Dimensions Research Articles

Engineering Properties of Foxtail Millet Seeds for Development of Planter for Millet Crop

Millets are sown by broadcasting and drilling by traditional implements. By using these tools, seeds were not distributed uniformly, which led to an excess of plants and irregular spacing. Furthermore, if seed is sown in a line rather than a spread pattern, harvesting and threshing activities become more efficient. In keeping the views of above facts, planter for millet seeds was developed. A study was carried out to measure the engineering properties of foxtail millet seeds for development of planter for millet crop. Engineering properties helps in design of machine parameters like type of seed metering mechanism, size of cell and hopper capacity etc. The engineering properties such as mean linear dimensions viz., length, width and thickness are 2.20 mm, 1.23 mm and 1.11 mm respectively. The physical properties like geometrical mean diameter, bulk density and true density of are 1.43 mm, 670.33 kg m-3, 1156.2 kg m-3 respectively. The frictional properties such as angle of repose, co-efficient of internal and external friction are 24.30 degrees, 1.23 and 0.45 degrees respectively. In developing the millet planter, these measured engineering parameters were taken into account.

On the optimal control function diagrams in the problem of the movement of a platform with oscillators

We consider the problem of the time-optimal movement of a rigid body moving translationally along a horizontal straight line and carrying n-linear oscillators. The only control force is applied to the platform and is limited in magnitude, there is no friction. The system is transferred from a state of rest to a specified distance with vibration damping. The evolution of optimal control functions depending on the distance of movement is investigated. A general approach to constructing a visual diagram reflecting such evolution is proposed. To do this, a geometric interpretation of the necessary optimality conditions is used as properties of an auxiliary “control” curve in n-dimensional space. Numerical examples of constructing diagrams of optimal control functions for a platform with three oscillators are given.

Islanding as a vernacular creative response to crisis: Emotional geographies of pandemic musicianship

Emotional geographies of creativity during disruptive crises have received scant attention yet are deeply entwined with unfolding spatial reconfigurations. Drawing upon ethnographic research on the shifting geographies of creative work in the post-pandemic city, we highlight and discuss one distinctive emotional-geographic experience: what Wakefield (2021) calls ‘islanding’, a ‘retreat’ among creative workers to home spaces, consolidating activities and finding ways to inhabit upheaval. Describing the case of four male musicians who play together regularly in Sydney, Australia, we show how, in response to forced lockdowns, collaboration shifted from dispersed networks to home spaces of rehearsal, composition, production and performance. Within these reworked home spaces, creativity and sociality were reassembled. The emotional dimensions of music-making, support from family and friends, gender nuances, and bonds of collective creativity, acquired heightened significance. Pandemic upheaval afforded unforeseen opportunities to experiment, while ‘islanded’ spaces proved vital, socially, as the musicians faced multiple health, employment, and psychological challenges. Transcending masculine tropes, gathering at home to play music proved an avenue to perform care work, share the joys of music, express love and admit vulnerability. We conclude that emotional, gendered, and spatial dimensions must figure more prominently within analyses of creative responses to crisis, including post-pandemic discussions of the creative sectors.

Spatial–Temporal Development Trends and Influencing Factors of Government Environmental Information Disclosure: Empirical Evidence Based on China’s Provincial Panel Data

Government environmental information disclosure (GEID) plays an important role in promoting the digital transformation of environmental governance, leading the concept of sustainable development, enhancing public oversight capacity, and promoting democratic decision-making governance. Using provincial panel data from China spanning from 2009 to 2021, we conducted spatial data exploratory analysis and used the dynamic spatial panel model to investigate the spatial–temporal development trends and influencing factors of GEID. The results show that (1) GEID in China exhibits significant spatial agglomeration characteristics, with an “H-H” (High-High aggregation type) agglomeration characteristic observed in three national strategic development regions: Yangtze River Delta, southeast coastal areas, and Beijing–Tianjin–Hebei region. (2) The spillover effect from the southeast coastal provinces gradually radiates to the northwest, resulting in an overall westward movement of GEID. (3) GEID exhibits a significant path-dependency feature in the temporal dimension and a “peer effect” in the spatial dimension. (4) Population size has the greatest impact on GEID. Population size, public participation, and the industrial and transportation sectors positively influence GEID improvement at the local level. However, they generate negative spillover effects to neighbouring provinces. Environmental status and the size of the Real Estate sector have no significant effect. Therefore, China should strengthen regional cooperation, narrow regional disparities, cultivate new quality productive forces, establish a government-led proactive disclosure mechanism under public supervision, and improve the level of GEID at the national level.

