Function Network Research Articles

A Semi-Supervised Active Learning Method for Structured Data Enhancement with Small Samples

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F1macro value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods.

Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function

BackgroundCocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. MethodsResting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22–55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics – betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) – were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. ResultsParticipants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. ConclusionsWidespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.

Micropeptide MPM regulates cardiomyocyte proliferation and heart growth via the AKT pathway

The role of micropeptide in cardiomyocyte proliferation remains unknown. We found that MPM (micropeptide in mitochondria) was highly expressed in cardiomyocytes. Compared to MPM+/+ mice, MPM knockout (MPM−/−) mice exhibited reduction in left ventricular (LV) mass, myocardial thickness and LV fractional shortening. RNA-sequencing analysis in H9c2, a rat cardiomyocyte cell line, identified downregulation of cell cycle-promoting genes as the most significant alteration in MPM-silencing cells. Consistently, gain- and loss-of-function analyses in H9c2 cells revealed that cardiomyocyte proliferation was repressed by silencing MPM but was promoted by overexpressing MPM. Moreover, the cardiomyocytes in the hearts of MPM−/− mice displayed reduced proliferation rates. Mechanism investigations disclosed that MPM is crucial for AKT activation in cardiomyocytes. We also identified an interaction between MPM and PTPMT1, and found that silencing PTPMT1 attenuated the effect of MPM in activating the AKT pathway, whereas inhibition of the AKT pathway abrogated the role of MPM in promoting cardiomyocyte proliferation. Collectively, these results indicate that MPM may promote cardiomyocyte proliferation and thus heart growth by interacting with PTPMT1 to activate the AKT pathway. Our findings identify the novel function and regulatory network of MPM and highlight the importance of micropeptides in cardiomyocyte proliferation and heart growth.

Finite-time error constraint control for multi-AUV systems with unknown uncertainties based on observer and tan-type barrier Lyapunov function

PurposeAutonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.Design/methodology/approachThe tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.FindingsThe containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.Originality/valueThe state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

Generalized [formula omitted]-sparse algorithms for constructing fault tolerant RBF networks

In the construction process of radial basis function (RBF) networks, two common crucial issues arise: the selection of RBF centers and the effective utilization of the given source without encountering the overfitting problem. Another important issue is the fault tolerant capability. That is, when noise or faults exist in a trained network, it is crucial that the network’s performance does not undergo significant deterioration or decrease. However, without employing a fault tolerant procedure, a trained RBF network may exhibit significantly poor performance. Unfortunately, most existing algorithms are unable to simultaneously address all of the aforementioned issues. This paper proposes fault tolerant training algorithms that can simultaneously select RBF nodes and train RBF output weights. Additionally, our algorithms can directly control the number of RBF nodes in an explicit manner, eliminating the need for a time-consuming procedure to tune the regularization parameter and achieve the target RBF network size. Based on simulation results, our algorithms demonstrate improved test set performance when more RBF nodes are used, effectively utilizing the given source without encountering the overfitting problem. This paper first defines a fault tolerant objective function, which includes a term to suppress the effects of weight faults and weight noise. This term also prevents the issue of overfitting, resulting in better test set performance when more RBF nodes are utilized. With the defined objective function, the training process is designed to solve a generalized M-sparse problem by incorporating an ℓ0-norm constraint. The ℓ0-norm constraint allows us to directly and explicitly control the number of RBF nodes. To address the generalized M-sparse problem, we introduce the noise-resistant iterative hard thresholding (NR-IHT) algorithm. The convergence properties of the NR-IHT algorithm are subsequently discussed theoretically. To further enhance performance, we incorporate the momentum concept into the NR-IHT algorithm, referring to the modified version as “NR-IHT-Mom”. Simulation results show that both the NR-IHT algorithm and the NR-IHT-Mom algorithm outperform several state-of-the-art comparison algorithms.

