Model Parameters Research Articles

Comprehensive slope stability analysis of bauxite storage facility

ABSTRACT This study evaluates dykes stability of bauxite residue storage facility using limit equilibrium (LEM) and finite element methods (FEM), considering diverse construction phases. In LEM, steady state seepage is simulated using piezometric line while factor of safety (FOS) is determined by Morgenstern-Price method using SLOPE/W. In FEM, actual loading rates and time dependent seepage is modelled by coupled stress-pore water pressure analysis in SIGMA/W and dyke stability is assessed by stress analysis in SLOPE/W, referencing SIGMA/W analysis as a baseline model. Both the analysis incorporated suction and volumetric water content functions to determine FOS. FEM predicted pore pressures are validated against in-situ piezometer data. The results highlight that coupled hydro-mechanical analysis offers accurate stability assessment by integrating stress-strain behaviour, pore pressure changes, seepage paths, and dyke displacements with time. It is found that inclusion of unsaturated parameters in Mohr-Coulomb model improved the reliability in FOS predictions.

The Kolumbo Volcanic Field, Greece: expert elicitation findings supporting volcanic hazard and risk assessment

A detailed hazard and risk assessment has been undertaken for the Kolumbo Volcanic Field (KVF), which includes the Kulumbo Central Volcano, an active volcano situated in the Aegean Sea, Greece. In support, a comprehensive structured expert judgment exercise was conducted using the Classical Model for pooling specialists’ uncertainty quantifications concerning Target Item questions concerning volcanological model parameters, empirical variables, and event probabilities of interest. In total, 64 Target Items, organized according to a new event tree model for the KVF, were elicited from 15 specialists who participated in two workshops. Related statistical distributions and associated uncertainties were quantified via ELICIPY, a recently released Python tool implementing the Classical Model algorithm. The overarching methodological approach is summarized, and a number of examples of ELICIPY solutions for specific Target Items are given, illustrating the nature and key probabilistic features of such results. The Target Items included questions aimed at reconstructing the 1650 CE eruption of Kolumbo central Volcano and providing statistical characterizations as direct inputs to various hazard and risk models for a potential near-future eruption in the KVF. In addition, a new type of elicitation metric, the ‘Conformity Score’, is introduced in two alternative formulations for ranking the item-wise extents of agreement/disagreement between experts providing judgments; these rankings help inform stake holders which specific Target Items are well-constrained by expert elicitation, while also identifying where possible major knowledge gaps among experts may exist.

The full radiostereometric analysis migration pattern of tibial components is associated with aseptic loosening : introducing MTPMemax (MTPM estimated maximum).

Thresholds for acceptable amounts of migration tibial components measured with radiostereometric analysis (RSA) are limited to specific follow-up moments and do not use the full migration pattern. The Michaelis-Menten (MM) model, a non-linear model from biochemistry, could potentially be used to model the entire migration pattern. The purpose of this study was therefore to determine if MM models can be fitted to RSA migration data of tibial components, and if these fitted model parameters can be used for early detection of tibial components at high risk for aseptic loosening. Migration patterns of tibial component maximum total point motion (MTPM) over six months, one year, and two years, as well as revision rates for aseptic loosening from previously published systematic reviews, were used. Fitted MM models gave the estimated maximal MTPM (MTPMemax) and a constant (K), which is the time in months at which half the MTPMemax is reached for tibial component designs. To assess model fit, intraclass correlation coefficients (ICCs) were calculated for the modelled MTPMemax and reported five-year MTPM values. The estimated MTPMemax and K-values were plotted against their corresponding five-year revision rate for aseptic loosening. For six-month, one-year, and two-year migration patterns, the ICC was 0.81, 0.83, and 0.91, respectively, suggesting excellent agreement between calculated MTPMemax values and the known five-year MTPM values. MTPMemax up to 1.3 mm was considered to be safe based on association with aseptic loosening revision rate, while MTPMemax of more than 1.3 mm was unsafe. The K-value could not be used as a predictor for safe versus unsafe implants. MTPMemax values may be used for early detection of tibial components at high risk for aseptic loosening, possibly offering improvements over the older threshold system.