Beyond four dimensions: An extended study of f(R,T) theory with multidimensional metric

The present communication explores [Formula: see text]-dimensional cosmological models within the framework of the [Formula: see text] theory. The exploration is motivated by Kaluza–Klein theory, where the existence of extra dimensions becomes a focal point in understanding the fundamental forces of the universe. A cosmological model has been constructed by assuming Fractional quadratic deceleration parameter. [Formula: see text] and [Formula: see text] provide a crucial link between theoretical predictions and empirical observations. Through rigorous mathematical analysis and theoretical modeling, we investigate the implications of this extended gravitational theory on cosmic evolution and the structure of spacetime. Our findings shed light on the interplay between geometry, matter and gravitational dynamics in multidimensional space, offering new insights into the nature of gravity and cosmological phenomenon.

Articulating environmental sustainability dynamics with space-time cube

Conceptually, environmental sustainability involves maintaining crucial environmental functions while considering both present and future development. However, existing methods for expressing environmental sustainability are mainly derived from a steady state with minimal spatial explicitness. Furthermore, the environmental impact of certain events may exhibit a lag, particularly in basins. Here, we propose a framework that employs a space-time cube to articulate environmental sustainability. This cube can visualize the environment's evolution over time, identify hot and cold spots in space, and concurrently determine underlying influencing factors via spatial regression analysis. Unlike traditional methods, the space-time cube incorporates not only spatial dimensions but also temporal dimensions. We applied this framework to China's upper Han River basin, using the Remote Sensing Ecological Index (RSEI) as an indicator of environmental sustainability. It enabled us to chart the basin's ecological trajectory with spatial and temporal explicitness from 1990 to 2020. Our findings reveal that climate change (represented by temperature and precipitation changes) and human activities (represented by nighttime light) were the main factors driving changes in environmental sustainability from 2000 to 2020 in the basin. Therefore, our proposed spatial-temporal integration framework proves to be an efficient tool in articulating environmental sustainability.

A Study on the Spatial Layout of Newly Built Townhouses in Kaohsiung City

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including the facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing a reference for the future planning and design of such structures. The study results showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m2, a facade width between 4.1 and 6.38 m, and a building depth from 7.67 to 12.68 m. In addition, they showed a low negative correlation between the facade width and total floor area, a high positive correlation between the building depth and total floor area, and a moderate negative correlation between the facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable rooms and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable rooms increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used. This means that planning of staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses.

Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.

This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.

Imprint of a dead-ice environment on lidar imagery—Spatial patterns at the last Scandinavian Ice Sheet margin (N Poland)

There is a unique glacial landscape system in the southern Żarnowiec Moraine Plateau of northern Poland. Here the terrain is characterized by a very high concentration of ridges that are often adjacent to pits, which together constitutes a special type of hummocky topography. The morphological diversity of the study-area ridges and pits was determined from high-resolution lidar imagery that was qualitatively and quantitatively processed, then analysed. Fourteen test fields of spatial dimensions 0.5 km × 0.5 km were selected within the moraine plateau for detailed studies. The results show that the ridges and pits constitute a very dense network of approximately circular, diverse-oriented forms of various sizes, most of which are linked to each other in a step-wise fashion. The pits are surrounded by ridges of various heights and widths that separate one pit from another. A characteristic feature of the studied pits is the orientation of their long axis. Form-based morphological analysis revealed that their genesis was associated with a glacial rather than a periglacial environment. It is believed that ridge orientation reflects a spatial pattern related to ice crevasses. A moraine plateau constituted a terrain obstacle that acted as a counterslope to the advancing ice sheet. The studied landforms and landscape resulted from the ice sheet's breaking up into dead-ice blocks of various sizes. The very dense system of perpendicular, parallel, and diagonal crevasses was related to ice sheet advance from a northerly direction, resulting in a flow around the obstacle and differences in ice-mass speed.

