Representation Of Behavior Research Articles

Numerical Modeling, Trim, and Linearization of a Side-by-Side Helicopter in Hovering Conditions

In the present paper, a flight dynamics model is adopted to represent the trim and stability characteristics of a side-by-side helicopter in hovering conditions. This paper develops a numerical representation of the rotorcraft behavior and proposes a set of guidelines for trimming and linearizing the highly coupled rotor dynamics derived by the modeling approach. The trim algorithm presents two nested loops to compute a solution of the steady-state conditions averaged around one blade’s revolution. On the other hand, a 38-state-space linear representation of the helicopter and rotor dynamics is obtained to study the effects of flap, lead–lag, and inflow on the overall stability. The results are compared with an analytical framework developed to validate the rotorcraft stability and compare different modeling approaches. The analysis showed that non-uniform inflow modeling led to a coupled longitudinal inflow–phugoid mode which made the vehicle prone to dangerous instabilities. The flap and lead–lag dynamics introduced damping in the system and can be considered beneficial for rotor dynamics.

Conflict during learning reconfigures the neural representation of positive valence and approach behavior.

Punishing and rewarding experiences can change the valence of sensory stimuli and guide animal behavior in opposite directions, resulting in avoidance or approach. Often, however, a stimulus is encountered with both positive and negative experiences. How is such conflicting information represented in the brain and resolved into a behavioral decision? We address this question by dissecting a circuit for sexual conditioning in C.elegans. In this learning paradigm, an odor is conditioned with both a punishment (starvation) and a reward (mates), resulting in odor approach. We find that negative and positive experiences are both encoded by the neuropeptide pigment dispersing factor 1 (PDF-1) being released from, and acting on, different neurons. Each experience creates a distinct memory in the circuit for odor processing. This results in the sensorimotor representation of the odor being different in naive and sexually conditioned animals, despite both displaying approach. Our results reveal that the positive valence of a stimulus is not represented in the activity of any single neuron class but flexibly represented within the circuit according to the experiences and predictions associated with the stimulus.

Concepts of reproductive behaviour and motherhood in Soviet cinema of the 1920–1930s

The article examines representations of motherhood and reproductive behaviour in Soviet cinema from Revolution 1917 to the mid-1930s (films “Children are the Flowers of Life”, “The Road to Happiness”, “Katka's Reinette Apples”, “Bed and Sofa”, etc.) through the study of the reproductive choice mechanism, demonstration of pregnancy, childbirth and child care. Conclusions are drawn about the presence of several main conceptual frameworks, such as happy Soviet motherhood as opposed to pre-revolutionary motherhood; the unreliability of the parental function of the biological father, but the support of the collective and the acquisition of a new partner, which means the creation of a two-parents family; transition from the demonstration of medical instruments and the process of childbirth to the withdrawal of this segment; etc.

Distance similarity entropy: a sensitive nonlinear feature extraction method for rolling bearing fault diagnosis

Entropy-based methods are widely used in machinery fault diagnosis for characterizing system disorder and complexity. However, conventional entropy techniques often fail to capture local signal variations when analyzing relationships between vectors, especially in complex settings. This leads to incomplete representations of subtle features and dynamic behaviors, resulting in inaccurate estimations of system complexity and affecting diagnostic accuracy and reliability. To address these limitations, a novel distance similarity entropy (DSEn) is proposed in this paper: (1) It leverages element-wise distances to precisely capture local shifts and subtle distortions between subsequences. (2) It employs a Gaussian kernel function for vector similarity, enhancing signal pattern analysis by preserving subtle differences and mitigating the impact of outliers. (3) It uses probability density estimation of distance similarities between adjacent vectors to track changes in internal signal patterns, enabling more accurate and sensitive estimations of signal complexity. Synthetic signal experiments demonstrate that DSEn excels in detecting dynamic time series changes and characterizing signal complexity. Tests on two bearing datasets reveal that DSEn's extracted features show significant differences, highlighted by Hedges’ g effect size. Compared to other commonly used entropies (SampEn, PermEn, FuzzEn, DistEn, etc.), DSEn shows superior fault identification accuracy, computational efficiency, and noise resistance.

