Real-world Materials Research Articles

Analysis of Al-‘Arabiyyah Lil Hayah Books as Teaching Material from the Perspective of Brian Tomlinson and Hitomi Masuhara’s Theory

Teaching materials are crucial in the learning process, especially in Arabic language acquisition. "Al-'Arabiyyah Lil Hayah" serves as a reference guide for new students enrolled in the Special Program for Arabic Language Development (PKPBA) at UIN Maulana Malik Ibrahim Malang, providing a structured approach to enhance Arabic language skills across various academic disciplines. This study analyzes the book through the lens of the theory of different levels of teaching materials proposed by Brian Tomlinson and Hitomi Masuhara. Utilizing a literature study with a qualitative approach, data were collected through documentation techniques and analyzed using the Miles, Huberman, and Saldana model, which includes data condensation, presentation, and conclusion drawing. The findings indicate that the four volumes of "Al-'Arabiyyah Lil Hayah" largely align with the recommendations of Tomlinson and Masuhara at basic, intermediate, and high levels. However, certain gaps remain, notably the absence of a comprehensive comprehension approach in Volume 1 and a deficiency in extensive reading and listening activities in Volumes 2 and 3. Additionally, Volume 4 lacks real-world professional language use materials, such as newspapers and research reports, which are essential for high-level learners. These shortcomings highlight a disconnect between the theoretical framework and the practical needs of students. Consequently, this research underscores the need for future studies to conduct a more thorough analysis of the suitability of the content, learning activities, and instructional approaches utilized in "Al-'Arabiyyah Lil Hayah," ensuring they effectively address the specific needs and characteristics of students at UIN Maulana Malik Ibrahim Malang.

Laser Desorption of Explosives from the Surface of Different Real-World Materials Studied Using C2Cl6-Dopant-Assisted Ion Mobility Spectrometry.

A highly efficient and sensitive ion mobility spectrometry (IMS) system with laser desorption sampling was applied for rapid explosive detection using different surface materials. This portable IMS detector, powered by a battery, offers mobility and is suitable for use in the field or combat zones. The laser desorption (LD) sampling of common explosives (Trinitrotoluene-TNT; Dinitrotoluenes-DNTs; Hexogene-RDX; pentaerythritol tetranitrate-PETN; plastic explosives-Compound 4 (C-4) and Semtex) on a wide range of common surface materials, such as metal, ceramic, plastic, glass, drywall, paper, wood, and textiles, was studied. Successful detection was achieved on nearly all surfaces except flammable materials (paper, wood, and textiles). The limit of detection (LOD) was determined for each explosive and specific surface, demonstrating an impressive LOD of 7 ng/mm2 for TNT. RDX, C-4, PETN, and Semtex achieved LOD values of 15 ng/mm2, while DNTs showed an LOD of approximately 50 ng/mm2.

Microwave-assisted bio-derived bismuth oxybromide (BiOBr) decorated polyaniline nanocomposite for highly sensitive electrochemical determination of endocrine disruptor bisphenol-A

In the presence of polyaniline (PANI), the novel nanocomposites of PANI-BiOBr bismuth oxybromide (BiOBr) were synthesized using a microwave-assisted green synthesis method and used to detect bisphenol A (BPA). By using fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and electrochemical methods, the hybrid PANI-BiOBr nanocomposite was confirmed. The higher conductivity, improve active surface-to-volume ratio, and enhanced electrochemical behaviour of the PANI-BiOBr nanoplatform can be attributed to the synergistic actions of PANI and BiOBr. With a high sensitivity of 11.7 μA (log nM)−1 cm2 and a low limit of detection of 0.19 × 10−9 μM, the constructed PANI-BiOBr sensor exhibits a broad linear range from 0.19 × 10−9 μM to 3.04 × 10−3 μM. In addition, this sensor produced adequate findings with outstanding selectivity, stability, repeatability, and reproducibility. Furthermore, the developed electrochemical PANI-BiOBr nanoplatform is essential in analysing real-world materials to detect BPA. The results obtained from these real sample analyses not only confirmed the effectiveness of the PANI-BiOBr nanocomposite sensor for BPA detection but also underscored its capability to detect other compounds with estrogenic properties, beyond just BPA.

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set. Traditional performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN’s significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN) as a case study for the perovskites dataset, and provide insights to improve their performance.