Experimental Determination and Simulation Validation: Johnson–Cook Model Parameters and Grinding Simulation of 06Cr18Ni11Ti Stainless Steel Welds

Hydrogen permeation resistance in the welded region of 06Cr18Ni11Ti steel is relatively weak due to surface defects, which need high integrity surface machining. The parameters of the welding material for 06Cr18Ni11Ti steel are currently unavailable, which causes some inconvenience for simulation studies. To fill the lack of 06Cr18Ni11Ti steel weld material parameters in the relevant literature at the present stage, the quasi-static tensile test at different strain rates and notch specimen tensile tests were conducted in this paper and determined the Johnson–Cook (J-C) constitutive model parameters and Johnson–Cook failure model parameters. Subsequently, a multi-grain grinding simulation model was built based on W-M fractal dimension theory by using the determined material parameters. The influence of processing parameters on grinding heat was analyzed. Grinding experiments were conducted to analyze the influence of processing parameters on grinding heat and grinding force. By comparing the simulation and experimental results, it is revealed that the average error is 9.37%, indicating relatively small discrepancy. It is demonstrated that the grinding simulation model built in this paper could efficiently simulate the grinding process, and the determined weld material parameters of 06Cr18Ni11Ti steel have been verified to possess high accuracy and reliability.

Anatomy of null contractions

We introduce null contractions of the Poincare and relativistic conformal algebras. The longitudinal null contraction involves writing the algebra in lightcone coordinates and contracting one of the null directions. For the Poincare algebra, this yields two non-overlapping co-dimension one Carroll algebras. The transverse contraction is a limit on the spatial dimensions and yields two non-overlapping co-dimension one Galilean algebras. We find, similar to Susskind’s original observation of the non-relativistic case, that the Poincare algebra, written in the lightcone coordinates, naturally contains Carrollian sub-algebras in one lower dimension. The effect of the longitudinal contraction, which essentially focusses on the null direction, is to disentangle the two Carroll algebras that now correspond to the symmetries of the two null boundaries. The transverse contraction similarly separates the overlapping Galilean sub-algebras of the original Poincare algebra. We discuss aspects of the conformal case, where we get lower dimensional Carroll Conformal algebras and Schrödinger algebras.

Southern Hemisphere baroclinic activity in seasonal forecasts

Accurate prediction of mid-latitude baroclinic activity is extremely relevant for understanding global climate dynamics and improving long-term weather forecasts. However, current seasonal forecast models struggle to accurately represent the variability of baroclinic activity in the Southern Hemisphere, which may affect their reliability and usefulness. Baroclinic instability in the mid-latitudes is a significant component of the climate system, as it is associated with the meridional transport of a large amount of energy and momentum. Therefore, the ability of the models to correctly predict the properties of the atmospheric circulation in this latitudinal region is a very important requirement. The aim of this study is to estimate the energy of atmospheric phenomena typical of the mid-latitudes, such as baroclinic perturbations, and to understand how seasonal forecasts can be practically used to assess the energy transfer in the atmosphere. We compare the Southern Hemisphere mid-latitude winter variability of the seasonal forecasts of the ECMWF, DWD and Météo France forecasting systems with the ERA5 reanalysis. The analysis is performed by computing the Hayashi spectra of the 500-hPa geopotential height field. Both the reanalysis and the seasonal forecast show a series of peaks in the spectral region of eastward-travelling waves, which corresponds to the high frequency and high wavenumber domain. We quantify the amount of energy released from the atmosphere by calculating the Baroclinic Amplitude Index. The results suggest that seasonal forecasts may not accurately capture the variability of geopotential height power spectra in the Southern Hemisphere, which poses a challenge in correctly distributing the energy over spatial and temporal dimensions. This study will show that this problem is particularly pronounced for wavenumber 4 over a period of 8 days. This misrepresentation likely contributes to the uncertainties in precipitation forecasting, with discrepancies exacerbated by a suboptimal description of baroclinic instability and dynamical components in the models. Our findings highlight the need for an improved representation of baroclinic processes in seasonal forecast models, which could lead to substantial advancements in long-term weather prediction capabilities and in a more complete understanding of climate dynamics.