Reliability analysis of support strategies in tunnel construction: Insights from geomechanical analysis of jointed rock masses

In this study, the dynamics of jointed rock masses in the challenging geological setting of the Himalayas, with a focus on tunneling activities and recommendations of support for jointing rock mass, were investigated. The jointing in Himalayan rocks mass poses significant implications for tunnel stability, demanding a detailed analysis of joint characteristics, such as joint connectivity and spacing. The parameter of joint connectivity rate along the Himalayas becomes a key focus, impacting rock mass strength for tunneling. The study utilized quadratic polynomial and reciprocal expressions of power functions, to characterize the nonlinear variation of jointed rock mass strength concerning joint connectivity rate. Integration of these results leads to unified equations that consider rock type dependence, having a realistic representation of jointed rock behavior. Numerical analysis reveals that jointing in rock affects instability through complex stress regimes, identifying critical stress concentration points. The effectiveness of support measures like rock bolts and shotcrete are validated, demonstrating their role in reducing displacements in tunnels.

An Enhanced Progressive Damage Model for Laminated Fiber-Reinforced Composites Using the 3D Hashin Failure Criterion: A Multi-Level Analysis and Validation.

This paper presents a progressive damage model (PDM) based on the 3D Hashin failure criterion within the ABAQUS/ExplicitTM 2021 framework via a VUMAT subroutine, enhancing the characterization of the mechanical performance and damage evolution in the elastic and softening stages of composite materials via the accurate calculation of damage variables and accommodation of non-monotonic loading conditions. In the subsequent multi-level verification, it is found that the model accurately simulates the primary failure modes at the element level and diminishes the influence of element size, ensuring a reliable behavior representation under non-monotonic loading. At the laminate level, it also accurately forecasts the elastic behavior and damage evolution in open-hole lamina and laminates, demonstrating the final crack band at ultimate failure. This paper also emphasizes the importance of correct characteristic length selection and how to minimize mesh size impact by selecting appropriate values. Compared to ABAQUS's built-in 2D model, the 3D VUMAT subroutine shows superior accuracy and effectiveness, proving its value in characterizing the mechanical behavior and damage mechanisms of fiber-reinforced polymer (FRP) materials. The enhanced 3D PDM accurately characterizes the softening processes in composite materials under simple or complex stress states during monotonic or non-monotonic loading, effectively minimizes the mesh dependency, and reasonably captures failure crack bands, making it suitable for future simulations and resolutions of numerical issues in composite material models under complex, three-dimensional stress states.

Constitutive description of snow at finite strains by the modified cam‐clay model and an implicit gradient damage formulation

AbstractSnow, characterized as a unique granular and low‐density material, exhibits intricate behavior influenced by the proximity to its melting point and its three‐phase composition. This composition entails a structured ice skeleton surrounded by voids filled with air and spread with liquid water. Mechanically, snow experiences dynamic transformations, including bonding/degradation between its grains, significant inelastic deformations, and a distinct rate sensitivity. Given snow's varied structures and mechanical strengths in natural settings, a comprehensive constitutive model is necessary. Our study introduces a pioneering formulation grounded on the modified Cam‐Clay model, extended to finite strains. This formulation is further enriched by an implicit gradient damage modeling, creating a synergistic blend that offers a detailed representation of snow behavior. The versatility of the framework is emphasized through the careful calibration of damage parameters. Such calibration allows the model to adeptly capture the effects of diverse strain rates, particularly at high magnitudes, highlighting its adaptability in replicating snow's unique mechanical responses across various conditions. Upon calibration against established experimental benchmarks, the model demonstrates a suitable alignment with observed behavior, underscoring its potential as a comprehensive tool for understanding and modeling snow behavior with precision and depth.

Influence of Binding Energies on Required Process Conditions in Aerosol Deposition

AbstractWith the high interest in aerosol deposition in order to form high-quality coatings by solid-state impact, there is an increasing demand for developing general guidelines to estimate needed particle velocities and thus process parameter sets for successful deposition of ceramic materials. By using modeling approaches, rather different material properties in first instance can be expressed in terms of binding energies. Needed velocities for possible bonding can then derived by impact simulations and compared to experimental results from the literature. In order to study the role of binding energy on the impact behavior of ceramic particles in aerosol deposition, a molecular dynamics study is presented. Single-particle impacts are simulated for a range of binding energies, particle sizes and impact velocities. The results show that increasing the binding energy from 0.22 to 0.96 eV results in up to three times higher characteristic velocities corresponding to the threshold of bonding or grain size-dependent fragmentation of the particles. However, regardless of the binding energy, exceeding the characteristic velocities results in a similar deformation and fragmentation pattern. This allows for a general representation of the impact behavior as a function of normalized impact velocity for different ceramic materials. Apart from dealing with prerequisites for bonding of different materials by aerosol deposition, this study could also be generally relevant to the high-velocity deformation behavior of ceramics with different grain sizes.