Impedance Spectroscopy – comparison of experimental ceramic results with model parameters

Abstract Impedance spectroscopy is a non-destructive test method belonging to the group of electrical engineering measurements. The method is suitable for monitoring the quality of building materials even with low conductivity. Although the method focuses on the intrinsic conductivity of systems, most IS techniques, including data analysis methods, conceive of the material in terms of a lossy dielectric. In selecting the material for testing with the IS method, an attempt was made to bring the IS capabilities as close as possible to practical, real-world materials. This paper is devoted to a comparison of the impedance characteristics of ceramics with three types of dielectric models that are described in theory or referenced in the literature. The ceramic samples were fabricated using different material additions, such as bentonite, phosphoric acid, or sodium water glass. Experimental curves of the loss factor versus frequency for all samples are described and then approximations of selected samples are shown successively for all three models considered. Experimental results were obtained from more than 50 points in each characterization. Measurements were performed for frequencies from 40 Hz to 1 MHz.

USING AUTHENTIC MATERIALS TO DEVELOP STUDENTS’ CRITICAL READING ABILITY

This article uncovers the potential of authentic materials, such as real-world texts, materials and resources, in developing students' critical reading abilities. It argues that traditional textbook-based approaches often fail to engage students in meaningful ways and lack the complexity and relevance required for fostering critical thinking skills. The article examines specificstrategies for incorporating authentic materials into the classroom, highlighting their effectiveness in motivating students, promoting deeper understanding of content, and developing essential critical reading skills such as analysis, evaluation, and synthesis. The benefits of using authentic materials for various subject areas and age groups are discussed, alongside practical tips and resources for educators.

Dislocation Majorana bound states in iron-based superconductors

We show that lattice dislocations of topological iron-based superconductors such as FeTe1−xSex will intrinsically trap non-Abelian Majorana quasiparticles, in the absence of any external magnetic field. Our theory is motivated by the recent experimental observations of normal-state weak topology and surface magnetism that coexist with superconductivity in FeTe1−xSex, the combination of which naturally achieves an emergent second-order topological superconductivity in a two-dimensional subsystem spanned by screw or edge dislocations. This exemplifies a new embedded higher-order topological phase in class D, where Majorana zero modes appear around the “corners” of a low-dimensional embedded subsystem, instead of those of the full crystal. A nested domain wall theory is developed to understand the origin of these defect Majorana zero modes. When the surface magnetism is absent, we further find that s± pairing symmetry itself is capable of inducing a different type of class-DIII embedded higher-order topology with defect-bound Majorana Kramers pairs. We also provide detailed discussions on the real-world material candidates for our proposals, including FeTe1−xSex, LiFeAs, β-PdBi2, and heterostructures of bismuth, etc. Our work establishes lattice defects as a new venue to achieve high-temperature topological quantum information processing.

Exploring the Limits of a Deflection-Based Method for the Estimation of Dynamic Stress in Beams

Estimation of the dynamic stress in structures, such as beams and plates, has previously been made using the relationship between stress and velocity spatial maxima based on farfield assumptions. This paper presents a method for the estimation of dynamic stress in a beam using Euler-Bernoulli beam theory, where deflection data from a grid of measurement points on the surface of the beam is used to estimate the dynamic bending stress in the structure. The limitations of the method are investigated via response data provided by a numerical model of a free-free beam. A non-dimensional wavenumber analysis is used to determine the number of points required for an accurate estimate of stress. Beams with a range of material and geometric parameters are modelled in order to explore the limits of the estimation method, and parameters representative of several real-world materials are used to assess the suitability of the method for practical applications.

Determining individual glomerular proteinuria and periglomerular infiltration in a cleared murine kidney by a 3-dimensional fast marching algorithm

Three-dimensional imaging has advanced basic research and clinical medicine. However, limited resolution and imperfections of real-world 3D image material often preclude algorithmic image analysis. Here, we present a methodological framework for such imaging and analysis for functional and spatial relations in experimental nephritis. First, optical tissue clearing protocols were optimized to preserve fluorescence signals for light-sheet-fluorescence-microscopy and compensated attenuation effects using adjustable 3D correction fields. Next, we adapted the Fast — Marching — Algorithm to conduct backtracking in 3D environments and developed a tool to determine local concentrations of extractable objects. As a proof-of-concept application, we used this framework to determine in a glomerulonephritis model the individual proteinuria and periglomerular immune cell infiltration for all glomeruli of half a mouse kidney. A correlation between these parameters surprisingly did not support the intuitional assumption that the most inflamed glomeruli are the most proteinuric. Instead, the spatial density of adjacent glomeruli positively correlated with the proteinuria of a given glomerulus. Since proteinuric glomeruli appear clustered, this suggests that the exact location of a kidney biopsy may affect the observed severity of glomerular damage. Thus, our algorithmic pipeline described here allows analysis of various parameters of various organs composed of functional subunits, such as the kidney, and can theoretically be adapted to processing other image modalities.