Bayesian Inference and the Principle of Maximum Entropy

Bayes’ theorem incorporates distinct types of information through the likelihood and prior. Direct observations of state variables enter the likelihood and modify posterior probabilities through consistent updating. Information in terms of expected values of state variables modify posterior probabilities by constraining prior probabilities to be consistent with the information. Constraints on the prior can be exact, limiting hypothetical frequency distributions to only those that satisfy the constraints, or be approximate, allowing residual deviations from the exact constraint to some degree of tolerance. When the model parameters and constraint tolerances are known, posterior probabilities follow directly from Bayes’ theorem. When parameters and tolerances are unknown a prior for them must be specified. When the system is close to statistical equilibrium the computation of posterior probabilities is simplified due to the concentration of the prior on the maximum entropy hypothesis. The relationship between maximum entropy reasoning and Bayes’ theorem from this point of view is that maximum entropy reasoning is a special case of Bayesian inference with a constrained entropy-favoring prior.

Blind single-shot phase retrieval based on a self-supervised physics-adaptive neural network

Recently, single-shot phase retrieval techniques, which aim to reconstruct an original sample from a single near-field diffraction pattern, have garnered significant attention. Despite their promise, existing methods are highly dependent on precise physical forward models, constraining their effectiveness in real-world scenarios. To overcome the challenges posed by unknown diffraction distances in blind single-shot phase retrieval, this paper introduces a self-supervised physics-adaptive neural network termed BlindPR-SSPANN. The proposed method jointly optimizes the physical parameters of the forward propagation model alongside the trainable parameters of the reconstruction network. To achieve this, BlindPR-SSPANN incorporates a novel network architecture that integrates tunable physical parameters within a multi-stage, coupled reconstruction process. The proposed network is trained under a self-supervised scheme facilitated by a refined physics-consistent loss function. Simulation and experimental results demonstrate that BlindPR-SSPANN delivers high-performance reconstructions from a single intensity measurement, even under large diffraction distance errors, enabling self-calibrated snapshot coherent diffraction imaging.

Gaussian mixture autoencoder for uncertainty-aware damage identification in a floating offshore wind turbine

Abstract. This work proposes an uncertainty-aware approach to the inverse problem of damage identification in a floating offshore wind turbine (FOWT). We design an autoencoder architecture, where the latent space represents the features of the target damage condition. The inverse operator (encoder) is a deep neural network that maps the measurable response to the parameters (means, variances, and weights) of a multivariate Gaussian mixture model. The Gaussian mixture model provides a convenient distributional description that is flexible enough to accommodate complex solution spaces. The decoder receives samples from the Gaussian mixture and maps the damage condition (states) to the system's measurable response. In such a problem, and depending on the quantities being observed (sensor positioning), it is possible that multiple damage states may correspond to similar measurement records. In this context, the main contribution of this work lies in developing a method to quantify the uncertainty within the context of a possibly ill-posed damage identification problem. We employ the Gaussian mixture to express the multimodal solution space and explain the uncertainty in the damage condition estimates. We design and validate the methodology using synthetic data from a FOWT in the commonly adopted OpenFAST software and consider two damage types frequently occurring in mooring lines: biofouling and anchor displacement. The method allows for the estimation of the damage state while capturing the uncertainty in the estimations and the multimodality of the solution under the availability of a limited number of response measurements.

Foot-end contact state estimation for quadruped robots based on proprioceptive sensing

Abstract To solve the problem of estimating the early or delayed foot-end contact state that may occur on uneven road surfaces for quadruped robots, a generalized momentum end-of-foot contact force estimation method combined with Kalman filtering without relying on external sensors is proposed, and an end-of-foot contact state estimation is realized based on Proprioceptive Sensing. First, the quadruped robot model structure and parameters are introduced. Then, based on the dynamics model and generalized momentum theory, the estimation of foot-end contact force is achieved by combining Kalman filtering, and the foot-end contact force estimation method is improved. Finally, the contact probability models of phase progress planning, external force at the foot end and height of the foot end from the ground are established, and the Kalman filter algorithm is used to fuse the above probability models to achieve the estimation of the contact state of the foot end of the quadruped robot. The experimental results show that the method can effectively improve the accuracy and robustness of the foot-end contact state estimation.

Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention

To address the temporal jitter with low segmentation accuracy and the lack of high-precision transformations for specific object classes in video generation, we propose the fish body pattern sync-style network for ornamental fish videos. This network innovatively integrates dynamic texture transfer with instance segmentation, adopting a two-stage processing architecture. First, high-precision video frame segmentation is performed using Mask2Former to eliminate background elements that do not participate in the style transfer process. Then, we introduce the wavelet-gated styling network, which reconstructs a multi-scale feature space via discrete wavelet transform, enhancing the granularity of multi-scale style features during the image generation phase. Additionally, we embed a convolutional block attention module within the residual modules, not only improving the realism of the generated images but also effectively reducing boundary artifacts in foreground objects. Furthermore, to mitigate the frame-to-frame jitter commonly observed in generated videos, we incorporate a contrastive coherence preserving loss into the training process of the style transfer network. This enhances the perceptual loss function, thereby preventing video flickering and ensuring improved temporal consistency. In real-world aquarium scenes, compared to state-of-the-art methods, FSSNet effectively preserves localized texture details in generated videos and achieves competitive SSIM and PSNR scores. Moreover, temporal consistency is significantly improved. The flow warping error index decreases to 1.412. We chose FNST (fast neural style transfer) as our baseline model and demonstrate improvements in both model parameter count and runtime efficiency. According to user preferences, 43.75% of participants preferred the dynamic effects generated by this method.

Operation at Reduced Atmospheric Pressure and Concept of Reliability Redundancy for Optimized Design of Insulation Systems

Electrified transportation is calling for insulation design criteria that is adequate to provide elevated levels of power density, power dynamics and reliability. Increasing voltage levels are expected to cause accelerated intrinsic and extrinsic aging effects which will not be easily predictable at the design stage due to a lack of suitable modeling. Designing reliable insulation systems would require finding solutions able to control accelerated aging due to an unpredictable increase of intrinsic stresses and the onset of extrinsic stresses as partial discharges. This paper proposes the concept of reliability redundancy for the insulation design of aerospace electrical asset components, which is also validated at lower-than-standard atmospheric pressure. The principle is that extrinsic-aging-free design might be achieved upon determining the aging stress or abnormal service stresses distribution and being sure that aging will not generate conditions that can incept extrinsic aging (partial discharges) during operation life. However, such information is never, in practice, fully available to insulation system designers. Hence, especially in critical applications such as electrified aircraft, aerospace, and combat ships a further level of reliability should be added to a partial-discharge-free design, which can consist of the use of corona-resistant materials and/or of life models able to consider the accelerated aging effect of partial discharges (or any other type of extrinsic-accelerated aging factor). Innovative life modeling considering both extrinsic and intrinsic aging stresses, insulating material testing to estimate model parameters, and a metric for quantifying the extent of corona (or partial discharge) resistance can lead to establishing feasibility and limit conditions for optimized or fully reliability-redundant design. It is shown in the paper that if an extrinsic-aging-free design is not feasible, and it is therefore replaced by a redundant design, a further level of reliability redundancy can be provided by effective condition monitoring plans.

An integrated framework based on the analytical hierarchy process and auto-calibration of the storm water management model for implementing and assessing low impact development practices

Abstract Low Impact Development (LID) practices have emerged as promising solutions for mitigating stormwater runoff in urbanized watersheds. However, the constraints of limited space for the large LID units and the absence of a standardized framework for simulating, calibrating, and implementing these practices introduce significant uncertainties. This study proposes a comprehensive framework for the implementation and simulation of three LID practices—rain barrels, cisterns, and drywells—focusing on their runoff reduction potential in a highly urbanized, flood-prone watershed in Istanbul, Turkey. The framework integrates expert knowledge through the Analytical Hierarchy Process to develop site-specific implementation strategies and employs an auto-calibrated Storm Water Management Model for hydrologic simulations. Key findings demonstrate the efficacy of the Optimization Software Tool for Research Involving Computational Heuristics in calibrating sensitive model parameters. Implementation of the proposed LID practices resulted in significant reductions in runoff peak and volume, along with notable improvements in the time to peak during short-duration (2-h) storm events, within geospatial, environmental, and feasibility constraints. However, their effectiveness was comparatively lower under the long-duration storm scenario tested. The employed methodology is transferable to other densely urbanized metropolitan areas, offering valuable insights for decision-makers in designing effective and sustainable urban stormwater management strategies.