The influence of fiscal transparency on expenditure by regional and provincial governments in Vietnam

The intricate relationship between budget transparency and government expenditure has been confirmed, with a particular focus on how these dynamics are influenced by spatial and temporal factors. This study deals with the spatio-temporal effects of transparent communal budgets on local government spending, specifically across the 63 provinces in Vietnam. By analyzing data over time, the study uncovers critical insights into how the disclosure of budget information influences government spending patterns, both in the short term and the long term. The findings reveal a nuanced relationship, where both the direct and indirect effects of budget transparency on expenditure tend to be negative, suggesting that increased transparency may lead to a reduction in government spending. This trend is observed from both a short-term perspective, where immediate changes in budget disclosure affect spending, and a long-term perspective, where the effects of sustained transparency become more evident. The study highlights significant regional disparities in the impact of budget transparency on government expenditure. It finds that the influence of budget disclosure is less pronounced in northern Vietnam compared to the southern regions. The results advocate for policies that support targeted resource allocation at the provincial or regional levels. Such tailored approaches are essential for achieving more effective and equitable government budget management, ensuring that transparency initiatives are adapted to the specific needs and conditions of different regions within Vietnam. Overall, this research contributes to the broader understanding of how transparency in budgetary processes can influence government spending. By highlighting the spatial and temporal dimensions of this relationship, the study provides valuable insights for policymakers seeking to enhance fiscal accountability and optimize public resource allocation in Vietnam.

Self-Presentational Communication in Context Digital Realities of Modern Society

Introduction. In the age of the digital economy and hyper-competition, self-presentation communication as a type of self-promotion, as the ability to present oneself to a communication partner, employer, business partner or target audience, is considered by experts as a key skill of the 21st century. It is also considered as one of the significant factors of competition both in the modern highly competitive labor market and in the consumer market, both global and local. The article discusses the theoretical foundations of self-presentational communication in its historical retrospective, traditions, innovations and development trends in the conditions of new realities.Methodology and sources. The methodology of philosophical, sociological and interdisciplinary approaches is used. The article uses the following sources: specialized literature from domestic and foreign authors, scientific research, publications and websites that allow one to explore the specifics of digital self-presentation, its new formats and current practices.Results and discussion. Digital society is a society whose distinctive feature is the electronic-digital method of receiving, storing and distributing information, as well as the electronic-digital mediation of social relations and communicative interaction. One of the ways of such interaction is digital self-presentation, which combines the traditions of this content and innovations caused by the development of new technologies.Conclusion. Self-presentational communication, like the entire communicative component of modern society, including the «human-AI» interface, is carried out in three spatial dimensions: real/offline/Physics, virtual/online/Digital, as well as in their interaction offline online/Phygital. In each dimension, self-presentational communication has its own specifics and could can be considered both as a separate phenomenon and in the process of continuity and development.

