Mechanism-based Model Research Articles

An effectual underwater image enhancement framework using adaptive trans-resunet ++ with attention mechanism

ABSTRACT The intricacy of the underwater setting makes it difficult for optical lenses to capture clear underwater photos without haze and colour distortion. Some studies use domain adaptation and transfer learning to address this issue, they aim to reduce the latent mismatch between composition and real-world data, making the space of latent data difficult to read and impractical to control. The background light is a crucial component of the decaying paradigm that directly impacts how well images are enhanced. Thus, to improve the quality of the images over the underwater, new deep-learning techniques are being designed in this paper. Here, the Adaptive Trans-ResUnet++ with Attention Mechanism-based model performs the real-time underwater image enhancement process. In addition, a novel Random Enhanced Artificial Gorilla Troops Optimizer algorithm model is used for optimising the parameters over the given model to further enhance the given model’s performance. A diverse quantitative and qualitative validation is also carried out to learn the enhancement of underwater image quality. The enhanced underwater image may be also useful in the underwater object detection process. Thus, the enhanced images obtained from the developed model are compared with the existing techniques to confirm the efficacy of the suggested underwater image enhancement process.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Predictive mechanical property and fracture behavior in high-carbon steel containing high-density carbides via artificial RVE modeling

The mechanical properties and fracture behavior of high-carbon steel are closely related to the microstructural characteristics. This work developed the artificial representative volume element (RVE) model to explore the effects of microstructural characteristics on mechanical properties and fracture behavior of high-carbon steel containing high-density carbides under uniaxial tension. A series of RVEs with different ferrite grain sizes, particle volume fractions, and particle sizes were generated based on the RSA algorithms. The mechanism-based plasticity model and the three uncoupled damage models were implemented into the RVE modeling. The model parameters were calibrated by the corresponding simulation between in-situ μ DIC and microstructure-based RVE simulation. The predicted mechanical properties and fracture strain from the RVE simulation were in good agreement with the experimental results. Simulated results from a series of RVEs quantified the effects of ferrite grain sizes, particle volume fractions, and particle sizes on strength, elongation, and damage evolution of high-carbon steel containing high-density carbides: strength increases with increasing particle volume fraction while elongation decreases, as well as excessively large or small grain and particle size were not favored to improve elongation. These results were attributed to the damage and internal stress partition.

Mechanism based four-linear cohesive zone model for mode I fracture of different stacking sequence CFRP laminates

Delamination, a prevalent failure mode observed in laminated composites, exerts a significant impact on structural integrity and performance. The occurrence of fiber bridging during the fracture process adds complexity and elevates the research challenges associated with this phenomenon. Existing models exhibit limitations in accurately capturing bridging behavior and discerning its underlying mechanical mechanisms. This study addresses these limitations by analyzing experimental results, employing the J-integral, and analyzing R-curve behavior, proposing a mechanism-based four-linear cohesive zone model along with a new finite element implementation method. Comprising three overlapping bi-linear CZMs, this model effectively simulates mode I fracture behavior in laminates with different stacking sequences. Moreover, it intuitively illustrates the mechanical mechanisms during crack propagation and offers simplicity in implementation. This research contributes to a deeper understanding of composite fracture mechanics and provides a practical model for predicting delamination behavior in laminated structures.

Model-based translation of results from in vitro to in vivo experiments for afabicin activity against Staphylococcus aureus.

Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative. To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome. A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters. A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range. The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model.

Research on the bonding performance and mechanism of hot-rolled Ti/steel clad plates based on surface state