Application of data twinning based on deep time series model in smart city traffic flow prediction

This paper introduces an intelligent traffic flow prediction system that combines data twinning and deep learning, aiming to improve the prediction accuracy and model adaptability by integrating grey prediction model (GM(1,1)), long-short-term memory network (LSTM) and particle swarm optimization (PSO). The system construction starts from physical layer data acquisition, deals with missing data through smoothing and interpolation, and applies the GM(1,1) model to construct a digital twin layer for preliminary prediction. The information fusion mechanism combines real-time data and forecast data to optimize the model inputs, and the PSO algorithm is used to optimize the model parameters. The LSTM model is used as a deep time-series model, and combined with the output of the data twin prediction to make a comprehensive prediction, and at the same time, feature engineering is utilized to enhance the model performance. The system implements dynamic feedback adjustment to optimize the model in real time according to the prediction error. Empirical analysis shows that the LSTM model with integrated data twin (LSTM + DT) outperforms the traditional model in prediction accuracy, stability, and generalization ability, especially in peak hours, holidays, and emergency response, and provides a reliable solution for intelligent traffic management despite the long training time.

Privacy Preserving Prompt Engineering: A Survey

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a direct correlation between the performance of these models and their sizes. As a result, the sizes of these models have notably expanded in recent years, persuading researchers to adopt the term large language models (LLMs) to characterize the larger-sized PLMs. The size expansion comes with a distinct capability called in-context learning (ICL), which represents a special form of prompting and allows the models to be utilized through the presentation of demonstration examples without modifications to the model parameters. Although interesting, privacy concerns have become a major obstacle in its widespread usage. Multiple studies have examined the privacy risks linked to ICL and prompting in general, and have devised techniques to alleviate these risks. Thus, there is a necessity to organize these mitigation techniques for the benefit of the community. In this survey, we provide a systematic overview of the privacy protection methods employed during ICL and prompting in general. We review, analyze, and compare different methods under this paradigm. Furthermore, we provide a summary of the resources accessible for the development of these frameworks. Finally, we discuss the limitations of these frameworks and offer a detailed examination of the promising areas that necessitate further exploration.

Algebraic differentiation for fast sensitivity analysis of optimal flux modes in metabolic models.

Sensitivity analysis is a useful tool to identify key parameters in metabolic models. It is typically only applied to the growth rate, disregarding the sensitivity of other solution variables to parameters. Further, sensitivity analysis of elementary flux modes could provide low-dimensional insights into optimal solutions, but they are not defined when a model is subject to inhomogeneous flux constraints, such as the frequently used ATP maintenance reaction. We introduce optimal flux modes (OFMs), an analogue to EFMs, but specifically applied to optimal solutions of constraint-based models. Further, we prove that implicit differentiation can always be used to efficiently calculate the sensitivities of both whole-model solutions and OFM-based solutions to model parameters. This allows for fine-grained sensitivity analysis of the optimal solution, and investigation of how these parameters exert control on the optimal composition of OFMs. This novel framework is implemented in DifferentiableMetabolism.jl, a software package designed to efficiently differentiate solutions of constraint-based models. To demonstrate scalability, we differentiate solutions of 342 yeast models; additionally we show that sensitivities of specific subsystems can guide metabolic engineering. Applying our scheme to an Escherichia coli model, we find that OFM sensitivities predict the effect of knockout experiments on waste product accumulation. Sensitivity analysis of OFMs also provides key insights into metabolic changes resulting from parameter perturbations. Software introduced here is available as open-source Julia packages DifferentiableMetabolism.jl (https://github.com/stelmo/DifferentiableMetabolism.jl) and ElementaryFluxModes.jl (https://github.com/HettieC/ElementaryFluxModes.jl), which both work on all major operating systems and computer architectures. Code to reproduce all results is available from https://github.com/HettieC/DifferentiableOFMPaper, and as an archive from https://doi.org/10.5281/zenodo.15183208. Supplementary data are available at Bioinformatics online.

WMC-RTDETR: a lightweight tea disease detection model

Tea pest and disease detection is crucial in tea plantation management, however, challenges such as multi-target occlusion and complex background impact detection accuracy and efficiency. To address these issues, this paper proposes an improved lightweight model, WMC-RTDETR, based on the RT-DETR model. The model significantly enhances the ability to capture multi-scale features by introducing wavelet transform convolution, improving the feature extraction accuracy in complex backgrounds, and increasing detection efficiency while reducing the number of model parameters. Combined with multiscale multihead self-attention, global feature fusion across scales is realized, which effectively overcomes the shortcomings of traditional attention mechanisms in small target detection. Additionally, a context-guided spatial feature reconstruction feature pyramid network is designed to refine the target feature reconstruction through contextual information, thereby improving the robustness and accuracy of target detection in complex scenes. Experimental results show that the proposed model achieves 97.7% and 83.1% respectively in mAP50 and mAP50:95 indicators, which outperform the original model. In addition, the number of parameters and floating-point operations are reduced by 35.48% and 40.42% respectively, enabling highly efficient and accurate detection of pests and diseases in complex scenarios. Furthermore, this paper successfully deploys the lightweight model on the Raspberry Pi platform, which proves that it has good real-time performance in resource-constrained embedded environments, providing a practical solution for low-cost disease monitoring in agricultural scenarios.

