing cycles. Conversely, reconfigurable flexible assembly fixtures offer universality, efficiency and ease of maintenance but heavily rely on designers' experience, hindering knowledge reuse. Existing research on computer-aided fixture design (CAFD) primarily addresses specialized fixtures, neglecting reconfigurable flexible ones, lacking fixture design knowledge systems. Aiming at the above problems, the study proposes an intelligent design method for the reconfigurable flexible assembly fixture for aircraft panels based on the smart composite jig model (SCJM) and knowledge graph (KG). Firstly, based on the SCJM of the aircraft panel type assembly fixture (PSCJM), a configuration design method is proposed to carry out the multi-level design of reconfigurable assembly fixture to instantiate PSCJM containing fixture configuration rules and a module library based on model definition (MBD). Then, a knowledge retrieval method is proposed to search the suitable fixture design knowledge based on KG, assisting designers in achieving the flexible design of aircraft panel reconfigurable assembly fixtures. Finally, practical engineering design validation confirms the method's feasibility and effectiveness in obtaining the reconfigurable flexible assembly fixture design scheme for aircraft panels.

Application of carbon fiber composite materials in aircraft

Carbon fiber composite is a material with excellent mechanical properties. Compared with other high-performance fiber materials, carbon fiber composites have the advantages of high strength and high modulus even at ultra-high temperatures. In addition, the carbon fiber composite material also has excellent electrical and thermal conductivity and electromagnetic shielding performance. Carbon fiber composites are widely used in the aircraft manufacturing industry due to their incomparable performance with other composite materials. In recent years, the amount of carbon fiber composite materials in newly developed aircraft products has reached more than 50%. This paper aims to explore the application of carbon fiber in aircraft, and to reflect the importance of carbon fiber composite materials by comparing models that use composite materials and models that do not use composite materials. The study explores the application of carbon fiber composite materials in aircraft through a comprehensive literature analysis and review. Key findings indicate significant advantages of these materials in enhancing aircraft performance, including reduced weight and increased strength. The paper also discusses the challenges in manufacturing and environmental impacts, offering insights into future research directions for sustainable aviation technologies.

Monte Carlo Simulation of the CO2 Flooding Efficiency at a Core Scale for Different Oil Compositions

The evaluation of water-alternating-gas (WAG) efficiency and profitability is complicated by a large number of reservoir, operating, and economic parameters and constraints. This study aims at understanding the influence of the oil composition on different WAG injections. By employing compositional reservoir modeling and the Monte Carlo method to characterize the diversity of oils occurring in nature, we simulate the microscopic displacement efficiency of CO2 flooding when it is applied to both light- and heavy-oil reservoirs. We find that the economic performance of WAG in both miscible and immiscible scenarios is mainly characterized by the dimensionless injection rate and the oil density at surface conditions. Neither the bubble point pressure nor the minimum miscibility pressure can be used for the quantification of the optimal WAG parameters. We present our estimates of the best strategies for the miscible and immiscible injections and verify some of our previous results for randomly sampled oils. In particular, we demonstrate that CO2 flooding is better to apply at higher-dimensionless injection rates. We show that the injection of CO2 organized at a light-oil reservoir results in a higher profitability of WAG, although this comes at the cost of lower carbon storage efficiency.

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast magnetic resonance imaging (MRI) to improve upon conventional risk assessment models in determining culprit lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone [area under curve (AUC), 0.819 ± 0.002 vs. 0.689 ± 0.019 respectively, p = 0.014]. The radiomic model alone also provided value over the conventional model [AUC, 0.805 ± 0.003 vs. 0.689 ± 0.019 respectively, p = 0.031]. T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM) and Grey Level Size Zone Matrix (GLSZM) sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischemic cerebrovascular events.

A linearly polarized light emission with a composite nanowire grating in whole white band

Abstract To obtain a highly linearly polarized light, a composite model consisting of white light emission, anti-reflection film, and metal-dielectric-metal nanowire grating was designed, analyzed, optimized, and fabricated. Based on the finite-difference time-domain method, the impacts of material, period, height, and incidence angle on the polarization performance of the composite model were discussed. The metal-dielectric-metal nanowire grating was fabricated on blue chip and fluorescent ceramics using nanoimprint technology. The employed materials of metal-dielectric-metal nanowire grating were aluminum and PMMA, with the period of 200 nm, wire width of 100 nm, and the height of metal and dielectric were 100 nm and 120 nm. Additionally, the anti-reflection film consisting of PMMA with the thickness of 45 nm was incorporated on fluorescent ceramics to enhance energy efficiency. Finally, through a series of test experiments, the composite model can be realized by the extinction ratio of 40 dB, while the transmittance of TM mode exceeds 50% at 450-750 nm. The theoretical analysis of this study is verified by experiments, and it has significant potential in the pursuit of high brightness, ultra-thin micro displays.