The interfacial bonding strength of Ti/steel clad plates is a crucial factor that affects their application. However, the effect of the surface state, which is a significant determinant, is often neglected. In this study, various surface treatment processes were employed to create different surface states based on the hot-rolling of double-layer steel billets, and the effects and mechanisms of these surface states on the bonding performance of hot-rolled Ti/steel clad plates were systematically examined. The results showed that the Ti/steel clad plates pretreated with a louver wheel exhibited the highest bonding performance, with the average bonding strength peaking at 328.67 MPa and stabilising at approximately 300 MPa. This strength was approximately 50 % greater than that achieved with wire brush treatment and significantly surpassed the results obtained with sanding belts and diamond grinding discs. The analysis of the surface properties and microstructural characteristics revealed that various surface treatments led to different levels of work hardening and lattice distortion at the surface, and the interface bonding strength depended on the degree of matching between these factors. Proper surface hardening can promote the transformation of lattice distortion energy into a diffusion-driving force of elements on both sides of the interface during rolling, enabling sufficient diffusion of elements on both sides of the interface and obtaining good interface bonding performance. A phenomenological prediction mechanism-based model was established to quantify the relationship between the surface state and the bonding strength. This study elucidats the mechanism by which the surface state of materials influences the interfacial bonding performance of hot-rolled Ti/steel clad plates. These findings have significant implications for enhancing the interfacial properties of these composite plates and for selecting suitable pre-rolling surface treatment processes.

Supercritical hydrothermal combustion and enhanced degradation characteristics of phenol

Supercritical hydrothermal combustion is a green combustion technology that produces flames in supercritical water (SCW), which can realize efficient disposal of aqueous wastes. In this study, the typical pollutant phenol is investigated through tubular reactor experiments and mechanism analysis to explore its hydrothermal combustion properties and enhanced degradation effects on refractory species. The results show that 3.06 and 3.41 wt% phenol can be self-ignited in the tubular reactor with a critical preheating temperature of 460 °C. Methanol could improve the ignition and burnout characteristics of phenol. As the COD ratio of methanol was greater than 0.35, the ignition temperature of phenol was reduced to 420 °C, with TOC removal rate achieving 99.9%. At 460 °C, phenol positively influenced the heat release during combustion of phenol/methanol. In oxidation of phenol/ammonia, 1.61 wt% phenol resulted in the reaction temperature rising by around 62.0 °C, allowing a NH4-N removal rate of 92.8% through its synergistic kinetic and thermal effects. Whereas, the nitrogen-containing intermediates from ammonia inhibited phenol oxidation. Finally, a mechanism-based kinetics model for oxidation of phenol in SCW was developed by means of real gas properties and sensitive reaction step modifications, which was viable for simulating the ring-opening path of phenol.

A Review on Lithium-Ion Battery Modeling from Mechanism-Based and Data-Driven Perspectives

As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen to prominence in the automotive industry. The design and utilization of lithium-ion batteries (LIBs), which are core component of NEVs, are directly related to the safety and range performance of electric vehicles. The requirements for a refined design of lithium-ion battery electrode structures and the intelligent adjustment of charging modes have attracted extensive research from both academia and industry. LIB models can be divided into mechanism-based models and data-driven models; however, the distinctions and connections between these two kinds of models have not been systematically reviewed as yet. Therefore, this work provides an overview and perspectives on LIB modeling from both mechanism-based and data-driven perspectives. Meanwhile, the potential fusion modeling frameworks including mechanism information and a data-driven method are also summarized. An introduction to LIB modeling technologies is presented, along with the current challenges and opportunities. From the mechanism-based perspective of LIB structure design, we further explore how electrode morphology and aging-related side reactions impact battery performance. Furthermore, within the realm of battery operation, the utilization of data-driven models that leverage machine learning techniques to estimate battery health status is investigated. The bottlenecks for the design, state estimation, and operational optimization of LIBs and potential prospects for mechanism-data hybrid modeling are highlighted at the end. This work is expected to assist researchers and engineers in uncovering the potential value of mechanism information and operation data, thereby facilitating the intelligent transformation of the lithium-ion battery industry towards energy conservation and efficiency enhancement.

Digitally enhanced development of customised lubricant: Experimental and modelling studies of lubricant performance for hot stamping

Digitally enhanced technologies are transforming every aspect of the manufacturing sector towards the era of digital manufacturing. Traditional lubricant development methods involving systematic but time-consuming iterative processes is still extensively used in the metal forming industry. In the present study, a novel digitally enhanced lubricant development scheme was proposed by leveraging a mechanism-based interactive friction modelling framework and quantitative and comprehensive evaluation of lubricant performance via the data-centric lubricant limit diagrams. By predicting transient lubricant behaviour following the complex contact condition evolution experienced in actual forming operations, a close association and quantified relation between the lubricant performance and its properties such as viscosity, evaporation rate and fraction of dry matter was established. This can facilitate the optimisation efficiency of lubricant parameters and minimise the experimental cost for iterative lubricant trials. A case study was conducted in this work to develop a customised lubricant using this digitally enhance scheme for the target hot stamping process based on a benchmark lubricant as a reference. Further industrial forming tests of an automotive component were conducted to validate the ideal performance of the customised lubricant.

