Search Results (78)

Urban informal settlements (UISs), characterized by self-organized housing, a high population density, inadequate infrastructure, and insecure land tenure, constitute a critical, yet underexplored, aspect of contemporary urbanization. They necessitate scholarly scrutiny to tackle pressing challenges pertaining to equity, sustainability, and urban governance. The automated, accurate, and rapid extraction of UISs is of paramount importance for sustainable urban development. Despite its significance, this process encounters substantial obstacles. Firstly, from a remote sensing standpoint, informal settlements are typically characterized by a low elevation and a high density, giving rise to intricate spatial relationships. Secondly, the remote sensing observational features of these areas are often indistinct due to variations in shooting angles and imaging environments. Prior studies in remote sensing and geospatial data analysis have often overlooked the cross-modal interactions of features, as well as the progressive information encoded in the intrinsic hierarchies of each modality. We introduced a spatial network to solve this problem by combining panoramic and coarse-to-fine asymptotic perspectives, using remote sensing images and urban street view images to support a hierarchical analysis through fusion. Specifically, we utilized a multi-linear pooling technique and then established coarse-to-fine-grained and panoramic viewpoint details within an integrated structure, known as the panoramic fusion network (PanFusion-Net). Comprehensive testing was performed on a self-constructed WuhanUIS dataset as well as two open-source datasets, ChinaUIS and $S^{2}UV$. The experimental results confirmed that the performance of the introduced PanFusion-Net exceeded all comparative models across all of the above datasets. Full article

This paper focuses on optimisation of material parameters to describe the elastoplastic stress–strain relationship in finite element solvers. Two new methods are introduced to minimise the numerical error that occurs in the interspace between the experimental cyclic stress–strain curve and its representation using multilinear interpolation. Specifically, both methods are integrated into a Prandtl operator approach, which can be used to simulate the elastoplastic response of mechanical components subjected to variable thermomechanical loadings. The improvement as compared to standard interpolation is most substantial when the number of yield planes is limited, especially in the case of bilinear stress–strain curves. The innovation of this study is an algorithm that optimises positions of the stress–strain points across the temperature range of interest considering several input temperatures. It is shown that these methods are especially applicable for optimisation of material parameters when the stress–strain curves are available for a range of test temperatures that are needed for simulating thermomechanical fatigue. The improvement in the interpolation using these methods is exhibited for two materials with available experimental results: stainless steel EN 1.4512 and polyamide PA12. Full article

New brain imaging modalities and neuropsychological testing tools are used to study neuronal changes in brain injuries such as mild traumatic brain injury (mTBI). Here we utilized diffusion tensor imaging (DTI) parameters and Wisconsin Card Sorting Test (WCST) variables to investigate patients with chronic mTBI. Neuropsychological assessments for mTBI evaluate impairments across a broad spectrum of executive functions. Our study aims to examine the relationship between fractional anisotropy (FA) and WCST covariates in patients with chronic mTBI. We hypothesize that patients who suffered chronic mTBI have significantly reduced FA in frontal white matter regions in association with significant deviation from standard percentile scores in WSCT. Utilizing multi-linear regression models alongside analyzing DTI scans, WCST covariates were linearly regressed to produce positive and negative contrasts to identify specific regions of interest (ROIs) with reduced FA. Results show that WCST covariates (such as percentile perseverative responses (Ep), non-perseverative responses (Enp), and conceptual response (CResp)) significantly deviate beyond standard percentile scores and correlate with lower FA in white matter regions in the frontal cortex, demonstrating executive function deficits. These frontal regions include the inferior frontal, superior frontal, and corpus callosum (CC), correlated with greater errors in WCST percentile scores. This study investigates the correlation between WCST covariates and DTI parameters as valuable tools in the diagnosis and prognosis of persistent cognitive impairment for patients with a history of chronic traumatic brain injury. Full article