Snapshot computed tomographic microscopic imaging spectrometer and its video-level tracking of poisonous Microcystis aeruginosa cells in mixed algae

Conventional microscopic spectral imaging suffers from extended scanning times across wavelength or spatial dimension. To improve capabilities of dynamic microscopic spectral imaging, we developed a snapshot computed tomographic microscopic imaging spectrometer (CTMIS) based on the zeroth and first orders dispersive diffraction of a two-dimensional grating. Utilizing the CTMIS-UNET reconstruction algorithm, we can reconstruct a spectral cube (541x541x26) for each frame of micro spectral imaging video. Experimental results demonstrate a sub-4 μm spatial resolution achievable through a 20x objective lens and a spectral resolution better than 10 nm among 450–700 nm, while maintaining spectral cosine similarities exceeding 0.9989 when comparing reconstructed spectra with ground truth data. Spectral imaging videos of four species of algae and mixed algae were captured under 10 ms exposure time using the CTMIS system. Leveraging the self-developed UNET-SI26 algae recognition network, precise identification and tracking of four types of algae and poisonous microcysts aeruginosa in mixed algae were conducted. The pixel-level recognition accuracy exceeds 95 %, while the accuracy for counting the numbers of different types of cells surpasses 85 %, offering an efficient and accurate spectral imaging method for real-time monitoring and early warning of harmful algae at the cellular level.

Microcracking evolution and clustering fractal characteristics in coal failure under multi step and cyclic loading

During the deep mining process, coal mass encounter intricate geo-environmental stress, such as periodic weighting loading and repeatedly excavation unloading–reloading cycles, which weakens coal’s mechanical integrity and predisposing it to severe coalburst accidents. To investigate the microcracking damage mechanisms and predictive indicators in coal failure under in-situ stress analogs, the multistage step and cyclic loading experiments are conducted on cubic coal specimens. Acoustic emission (AE) technology is employed to track the spatiotemporal-energy evolution of stress-induced damages and discern the microcracking nature through AF/RA assessments, and the power-law scaling relation of AE activity near the catastrophic failure of coal is investigated. Then the clustering fractal structures of microcracking events in the stressed coal are quantified across temporal, spatial and energetic domains, utilizing correlation integral methodologies and b-value derivations from magnitude-frequency relation. Findings indicate that irrespective of the loading mode (step or cyclic), escalating stress triggers an intensification of irreversible fatigue deformations. AE characteristic parameters manifest a gradual rise, culminating in a precipitous peak coinciding with the critical failure point. This escalation adheres to a power-law correlation between AE occurrence frequency and time to failure, observable in the immediate pre-failure seconds, reflecting a universal attribute of coal fracture. Prior to ultimate failure, a marked increase in shear microcracks is discernible, despite tensile-dominated cracks (constituting about 80 % of total microcracks) prevailing as inferred from the variation of AF/RA values, aligning with an inferred “X” conjugate wedge splitting pattern from AE event density and energy mapping. The microcracking events in the loaded coal exhibit a clustering fractal structure that spans across temporal, spatial, and energetic (or magnitude) domains. Notably, the temporal fractal dimension, spatial fractal dimension, and b-value (i.e., a parameter characterized the energetic fractal dimension) all follow a parallel decrease pattern as the loading stress escalates, with a pronounced diminution becoming especially evident as the specimen approaches its catastrophic failure threshold. This insight offers fresh perspectives for predicting rock/coal dynamic disasters, emphasizing the necessity of concurrently monitoring the shift from diffuse microcracking to localized failure across time, space and energy domains. These research findings contribute to a deeper understanding of microcracking damage evolution and failure mechanism of loaded coal, and provide a foundational basis for early warning of rock failure such as the coalburst disasters.

Hot spots for risk-based planning in Greece – the cases of floods and forest fires

ABSTRACT Recent flood and forest fire disasters in Greece highlight the local spatial development history and territorial vulnerability as principal causes. However, despite the strong spatial aspects of disaster risk, disaster management rarely finds its way into spatial planning. The article attempts to locate high disaster risk areas (hot-spots) in Greece and activate risk-based planning. The approach combines theoretical assumptions on the spatial dimension of disaster risk; macro-scale hazard zoning at the national-regional level through past disaster event mapping; meso-scale territorial vulnerability analysis to delimit disaster risk hot-spots; and discussion on statutory planning tools and scales of intervention for risk mitigation.

