Artificial Neural Network Prediction Research Articles

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

Three-dimensional simulation and artificial neural network optimization of a flat plate heat pipe for the cooling of telecommunication equipment

To address the significantly increased heat dissipation requirements of 5G base stations, a flat plate heat pipe (FPHP) of 500 × 200 × 3 mm3 comprising of 19 independent channels was developed. To solve the problem of simulation distortion due to the high superheat of the initial boiling of the working fluid in the ultra-long channel of 490 × 5 × 1.5 mm3, this paper adopts the Lee model considering the superheat, and simulates based on the Volume of fluid method, and the simulation results are compared with the experiments, and the average relative error is less than 5 %. The FPHP was explored at 100–500 W heat power and filling ratio of 30 %-70 %, depending on the application scenarios of the base station. By partitioning the 19 channels and calculating the thermal resistance of the FPHP based, an Artificial neural network (ANN) model with 10 hidden layer nodes is established based on 675 data sets. The results showed that the mean square error and correlation coefficient were 1.30 × 10-5 and 0.9990. The ANN prediction results are validated, the mean relative error is 5.4 %, demonstrating that the ANN model can be an effective tool to optimize the FPHP for the telecommunication equipment.

Electroelastic wave dispersion in the rotary piezoelectric NEMS sensors/actuators via nonlocal strain gradient theory

This article introduces a computational means for investigating the electroelastic nonlinear wave dispersion traits of the nano-dimension sandwich pipe, which is composed of a core formed of a bi-directional functionally graded (Bi-FG) material, together with a piezoelectric sensor/actuator. A combination of Hamilton’s principle, first-order shear deformation, along with Von-Karman nonlinearity, is used for modeling and obtaining the nonlinear governing equations of the nano-sized sandwich pipe connected to a piezoelectric part. The nonlinear governing equations to obtain the nonlinear phase velocity of the current system are determined using a combination of analytical and multiple scales approaches. Due to some computational cost for choosing the precise values of both the nonlocality factor and length scale of the nanopipe in the laboratory, for the first time in this research, with the aid of COMSOL multi-physics finite element simulation, the results are verified, and new outcomes for obtaining the exact functions for nonlocal and length scale factors is presented. In addition, an artificial neural network (ANN) is utilized in this study for the prediction of the results. The mathematical and finite element results were used to train the ANN. A newly presented optimization algorithm is exploited for the first time for optimizing the ANN parameters concluding higher accuracy of the ANN predictions. Consequently, to explore the influences of the location of the piezoelectric patch, nonlocality and length scale factors, and applied voltage parameter on the phase velocity characteristics of the nano-dimension sandwich pipe made of a Bi-FG core and electrically patch, an effort is performed. As an applicable result that can be useful in the related nano-industries, the current work presents exact nonlocal and length scale functions for different conditions.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Unlocking waste potential: A neural network approach to forecasting sustainable acetaldehyde production from ethanol upcycling in biomass waste gasification

The upcycling of biomass waste gasification process for ethanol and acetaldehyde production has been augmented by integrating secondary and tertiary processes. A simulation model using Aspen Plus, developed from experimental investigations to assess the viability of this proposed system. Furthermore, this model has been utilized to create an Artificial Neural Network (ANN) prediction model. Process sustainability analysis demonstrated an energy efficiency of 64 % while economic viability up to 80 % process efficiency with an Internal Rate of Return (IRR) of 6 % and a payback period of 2107 days. An optimization strategy and an artificial neural network (ANN)-based predictive model have been developed. Optimization results revealed that a gasifier temperature of around 600 °C, a gasifying agent ratio of 2.0, and an acetaldehyde reactor temperature of 300 °C yield better process outcomes and revenue. The ANN model exhibited robust performance with coefficient of determination (R2) values ranging from 0.92 to 0.98 for acetaldehyde, hydrogen, and total revenue. Mean absolute error (MAE) and mean absolute percentage error (MAPE) fell within the range of 0.03–0.1 and 0.1–1.12 %, respectively. Consequently, given the favorable sustainability and predictive model performance, this study can be used for similar endeavors.