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR. Full article

In coal seam mining operations, the presence of overlying water bodies presents persistent challenges, particularly during multi-seam extraction, where water accumulation in upper seam goafs requires careful management. This study examined the Lingzhida Coal Mine, focusing on the geological conditions of the 3# seam (upper) and the 15# seam (lower), as well as the distribution of water accumulation in the corresponding goafs. The mechanism of water inrush from the upper goaf was studied, and the role of the water-resisting belt (WRB) is suggested. By utilizing empirical equations and field measurements, a method for calculating the floor fracture depth of the 3# seam and the roof fracture height of the 15# seam was derived through multi-linear regression analysis. Based on the relationship between the thickness of the WRB (H_w) and the protective layer (H_p), a classification criterion for the water-inrush risk (the likelihood of water entering the lower seam from the upper goaf) is proposed. The mining area was divided into four risk zones: high-risk (H_w < 0), medium-risk (0 ≤ H_w < 0.5H_p), low-risk (0.5H_p ≤ H_w < H_p), and safe (H_w ≥ H_p). Then, an adaptive zoning approach for water-preserved mining was introduced, considering the spatial distribution of goaf water. This approach incorporates water-preserved mining technologies, including the staggered layout of working faces, reduction in mining height, and the transfer–storage of water resources. These research findings provide crucial insights for ensuring the safe and efficient extraction of the multi-seam. Full article

Biomimetic chromatography is a powerful tool used in the pharmaceutical industry to characterize the physicochemical properties of molecules during early drug discovery. Some studies have indicated that biomimetic chromatography may also be useful for the evaluation of toxicologically relevant molecules. In this study, we evaluated the usefulness of the biomimetic chromatography approach for determining the lipophilicity, affinity to phospholipids, and bind to plasma proteins of selected organophosphate pesticides. Quantitative structure–retention relationship (QSRR) models were proposed to understand the structural features that influence the experimentally determined properties. ACD/labs, Chemicalize, and alvaDesc software were used to calculate theoretical descriptors. Multilinear regression was used as the regression type, and feature selection was supported by a genetic algorithm. The obtained QSRR models were validated internally and externally, and they demonstrated satisfactory performance with key statistical parameters ranged from 0.844 to 0.914 for R² and 0.696–0.898 for R²_ext, respectively, indicating good predictive ability. Full article

Medium manganese steels provide a good combination of tensile strength and ductility due to their multiphase microstructure produced during the multi-step heat treatment process. This study primarily focused on testing and analyzing the tensile properties of 0.17C-5Mn-0.76Al-0.9Si-Nb medium manganese quenching and partitioning (QP) steel using both the experimental and finite element method (FEM) in the multilinear isotropic hardening material model. The 7 mm and 12 mm thick plates exhibited a similar microstructure of tempered primary martensite, lath-type retained austenite, and secondary martensite. The experiments measured tensile strengths of 1400 MPa for 12 mm round specimens and 1325 MPa for 7 mm flat specimens, with total elongations of 15% for round specimens and 11% for flat specimens. The results indicated that the sample’s geometry has some effect on the UTS and ductility of the studied medium-Mn QP steel. However, the more important is the complex relationship between the plate thickness and yield stress and ductility, which are affected by finishing hot rolling conditions. The FEM results showed that the von Mises stresses for flat and round specimens were 1496 MPa and 1514 MPa, respectively, and were consistent with the calculated true stresses of experimental results. This shows that numerical modeling, specifically a multilinear isotropic hardening material model, properly describes the material properties beyond the yield stress and accurately predicts the plastic deformation of the investigated multiphase QP steel. Full article

The objective of this paper is to develop assessment models to quantitatively evaluate the seismic damage caused to resilient concrete columns intended for buildings located in strong-earthquake-prone regions such as Japan and China. The proposed damage assessment models are based on the fractal analysis of crack patterns on the surface of damaged concrete columns and expressed in the form of a fractal dimension (FD) versus transient drift ratio relationship. To calibrate the proposed damage assessment models, a total of eighty images of crack patterns for eight concrete columns were utilized. All the columns were reinforced by weakly bonded ultra-high-strength (WBUHS) rebars and tested under reversed cyclic loading. The experimental variables covered the shear span ratio of the column, the concrete strength, the axial load ratio, and the amount of steel in the WBUHS rebars. A box-counting algorithm was adopted to calculate or derive the FD of the crack pattern corresponding to each transient drift ratio. The test results reveal that the FD is an efficient image-based quantitative indicator of seismic damage degree for resilient concrete columns and correlates strongly with the transient drift ratio and is subjected to the influence of the shear span ratio. The influence of the other experimental variables on the derived FDs is, if any, little. Based on the test results, a linear equation was developed to define the relationships between the FD and transient drift ratio, and a multi-linear equation was formulated to relate the transient drift ratio to the residual drift ratio, an important index adopted in current design guidelines to measure the repairability of damaged concrete structures. To further verify the efficiency of the drift ratio-based FD in seismic damage assessment, the correlation between the FD and relative stiffness loss (RSL), an indicator used to measure the overall damage degree of concrete structures, was also examined. The driven FD exhibited very strong correlation with RSL, and an empirical equation was developed to reliably assess the overall seismic damage degree of resilient concrete columns with an FD. Full article