Prediction of mine water quality by the Seq2Seq model based on attention mechanism

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

Topography Modeling of Surface Grinding Based on Random Abrasives and Performance Evaluation

The surface morphology and roughness of a workpiece are crucial parameters in grinding processes. Accurate prediction of these parameters is essential for maintaining the workpiece’s surface integrity. However, the randomness of abrasive grain shapes and workpiece surface formation behaviors poses significant challenges, and accuracy in current physical mechanism-based predictive models is needed. To address this problem, by using the random plane method and accounting for the random morphology and distribution of abrasive grains, this paper proposes a novel method to model CBN grinding wheels and predict workpiece surface roughness. First, a kinematic model of a single abrasive grain is developed to accurately capture the three-dimensional morphology of the grinding wheel. Next, by formulating an elastic deformation and formation model of the workpiece surface based on Hertz theory, the variation in grinding arc length at different grinding depths is revealed. Subsequently, a predictive model for the surface morphology of the workpiece ground by a single abrasive grain is devised. This model integrates the normal distribution model of abrasive grain size and the spatial distribution model of abrasive grain positions, to elucidate how the circumferential and axial distribution of abrasive grains influences workpiece surface formation. Lastly, by integrating the dynamic effective abrasive grain model, a predictive model for the surface morphology and roughness of the grinding wheel is established. To examine the impact of changing the grit size of the grinding wheel and grinding depth on workpiece surface roughness, and to validate the accuracy of the model, experiments are conducted. Results indicate that the predicted three-dimensional morphology of the grinding wheel and workpiece surfaces closely matches the actual grinding wheel and ground workpiece surfaces, with surface roughness prediction deviations as small as 2.3%.

A Rigorous Mechanism-Based Kinetic Modeling Method for Optimization of Solid Oxide Fuel Cell

A Rigorous Mechanism-Based Kinetic Modeling Method for Optimization of Solid Oxide Fuel Cell

High-frequency electrical stimulation increases cortical excitability and mechanical sensitivity in a chronic large animal model.

Translational models of the sensitized pain system are needed to progress the understanding of involved mechanisms. In this study, long-term potentiation was used to develop a mechanism-based large-animal pain model. Event-related potentials to electrical stimulation of the ulnar nerve were recorded by intracranial recordings in pigs, 3 weeks before, immediately before and after, and 3 weeks after peripheral high-frequency stimulation (HFS) applied to the ulnar nerve in the right forelimb (7 pigs) or in control animals (5 pigs). Event-related potential recordings and peripheral HFS were done during anesthesia. Two weeks before and after the HFS, behavioral responses reflecting mechanical and thermal sensitivity were collected using brush, noxious limb-mounted pressure algometer, and noxious laser stimuli. The HFS intervention limb was progressively sensitized to noxious mechanical stimulation in week 1 and 2 compared with baseline (P = 0.045) and the control group (P < 0.034) but not significantly to laser or brush stimulation. The first negative (N1) peak of the event-related potential was increased 30 minutes after HFS compared with before (P < 0.05). The N1 peak was also larger compared with control pigs 20 to 40 minutes after HFS (P < 0.031) but not significantly increased 3 weeks after. The relative increase in N1 30 minutes after HFS and the degree of mechanical hyperalgesia 2 weeks post-HFS was correlated (P < 0.033). These results show for the first time that the pig HFS model resembles the human HFS model closely where the profile of sensitization is comparable. Interestingly, the degree of sensitization was associated with the cortical signs of hyperexcitability at HFS induction.

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Abstract Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.

Population pharmacokinetic and exposure-toxicity analyses of nab-paclitaxel after pegylated recombinant human granulocyte colony-stimulating factor administration in patients with metastatic breast cancer.