Seasonal dynamics and driving mechanisms of microbial biogenic elements cycling function, assembly process, and co-occurrence network in plateau lake sediments

Microbial community diversity significantly varies with seasonality. However, little is known about seasonal variation of microbial community functions in lake sediments and their associated environmental influences. In this study, metagenomic sequencing of sediments collected from winter, summer, and autumn from Caohai Lake, Guizhou Plateau, were used to evaluate the composition and function of sediment microbial communities, the potential interactions of functional genes, key genes associated with seasons, and community assembly mechanisms. The average concentrations of nitrogen (TN) and phosphorus (TP) in lake sediments were higher, which were 6.136 and 0.501 g/kg, respectively. TN and organic matter (OM) were the primary factors associated with sediment community composition and functional profiles. The diversity and structure of the microbial communities varied with seasons, and Proteobacteria relative abundances were significantly lower in summer than in other seasons (58.43–44.12 %). Seasons were also associated with the relative abundances of functional genes, and in particular korA, metF, narC, nrfA, pstC/S, and soxB genes. Network complexity was highest in the summer and key genes in the network also varied across seasons. Neutral community model analysis revealed that the assembly mechanisms related to carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) cycle-related genes were primarily associated with random processes. In summary, diverse functional genes were identified in lake sediments and exhibited evidence for synergistic interactions (Positive proportion: 74.91–99.82 %), while seasonal factors influenced their distribution. The results of this study provide new insights into seasonal impacts on microbial-driven biogeochemical cycling in shallow lakes.

Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model.

Agriculture is the main source of livelihood for most of the population across the globe. Plants are often considered life savers for humanity, having evolved complex adaptations to cope with adverse environmental conditions. Protecting agricultural produce from devastating conditions such as stress is essential for the sustainable development of the nation. Plants respond to various environmental stressors such as drought, salinity, heat, cold, etc. Abiotic stress can significantly impact crop yield and development posing a major threat to agriculture. SNARE proteins play a major role in pathological processes as they are vital proteins in the life sciences. These proteins act as key players in stress responses. Feature extraction is essential for visualizing the underlying structure of the SNARE proteins in analyzing the root cause of abiotic stress in plants. To address this issue, we developed a hybrid model to capture the hidden structures of the SNAREs. A feature fusion technique has been devised by combining the potential strengths of convolutional neural networks (CNN) with a high dimensional radial basis function (RBF) network. Additionally, we employ a bi-directional long short-term memory (Bi-LSTM) network to classify the presence of SNARE proteins. Our feature fusion model successfully identified abiotic stress in plants with an accuracy of 74.6%. When compared with various existing frameworks, our model demonstrates superior classification results.

Micro RNA Dysregulation in Keratinocyte Carcinomas: Clinical Evidence, Functional Impact, and Future Directions.

The keratinocyte carcinomas, basal cell carcinoma (BCC), and cutaneous squamous cell carcinoma (cSCC), are the most common cancers in humans. Recently, an increasing body of literature has investigated the role of miRNAs in keratinocyte carcinoma pathogenesis, progression and their use as therapeutic agents and targets, or biomarkers. However, there is very little consistency in the literature regarding the identity of and/or role of individual miRNAs in cSCC (and to a lesser extent BCC) biology. miRNA analyses that combine clinical evidence with experimental elucidation of targets and functional impact provide far more compelling evidence than studies purely based on clinical findings or bioinformatic analyses. In this study, we review the clinical evidence associated with miRNA dysregulation in KCs, assessing the quality of validation evidence provided, identify gaps, and provide recommendations for future studies based on relevant studies that investigated miRNA levels in human cSCC and BCC. Furthermore, we demonstrate how miRNAs contribute to the regulation of a diverse network of cellular functions, and that large-scale changes in tumor cell biology can be attributed to miRNA dysregulation. We highlight the need for further studies investigating the role of miRNAs as communicators between different cell types in the tumor microenvironment. Finally, we explore the clinical benefits of miRNAs as biomarkers of keratinocyte carcinoma prognosis and treatment.

A classifier based on mixed radial basis function network and combinatorial optimization model for medical diseases diagnosis

A classifier based on mixed radial basis function network and combinatorial optimization model for medical diseases diagnosis

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

AimRecent imaging studies have found significant abnormalities in the brain’s functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.MethodsWe conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.ResultsIn the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).ConclusionHM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications.

Advanced Physics-informed neural networks for numerical approximation of the coupled Schrödinger–KdV equation

Physics-informed neural networks (PINNs) has been shown to be an effective tool for solving partial differential equations (PDEs). PINNs incorporate the PDEs residual into the loss function, seamlessly integrating it as part of the neural network architecture. This novel methodology has exhibited success in tackling a wide range of both forward and inverse PDE problems. However, a limitation of the first generation of PINNs is that they usually have limited accuracy even with many training points. Here, we propose two advanced methodologies: PINNs utilizing an H1 loss function and parallel physics-informed neural networks (P-PINNs). The former involves modifying the loss function with the H1 norm, while the latter entails solving the coupled Schrödinger–Korteweg–de Vries (Sch–KdV) equation separately with two parallel networks. These advancements are designed to enhance both accuracy and training efficiency. We have extensively tested these two advanced methods through a series of experiments and demonstrated the accuracy and effectiveness in approximation of the Sch–KdV system.