Optimasi Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Penduduk Nasional

Predictions or forecasting are important parameters in determining policy that will be implemented in the future. The higher the population of a country, the more it will affect the balance of that country. This research aims to predict the national population using the Artificial Neural Network (ANN) approach. The sample for this research is population data for each province obtained from the National Central Statistics Agency (BPS). The data is iterated from 2011 to 2022. The approach method uses backpropagation Artificial Neural Networks (ANN). The architectural model is determined with 2 hidden layers and 1 output, namely 5-20-10-1 with learning rate variations of 0.01, 0.02, and 0.03. ANN prediction results are able to predict the national population. The success of the three models shows that the ANN model with a Learning rate of 0.01 has the highest accuracy level of 96.9697, equivalent to 97%. This is proven by the model's success in predicting 32 out of 33 data samples with a time duration of 117 seconds. The prediction results showed that the highest population numbers were in three provinces, namely Central Java, West Java, and East Java. Meanwhile, the lowest population is in West Papua province. Keywords: BPS, JST, Learning Rate, Optimization.

Machine learning-based Nusselt number prediction for falling-film evaporators in absorption refrigeration systems

The evaporator serves as a pivotal component of an absorption refrigeration system (ARS), bolstered by improved wettability and mitigation of the irregular arrangement of the liquid film. The complexities of the evaporation heat transfer characteristics of falling films have led to numerous empirical correlations for falling-film Nusselt number (Nu) estimation, demanding substantial computational resources and experimental expenses and limiting their universal applicability. This study used machine learning to predict Nu of a falling-film evaporator. Artificial neural network (ANN) and random forest regression (RFR) models were used to predict heat transfer characteristics and establish a novel Nu correlation using an experimentally derived dataset. Correlations of falling-film Nu were established for six tube types using empirical data, ANN, and RFR predictions and then compared based on experimental falling-film Nu. The ANN and RFR provided high prediction performance within 10 % error and an R2 of 0.985 and 0.999, respectively. The Nu correlations derived from ANN and RFR demonstrated satisfactory agreement, typically within ±15 % error of experimental results lower than that of the empirical correlations. The optimized ANN and RFR demonstrates their promising ability to predict heat transfer characteristics and establish Nu correlations, thereby reducing the need for immense experimental efforts.

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments.

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter's height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

Process control and energy saving in the ladle stage of a metal casting process through physics-based and ANN-based modelling approaches

The process and temperature control of metal casting applications is of utmost importance both to guarantee the good quality of the final product and also to pursue an energy saving policy. For this purpose, in this paper two different modelling approaches have been proposed to predict the liquid steel temperature inside a ladle for metal casting, shortly before the casting process. The first is a physics-based grey-box model relying on equations for the characterization of the heat transfer mechanisms inside the ladle structure, whereas the second approach relies on artificial neural networks (ANNs). Both methods have been calibrated with experimental data of a case study plant, and subsequently assessed and compared in terms of prediction accuracy. Results show that the physics-based approach is able to predict the casting temperature with a higher mean absolute error (MAE) of 14 °C, whereas the ANNs predictions result to be better, with MAEs around 6 °C. On the other hand, it has been demonstrated that the ANNs approach may lack of reliability, especially if input data strongly differ from the calibration dataset, whereas the physics-based approach results to be more consistent and trustworthy. Finally, an energy analysis is conducted to demonstrate the feasibility of the model in evaluating the potential energy saving compared with situations in which decisions are taken by operators without the aid of a model predictive control.

Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

In this work, a previously developed three-feature Artificial Neural Network (ANN) model with dimensional inputs is directly applied to predict the cell size of hydrocarbon/ammonia/nitrous oxide mixtures and compare these to experimental data. This model uses as inputs three ZND parameters (MCJ, ΔI, and σ˙max), which are mainly calculated using Konnov’s and Mével’s mechanisms. A similar prediction is obtained with the two mechanisms for the biogas–O2, H2–O2, H2–N2O, and NH3–O2 mixtures, indicating that the model is not only limited to Konnov’s chemical kinetic mechanism which was used for its training. The overall good agreement between the ANN predictions and the actual experimental values for the aforementioned mixtures, which are not used in the original training of the ANN model, is promising and shows its potential for application and extension to other mixtures and initial conditions, provided that the chemical kinetic parameters describing the ideal reaction zone structure could be computed. The model is then used to compare experimental cell size data from two detonation tube facilities, and also different chemical kinetic mechanisms for NH3-N2O mixtures. In the end, the original ANN model is expanded with the inclusion of additional cell size data, showing a slightly lower mean error for the predicted cell sizes if the data for the mixtures considered in this study are taken into account for the training of the new ANN model.