Abstract PS04-08: Sociodemographic Risk Factors and Prediction of Aromatase Inhibitor Non-Adherence in Women with Breast Cancer Enrolled in SWOG S1105

Abstract Background: Non-adherence to aromatase inhibitors (AIs) for breast cancer is common and increases risk of recurrence. Few prospective studies have systematically evaluated non-adherence and non-clinical factors associated with it. We analyzed baseline sociodemographic characteristics and financial factors associated with non-adherence to create a baseline composite risk-score for AI non-adherence. Methods: Patients enrolled in S1105 were required to have been on an AI for ≥30 days at enrollment. Patients were assessed for non-adherence of AIs every 3 months for 36 months, with non-adherence defined as urine AI metabolite assay results satisfying any of the following: < 10 ng/mL, undetectable, specimen submitted outside of the ± 21-day follow-up appointment window, or no submitted specimen. Factors included race, Hispanic ethnicity, age, education, income, health insurance type, and prescription cost. We also included socioeconomic deprivation and rural or urban residence using trial participants’ residential zip codes linked to the Area Deprivation Index (ADI) and Rural Urban Continuum codes (RUCC) respectively. For numeric factors and categorical factors with more than two levels, we used cutpoint analysis to determine the highest risk range by optimizing the Chi-squared statistic. For each resulting binary factor, we performed a logistic regression, adjusting for the study stratification factors and stratifying by treatment arm. Factors associated with adherence at the alpha=0.10 level were combined into a composite risk score, with study participants given one point for each baseline adverse risk factor. Secondarily, we examined a composite risk score including all factors, regardless of statistical significance. Results: In total, 724 patients were registered from 40 institutions between May 2012 and September 2013. The median age was 60.9 years, and 64.5% were on AI < 12 months prior to registration. Only 8% had out of pocket (OOP) cost >$30. Observed adherence at 36 months was 35.9%. Patients living in metro areas with populations ≥ 250,000 (RUCC ≤ 3) were more likely to be non-adherent (37.5% vs 28.3%, OR=1.50, p=.08); as were those who were younger, those without a college education, and those with limited (≤$5) OOP cost. ADI, race and ethnicity were not associated with non-adherence. The composite risk score was strongly associated with 36-month adherence. For each 1 unit increase in risk level, the risk of non-adherence increased 47% (OR=1.47, p< .001); the risk increased 64% for those with >2 vs. ≤2 risk factors (OR=1.64, p=.002). Results using all factors regardless of statistical significance were consistent. Conclusions: A composite model comprised of sociodemographic risk factors can identify patients on AI’s at much greater risk of long-term AI non-adherence. In addition to mitigating side effects, targeted interventions to improve adherence should focus on structural barriers among those at highest risk. Non-adherence (+/- 6 months) = factor + AI duration + AI type, stratified by arm Citation Format: Dawn Hershman, Anna Moseley, Kathryn Arnold, Julie Gralow, Alfred Neugut, Scott Ramsey, N. Lynn Henry, Joseph Unger. Sociodemographic Risk Factors and Prediction of Aromatase Inhibitor Non-Adherence in Women with Breast Cancer Enrolled in SWOG S1105 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS04-08.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Modeling Risk Assessment of Soil Heavy Metal Pollution using Partial Least Squares and Fuzzy Logic: A Case Study of a Gully Type Coal-Based Solid Waste Dumpsite

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R2 C > 0.80 and R2 P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Thermal analysis of permanent magnet synchronous linear motor accounting for three-phase current unbalance

Three-phase current unbalance is a common winding fault of the motor, which will bring local overheating, cause security risks and economic losses, accurate temperature rise calculation and thermal flux characteristics analysis of the motor working in three-phase current unbalance are necessary and challenging. However, little research has been conducted on the thermal characteristics and analysis methods for the permanent magnet synchronous linear motor (PMSLM) working in three-phase current unbalance. To address this problem, a multilayer composite flat wall model contained heat source of the primary is proposed firstly to analysis the heat transfer characteristics of the PMSLM caused by three-phase current unbalance qualitatively. Then, the heat transfer and temperature distribution characteristics of the PMSLM are obtained by calculating the fluid-thermal coupling model of the PMSLM directly. What's more, the effect of current grade on heat transfer is analyzed by comparing the heat transfer characteristics of the PMSLM working in four different three-phase current balance cases. Finally, the qualitative analysis and fluid-thermal analysis results are verified experimentally with a PMSLM adopting four different three-phase current unbalance conditions. Thermal analysis of the PMSLM working in three-phase current unbalance makes it is possible to prevent local heat accumulation and avoid unexpected interruptions for the redundant motor system and the fault tolerant motor system.

