Generic Model Research Articles

In‐Situ Wavefront Correction via Physics‐Informed Neural Network

AbstractWavefront distortions pose a significant limitation in various optical applications, hindering further advancements in optical system performance. In this study, a novel generic calibration model based on Zernike‐fitting neural network (ZFNN) is proposed, which enables insitu wavefront correction with just a single‐shot measurement. The experimental setup follows a standard or equivalent focal‐field imaging optical path, allowing calibration without the need to remove any components from the optical system. The ZFNN, a physics‐informed neural network, offers the advantage of not requiring prior training, eliminating the need for extensive labeled data. With a fully connected network architecture and a modest number of neurons (469), the ZFNN achieves exceptionally fast optimization speed and meets the basic requirements for real‐time calibration. Consequently, this approach holds great potential for applications such as rapid calibration of optical systems, high‐precision light field modulation, and various advanced imaging techniques.

Potential functions of construction worker–machine interaction safety assessment

The interaction of several workers with intelligent construction machinery can lead to serious collisions. Typically, the safety distance is used as an indicator of the safety of worker–machine interactions (WMI). However, the degree of risk does not increase linearly with decreasing worker–machine distances. To further reveal the essence of WMI safety, this study proposes a new method for assessing the safety state of WMIs, namely, the construction safety potential field. It is used to describe the factors and patterns associated with the spatial overlap and decay of hazardous energy in WMI operations. The proposed method was tested in an earthworks construction WMI operation and the results were valid. A preliminary discussion of the relevant parameters constituting the construction safety potential field model is presented. The contributions of the research is proposing a generic energy-based model, which provides a novel idea for the interpretation of safety issues in construction WMI operations and opens up a new foundation for the development of active safety control.

A flexible planning approach for integrated lot sizing and rework planning with random proportion of defective products

We consider a stochastic capacitated lot sizing problem with in-line rework. Both defective and defect-free products can be the result of an imperfect production process in real-world production systems. However, the proportion of defective items in a production lot is subject to uncertainty. For economic and environmental reasons, the possibility to rework defective products appears reasonable since these products usually have considerable value. In this paper, a nonlinear model formulation for integrated lot sizing and rework planning is proposed. We use a sample average approach to approximate the generic nonlinear model formulation. To cope with the randomness of the proportion of defective products, we apply a flexible planning approach that allows the adjustment of production and rework quantities. We conduct extensive numerical investigations to evaluate the performance of the proposed planning approach.

Nonlinear low-velocity impact analysis of sandwich plates with titanium face sheets and porous aluminum core reinforced by GPLs

This study examines the nonlinear dynamic response of sandwich plates under low-velocity impacts (LVIs), which consist of a porous aluminum foam core with functionally graded (FG) graphene platelets (GPLs) reinforcement and two titanium alloy face sheets. The finite element method is employed to perform the numerical simulations and the Hertz contact theory is used to model the impact behavior. The generic Halpin-Tsai model, which incorporates the porosity effect, is adopted to estimate the mechanical properties of core made of the FG-GPLRC, based on the experimental data of the closed-cell aluminum alloy foam. The simulation results reveal that the FG-X configuration has the highest impact resistance, and that the addition of GPLs improves the impact performance. Moreover, a thicker core can reduce both the first contact time and the vibration period of plates, while a looser boundary condition can prolong the vibration time after the first impact. Additionally, a higher impactor velocity, mass and radius lead to a higher deflection and contact force. The oblique impact decreases the impact energy. This work provides a lighter and stiffer design for structure engineers when components suffer low-velocity impact.

The minimal massive Majoron Seesaw Model

A convincing explanation of the smallness of neutrino masses is represented by the Type-I Seesaw mechanism, where the two measured neutrino mass differences can be generated by introducing at least two right-handed neutrinos. In an ultraviolet complete model, it is possible to dynamically generate the heavy Majorana scale through the spontaneous symmetry breaking of a global Abelian symmetry and the most economical realisation consists in coupling the two exotic neutral leptons to a singlet complex scalar field. The associated Goldstone boson is often dubbed as Majoron, which may achieve a non-vanishing mass by means of a small term that explicitly breaks the Abelian symmetry. In a generic model, the neutrino and Majoron mass generation mechanisms are completely uncorrelated. In this paper, instead, we reduce the landscape of possible models proposing a unique, minimal and predictive framework in which these two types of masses are strictly tied and arise from the same source. Bounds from various terrestrial and astrophysical experiments are discussed.

Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method

We present a generic model framework for coupled heat and water transfer (CHWT) in deformable (non-rigid) soils with spatial variations of soil properties. The model backbone is a mixed finite element method (FEM), which solves the Philip and de Vries (1957) CHWT model and achieves conservation of mass and energy on both local and global scales. Spatial variations occur in soil hydraulic and thermal properties due to transient water content and temperature distributions. Based on the mixed FEM scheme, a gradient measure and a clustering model (“k-means”) are proposed to trace the regions with large spatial variations of soil properties, and an adaptive mesh refinement technique is developed to improve the spatial resolution and simulation accuracy. Deformation perturbates local soil topography and alters transient soil water and temperature regimes in the deformed regions. A quasi-static deformation model is presented, and the deformation effects are incorporated into the mixed FEM scheme. When external load exists, soil deformation is simulated with an updated Lagrangian formulation, and the local water content and temperature variations due to soil volume changes are also updated in the CHWT model. Numerical examples, including thermally induced soil water transfer and water infiltration, illustrate the model ability to provide plausible CHWT results, especially the refined solutions near the wetting fronts and the water content and temperature distributions when the soil is deformable. In conclusion, the proposed model framework provides an effective pipeline to incorporate and process the spatial variations of soil properties and soil deformation in CHWT simulations.

Full harmonic spectrum calculation of magnetic pressures in synchronous permanent magnet machines based on a new realistic partial analytical model: application to SVPWM and DPWM2

PurposeThe purpose of this paper is to propose a new method that allows to compare the magnetic pressures of different pulse width modulation (PWM) strategies in a fast and efficient way.Design/methodology/approachThe voltage harmonics are determined using the double Fourier integral. As for current harmonics and waveforms, a new generic model based on the Park transformation and a dq model of the machine was established taking saturation into consideration. The obtained analytical waveforms are then injected into a finite element software to compute magnetic pressures using nodal forces.FindingsThe overall proposed method allows to accelerate the calculations and the comparison of different PWM strategies and operating points as an analytical model is used to generate current waveforms.Originality/valueWhile the analytical expressions of voltage harmonics are already provided in the literature for the space vector pulse width modulation, they had to be calculated for the discontinuous pulse width modulation. In this paper, the obtained expressions are provided. For current harmonics, different models based on a linear and a nonlinear model of the machine are presented in the referenced papers; however, these models are not generic and are limited to the second range of harmonics (two times the switching frequency). A new generic model is then established and used in this paper after being validated experimentally. And finally, the direct injection of analytical current waveforms in a finite element software to perform any magnetic computation is very efficient.

Increasing accuracy of 3-D geomechanical-numerical models

SUMMARY The current crustal stress field is of key importance to understand geodynamic processes and to assess stability aspects during subsurface usage. To provide a 3-D continuous description of the stress state, linear elastic forward geomechanical-numerical models are used. These models solve the equilibrium of forces between gravitational volume forces and surfaces forces imposed mainly by plate tectonics. The latter are responsible for the horizontal stress anisotropy and impose the inverse problem to estimate horizontal displacement boundary conditions that provide a fit best to horizontal stress magnitude data within the model volume. However, horizontal stress magnitude data have high uncertainties and they are sparse, clustered and not necessarily representative for a larger rock volume. Even when Bayesian statistics are incorporated and additional stress information such as borehole failure observations or formation integrity test are used to further constrain the solution space, this approach may result in a low accuracy of the model results, that is the result is not correct. Here, we present an alternative approach that removes the dependence of the solution space based on stress magnitude data to avoid potential low accuracy. Initially, a solution space that contains all stress states that are physically reasonable is defined. Stress magnitude data and the additional stress information are then used in a Bayesian framework to evaluate which solutions are more likely than others. We first show and validate our approach with a generic truth model and then apply it to a case study of the Molasse foreland basin of the Alps in Southern Germany. The results show that the model's ability to predict a reliable stress state is increasing while the number of likely solutions may also increase, and that outlier of stress magnitude data can be identified. This alternative approach results in a substantial increase in computational speed as we perform most of the calculations analytically.

Bifurcations and bursting in the Epileptor.