This study aimed to establish a population pharmacokinetic (PK) model to evaluate the dynamic relationship between the concentrations of total and unbound paclitaxel, and the exposure-response analysis of albumin-bound paclitaxel (nab-paclitaxel) after pegylated recombinant human granulocyte colony-stimulating factor (PEG-G-CSF) administration in patients with metastatic breast cancer. A total of 653 concentrations corresponding to total paclitaxel and 334 concentrations corresponding to unbound paclitaxel were analyzed in 24 subjects who randomized received a single 260 mg/m2 dose of two nab-paclitaxel formulations with a 21-35-day washout period. PEG-G-CSF was administered to all the patients in each cycle to prevent neutropenia. The exposure-response relationships were evaluated using the exposure to total, albumin-coated, and unbound paclitaxel, as well as the reduction in neutrophil count. The exposure data were analyzed using nonlinear mixed-effect modeling. A linear regression model was used to test the statistical significance of the correlation between percentage of reduction in neutrophil count and exposure. The PK characteristics of total paclitaxel were described using a three-compartment model with first-order elimination, and a mechanism-based model incorporating linear release of nab-paclitaxel and the saturated binding of unbound paclitaxel to plasma components was established. The release ratio of paclitaxel from nab-paclitaxel was estimated to be 4.60% and the maximum unbound fraction (2.76%) was reached at the end of the infusion. The study found that a longer duration of total paclitaxel concentration > 0.19 µmol/L was significantly correlated with a reduction in neutrophil count (r2 = 0.23, P = 0.00062). Specifically, a duration of > 8.6h was a predictor of a decreased neutrophil count. The decrease in neutrophils induced by nab-paclitaxel was significantly correlated with the duration above a total paclitaxel concentration of 0.19 µmol/L despite the use of PEG-G-CSF.

A Mechanism-Based Reaction-Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis.

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction-diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., Tmax). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model's potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

Lower-order mechanism-based strain gradient plastic model considering stress gradient effect

Lower-order mechanism-based strain gradient plastic model considering stress gradient effect

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models.

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Improved Message Mechanism-Based Cross-Domain Security Control Model in Mobile Terminals

Dual-domain terminal with two built-in independent operating systems - Life Domain and Work Domain, provides convenience for daily use and mobile office. However, the security isolation between the two domains also causes that message reminders cannot be delivered and viewed across domains, which restricts the improvement of work efficiency and the expansion of mobile services. This paper conducts an in-depth study on this pain point and proposes the concept and implementation method of a cross-domain instant messaging reminder service system for mobile office, focusing on solving the problems of: cross-domain isolated boundary exchange of message reminders, timeliness and delivery rate guarantee of message reminders, and security check filtering of message contents. Technically, on the side of mobile office platform, based on AMQP technical framework and protocol, the cross-domain isolated border message queue push and synchronization services are built, which are real-time, reliable and high-throughput.

Modeling and Control Research of Fractional-Order Cascaded H-Bridge Multilevel STATCOM

Abstract: This paper introduces fractional-order capacitors and fractional-order inductors into the conventional integer-order cascaded H-bridge multilevel static compensator (ICHM-STATCOM), thereby constructing the main circuit of the fractional-order cascaded H-bridge multilevel static compensator (FCHM-STATCOM). Mechanism-based modeling is employed to establish switching function models and low-frequency dynamic models for the FCHM-STATCOM in the three-phase stationary coordinate system (a-b-c). Subsequently, fractional-order rotating coordinate transformation is introduced to establish the mathematical model of the FCHM-STATCOM in the synchronous rotating coordinate system (d-q). Additionally, a fractional-order proportional-integral (FOPI)-based fractional-order dual closed-loop current decoupling control strategy is proposed. Finally, this paper validates the correctness of the established mathematical models through digital simulation. Moreover, the simulation results demonstrate that by appropriately selecting the order of fractional-order capacitors and fractional-order inductors, the FCHM-STATCOM exhibits superior dynamic and static characteristics compared to the conventional ICHM-STATCOM, and the FCHM-STATCOM provides a more flexible reactive power compensation solution for power systems.