Since multi-view learning leverages complementary information from multiple feature sets to improve model performance, a tensor-based data fusion layer for neural networks, called Multi-View Data Tensor Fusion (MV-DTF), is used. It fuses M feature spaces ${\mathcal{X}}_1,\cdots ,{\mathcal{X}}_M$, referred to as views, in a new latent tensor space, $\mathcal{S}$, of order P and dimension $J_1\times \cdots \times J_P$, defined in the space of affine mappings composed of a multilinear map $\mathcal{T}:{\mathcal{X}}_1\times \cdots \times {\mathcal{X}}_M\to \mathcal{S}$—represented as the Einstein product between a $(P+M)$-order tensor $\mathcal{A}$ anda rank-one tensor, $\mathcal{X}={\mathbf{x}}^{(1)}\otimes \cdots \otimes {\mathbf{x}}^{(M)}$, where ${\mathbf{x}}^{(m)}\in {\mathcal{X}}_m$ is the m-th view—and a translation. Unfortunately, as the number of views increases, the number of parameters that determine the MV-DTF layer grows exponentially, and consequently, so does its computational complexity. To address this issue, we enforce low-rank constraints on certain subtensors of tensor $\mathcal{A}$ using canonical polyadic decomposition, from which M other tensors ${\mathcal{U}}^{(1)},\cdots ,{\mathcal{U}}^{(M)}$, called here Hadamard factor tensors, are obtained. We found that the Einstein product $\mathcal{A}{⊛}_M\mathcal{X}$ can be approximated using a sum of R Hadamard products of M Einstein products encoded as ${\mathcal{U}}^{(m)}{⊛}_1{\mathbf{x}}^{(m)}$, where R is related to the decomposition rank of subtensors of $\mathcal{A}$. For this relationship, the lower the rank values, the more computationally efficient the approximation. To the best of our knowledge, this relationship has not previously been reported in the literature. As a case study, we present a multitask model of vehicle traffic surveillance for occlusion detection and vehicle-size classification tasks, with a low-rank MV-DTF layer, achieving up to $92.81\%$ and $95.10\%$ in the normalized weighted Matthews correlation coefficient metric in individual tasks, representing a significant $6\%$ and $7\%$ improvement compared to the single-task single-view models. Full article

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g⁻¹. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure–Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R² = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area (S_dft) and pore size fractions of 1.1–1.2 nm ($V_{N_2\left[1.1–1.2\right]}$) and 3.3–3.4 nm ($V_{N_2\left[3.3-3.4\right]}$) for pores. Full article

Evidence-based research on the effects of rainfall, temperature, and relative humidity variability on maize yield is essential for understanding the climate dynamics of, and paving the way for informed adaptive solutions to future potential negative impacts in, Dschang-Cameroon. This study employed the non-parametric Mann–Kendall and Sen’s slope method to detect trends in climate variables and maize yield in the period between 1990 to 2018. Pearson correlation and multilinear regression (MLR) analyses were also used to establish the linear relationship between climate variables and maize yield, and to explore the behavior of the response variable (maize yield) with the predictor variables (climatic variables), respectively. In addition, perceptions of climate variability and its impact on maize yield from a hundred farmers were collected through a questionnaire and analyzed in SPSS. Twenty key informants’ interviews (KII) were conducted using a semi-structured interview and analyzed by thematic analysis. The results showed that the minimum temperature exhibited a decreasing trend at a rate of 0.039 °C per annum, whereas relative humidity had an increasing trend of 0.25% per annum with statistical significance at p = 0.001. In addition, a decreasing trend of rainfall, at a rate of 4.94 mm per annum, was observed; however, this had no statistical significance. Furthermore, the MLR analysis showed that mean temperature and relative humidity have an inversely proportional but statistically significant relationship with maize yield (p = 0.046 and p = 0.001, respectively). The analysis of farmers’ perceptions confirmed the results of trend analyses of decreasing rainfall and increasing maximum temperatures. Moreover, the farmers asserted that the vulnerability of farmers to climate variability is also linked to gender and locality, where women’s outputs are more assailable and farms in low-lying areas are more prone to floods. The high price of farm inputs was also reported as a key factor, other than climate variability, hindering the flourishing of the maize sector in Dschang. Finally, an analysis of the KII indicated the inadequate implementation of flagship agricultural programs in the locality. Full article