The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.

Metacognitive strategies mediate the association between metacognitive beliefs and perceived quality of life.

Quality of life may be understood as a multidimensional evaluation of life circumstances in relation to values, expectations, and perceived well-being. Quality of life is thus dependent on the subjective perception of the current life situation, not only objective circumstances. According to metacognitive theory, metacognition guides the appraisal of inner experiences (i.e., thoughts and feelings) and influences how one relates to external stressors. Hence, dysfunctional metacognitive beliefs and the cognitive attentional syndrome (CAS), which includes perseverative thinking, threat monitoring and ineffective coping strategies, may negatively influence subjective quality of life. Therefore, we aimed to investigate if metacognitive beliefs and CAS strategies were associated with quality of life. A sample of 503 participants (77.1% women, mean age 41.0, SD = 11.5) completed the metacognitions questionnaire 30 (MCQ-30), the CAS-1 and the quality of life scale (QOLS). We used structural equation modelling (SEM) to estimate associations between the variables founded in metacognitive theory. The results of the SEM showed a significant direct relationship between metacognitive beliefs and quality of life. CAS strategies mediated the effect of metacognitive beliefs on quality of life. Higher level of metacognitive beliefs was associated with greater use of CAS strategies, which in turn was associated with lower quality of life. Further, more CAS strategies were associated with lower quality of life. The results support the generic metacognitive model and suggest that stronger endorsement of dysfunctional metacognitive beliefs and corresponding CAS strategies are associated with lower quality of life. This observation held even when controlling for relevant covariates and suggests that modifying metacognitive beliefs may impact on subjective quality of life.

An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.

Prevalence and clinical significance of late gadolinium enhancement in children and adolescents with hypertrophic cardiomyopathy: a systematic review and meta-analysis.

Hypertrophic cardiomyopathy is the leading cause of sudden cardiac death among the paediatric population. The aim of this study is to investigate the prevalence and clinical significance of late gadolinium enhancement, as assessed by cardiac MRI, in paediatric hypertrophic cardiomyopathy. A systematic literature search was conducted in PubMed, SCOPUS, and Ovid SP to identify relevant studies. Pooled estimates with a 95% confidence interval were calculated using the random-effects generic inverse variance model. Statistical analysis was performed using Review Manager v5.4 and R programming. Seventeen studies were included in this meta-analysis, encompassing a total of 778 patients. Late gadolinium enhancement was highly prevalent in paediatric hypertrophic cardiomyopathy, with a pooled prevalence of 51% (95% confidence interval, 40-62%). The estimated extent of focal fibrosis expressed as a percentage of left ventricular mass was 4.70% (95% confidence interval, 2.11-7.30%). The presence of late gadolinium enhancement was associated with an increased risk of adverse cardiac events (pooled odds ratio 3.49, 95% confidence interval 1.10-11.09). The left ventricular mass index of late gadolinium enhancement-positive group was higher than the negative group, with a standardised mean difference of 0.91 (95% confidence interval, 0.42-1.41). This meta-analysis demonstrates that prevalence of late gadolinium enhancement in paediatric hypertrophic cardiomyopathy is similar to that in the adult population. The presence and extent of late gadolinium enhancement are independent predictors of adverse cardiac events, underscoring their prognostic significance among the paediatric population.

Investigation of stair ascending and descending activities on the lifespan of hip implants

Total hip arthroplasty (THA) surgeries among young patients are on the increase, so it is crucial to predict the lifespan of hip implants correctly and produce solutions to improve longevity. Current implants are designed and tested against walking conditions to predict the wear rates. However, it would be reasonable to include the additional effects of other daily life activities on wear rates to predict convergent results to clinical outputs. In this study, 14 participants are recruited to perform stair ascending (AS), descending (DS), and walking activities to obtain kinematic and kinetic data for each cycle using marker based Qualisys motion capture (MOCAP) system. AnyBody Modeling System using the Calibrated Anatomical System Technique (CAST) full body marker set are performed Multibody simulations. The 3D generic musculoskeletal model used in this study is a marker-based full-body motion capture model (AMMR,2.3.1 MoCapModel) consisting of the upper extremity and the Twente Lower Extremity Model (TLEM2). The dynamic wear prediction model detailing the intermittent and overall wear rates for CoCr-on-XLPE bearing couple is developed to investigate the wear mechanism under 3D loading for AS, DS, and walking activities over 5 million cycles (Mc) by using finite element modelling technique. The volumetric wear rates of XLPE liner under AS, DS, and walking activities over 5-Mc are predicted as 27.43, 23.22, and 18.84 mm3/Mc respectively. Additionally, the wear rate was predicted by combining stair activities and gait cycles based on the walk-to-stair ratio. By adding the effect of stair activities, the volumetric wear rate of XLPE is predicted as 22.02 mm3/Mc which is equivalent to 19.41% of walking. In conclusion, in this study, the effect of including other daily life activities is demonstrated and evidence is provided by matching them to the clinical data as opposed to simulator test results of implants under ISO 14242 boundary conditions.