RGBD Synergetic Model for Image Enhancement in Animation Advertisements

This paper proposes a depth image symbiosis model to solve the problem of insufficient depth image quality in animated advertising. The model uses image surface information and image edge cues as the main guidance information to obtain image symbiosis information. Research data show that the model designed in this paper performs well in convergence, and enters a stable convergence process when the number of iterations is less than 5. Its PSNR data curve has the highest position and best performance, while a composite model structure has been adopted. Compared with the unitary model, the PSNR of this model reaches 41 dB when the number of iterations reaches 5, and the convergence effect of the three-step training is also better. Finally, in practical applications, the average PSNR value of the model mentioned in this article is the highest, 37.1 dB. From this comprehensive perspective, the depth image enhancement model in this study has better comprehensive performance and can provide better image enhancement effects for animation advertising depth images.

Exploring potential predictors of Henoch-Schönlein purpura nephritis: a pilot investigation on urinary metabolites.

Henoch-Schönlein purpura nephritis (HSPN) is the most severe manifestation of Henoch-Schönlein purpura (HSP). This study aimed to determine the role of urine metabolomics in predicting HSPN and explore the potential mechanisms of HSP. A liquid chromatography-tandem mass spectrometry-based untargeted metabolomics analysis was performed to investigate the urinary metabolic profiles of 90 participants, comprising 30 healthy children (group CON) and 60 patients with HSP, including 30 HSP patients without renal involvement (group H) and 30 HSPN patients (group HSPN). The differentially expressed metabolites (DEMs) were identified using orthogonal partial least squares discriminant analysis (OPLS-DA), and subsequent bioinformatics analysis was conducted to elucidate the perturbed metabolic pathways. A total of 43 DEMs between H and HSPN groups were analyzed by the Kyoto Encyclopedia of Gene and Genome (KEGG) database, and the result indicates that glycine, serine and threonine metabolism, and cysteine and methionine metabolism were significantly disturbed. A composite model incorporating propionylcarnitine and indophenol sulfate was developed to assess the risk of renal involvement in pediatric patients with HSP. Conclusion: This study reveals the metabolic alterations in healthy children, HSPN patients, and HSP patients without renal involvement. Furthermore, propionylcarnitine and indophenol sulfate may be potential predictive biomarkers of the occurrence of HSPN. What is Known: • HSP is the predominant type of vasculitis observed in children. The long-term prognosis of HSP is contingent upon the extent of renal impairment. In severe nephritis, a delay in appropriate treatment may lead to fibrosis progression and subsequent development of chronic kidney disease (CKD), even leading to renal failure. • The application of metabolomics in investigating diverse renal disorders has been documented. Urine is a robust and sensitive medium for metabolomics detection. What is New: • The metabolic profiles were identified in urine samples of healthy children and those with HSP at the early stage of the disease. Different metabolites were identified between HSP patients without nephritis and those who developed HSPN. • These different metabolites may affect oxidative stress in the progression of HSPN.

Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea hypopnea syndrome.

Obstructive sleep apnea hypopnea syndrome (OSAHS) poses a significant health hazard, as intermittent hypoxia inflicts damage throughout the body and is considered a critical risk factor for metabolic disorders. The aim of this study was to establish a metabolic profile for patients with OSAHS using nontargeted metabolomics detection techniques, providing a basis for OSAHS diagnosis and novel biological marker identification. Forty-five patients with OSAHS composed the OSAHS group, and 44 healthy volunteers composed the control group. Nontargeted metabolomics technology was used to analyze participants' urinary metabolites. Differentially abundant metabolites were screened and correlated through hierarchical clustering analysis. We constructed a composite metabolite diagnostic model using a random forest model. Simultaneously, we analyzed the relationships between 20 metabolites involved in model construction and OSAHS severity. The urinary metabolomics pattern of the OSAHS group exhibited significant changes, demonstrating noticeable differences in metabolic products. Urinary metabolite analysis revealed differences between the mild-moderate OSAHS and severe OSAHS groups. The composite metabolite model constructed in this study demonstrated excellent diagnostic performance not only in distinguishing healthy control participants from patients with mild-moderate OSAHS (AUC = 0.78) and patients with severe OSAHS (AUC = 0.78), but also in discriminating between patients with mild-moderate and severe OSAHS (AUC = 0.71). This study comprehensively analyzed the urinary metabolomic characteristics of patients with OSAHS. The established composite metabolite model provides robust support for OSAHS diagnosis and severity assessment. Twenty metabolites associated with OSAHS disease severity offer a new perspective for diagnosis.

