Capacity Model Research Articles

Tailoring Wear Properties of Nodular Cast Iron through Induction Hardening Optimization

Abstract The current study investigated the wear performances of GGG60 nodular cast iron under different loads after induction hardening at different powers and durations. The background of this study is the optimization of induction hardening parameters applied to increase the wear resistance of GGG60 nodular cast iron, which is the raw material of parts subjected to high wear loads such as crankshafts, gear systems and flywheels. With the obtained wear results, the induction parameters were optimized by RSM. Induction hardening was treated at four induction powers and two induction durations. Wear tests were conducted using the ball-on-disc method under dry wear conditions for 60 min. Three different wear loads of 10, 20, and 30 N were used. The samples were analyzed in terms of microstructure, hardness, and wear characterization. Subsequently, a RSM model was constructed using the wear results Untreated specimens have significantly lower performance characteristics than treated ones. The hardness, penetration depth, and wear performance indicated a strong relationship with the increase in both induction power and duration. The optimization model presented that the most critical parameter of volume loss is the wear load. In addition, induction power and wear load were found to be significant in the wear rate. The model's prediction capacity for volume loss is satisfactory. Nevertheless, the model's prediction capacity for the wear rate experienced a slight decrease. Conclusions indicate the model can estimate the induction hardening parameters.

Research on the bearing performance of HSCA high-strength preloaded expansion piles in calcareous sand foundation

With the rapid development of infrastructure construction on oceanic reefs, calcareous sand, as the primary medium of these reefs, exhibits unique physical and mechanical properties such as high void ratio, low strength, and susceptibility to particle breakage. These characteristics reduce the bearing capacity and stability of pile foundations in calcareous sand foundations. This study investigates the bearing characteristics of high-strength preloaded expansion piles in calcareous sand foundations, taking into account the influence of HSCA high-performance expansion agent dosage through a series of indoor model tests and in-situ tests. The research delves into the load-settlement curves of expansion piles, the distribution of axial force and side resistance of piles, and the effects of Calcareous sand compaction and reinforcement around the piles. The results indicate that adding the HSCA high-performance expansion agent results in compaction preloading of the Calcareous sand around the pile, significantly increasing the expansion stress on the pile side, thereby enhancing the resistance on both the pile side and pile tip. When the expansion agent dosage is 20%, the ultimate bearing capacity can be increased by 56%, and the ultimate side resistance by 63%. The Coulomb strength theory of non-cohesive soil is employed to accurately calibrate the incremental side resistance of the expansion section. A prediction model for the bearing capacity of the expansion pile is established by combining the side resistance prediction model with the ultimate side resistance load-sharing ratio. The research outcomes provide important guidance for the optimization, design, and construction of high-strength preloaded expansion piles in calcareous sand foundations.

Influence of screw angle and position on the extraction dynamics of Cross-Laminated Timber: an empirical analysis and comparative modeling study

ABSTRACT The design of dowel-type fastener connections is crucial for the structural integrity of Cross-Laminated Timber (CLT) systems, with individual fastener anchorage capacity as a key factor. This study investigates the withdrawal performance of Self-Tapping Screws (STSs) in CLT systems using Canadian Douglas fir. Four driving angles—45°, 60°, 75°, and 90°—were tested, alongside screw placements within panel gaps. Optimal performance was observed at 75° and 90°, with 90° placement in gaps enhancing stiffness and ductility but reducing energy dissipation. The ratios of experimental capacities to model predictions were 1.26–1.49 for the Blaß and Uibel (B&U) model and 1.36–1.56 for Eurocode 5 (EC5). The new EC5 code, compared to the previous version, includes several advancements such as more accurate material properties and improved calculation methods for fastener withdrawal capacity, offering enhanced prediction accuracy for STS performance in CLT. The discrepancies in the over-strength factors predicted by the new EC5 version ranged from 1.07 to 1.37. These findings suggest the need to consider the incorporation of a specific calculation model for STS withdrawal capacity in CLT, such as the Ringhofer et al. model, in the next revision of EC5.