Study on the influence of transient processes in pantograph-catenary contact-loss on the characteristics of glue rail insulation

Electrified railways are complex electromagnetic systems that feature strong-weak electrical interactions and the coexistence of dynamic and static components. In this system, rails serve dual roles as signaling circuits and traction return channels. With the increase in operating speeds and the optimization of operational strategies, greater traction currents and more frequent traction switching when passing neutral zones pose higher demands on the signaling system’s EMI immunity, and the insulation joints’ voltage tolerance. This paper investigates the impact of pantograph-catenary contact-loss during train passing neutral zones on the return current cut-off insulation joints (referred to as ‘cut-off joints’). First, a circuit model is established based on the traction return system principles and the cut-off joints’ configuration scheme. Key model parameters are determined to clarify the mechanisms, influencing factors, and coupling paths of transient pulses during the contact-loss process. The propagation characteristics of these pulses in rails are analyzed. The results indicate that in typical scenarios, cut-off joints experience an impulse voltage peak of 4 kV and a frequency of 109 kHz as the train exits the neutral zone. Using the ANSYS Maxwell platform, a finite element modeling simulation is conducted to analyze the electric field distribution of the insulation joint structure under both ideal and defect conditions when subjected to impulse voltages. Finally, all results are validated through field tests, and optimization suggestions are proposed. This study contributes to fault tracing in glue rail insulation, provides a basis for setting electrical standards, and offers guidance for designing stationary current channels.

A real-time insulator condition detection model for UAV inspection based on FG-YOLO

Abstract In the field of target detection, especially for UAV inspection insulator state detection, accurate identification of small targets and complex background environment is always a major challenge. Traditional methods solve this problem by strengthening feature acquisition, but ignore the increase of computational complexity and resource consumption, resulting in insufficient hardware resources of UAV. We innovatively enhance YOLOv8-N model comprehensively and propose a Feature Guided-You Only Look Once (FG-YOLO), which effectively solves the problems of background noise interference and insufficient global information capture by designing context anchor concat and C2 Locality-Aware Attention (C2fLA) modules. Secondly, we introduce lightweight neck networks such as SCDown, DySample and lightweight guided convolutional detection detector head to maintain the original performance of the model, while reducing the model parameters and computational complexity and enhancing the robustness of features. In addition, we design global channel directed attention mechanism module to improve the sensing ability of network to size targets through multi-scale feature fusion. Experiments show that the improved FG-YOLO has excellent performance and potential in real-time detection and feature capture in three public datasets, with mAP50 reaching 88.2, 99.6 and 99.8 respectively. Moreover, FG-YOLO has inference speed of 25 frames s−1 in edge device MAIX-II Axera-Pi, meeting the real-time detection requirements of insulator defects.

Edge‐Side Fault Diagnosis for Machinery Health Management Based on Lightweight Model and Uncertainty Analysis

ABSTRACTConsidering scenarios in mechanical equipment involving continuous high‐frequency signal acquisition and challenges in data transmission, this paper proposes an edge‐based fault diagnosis framework (EdgeDiag) based on high‐confidence information extraction to enhance model training and improve the efficiency of diagnostic information processing, including data storage and uploading. First, to ensure diagnostic accuracy while reducing computational burden on edge devices, a lightweight model is designed to minimize the number of model parameters. Next, a high confidence–based training strategy and information processing framework considering risk levels are developed, utilizing uncertainty analysis to evaluate the diagnostic performance of each class in the data. Finally, the effectiveness of the proposed method is validated through Raspberry Pi and fault diagnosis of critical components in mechanical systems. This approach addresses the challenge of transmitting all data from edge devices, significantly improving the efficiency of fault diagnosis.

Children's and Adolescents' Actual Motor Competence, Perceived Physical Competence and Physical Activity: A Structural Equation Modelling Meta-Analysis.