Competitive Strategies and Performance of Kenya Airways Limited in Nairobi County, Kenya

Kenya Airways has experienced significant financial losses over a prolonged period, which has led to the requirement of a government bailout and the adoption of a restructuring initiative. Kenya Airways is a major player in the Kenyan aviation sector but has struggled to stay profitable compared to Ethiopian Airlines. Ethiopian Airlines has reported consistent profits for several years, in contrast. Kenya Airways is adopting competitive tactics to improve its performance, but there is a dearth of empirical research on the subject. The objective of this study was to analyse the impact of competitive strategies on the performance of Kenya Airways. The purpose of the study was to examine the impact of differentiation, cost leadership, focus, and innovation on the performance of Kenya Airways Limited. This study was anchored on the following theories: porter’s generic strategies model, dynamic capabilities, resource-based view, strategy-structure-performance, and balanced score card. This study employed a descriptive inquiry approach. The focus group for the study was comprised of a total of 180 staff members of Kenya Airways Limited. The sample size consisted of 124 employees, who were selected using a basic random sampling technique. A semi-structured questionnaire was used to collect primary data. This research assessed the accuracy of the research tool by utilizing the viewpoints of experts through a content validity method. Cronbach's alpha co-efficient was utilized to evaluate the dependability of the research instrument, and a minimum threshold of 0.7 was established. Data was analysed using descriptive statistics using means and standard deviations and inferential statistics. The study made use of correlation analysis and multiple regression analysis to deduce connections among different variables. Differentiation strategy had a positive and significant effect on performance of Kenya Airways of (β= 0.513, p < 0.05). Cost leadership strategy had a positive and significant effect on performance of Kenya Airways of (β=0.458, p < 0.05). Focus strategy had a positive and significant effect on performance of Kenya Airways of (β= 0.540, p < 0.05). Innovation strategy had a positive and significant effect on performance of Kenya Airways of (β=0.651, p < 0.05). The study concluded that differentiation strategy improves the performance of Kenya Airways. Cost leadership strategy helps to improve the performance of Kenya Airways Limited. Focus strategy enhances performance of Kenya Airways Limited and innovation strategy helps to improve performance of Kenya Airways Limited. The study recommended that the company should continue to offer unique features that sets it apart from other airlines. The company should operate efficiently in order to keep costs low.

Optimal Inference for Spot Regressions

Betas from return regressions are commonly used to measure systematic financial market risks. “Good” beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The “local Gaussian” property of the generic continuous-time benchmark model enables optimal “finite-sample” inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures. (JEL C22, C58, G12, G23)

Improved Search for Integral, Impossible Differential and Zero-Correlation Attacks