Iterative experimental design and identifiability analysis of composite material failure models

The parameter identification of failure models for composite plies can be cumbersome, due to multiple effects as the consequence of brittle fracture. Our work proposes an iterative, nonlinear design of experiments (DoE) approach that finds the most informative experimental data to identify the parameters of the Tsai-Wu, Tsai-Hill, Hoffman, Hashin, max stress and Puck failure models. Depending on the data, the models perform differently, therefore, the parameter identification is validated by the Euclidean distance of the measured points to the closest ones on the nominal surface. The resulting errors provide a base for the ranking of the models, which helps to select the best fitting. Following the validation, the sensitivity of the best model is calculated by partial differentiation, and a theoretical surface is generated. Lastly, an iterative design of the experiments is implemented to select the optimal set of experiments from which the parameters can be identified from the least data by minimizing the fitting error. In this way, the number of experiments required for the identification of a model of a composite material can be significantly reduced. We demonstrate how the proposed method selected the most optimal experiments out of generated data. The results indicate that if the dataset contains enough information, the method is robust and accurate. If the data set lacks the necessary information, novel material tests can be proposed based on the optimal points of the parameters' sensitivity of the generated failure model surface.

Bayesian compositional models for ordinal response.

Ordinal response is commonly found in medicine, biology, and other fields. In many situations, the predictors for this ordinal response are compositional, which means that the sum of predictors for each sample is fixed. Examples of compositional data include the relative abundance of species in microbiome data and the relative frequency of nutrition concentrations. Moreover, the predictors that are strongly correlated tend to have similar influence on the response outcome. Conventional cumulative logistic regression models for ordinal responses ignore the fixed-sum constraint on predictors and their associated interrelationships, and thus are not appropriate for analyzing compositional predictors.To solve this problem, we proposed Bayesian Compositional Models for Ordinal Response to analyze the relationship between compositional data and an ordinal response with a structured regularized horseshoe prior for the compositional coefficients and a soft sum-to-zero restriction on coefficients through the prior distribution. The method was implemented with R package rstan using efficient Hamiltonian Monte Carlo algorithm. We performed simulations to compare the proposed approach and existing methods for ordinal responses. Results revealed that our proposed method outperformed the existing methods in terms of parameter estimation and prediction. We also applied the proposed method to a microbiome study HMP2Data, to find microorganisms linked to ordinal inflammatory bowel disease levels. To make this work reproducible, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCO.

On the importance of integrating convolution features for Indian medicinal plant species classification using hierarchical machine learning approach

This work proposes a novel hierarchical classification framework designed to categorize hundred Indian medicinal plant species. The innovation lies in introducing a comprehensive feature representation by integrating convolutional features with geometric, texture, shape, and multispectral features for classification tasks. In this study, a two-level hierarchical plant classification model is proposed to address the challenges of inter-class similarity and intra-class variations. The first level classifies 100 medicinal plant species into 11 groups based on visual similarities among the plants. At level two, the specific plant species containing in each group are predicted using Random Forest classifier. The evaluation is performed at two levels to analyze the effectiveness of the proposed model. The performance analysis compares the effectiveness of individual feature types against the composite feature model. Performance is also evaluated based on specific groups that demonstrate high similarity between classes and intra-class variations among the plant species separately. Furthermore, the generality of the model is tested using two self-created datasets-RTL80 and RTP40, requiring more than 300 man-hours to collect. Experimental results demonstrate a promising accuracy of 94.54% on GSL100 leaf dataset and 75.46% on RTL80 and RTP40 real-time datasets reflecting the superiority of the proposed hierarchical model over state-of-the-art methods.

Virtual unmodeled dynamic and data-driven nonlinear robust predictive control

This study presents a novel approach for controlling an industrial process that exhibits uncertainty and significant nonlinear features. The proposed method utilizes a virtual unmodeled dynamic and data-driven nonlinear robust predictive control strategy. The representation of a controlled object involves a composite state space model that combines both linear and high-order nonlinear elements. Moreover, a robust model predictive controller is developed using the linear component. In addition, the notion of one-step optimal feedforward is used in combination with a compensating controller to handle the high-order nonlinear factor specifically. Subsequently, a compensation controller with incremental characteristics is developed for a modified version of the high-order nonlinear term. Furthermore, the stability conditions of the closed-loop system are derived, and an analysis is conducted on the stability and convergence of the proposed approach. The TTS20 three-capacity water tank was utilized in both simulations and practical scenarios. The study demonstrated that the suggested approach successfully reduces system output variations and enhances overall performance in response to unpredictable changes in the process’s dynamic features.