Numerical Study of Convection Heat Transfer with Confinement Around a Square Cylinder Submerged in a Water-Based Nanofluid

The novelty of this work lies in the comprehensive investigation of Forced convection heat transfer a square cylinder inclined at 45° using CuO nanofluid employing a single phase approach. A heated square cylinder with constant wall temperature boundary condition, subjected to a flowing nanofluid between two parallel walls, undergoes a laminar, steady and two-dimensional flow within a Reynolds number range of 1 < Re > 40. To obtain solutions for the flow and energy transfer, a Finite Element Method (FEM) is employed to numerically solve the governing differential equations and boundary conditions. The objective of this work is to highlight the effects of Reynolds number (Re), confinement ratio (λ), volume concentration (Φ) and diameter of nanoparticles (dnp) on fluid flow and heat transfer characteristics of nanofluid. To capture the effect of Φ and dnp in nanofluid, the thermo-physical-properties of CuO nanofluid are determined experimentally. In the results, at Re = 40, a secondary separation zone (recirculation zone) is observed near the surface of the channel wall. The drag coefficient value rises as the Φ increases and the vdnp decreases, regardless of other factors such as Re and λ. Conversely, as the confinement ratio and volume fraction of nanoparticles increase, the average Nusselt number also rises, while maintaining a constant value of Re and dnp. In contrast, the size of the nanoparticles exhibits an inverse relationship with the average Nusselt number. The study contributes to the understanding of nanofluid behavior and provides practical insights for applications, supported by correlations and Artificial Neural Network predictions (Parrales et al.).

Prediction of laser-welded deformation using artificial neural networks

When predicting welding deformation of the laser-manufactured vehicles and aerospaces, analytical solutions or empirical formulas are not usually accessible in complex problems. Based on the inherent deformation method, a machine learning (ML) approach for predicting welding deformation of welded structures is proposed based on an artificial neural network (ANN). This method is a promising substitute for analytical, empirical, and finite element (FE) solutions due to its accuracy, easy-to-use, efficiency, and universality. First, the outputs of the ANN are determined via dimensionless analysis and comparison of numerical results, which are dimensionally independent. Then, based on the inherent deformation method, the training and validation sets of the ANN are generated through an elastic finite element analysis. At last, the structure of the ANN is determined by analyzing the ANN prediction accuracy with different hidden layers, numbers of neurons, and activation functions. The results show that the ML solutions are in good agreement with the FE results, verifying the effectiveness and generalization ability of the proposed method.

Adsorption/photocatalytic degradation of Cefixime by the green Bi2WO6/g-C3N4/ZIF-67 dual S-scheme heterojunction: Artificial neural network, genetic algorithm, density functional theory, and toxicity assessments

This study investigated the photocatalytic adsorption, degradation, and mineralization of Cefixime (CFX) through hydrothermally synthesized Bi2WO6(36 %)/g-C3N4(54 %)/ZIF-67(10 %) dual S-scheme heterojunction (BCZ). Full characterization analysis for the fresh and reused photocatalyst was explored to study the formation of heterojunction, stability, and reusability of the BCZ. VB-XPS, Mott-Schottky plots, and UV–Vis DRS defined the band structure and electron transfer mechanism of BCZ. The effects of ten operating condition factors, including reaction time, initial concentration of CFX, the dosage of photocatalyst, reaction temperature, initial pH of the reaction, visible and UV intensity, and concentration of Na2SO4, NaOH, and NaCl in the reaction solution were experimentally investigated. These factors were then modeled through artificial neural networks (ANN). The number of neurons, training algorithm, and the type of activation functions in the ANN were optimized by mean squared error metric followed by the visualizations of the ANN predictions. Two cost functions (the ratio of instantaneous CFX concentration to initial CFX concentration (C/C0) and the ratio of photocatalyst dosage to the amount of CFX removed) were employed to optimize the value of these operating condition factors separately and simultaneously through single and multi-objective genetic algorithms. Coupling LC-MS results and DFT calculations, a degradation pathway for Cefixime was proposed and then analyzed by QSAR. The toxicity of the treated solution was investigated using the wheat seed culture, along with MIC and MBC testing conducted by E. coli and S. aureus, and its eco-friendliness was confirmed by TOC and COD. Furthermore, ICP-OES confirmed that BCZ is a green photocatalyst.