Perceived physical competence (e.g. perceived motor skills, perceived athletic competence) is hypothesised to mediate the association between actual motor competence and physical activity in children, and this mediated association is expected to be stronger in older children and adolescents. However, no meta-analyses to date have synthesised the hypothesised mediation effect. The purpose of this study was to systematically identify and synthesise the existing literature on the hypothesised mediation model between actual motor competence, perceived physical competence and physical activity in children and adolescents using structural equation modelling meta-analysis. Five electronic databases were searched from inception to December 2023 using a range of keywords for actual motor competence, perceived physical competence, physical activity and children/adolescents. Machine learning assisted screening was used to identify studies which reported the association between at least two of the variables in the hypothesised model in children and adolescents aged 4-18years. One-stage structural equation modelling meta-analysis was used to test the hypothesised model. Moderation analysis was conducted to determine whether any of the model parameters differed as a function of children's age. A total of 218 reports that reported on 213 studies met the inclusion criteria and were included in the meta-analysis. Results from studies which examined the concurrent association between actual motor competence (gross motor, locomotion, object control), perceived physical competence, and physical activity demonstrated that perceived physical competence only had a small absolute (0.029 ≤ r ≤ 0.034) and relative (16.7-20.6% of total effect) mediating effect on the association between actual motor competence and physical activity. Results from studies which examined lagged associations (11% of included studies) also demonstrated a small bidirectional mediation effect of perceived physical competence. The moderation model demonstrated the mediation effect was significantly stronger in adolescents than children, albeit still weak. Perceived physical competence is not a strong mediator of the association between actual motor competence and physical activity in children and adolescents. Given that the association between perceived physical competence and actual motor competence with physical activity are largely independent, there may be benefits to targeting both motor skills and perceived physical competence to increase engagement in physical activity.

A practical guide to economic frontiers for evaluating benefits of multispecies fisheries management

ABSTRACT Objective Management of most fisheries is currently based on single species, so multispecies interactions often constrain fishing, leading to suboptimal yields or overfishing. Ecosystem-based fisheries management (EBFM) uses a more holistic approach to resource access or allocation and takes advantage of multispecies interactions to spread risk and achieve target yields. Portfolio theory is a commonly applied financial tool that considers covariance between assets in an investment portfolio to reduce the risk of achieving economic return targets. This method can be adapted to EBFM for balancing risk with expected benefits for a portfolio of species’ revenue. Portfolio management considers species interdependencies (covariance in revenue time series), uncertainty, and sustainability constraints. Methods We demonstrate how to apply economic frontier analysis using publicly available landings and revenue data from commercial fisheries and calculate the risk gap between historic portfolios and the EBFM frontier to assess past fishery performance. We identify data challenges and offer guidance on practical decisions for applying portfolio analysis to derive annual efficient frontiers (trade-offs between revenue risk and return) and demonstrate the sensitivity of frontiers to these data decisions and model parameters. Results In accordance with previous portfolio analyses, results show that the multispecies portfolio approach outperformed single-species management and there was forgone revenue for the associated risk taken in the single-species approach. Conclusions These demonstrations as well as guidance on data and analysis are intended to facilitate broader evaluation and application of multispecies fishery management and portfolio theory to fisheries.

Late time phenomena in f(T,T) gravity framework: role of H0 priors

This study explored the behavior of the f(T,T) cosmological model with the use of various data set combinations. We also compared the results for this model between the Pantheon+ (without SH0ES) and the Pantheon+ &SH0ES (with SH0ES) data sets. Additionally, we incorporated data from BAO along with H0 priors. We observed that integrating SH0ES data points leads to a higher estimation of H0 than Pantheon+ (without SH0ES). We perform an extensive MCMC analysis for each combination of data sets, providing constraints on the model parameters. We also computed the χmin2 value for each combination of data sets to evaluate the chosen model against the standard ΛCDM model. Our primary finding is that the various dataset combinations in the f(T,T) model we examined relate to a range of Hubble constants, which could contribute to reducing the cosmic tension associated with this parameter. Additionally, we investigate the evolution of matter fluctuations by solving the density contrast evolution equation numerically. We calculate numerical solutions for the weighted growth rate fσ8 using these findings. We plotted the cosmological background parameters to check the behavior of the f(T,T) model in late-time. Based on the behavior of these background cosmological parameters, we conclude that our selected models reflect the late-time cosmic dynamics of the Universe.