High-resolution multicomponent LFM parameter estimation based on deep learning

This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, facilitated by convolutional neural networks. Initially, it analyzes the issues of peak shifts and the masking of weaker components due to spectral overlap in the FrFT domain of multi-component LFM signals. Convolutional neural networks are then employed to train and achieve high-resolution representations of FrFT parameters. Specifically, convolutional modules with residual structures are utilized to learn coarse features, while a weighted attention mechanism refines independent features across both channel and spatial dimensions. This approach effectively addresses the challenges posed by spectral peak overlap and frequency shifts in multi-component LFM signals, thereby enhancing the quality of high-resolution parameter estimation. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in processing multi-component LFM signals. Moreover, it exhibits robust detection capabilities for both weak and compact components, thereby underscoring its potential applicability in the field of complex signal processing.

MAFormer: A cross-channel spatio-temporal feature aggregation method for human action recognition

Human action recognition has been widely used in fields such as human–computer interaction and virtual reality. Despite significant progress, existing approaches still struggle with effectively integrating hierarchical information and processing data beyond a certain frame count. To address these challenges, we introduce the Multi-AxisFormer (MAFormer) model, which is organized in terms of spatial, temporal, and channel dimensions of the action sequence, thereby enhancing the model’s understanding of correlations and intricate structures among and within features. Drawing on the Transformer architecture, we propose the Cross-channel Spatio-temporal Aggregation (CSA) structure for more refined feature extraction and the Multi-Axis Attention (MAA) module for more comprehensive feature aggregation. Moreover, the integration of Rotary Position Embedding (RoPE) boosts the model’s extrapolation and generalization abilities. MAFormer surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.2% on NTU RGB+D 60 cross-subject split, 89.9% on NTU RGB+D 120 cross-subject split, and 97.2% on N-UCLA, offering a novel paradigm for hierarchical modeling in human action recognition.

Interpretable machine learning methods to predict the mechanical properties of ABX3 perovskites

This paper proposes the utility of interpretable ensemble learning models for predicting the mechanical properties (bulk, shear and Young moduli) of ABX3 perovskite compounds with the A, B, and X referring to the 3 elements that make the cubic 3-dimensional framework of the perovskite compounds. These models consist of 3 ensemble learning techniques namely CatBoost, Random Forest, and XGBoost. To expand the feature space, robust first-principles density functional theory calculations were used to generate some of the input features, namely elastic constants, density, volume per atom, and ground state energy per atom. The order of the input feature ranking that influences the machine learning (ML) model decisions was then determined. For this, we performed correlation analysis on the multi-dimensional input feature space, suppressed features with high collinearity, and selected features with limited correlation. We trained the three ensemble learning techniques on the desired vectorial input feature representation to predict the mechanical properties. Furthermore, we employed the Shapley Additive Explanations (SHAP) algorithm for analysing the intrinsic decision-making rationality of the ensemble learning models. We measured the performance in the context of the error metrics and coefficient of determination, R2. The results show that XGBoost outperforms other approaches when predicting the shear modulus or Young modulus of the perovskite compounds yielding the least error metrics and the highest R2 value (0.97) in the testing phase. However, both CatBoost and Random Forest outperformed XGBoost when attempting to predict the bulk modulus in the testing phase. The deficiency of the XGBoost in predicting the bulk modulus can be ascribed to an overfitting problem which can occur when the ML model gives accurate predictions for training data but not for test data. Furthermore, the SHAP algorithm provides an insight into the order of feature importance (from highest to lowest). Additionally, we conducted a post-analysis using a holistic ranking to analyse the relative importance of the SHAP feature impact comprehension for the examined ensemble learning techniques. Our findings indicate that the elastic constants are the most important input features influencing the predictive decision of the ensemble learning models.