Artificial neural network prediction of transverse modulus in humid conditions for randomly distributed unidirectional fibre reinforced composites: A micromechanics approach

This paper proposes an innovative micromechanics-based artificial neural network (ANN) method to efficiently investigate the transverse modulus of unidirectional fibre/epoxy composites under humid conditions. In this research, a novel approach is developed to establish relations between the geometrical, mechanical, and environmental properties of the microstructure and the material’s performance under transverse tension. A framework is developed to artificially generate periodic representative volume elements (RVEs) while taking into account the interphase region between fibre and matrix. The RVEs are analysed by the finite element method to obtain the transverse moduli of composites. Two-point correlation functions and principal component analysis techniques are applied to extract and compress statistical information from microstructure images. A database establishment framework is developed to create three batches of data with 1000, 2000, and 3000 sizes. An ANN-based prediction framework is developed by integrating 10-fold cross-validation and Bayesian optimisation to optimise the neural network architecture and establish an efficient structure-property linkage under the influence of humid conditions. The prediction results demonstrate the efficiency of ANN in mapping microstructural data to an effective transverse modulus. A parametric study by ANN reveals the role of microstructure geometrical features and humid environmental parameters on the transverse performance of the composite.

Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)

This research paper presents a comprehensive experimental and statistical approach for the analysis of vibration amplitudes in a high-speed rotating shaft employing a squeeze film damper (SFD). The research combines a comprehensive analysis that connects input parameters and response parameters, with a special emphasis on vibration amplitudes along the X and Z axes. This research utilizes the rigorous procedures of Response Surface Methodology (RSM) together with a Box-Behnken design and harnesses the capabilities of Artificial Neural Network (ANN) optimization techniques. The variables under scrutiny encompass critical factors such as shaft rotational speed, extending up to 8000 rpm, oil pressure, with a range extending up to 100 bar, and various oil mix ratios, spanning from 10 % to 50 %. Various statistical measures are computed to assess the errors and coefficients of determination of the projected models. The artificial neural network (ANN) model has shown somewhat reduced prediction errors and a greater coefficient of determination compared to the Response Surface Methodology (RSM) for both the x and z axes of vibration amplitude. The values of mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R-squared) are found for both x and z axes from RSM (3.50, 3.77), (4.50, 4.72), and (0.81, 0.79), (1.88, 1.70), (2.41, 2.12), and (0.94, 0.95), respectively, using the ANN model. The overall mean absolute percentage error (MAPE) from the ANN model of both the x and z axes (9.73 %, 9.38 %) is found to be lower compared to the RSM model (18.18 %–20.50 %). The conclusive research reveals that the artificial neural network (ANN) prediction model outperforms the regression model based on Response Surface Methodology (RSM), exhibiting superior accuracy in predicting vibration amplitudes. The ANN approach is an excellent option for calculating vibration amplitudes in high-speed rotating shafts. Additionally, it provides significant benefits in terms of effectiveness and time economy. This research provides valuable new insights into the most efficient modeling approaches for vibration management and highlights the benefits of using Artificial Neural Networks (ANN) for predictive assessments in this context. Particle Swarm Optimization is used to minimize the vibration amplitude of the shaft along the x and z axes using experimental data.

Abstract 499: Enhancing neoantigen cancer vaccine efficacy with a self-assembling peptide conjugates nanoparticle platform