Mechanistic Modelling for Optimising LTES-Enhanced Composites for Construction Applications

This study addresses the optimisation of latent heat thermal energy storage (LTES) composites for construction applications by utilising mechanistic modelling. The work focuses on enhancing the performance of phase change materials (PCMs) incorporated into expanded perlite (EP) for building energy efficiency by delivering sorption capacity models analysing factors such as particle size, surface area, and pore volume, particularly highlighting the performance of EP as a PCM carrier due to its high porosity (around 90%) and large surface area (up to 20 m2/g), which allowed for improved energy storage density and heat transfer. Key challenges in the integration of PCMs into construction materials, such as limited thermal conductivity and leakage during phase transitions, are explored. The model evaluates key parameters affecting sorption, such as temperature, pressure, and surface characteristics of the materials. The results indicate that while higher temperatures enhance sorption in larger pores, they reduce efficiency in smaller ones, leading to a slight overall decrease in total sorption capacity at elevated temperatures. The sorption capacity of water is a value slightly above 2 kg/kg EP, while the PCM RT27 exhibits a sorption capacity of 0.59 kg/kg EP. These results represent the optimised sorption performance in terms of temperature between 40 °C and 50 °C. Furthermore, applying vacuum impregnation is investigated in relation to the pore radii of the EP particles. Larger pore radii show a noticeable improvement in overall sorption capacity from 0.59 kg/kg EP to 0.68 kg/kg EP as pressure increases, especially beyond 4 × 105 Pa. The contribution of inter-particle sorption remains stable, while the intra-particle sorption in large pores drives the overall capacity upward. The findings convey significant findings in optimising the design of LTES-enhanced composites for improved energy storage, thermal regulation, and structural integrity in building applications.

DDSNet: a dual-domain supervised network for remote sensing image dehazing

Abstract Haze shrouds remote sensing images with a thick veil, severely affecting the extraction of valuable information and posing many obstacles to subsequent high-level vision tasks. However, current methods frequently concentrate solely on spatial information while neglecting frequency domain information. To tackle the above problem, we propose a novel model in this study that combines information from the spatial and frequency domains. Unlike most existing methods, We also investigate the relationship between phase and amplitude spectrum components in the frequency domain and haze degradation and use this connection to design a network structure. We have meticulously designed a central Spatial-frequency block containing a Global frequency supervised block (GFS), a Local spatial supervised block (LSS), and a Spatial frequency fusion block (SFF) and utilized parameter-free normalization representation to improve the model's capacity to manage instances with varying attributes. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on two remote sensing image dehazing datasets: SateHaze1k and RICE-1. The results indicate that our network performs exceptionally well, surpassing previous techniques in both quantitative assessments and visual quality. Our DDSNet demonstrates remarkable effectiveness through quantitative analysis, achieving the highest performance across three subsets of the SateHaze1k dataset, with measured values of 24.0053 dB PSNR and 0.9661 SSIM, 26.6054 dB PSNR and 0.9696 SSIM, and 21.3015 dB PSNR and 0.9208 SSIM.

Plant nutrient concentrations inform white-tailed deer diet limitations.

Management of large herbivores often involves increasing availability of forages sufficient in nutrient density to allow animals to meet dietary demands. Nutritional carrying capacity (NCC) models commonly are used to compare plant communities and management strategies, but failure to use the most limiting nutrient could result in overestimating NCC. Moreover, the relationship between limiting nutrients often is not considered, which may influence the utility of NCC models based on a single nutrient, especially when herbivores must simultaneously meet multiple constraints. We examined crude protein and phosphorus concentrations in 131 plant species commonly eaten by white-tailed deer (Odocoileus virginianus) to determine whether they would meet a minimum 14% crude protein and 0.3% phosphorus constraint for a lactating female. Crude protein and phosphorus demands were met in 43.9% and 18.8% of sampled forages, respectively. Concentrations of crude protein and phosphorus were greatest in young forb tissue, with an average of 18.6% crude protein and 0.28% phosphorus. Protein and phosphorus concentrations were positively correlated, but not all plants which met protein requirements simultaneously provided sufficient phosphorus. We created NCC models using crude protein and phosphorus and documented phosphorus tended to be more limiting, but variation existed among sites. Given that limiting nutrients may vary spatiotemporally, focusing conservation efforts on providing a diversity of plants, particularly to include forbs that simultaneously meet multiple nutritional demands, is likely the most practical management approach.