WRF-HEATS coupling: Incorporating human behaviors and city topography into urban heat stress evaluation

Urban human thermal stress can be inaccurately estimated along with less-understood heat heterogeneity due to the absence of high-resolution meteorological information and realistic human behavior representation. To this end, we coupled a regional climate model (weather research and forecasting model, WRF) and a human energy balance model (human-environment adaptive thermal stress model, HEATS) to predict pedestrian's dynamic thermal stress at a neighborhood scale. The WRF-human coupling system resolves human-environment heat exchanges based on meteorological and topographical information with the consideration of dynamic human activities. The coupling system has been tested and utilized to study dynamic heat stress in a typical hot, humid, and mountainous city, Hong Kong. Our results revealed widespread heat heterogeneity with up to 7 °C difference in Physiological Subjective Temperature (PST) in the core urban area, and extreme heat exposure (up to 45 °C PST) in calm-wind zones at noon. Heat stress can be further aggregated considering realistic human behaviors such as extra clothing (e.g., protective facemask during pandemics) and physical exercise (e.g., walking along inclined terrain). Optimal-thermal-comfort routes have been designed and suggested based on the simulated neighborhood-scale heat stress map.

The validity of studying healthy aging with cognitive tests measuring different constructs

A clinically useful characterization of the cognitive aging process requires the development of valid and robust behavioral tests, with an emphasis on explaining and understanding typical inter-individual variability in cognition. Here, using a dataset that includes behavioral scores collected with the National Institute of Health Toolbox Cognition Battery (NIHTB-CB) and other auxiliary tests, we examined (1) the differences between young and old adults across different cognitive domains, (2) the strength of across-subject correlations in behavioral test scores, (3) the consistency of low-dimensional behavioral representations across age using factor analysis, and (4) the accuracy of behavioral scores in predicting participants’ age. Our results revealed that (1) elderly females had better verbal episodic memory scores than elderly males, (2) across-subject correlations between behavioral tests varied with age group, (3) although a three-factor model explained the behavioral data in both age groups, some tasks loaded to different factors between the two groups, and (4) age-performance relationship (i.e. a regression model linking age to cognitive scores) in one group cannot be extrapolated to predict age in the other group, indicating an inconsistency in age-performance relationships across groups. These findings suggest that executive function tests might tap into different cognitive processes in different age groups, which might ultimately suggest that a statistically significant between-group difference in test performance might not always reflect differences in the same underlying cognitive processes. Overall, this study calls for more caution when interpreting age-related differences and similarities between age groups with different cognitive abilities even when the same tests are used.

A novel approach to the convergence analysis of chaotic dynamics in fractional order Chua’s attractor model employing fixed points

This article uses a fixed point method to connect with Chua’s attractor model, incorporating the Atangana–Baleanu derivative using a two-step Lagrange polynomial. We introduce novel fixed point theorems using some special contractions followed by graphical representations of the convergence behavior of the iterative process. The uniqueness and existence of fixed points are demonstrated through theoretical proofs and numerical simulations. This approach of its first kind substantiates the existence and uniqueness of the model, enriching the understanding of its chaotic dynamics. Furthermore, numerical investigations explore distinct scenarios of Chua’s model, encompassing the classic Chua’s attractor, the double scroll attractor, chaos in Chua’s circuit, and behaviors such as cycle behavior and higher parametric values. Each case is analyzed graphically, providing insights into the system’s complex dynamics and validating theoretical predictions.

Smartphone Screen Time Characteristics in People With Suicidal Thoughts: Retrospective Observational Data Analysis Study.