This research work extends the least squares criterion. The regression models which have been treated so far in the literature do not study multilinear relationships between variables. Such relationships are of a nonlinear nature. They take place whenever two or more than two univariate variables are the components of a multiple variable of order 2 or an order greater than 2. A multiple variable of order 2 is not a bivariate variable, and a multiple variable of an order greater than 2 is not a multivariate variable. A multiple variable allows for the construction of a tensor. The $\alpha$-norm of this tensor gives rise to an aggregate measure of a multilinear nature. In particular, given a multiple variable of order 2, four regression lines can be estimated in the same subset of a two-dimensional linear space over $\mathbb{R}$. How these four regression lines give rise to an aggregate measure of a multilinear nature is shown by this paper. In this research work, such a measure is an estimate concerning the expected return on a portfolio of financial assets. The metric notion of $\alpha$-product is used to summarize the sampling units which are observed. Full article

The current protocol used to determine if an individual is osteoporotic relies on assessment of the individual’s bone mineral density (BMD), which allows clinicians to judge the condition of a patient with respect to their peers. This, in essence, evaluates a person’s fracture risk, because BMD is a good surrogate measure for strength and stiffness. In recent studies, the authors were the first to produce fracture toughness (FT) data from osteoporotic (OP) and osteoarthritic (OA) patients, by using a testing technique which basically analyzes the prerequisite stress conditions for the onset of growth of a major crack through cancellous bone tissue. FT depends mainly on bone quantity (BV/TV, bone volume/tissue volume), but also on bone micro-architecture (mArch), the inner trabecular design of the bone. The working research hypothesis of the present study is that mArch offers added prediction power to BV/TV in determining FT parameters. Consequently, our aim was to investigate the use of predictive models for fracture toughness and also to investigate if there are any significant differences between the models produced from samples loaded across (A_C, transverse to) the main trabecular orientation and along (A_L, in parallel) the trabeculae. In multilinear regression analysis, we found that the strength of the relationships varied for a crack growing in these two orthogonal directions. Adding mArch variables in the Ac direction helped to increase the R² to 0.798. However, in the A_L direction, adding the mArch parameters did not add any predictive power to using BV/TV alone; BV/TV on its own could produce R² = 0.730. The present results also imply that the anisotropic layout of the trabeculae makes it more difficult for a major crack to grow transversely across them. Cancellous bone models and remodels itself in a certain way to resist fracture in a specific direction, and thus, we should be mindful that architectural quality as well as bone quantity are needed to understand the resistance to fracture. Full article

In this work, a multivariate approach was utilized for gaining some insights into the processing–structure–properties relationships in polyethylene-based blends. In particular, two high-density polyethylenes (HDPEs) with different molecular weights were melt-compounded using a twin-screw extruder, and the effects of the screw speed, processing temperature and composition on the microstructure of the blends were evaluated based on a Design of Experiment–multilinear regression (DoE-MLR) approach. The results of the thermal characterization, interpreted trough the MLR (multilinear regression) response surfaces, demonstrated that the composition of the blends and the screw rotation speed are the two most important parameters in determining the crystallinity of the materials. Furthermore, the rheological data were examined using a Principal Component Analysis (PCA) multivariate approach, highlighting also in this case the most prominent effect of the weight ratio of the two base polymers and the screw rotation speed. Full article

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective–physiological association. Full article