Influence of radiotherapy on control functions network fMRI connectivity in patients with lateralized mediobasal temporal lesions

According to the literature, radiotherapy (RT) used for brain tumors, along with a positive effect, can be accompanied by negative consequences in the form of the development of neurocognitive deficit due to the side effects of radiation on critical brain structures. At the same time, there are indications of a possible modulation of hippocampal neurogenesis with subsequent activation of a number of cognitive functions. An important component of human cognitive activity is the so-called executive functions (EF), which include the initiation, planning, regulation and control of any purposeful activity. Their structural and functional support is currently associated with the prefrontal and parietal sections of the hemispheres, as well as with the formations of the lower temporal cortex and the hippocampus. The work is aimed at dynamic assessment of the state of the EF-network according to the analysis of resting fMRI connectivity before and after 6 months after RT. In dynamics, 14 patients with lateralized tumor lesions of the mediobasal temporal lobe were examined: 7 with the left side, 7 with the right side. The control group consisted of 9 healthy subjects. Each participant underwent fMRI at rest – with further analysis of the functional connectivity between the given regions of interest, corresponding to the topography of the EF-network. The results were compared with the MRI morphometry tumor data. It has been shown that in patients 6 months after RT, against the background of a decrease in volume or stabilization of tumor growth, the functional effects are ambiguous and depend on the lateralization of the lesion: with a right-sided lesion they tend to normalize, while with a left-sided lesion they increase.

Epilepsy lesion localization method based on brain function network.

In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Safe dynamic sparse training of modified RBF networks for joint feature selection and classification

Developing an efficient embedded feature selection method for both binary and multiclass classification problems is a fundamental topic to be further studied. In this paper, the conventional radial basis function (RBF) network is modified for joint feature selection and classification. By utilizing the anisotropic Gaussian basis function, an individual weight parameter is assigned to each feature for feature weighting. The L1-regularized loss function is designed by combining the softmax cross entropy loss with L1 regularization terms imposing on feature and output weights. A specialized active-set limited-memory projected Quasi-Newton (SAL-PQN) algorithm is proposed to minimize the L1-regularized loss function, which can optimize all the adjustable parameters and zero out the redundant feature and output weights simultaneously. Furthermore, a safe dynamic pruning strategy is integrated into the SAL-PQN algorithm for dynamic sparse training of the modified RBF network, which continuously excludes the dynamically zeroed-out feature weights from the training process. It is theoretically assured of yielding the same gradient updates as SAL-PQN, thus the relevant computation of the zeroed-out feature weights in the forward and backward propagations can be safely eliminated. The experimental results demonstrate the effectiveness and efficiency of the proposed methods for joint feature selection and classification, and illustrate the practical utility of the inherent feature weighting capability.

Machine learning analysis of pressure fluctuations in a gas-solid fluidized bed

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems.

Neural network-based distributed intelligent supervisory control for multi-agent systems with unknown input powers

In this paper, a synchronisation problem is studied for a class of nonlinear leader-following systems. Based on adaptive control with some algebra lemmas, we propose a distributed control scheme. This scheme ensures each follower to asymptotically synchronise with the leader. Compared with the existing works where system input powers are assumed one, the input powers considered in this paper are unknown but larger than one. Based on Graph theory, Lyapunov theory and radial basis function (RBF) networks, we design a distributed adaptive supervisory control method. It is proven that the consensus among the leader and followers are achieved and all the signals including tracking errors asymptotically converge to a small neighbourhood of the origin. Finally, simulation results are displayed to demonstrate the effectiveness of control scheme.

Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality

This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes.

Numerical analysis of uncertain frequency of rotating composites using NURBS-based isogeometric HSDT approach: Rim-driven configuration

This study delves into the impact of material and fabrication uncertainties on the natural frequencies of rim-driven rotating structures. Utilizing a higher-order shear deformation theory (HSDT), an isogeometric analysis (IGA) model predicts these frequencies. Sensitivity analysis via a radial basis function network (RBFN) model, validated with Monte Carlo simulation, highlights parameter influence. Parametric investigations unveil sensitivity patterns and influential parameters. Employing a stochastic approach, the study compares the effects of random material properties on the free vibration response of rotating and non-rotating plates. Additionally, the RBFN-based surrogate model assesses reliability parameters concerning resonance failure in rim-driven rotating composite structures.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.