Smartphone-based monitoring in natural settings provides opportunities to monitor mental health behaviors, including suicidal thoughts and behaviors. To date, most suicidal thoughts and behaviors research using smartphones has primarily relied on collecting so-called "active" data, requiring participants to engage by completing surveys. Data collected passively from smartphone sensors and logs may offer an objectively measured representation of an individual's behavior, including smartphone screen time. This study aims to present methods for identifying screen-on bouts and deriving screen time characteristics from passively collected smartphone state logs and to estimate daily smartphone screen time in people with suicidal thinking, providing a more reliable alternative to traditional self-report. Participants (N=126; median age 22, IQR 16-33 years) installed the Beiwe app (Harvard University) on their smartphones, which passively collected phone state logs for up to 6 months after discharge from an inpatient psychiatric unit (adolescents) or emergency department visit (adults). We derived daily screen time measures from these logs, including screen-on time, screen-on bout duration, screen-off bout duration, and screen-on bout count. We estimated the mean of these measures across age subgroups (adults and adolescents), phone operating systems (Android and iOS), and monitoring stages after the discharge (first 4 weeks vs subsequent weeks). We evaluated the sensitivity of daily screen time measures to changes in the parameters of the screen-on bout identification method. Additionally, we estimated the impact of a daylight time change on minute-level screen time using function-on-scalar generalized linear mixed-effects regression. The median monitoring period was 169 (IQR 42-169) days. For adolescents and adults, mean daily screen-on time was 254.6 (95% CI 231.4-277.7) and 271.0 (95% CI 252.2-289.8) minutes, mean daily screen-on bout duration was 4.233 (95% CI 3.565-4.902) and 4.998 (95% CI 4.455-5.541) minutes, mean daily screen-off bout duration was 25.90 (95% CI 20.09-31.71) and 26.90 (95% CI 22.18-31.66) minutes, and mean daily screen-on bout count (natural logarithm transformed) was 4.192 (95% CI 4.041-4.343) and 4.090 (95% CI 3.968-4.213), respectively; there were no significant differences between smartphone operating systems (all P values were >.05). The daily measures were not significantly different for the first 4 weeks compared to the fifth week onward (all P values were >.05), except average screen-on bout in adults (P value = .018). Our sensitivity analysis indicated that in the screen-on bout identification method, the cap on an individual screen-on bout duration has a substantial effect on the resulting daily screen time measures. We observed time windows with a statistically significant effect of daylight time change on screen-on time (based on 95% joint confidence intervals bands), plausibly attributable to sleep time adjustments related to clock changes. Passively collected phone logs offer an alternative to self-report measures for studying smartphone screen time characteristics in people with suicidal thinking. Our work demonstrates the feasibility of this approach, opening doors for further research on the associations between daily screen time, mental health, and other factors.

Comparative analysis of event runoff coefficients and curve numbers in contrasting urban environments based on observed rainfall-runoff data

Recently, there has been a growing recognition that the generic values of runoff coefficient (C) and curve number (CN) from standard lookup tables in the literature may not accurately capture the specific characteristics of a given catchment. This study aims to estimate and compare the event C and CN of two contrasting urban catchments in Ljubljana, Slovenia using observed rainfall-runoff data. Estimated parameter values were compared to the tabulated values from the literature (i.e., ASCE, 1992; USDA-NRCS, 2004). Seasonal changes of C and CN, along with diurnal streamflow patterns, were also analyzed to understand the eco-hydrological processes in the urban forest. The results demonstrated that the urban mixed forest exhibited high variability of Cs across all rainfall events with a mean and median of 0.11 and 0.062, respectively. Most of the C values observed in the urban area are clustered around the central tendency with a mean and median of 0.60. These mean C values in both catchments were lower than the tabulated values in the ASCE (1992) design manual. Mean and median CN values in the urban area were 95.45 and 96.81, respectively, lower than the urban mixed forest’s CN values of 82.69 and 83.95. An asymptotic CN∞ of 90.69 was found for the urban area and 71.71 for the urban mixed forest. Central tendency-derived CN values tend to be slightly higher than the tabulated values from USDA-NRCS (2004) and gridded values from GCN250 (Jaafar et al., 2019), while asymptotic CN∞ values were lower. Using central tendency measures as the single lumped value of C and CN may provide a reasonable representation of the runoff behavior in the studied urban area. However, this may present certain challenges in the urban forest catchment. Additionally, pre-event soil moisture conditions and specific storm characteristics contributed to the observed variations in C and CN. Bi-monthly analysis showed that C and CN were high during the autumn and winter months. Diurnal streamflow pattern is most prevalent during low-flow and precipitation-free periods while exhibiting seasonal structures in terms of the shape and timing of maxima and minima. Local estimation of C and CN allows for a more tailored representation of the catchment’s hydrological behavior.

The good, the bad and the evil: A unified conceptualization of negative leadership behaviour

AbstractThe level of scholarly and practitioner interest in negative leadership behaviour has been increasing, but research in this field remains fragmented, and we lack a common understanding of what constitutes negative leadership behaviour and its antecedents, consequences and contexts. To address these research gaps, we systematically review 352 papers and identify 25 different negative leadership behaviours with large overlaps in their conceptualization and operationalization. Our analysis of conceptualizations reveals common attributes that constitute the behavioural intensity and the behavioural valence of negative leadership behaviour. Behavioural intensity attributes constitute negative leadership behaviour as active, reoccurring and intentional behaviour that targets subordinates’ psychological states. Behavioural valence attributes constitute negative leadership behaviour as being unethical, lack of empathy, leader self‐worthiness and self‐orientation, verbal hostility, use of power asymmetry and harming and belittling of others. The vast majority of operationalizations for negative leadership behaviour reflect the perceptions of subordinates or colleagues, whereas only a few operationalizations reflect supervisors’ self‐assessments. Our findings further reveal that the antecedents of negative leadership behaviour focus mainly on supervisors, whereas the consequences of negative leadership behaviour focus mainly on subordinates. We develop a unified conceptualization of negative leadership behaviour and discuss our findings in the light of an impactful future research agenda that revolves around the unified conceptualization and empirical representation of negative leadership behaviour on the basis of behavioural attributes, the role of subordinates and dyad‐related factors leading to negative leadership behaviour and the integration of the negative and positive psychological and economic consequences of negative leadership behaviour.