Dynamics of Contaminant Flow Through Porous Media Containing Random Adsorbers

Many porous media are mixtures of inert and reactive materials, manifesting spatio-chemical heterogeneity. We study the evolution of scalar transport in a chemically heterogeneous material that mimics a green roof soil substrate, fractionally composed of inert and reactive adsorbing particles. These adsorbing particles are equivalent to biochar within a real soil substrate. The scalar transport evolution is determined using experiments and simulations calibrated from experimental data. Experiment 1 is used to determine the equilibrium capacity and adsorption rate of two biochar types when immersed in a methylene blue solution. Breakthrough curves of a packed bed of glass beads with randomly interspersed biochar are determined in experiment 2. Simulations are then run to investigate the solute transport and adsorption dynamics at the pore-scale. An analytical model is proposed to capture the behavior of the biochar adsorption capacity, and the simulation results are compared with experiment 2. A pore-scale analysis showed that uniformly sized beds are superior in contaminant breakthrough reduction, which is related to the adsorptive surface area and the rate at which adsorption capacity is reached. Cases using the adsorption capacity model display a tight distribution of particle surface concentration at later simulation times, indicating maximum possible adsorption. The beds with dissimilar particle sizes create more channeling effects which reduce adsorptive particle efficiency and consequently higher breakthrough concentration profiles. Comparison between experiments and simulations show good agreement. Improved biochar performance can be achieved by maintaining particle size uniformity alongside high adsorption capacity and adsorption rates appropriate to the rainfall intensity.

Color Inside and Outside the Lines: Evidence From Eye‐Tracking Studies on Conformity to and Differentiation From Category Color Codes

ABSTRACTCategory color codes are commonly used hues in packaging within a specific product category. These colors establish visual standards for new products and influence consumer perception and product categorization. Product managers face a challenging decision: Should they conform to or differentiate from these established color norms? For example, should flour packaging adopt the typical white category color code or stand out with a distinctive color like purple? Retailers face a similar dilemma: Where should they place products that conform to or differ from these color norms to capture consumer attention effectively? Despite the relevance of category color codes for both manufacturers and retailers, the literature on this topic remains limited. Building upon the Limited Capacity Model of Motivated Mediated Message Processing, this paper examines how conformity or differentiation of main and secondary packaging colors from category codes impacts consumer attention. Findings from lab and field eye‐tracking studies reveal a negative interaction between the conformity of main and secondary colors with category codes on both the duration and number of fixations. Hybrid color combinations—where either the main or secondary color conforms while the other differentiates—are most effective in attracting consumer attention, which, in turn, influences consumer choice. This research expands the understanding of the trade‐off between conformity and differentiation in packaging color, offering theoretical contributions to the role of visual stimuli in attracting attention and providing practical guidance for managers in selecting optimal color combinations for their products.

Intelligent fault diagnosis based on multi-sensor data fusion and multi-scale dual attention enhanced network

Abstract As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a multi-sensor data fusion (MSDF) strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a multi-scale dual attention enhanced network (MSDAENet) is constructed, incorporating both time-frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model's capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.

Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques

ABSTRACTExisting studies mainly focus on short‐term economic forecasts, but research on long‐term projections, particularly for periods spanning 6–10 years, remains insufficient, despite its importance. This gap may arise from the limitations of traditional linear methods in prediction tasks and pattern recognition, whereas machine learning techniques may help overcome these challenges. To address this, we employ five widely used machine learning models—artificial neural networks (ANN), random forest regression (RF), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), and support vector regression (SVR)—using cross‐country data from 109 countries between 1961 and 2019. To ensure robustness, we employ two distinct sampling methods for model validation. Our findings reveal that the ANN model outperforms others, particularly in long‐term predictions (6–10 years), with an average out‐of‐sample prediction ‐squared of 0.89. Furthermore, analyses using permutation feature importance (PFI) and SHapley Additive exPlanations (SHAP) methods indicate that while current growth rates are critical for short‐term forecasts (1–3 years), two primary variables representing a country's foundational characteristics—real GDP per capita and “country‐feature,” akin to a country dummy variable—are crucial for long‐term predictions (4–10 years). This outcome demonstrates the ANN model's capacity to capture each country's unique characteristics and, through its highly non‐linear nature, successfully execute complex, long‐range forecasts. These results unveil the remarkable potential of machine learning in the realm of long‐term economic forecasting.