Abstract Tumor neoantigens, arising from genomic mutations during cancer cell proliferation, are absent in normal tissues, making them ideal immunotherapy targets. Predicting neoantigen peptide sequences using artificial neural networks (ANNs) like NetMHCpan offers a promising approach to developing personalized peptide cancer vaccines. However, limitations such as low affinity, short half-life, and weak immunogenicity hinder their efficacy. To address these challenges, we propose loading neoantigens into nanoparticles for protection and slow release within the body. We used ANN prediction to determine neoantigen peptide sequences for various cancer cell lines and engineered these peptides by conjugating them with Toll-like receptor 7 agonist (TLR7a) adjuvants, forming a self-assembling peptide conjugates nanoparticle (SPCN) platform. To assess the immunogenicity of the SPCN platform, we conducted enzyme-linked ELISpot assays and evaluated the activation of antigen-presenting cells (APCs) in draining lymph nodes using flow cytometry analysis. The therapeutic efficacy of the SPCN platform was determined by monitoring tumor growth curves in mouse models. The SPCN platform demonstrated broad applicability, carrying different neoantigens in vivo, resulting in robust and safe systemic anti-tumor responses. The SPCN enhanced immunogenicity, stimulating splenocytes to produce more interferon gamma and facilitating efficient absorption by antigen-presenting cells (APCs). Moreover, SPCNs loaded with neoantigens targeted different cancer models, including B16OVA, MB49, and RIL175. In conclusion, our self-assembling peptide conjugates nanoparticle platform enhances APC activation and neoantigen immunogenicity, leading to improved anti-tumor efficacy, offering valuable insights for developing synthetic peptide cancer vaccines. Citation Format: Yangfan Wu, Jian-Dong Huang. Enhancing neoantigen cancer vaccine efficacy with a self-assembling peptide conjugates nanoparticle platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 499.

Application of Artificial Neural Network for Prediction of Story Drift in High-Rise Buildings

The difficulties arising from growing population expansion and extensive urbanization are driving up the demand for high-rise structures. Comprehending the symmetry and composition of structural components correspondingly is becoming more and more important in light of current developments in computer technology, novel materials, and unique structures. The structural responses of multistory buildings predicted by Artificial Neural Networks (ANN) briefly outlined in a systematic manner. This review enhances the application of ANN for the prediction of story drift in multistory buildings with greater level accuracy. A multistory structure might fall because of the intense movement of the ground, resulting in fatalities and economic loss. As a result, it is imperative that the multistory structure be appropriately constructed to minimize the earthquake hazards. Based on a literature analysis, it can be concluded that artificial neural networks are widely used in simulating and predicting story drift in high-rise building systems. The objective of the current investigation is to present a comprehensive overview of the many approaches and uses of artificial neural network modeling research in the domain of structural engineering. Accordingly, the articles were categorized in this way based on author, year, algorithm used, software application and description of findings and conclusions. Here, the most influential parameter (input vector) is also rigorously reviewed. This information may be taken into account when creating a neural network model that operates more effectively. The review's conclusions might prove beneficial for structural and/or civil engineering problems. The approaches described here assist the structural practitioner in understanding the limitations and advantages of ANN in comparison to other traditional mathematical modelling. This review contributes a body of knowledge for the ANN modelling approach to forecast the story drift evaluation within the field of structural engineering.

Artificial neural network prediction and multi-objective genetic algorithm optimization of the microchannel heat sink with trapezoidal ribs

Trapezoidal rib microchannel heat sinks (TR-MCHS) are widely used for efficient heat dissipation in electronic integrated devices. To enhance the heat transfer and hydraulic performance of TR-MCHS, an optimization method using neural network prediction combined with multi-objective genetic algorithm NSGA-II is proposed. First, the effects of trapezoidal rib geometry parameters and inlet velocity (V) on the Nusselt number (Nu¯) and pressure drop (ΔP) of TR-MCHS are investigated using numerical simulation and Sobol sensitivity analysis. The results show that the trapezoidal rib height (ht) and inlet V have the most significant effect on the TR-MCHS, and the interaction effect between the parameters can be neglected. Then, optimization is performed with Nu¯ and ΔP as objectives. On the obtained Pareto solution set, Nu¯ can be improved by 7.3 % compared to the unoptimized TR-MCHS without consuming additional pumping power. In addition, if the same Nu¯ is obtained as the unoptimized TR-MCHS, the ΔP can be reduced to 223 Pa. On this basis, the TOPSIS method based on entropy weight is developed to solve the optimal compromise solution. The best compromise solution has the Nu¯ of 50.9 and the ΔP of only 184.6 Pa, which is 87.9 Pa lower than that of the unoptimized TR-MCHS. These results have important implications for the design of TR-MCHS for electronics cooling.