Social robot detection based on user behavioral representation

Social robots aim to facilitate our lives by mimicking our behaviors to deliver information and interact with us. Unfortunately, some social robots are bred for sinister intention, which often spread fake news, even threatening personal and national security. To effectively detect these robots, some existing works utilize basic user attributes for clustering, such as creation time and other information, but ignore the behavioral features of social robots, which are important a priori information for detection, and their absence leads to poor performance. To address this problem, we construct the Social Robot Behavior Detection model, which employs social robots' behavioral habits in disseminating content. Specifically, our method consists of three parts. First, to accurately characterize news, we analyze news features from multiple views, which facilitates representing users' habits of posting news. Second, a novel aggregation strategy is designed for aggregating user features to obtain accurate user representation. Finally, a classifier is used to detect whether user is social robots or not. We conduct analytical experiments on two public datasets, and the results show that our model has better detection performance, which demonstrates that our approach outperforms existing works on social robot detection. Our source code is available at https://github.com/NPURG/SRDB.

Bivariate Log-Symmetric Regression Models Applied to Newborn Data

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues.

IPSRM: An intent perceived sequential recommendation model

Objectives:Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process. Methods:This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process. Results:Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions. Conclusions:Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

Tailoring Failure Interactions for Validation Testing of Composite Laminate Progressive Failure Models

Efficient validation of numerical models for predicting failures in composite structures is imperative due to the intricate nature of such failures. The multifaceted aspects, including multimodal, multiscale, and interactive factors, pose challenges in the calibration and validation of models. Constraints in cost and time often limit the comprehensive testing required for model validation across diverse failure response scenarios. This study proposes an optimal test selection approach, building upon previous insights that optimizing measures derived from progressive failure responses can unveil failure mode interactions sensitive to stochastic variations. Calibration designs should minimize interaction while maximizing sensitivity to calibration parameters, whereas validation models must encompass varied interactions to understand predictability limits. However, optimizing progressive failure analyses for composites is computationally intensive. A straightforward design of experiments exploration is demonstrated here, focusing on efficiently identifying designs sensitive to stochastic effects, which reveal mode interactions and suitability for calibration/validation. This method is exemplified using computational models for composite bolted joints subjected to pin-bearing loads, showcasing their intricate failure modes. Designs displaying high sensitivity to parameter changes offer insights into failure mechanisms, aiding in model validation and enhancement. Thus, employing such critical designs as test cases can refine numerical models, resulting in more accurate representations of complex failure behaviors in diverse composite components.

Using well log data to predict rock compressibility and elasticity in Zubair formation/ southern of Iraq

The mechanical characteristics of rocks such as elasticity (Young's modulus, Poisson's ratio, and unconfined compressive strength) play an essential in sand production analysis, well design, and borehole stability assessment. In this study, the mechanical characteristics of rocks were indirectly estimated using gamma ray, density, and acoustic (compressional and shear) log data from well RU-X in the Rumaila oil field, specifically for the Zubair Formation. These estimated properties were compared to direct measurements obtained from triaxial and uniaxial mechanical tests conducted on well RU-X. The results showed a significant similarity between the indirect estimates and the direct measurements, indicating their reliability for sand production analysis and their valuable contribution to constructing the geomechanical model. Moreover, the static profile of Poisson's ratio was validated using laboratory core test results, demonstrating reasonable agreement. The validation process involved comparing laboratory-derived measurements with actual field measurements in the Zubair Formation. The higher Poisson's ratio observed in the shale was attributed to the slower propagation of acoustic waves, resulting in a good matching with an R2 value of 0.77. On the other hand, the lower Young's modulus in the shaly formations indicated lower resistance to deformation, with a comparative ratio of R²=0.96. Static measurements, which consider various influencing factors, provide a more realistic representation of rock behavior under different conditions. Regarding unconfined compressive strength, the comparative ratio was R²=0.83.