The Future of Plant Health: Deep Learning Solutions

Plant diseases must be identified early to prevent crop losses, and agriculture is essential to maintaining food security. With the use of a deep learning model based on Convolutional Neural Networks (CNNs). This research aims to automate the process of detecting plant leaf diseases. To distinguish between healthy and unhealthy plant leaves, the suggested system makes use of image processing techniques. To increase the model's capacity for generalization, data augmentation methods including zooming, rescaling, and horizontal flipping are used. Using categorical cross-entropy as the loss function and Adam optimizer, the CNN model is trained on a dataset of plant leaf images. Findings show that the model performs well in situations involving the real-time detection of diseases, with high accuracy observed in both training and validation datasets.

What happened next? A survey of review clients evaluating impacts of rapid reviews.

End-user evaluation of the impact of evidence syntheses is critical to demonstrating value. This study presents results of a survey evaluating the impact of rapid reviews undertaken by two teams based in Melbourne, Australia and Hamilton, Canada. Clients were invited to participate in a short, written survey following delivery of a rapid review. Survey items encompassed reach, usefulness and format; interactions with the review teams; and overall satisfaction. Twenty-five completed surveys from 53 invitations were received pertaining to 19 rapid reviews conducted between September 2021 and October 2023. Topics encompassed COVID-19, health and behaviour change; reports were an average of 31 pages; and were delivered over an average of 62 days. Evaluation findings were positive, with high satisfaction with reports and service delivery; very high satisfaction with report structure and length; good evidence of reach (reports read by decision makers and cited in other documents); and evidence that the rapid reviews made contributions to strategic planning, policy and program funding decisions. Rapid reviews are making impactful contributions, alongside other inputs, to policy and practice. Further research is required to build this evaluation dataset; examine the balance between timeliness and methodological rigor in evidence synthesis; and explore models of delivery and capacity within and outside of government. It is also critical to promote implementation efforts to harness the full potential of rapid reviews and other evidence syntheses to impact the lives of citizens.

Visual attention matters during word recognition: A Bayesian modeling approach.

It is striking that visual attention, the process by which attentional resources are allocated in the visual field so as to locally enhance visual perception, is a pervasive component of models of eye movements in reading, but is seldom considered in models of isolated word recognition. We describe BRAID, a new Bayesian word-Recognition model with Attention, Interference and Dynamics. As most of its predecessors, BRAID incorporates three sensory, perceptual, and orthographic knowledge layers together with a lexical membership submodel. Its originality resides in also including three mechanisms that modulate letter identification within strings: an acuity gradient, lateral interference, and visual attention. We calibrated the model such that its temporal scale was consistent with behavioral data, and then explored the model's capacity to generalize to other, independent effects. We evaluated the model's capacity to account for the word length effect in lexical decision, for the optimal viewing position effect, and for the interaction of crowding and frequency effects in word recognition. We further examined how these effects were modulated by variations in the visual attention distribution. We show that visual attention modulates all three effects and that a narrow distribution of visual attention results in performance patterns that mimic those reported in impaired readers. Overall, the BRAID model could be conceived as a core building block, towards the development of integrated models of reading aloud and eye movement control, or of visual recognition of impaired readers, or any context in which visual attention does matter.

Noninvasive Oral Hyperspectral Imaging-Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study.

Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics. The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms. Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People's Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance. Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model's capacity to accurately identify participants with HFpEF. This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care. China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133.

InterDIA: Interpretable Prediction of Drug-induced Autoimmunity through Ensemble Machine Learning Approaches.

Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these challenges, we developed InterDIA, an interpretable machine learning framework for predicting DIA toxicity based on molecular physicochemical properties. Multi-strategy feature selection and advanced ensemble resampling approaches were integrated to enhance prediction accuracy and overcome data imbalance. The optimized Easy Ensemble Classifier achieved robust performance in both 10-fold cross-validation (AUC value of 0.8836 and accuracy of 82.81%) and external validation (AUC value of 0.8930 and accuracy of 85.00%). Paired case studies of hydralazine/phthalazine and procainamide/N-acetylprocainamide demonstrated the model's capacity to discriminate between structurally similar compounds with distinct immunogenic potentials. Mechanistic interpretation through SHAP (SHapley Additive exPlanations) analysis revealed critical physicochemical determinants of DIA, including molecular lipophilicity, partial charge distribution, electronic states, polarizability, and topological features. These molecular signatures were mechanistically linked to key processes in DIA pathogenesis, such as membrane permeability and tissue distribution, metabolic bioactivation susceptibility, immune protein recognition and binding specificity. SHAP dependence plots analysis identified specific threshold values for key molecular features, providing novel insights into structure-toxicity relationships in DIA. To facilitate practical application, we developed an open-access web platform enabling batch prediction with real-time visualization of molecular feature contributions through SHAP waterfall plots. This integrated framework not only advances our mechanistic understanding of DIA pathogenesis from a molecular perspective but also provides a valuable tool for early assessment of autoimmune toxicity risk during drug development.

Mathematical assessment of the role of temperature on desert locust population dynamics.

This study presents a novel non-autonomous mathematical model to explore the intricate relationship between temperature and desert locust population dynamics, considering the influence of both solitarious and gregarious phases across all life stages. The model incorporates temperature-dependent parameters for key biological processes, including egg development, hopper growth, adult maturation, and reproduction. Theoretical analysis reveals the model's capacity for complex dynamical behaviors, such as multiple stable states and backward bifurcations, suggesting the potential for sudden and unpredictable population shifts. Sensitivity analysis identifies temperature-related parameters as critical drivers of population fluctuations, highlighting the importance of accurate temperature predictions for effective management. Numerical simulations demonstrate the significant impact of temperature on population growth, with optimal conditions promoting rapid development and increased survival, while extreme temperatures can hinder population growth and trigger phase transitions. By providing a deeper understanding of temperature-driven population shifts, this model enhances the ability to predict locust outbreaks, optimize control strategies, and reduce the socio-economic and ecological impacts of locust invasions.

SSIM over MSE: A new perspective for video anomaly detection.

Video anomaly detection plays a crucial role in ensuring public safety. Its goal is to detect abnormal patterns contained in video frames. Most existing models distinguish the anomalies based on the Mean Squared Error (MSE), which is hard to align with human perception, resulting in discrepancies between model-detected anomalies and those recognized by humans. Unlike the Human Visual System (HVS), those models are trained to prioritize texture over shape, which leads to poor model interpretability and limited performance. To address these limitations, we propose to optimize the video anomaly detection models from the perspective of human visual relevance. The optimization infrastructure includes a novel Structural Similarity Index (SSIM) based loss, a novel anomaly score calculation method based on SSIM, and a spatial-temporal enhancement block in 3D convolution (STE-3D). SSIM loss helps the model emphasize shape information in videos rather than texture. An anomaly score method based on SSIM evaluates video frames to align more closely with human visual perception. STE-3D improves the model's capacity to capture spatial-temporal features and compensates for the deficiency of the SSIM loss in capturing temporal features. STE-3D is lightweight in design and seamlessly integrated into existing video anomaly detection models based on 3D convolution. Extensive experiments and ablation studies were conducted in four challenging video anomaly detection benchmarks,i.e., UCSD Ped1, UCSD Ped2, CUHK Avenue, and ShanghaiTech. The experimental results validate the efficacy of the proposed approaches in improving video anomaly detection performance.

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation.

Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate remarkable abilities in navigation. Existing computational models often fall short in faithfully replicating the morphology of real insects and their interactions with the environment, hindering validation and practical application in robotics. To address these gaps, we present I2Bot, a novel simulation tool based on the morphological characteristics of real insects. This tool empowers robotic models with dynamic sensory capabilities, realistic modelling of insect morphology, physical dynamics and sensory capacity. By integrating gait controllers and computational models into I2Bot, we have implemented classical embodied navigation behaviours and revealed some fundamental navigation principles. By open-sourcing I2Bot, we aim to accelerate the understanding of insect intelligence and foster advances in the development of autonomous robotic systems.

A failure criterion of bond strength of corroded steel bars based on plasticity and limit analysis of concrete and the prediction model of shear capacity of beams based on CSCT and MCFT theories

A failure criterion of bond strength of corroded steel bars based on plasticity and limit analysis of concrete and the prediction model of shear capacity of beams based on CSCT and MCFT theories