Integral, impossible-differential (ID), and zero-correlation (ZC) attacks are three of the most important attacks on block ciphers. However, manually finding these attacks can be a daunting task, which is why automated methods are becoming increasingly important. Most automatic tools regarding integral, ZC, and ID attacks have focused only on finding distinguishers rather than complete attacks. At EUROCRYPT 2023, Hadipour et al. proposed a generic and efficient constraint programming (CP) model based on satisfiability for finding ID, ZC, and integral distinguishers. This new model can be extended to a unified CP model for finding full key recovery attacks. However, it has limitations, including determining the contradiction location beforehand and a cell-wise model unsuitable for weakly aligned ciphers like Ascon and PRESENT. They also deferred developing a CP model for the partial-sum technique in key recovery as future work.In this paper, we enhance Hadipour et al.’s method in several ways. First, we remove the limitation of determining the contradiction location in advance. Second, we show how to extend the distinguisher model to a bit-wise model, considering the internal structure of S-boxes and keeping the model based on satisfiability. Third, we introduce a CP model for the partial-sum technique for the first time. To show the usefulness and versatility of our approach, we apply it to various designs, from strongly aligned ones like ForkSKINNY and QARMAv2 to weakly aligned ones such as Ascon and PRESENT, yielding significantly improved results. To mention a few of our results, we improve the integral distinguisher of QARMAv2-128 (resp. QARMAv2-64) by 7 (resp. 5) rounds, and the integral distinguisher of ForkSKINNY by 1 round, only thanks to our cell-wise distinguisher modelings. By using our new bit-wise modeling, our tool can find a group of 2155 5-round ID and ZC distinguishers for Ascon in only one run, taking a few minutes on a regular laptop. The new CP model for the partial-sum technique enhances integral attacks on all SKINNY variants, notably improving the best attack on SKINNY-n-n in the single-key setting by 1 round. We also enhance ID attacks on ForkSKINNY and provide the first analysis of this cipher in a limited reduced-round setting. Our methods are generic and applicable to other block ciphers.

A Magnetic-Geared Machine With Improved Magnetic Circuit Symmetry for Hybrid Electric Vehicle Applications

Magnetic-geared machine (MGM) has been widely investigated as the power split device for hybrid electric vehicles with the inherently high-integration of multi-mechanical ports. It is revealed that the magnetic circuits of armature winding in MGM are essentially asymmetrical for different phases under the magnetic field modulation effect. This inevitably results in unbalanced phase back electromotive force (EMF) waveforms, severe torque ripples, acoustic noise, and difficulty for precise control. To alleviate the asymmetry problem, a generic winding design model is presented for MGMs in this paper, which allows for consideration of all potential winding configurations. Firstly, a novel back EMF harmonic factor is proposed to account for both the harmonic content and asymmetric level in MGM. The genetic algorithm (GA) is further employed to optimize the winding in terms of minimizing the back EMF harmonic factor, armature magnetomotive force harmonic contents, and phase winding resistance. The proposed winding layout exhibits superior filtering capability for three-phase asymmetric magnetic field harmonics. As compared with the conventional integral-slot distributed winding, the MGM with the proposed winding exhibits improved back EMF waveform symmetry, suppressed torque ripple, and reduced core losses. Finally, an experimental prototype is manufactured to validate the effectiveness of asymmetric magnetic field suppression for MGMs. The results show the investigated winding design method is an effective solution to the asymmetric issue of MGMs, paving the way to research opportunities for further improvements.

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

A Generic Model for Inertia-Based Fast Frequency Response of Wind Turbines and Other Positive-Sequence Dynamic Models for Renewable Energy Systems

A Generic Model for Inertia-Based Fast Frequency Response of Wind Turbines and Other Positive-Sequence Dynamic Models for Renewable Energy Systems

A novel DFN-DEM approach to simulate long-term behavior of crystalline rock under effects of glacial climate conditions

The safety assessment of nuclear waste repositories is a critical yet challenging task. Several processes acting on different time scales and multiphysical couplings must be taken into account. The complexity of such assessments often requires integrating different software into a single numerical workflow. This study focuses on the long-term behavior of fractured rock masses and the alteration of the hydrogeological system under changing global climate conditions. The investigated impacts include the response of the rock mass due to heating of a generic repository, development of permafrost, and advance and retreat of an ice-shield. To represent THM processes on a timescale of ∼100,000 years in crystalline rock, a novel DFN-DEM modelling approach has been developed. The modelling approach combines two numerical software tools. The thermomechanical calculations are carried out using the distinct element method (DEM) based tool 3DEC [1]. The flow field and the hydraulic heads in the fracture network are calculated with the DFN-tool DFN.Lab [2]. A proprietary script allows to couple and update parameters and variables that are needed to perform THM calculations linking the two different tools. The long-term THM simulations are tested on a generic model of crystalline rock that includes deterministic fault zones as well as a stochastic DFN as the key feature of the hydrogeological system. The modelling approach allows to analyze the behavior of the fracture network including fracture growth and the development of flow pathways within the fracture network as a response to the external THM impacts. The results of the simulations show that the superposition of the different THM processes favors the development of preferential flow pathways along large-scale faults during cold climate conditions.