Adaptive thermodynamic consistency control via interface thickness in pseudopotential lattice Boltzmann method across wide temperature ranges

Multiphase flow phenomena are ubiquitous in real-life applications, and the pseudopotential-based lattice Boltzmann multiphase simulation method has gained extensive usage in fields such as fuel cells, energy storage materials, and boiling heat transfer. Over the past two decades, significant improvements have been made to the pseudopotential model. These advancements have greatly enhanced its accuracy in simulating various processes. In this paper, we employ a numerical stability-based unit conversion scheme to ensure an accurate representation of real-world material properties. Additionally, we introduce a more precise non-ideal two-parameter gas state equation Modified Peng-Robinson (MPR) that closely aligns with experimental data, surpassing the commonly used single-parameter Peng-Robinson state equations. Furthermore, we compare the accuracy of the two-state equations for polar and non-polar substances, finding improved accuracy for non-polar substances and a higher degree of fidelity for polar substances when using the MPR equation. We analyze the constant correction coefficients chosen for thermodynamic consistency regulation from the perspectives of vapor-liquid interface thickness and numerical stability, both when held constant and when employing a dynamic correction approach that balances vapor-liquid interface width, numerical stability, and thermodynamic consistency. Finally, we validate to ensure its practical applicability.

Repeating and growing patterns in early mathematics textbooks

ABSTRACT There has been increased attention to the role of explicit pattern tasks in early mathematics learning, with growing recommendations to incorporate more pattern tasks in standards and curricula. One critical piece of this discussion is to consider whether the pattern tasks currently presented in formal curricula materials align with the research on the development of children’s early patterning skills. In the current paper, we quantified the prevalence of patterns and their core features in five early mathematics curricula used in the United States in kindergarten and first grade. Explicit patterns were rare—with one pattern every 20 pages in kindergarten and one pattern every 60 pages in first grade. Most of these explicit pattern tasks included repeating patterns (73%) instead of growing patterns (27%), though this trend was prominent in kindergarten texts and slightly reversed in the first-grade texts. The features of the pattern tasks were often misaligned with research recommendations; most tasks were simplistic, and there was little variability in the complexity of the pattern materials and structures. These findings highlight the need to pinpoint the specific features of pattern tasks that optimize children’s learning opportunities and how best to align these with real-world learning materials.

Delving into high-quality SVBRDF acquisition: A new setup and method

In this study, we present a new and innovative framework for acquiring high-quality SVBRDF maps. Our approach addresses the limitations of the current methods and proposes a new solution. The core of our method is a simple hardware setup consisting of a consumer-level camera, LED lights, and a carefully designed network that can accurately obtain the high-quality SVBRDF properties of a nearly planar object. By capturing a flexible number of images of an object, our network uses different subnetworks to train different property maps and employs appropriate loss functions for each of them. To further enhance the quality of the maps, we improved the network structure by adding a novel skip connection that connects the encoder and decoder with global features. Through extensive experimentation using both synthetic and real-world materials, our results demonstrate that our method outperforms previous methods and produces superior results. Furthermore, our proposed setup can also be used to acquire physically based rendering maps of special materials.

Nonlinear flutter response and reliability of supersonic smart hybrid nanocomposite rupture trapezoidal plates subjected to yawed flow using DQHFEM

In the realm of aerospace engineering, the analysis of flutter under supersonic flight conditions is of paramount importance. Hybrid nanocomposite materials, known for their lightweight and high-strength properties, are increasingly being employed in aerospace engineering. This article examines the supersonic flutter behavior and reliability of smart hybrid nanocomposite trapezoidal plates, considering various practical factors. The structure is subjected to yawed flow conditions, a scenario often encountered during supersonic flight. The importance of the hybrid nanocomposite in various applications such as aerospace, public security tools, and so on, therefore the hybrid nanocomposite core layer reinforced by carbon nanotubes (CNTs) and carbon fibers incorporates real-world material complexities such as agglomeration, waviness, and random orientation of fibers into the analysis. The complexities and the accurate characterization of the hybrid nanocomposite properties are determined by using the Halpin-Tsai model. Going beyond the current field of application, the core layer is integrated with actuators and sensors, respectively at the top and bottom layers. The structure is controlled by a Proportional-Derivative (PD) controller, simulating real-world control mechanisms used in aerospace systems for flight safety. The structure is modeled by the nonlinear Refined Zigzag Plate (RZP) theory. The advanced numerical technique of the Differential Quadrature Hierarchical Finite Element Method (DQHFEM) for the solution of the motion equations provides a robust framework for studying the intricate flutter and aerodynamic behavior of the smart hybrid nanocomposite trapezoidal plates. The examination of the reliability of the structure employs the three-term conjugate finite-step length (TCFS) method. This technique is constructed through the utilization of the conjugate gradient method, incorporating a controlled scalar parameter to ensure numerical stability. The results of this article have been validated against existing experimental and numerical studies, demonstrating the validity of the presented model and the numerical technique used. The effects of various parameters, such as the smart controller, applied output voltage to the actuator layer, volume percentage of CNTs, agglomeration, and waviness of carbon fibers, geometric parameters, the skew angle of the plate, rupture, and yawed angle flow are examined on the flutter behavior, critical aerodynamic pressure, nonlinear frequency, and reliability index. The results indicate that the controlled system exhibits an approximately 18% increase in the flutter critical aerodynamic pressure compared to the uncontrolled system, along with a significant 50% rise in flutter frequency. Additionally, the structure with rupture displays approximately 20% lower flutter aerodynamic pressure and a 25% lower flutter frequency compared to a healthy plate. In the absence of carbon nanotubes and under positive external voltage, a damage parameter exceeding 0.35 leads to a reliability index below 0.931, indicating a risk of structural failure. Two solutions involve switching to negative voltage or increasing the volumetric percentage of carbon nanotubes, resulting in a 43% or 118% increase in reliability, respectively.

FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition.

Efficient and accurate BRDF acquisition of real world materials is a challenging research problem that requires sampling millions of incident light and viewing directions. To accelerate the acquisition process, one needs to find a minimal set of sampling directions such that the recovery of the full BRDF is accurate and robust given such samples. In this article, we formulate BRDF acquisition as a compressed sensing problem, where the sensing operator is one that performs sub-sampling of the BRDF signal according to a set of optimal sample directions. To solve this problem, we propose the Fast and Robust Optimal Sampling Technique (FROST) for designing a provably optimal sub-sampling operator that places light-view samples such that the recovery error is minimized. FROST casts the problem of designing an optimal sub-sampling operator for compressed sensing into a sparse representation formulation under the Multiple Measurement Vector (MMV) signal model. The proposed reformulation is exact, i.e. without any approximations, hence it converts an intractable combinatorial problem into one that can be solved with standard optimization techniques. As a result, FROST is accompanied by strong theoretical guarantees from the field of compressed sensing. We perform a thorough analysis of FROST-BRDF using a 10-fold cross-validation with publicly available BRDF datasets and show significant advantages compared to the state-of-the-art with respect to reconstruction quality. Finally, FROST is simple, both conceptually and in terms of implementation, it produces consistent results at each run, and it is at least two orders of magnitude faster than the prior art.

Visibility Evaluation in Microfacet Theory.

Reflectance models capture many types of visual appearances. The most plausible reflectance models follow the Microfacet theory, which is specifically based on statistical representations, with an analytic visibility term. This visibility term has a significant impact on appearance. Visibility computed with the masking term proposed by Smith[1], and revisited by Ashikhmin et al.[2], is nowadays considered as the most plausible in the literature. It is simple and efficient to evaluate for statistical distributions, but it relies on assumptions that are not necessarily respected by real surfaces. This paper proposes an in-depth study of masking for meshed height-field surfaces, generated either from measured real-world materials or from functions derived from distributions of surface normals. We experimentally estimate the masking (and shadowing) of surfaces using a ray-casting technique, and compare their measurements with the theoretical model from Smith and Ashikhmin et al. We show that their assumptions are too restrictive for a majority of real-world surfaces. We propose a model capable of predicting how close the theoretical masking term can be from the masking term estimated by a ray-casting approach. Although most surfaces break their assumptions, our results show that the term from Smith and Ashikhmin et al. can still be reasonably employed for a fraction in a set of more than 400 measured surfaces, with low errors compared to a ray-casting masking estimation, much lower computation times, and very similar visual appearances. Our model can be used to predict the incurred error on a physically-based rendering simulation with a microfacet-based BRDF created from real-world surfaces, instead of explicitly calculating the masking term from its height field.

MetaNO: How to transfer your knowledge on learning hidden physics

Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification. Recently, partial differential equation (PDE)-solving deep learning methods, such as neural operators, are starting to make an important impact on learning and predicting the response of a complex physical system directly from observational data. Taking the material modeling problems for example, the neural operator approach learns a surrogate mapping from the loading field to the corresponding material response field, which can be seen as learning the solution operator of a hidden PDE. The microstructure and mechanical parameters of each material specimen correspond to the (possibly heterogeneous) parameter field in this hidden PDE. Due to the limitation on experimental measurement techniques, the data acquisition for each material specimen is commonly challenging and may also be costly. This fact calls for the utilization and transfer of existing knowledge to new and unseen material specimens, which corresponds to sampling efficient learning of the solution operator of a hidden PDE with a different parameter field. Herein, we propose a novel meta-learning approach for neural operators that can be seen as transferring the knowledge of solution operators between governing (unknown) PDEs with varying parameter fields. Our approach is a provably universal solution operator for multiple PDE solving tasks, with a key theoretical observation that underlying parameter fields can be captured in the first layer of neural operator models, in contrast to typical final-layer transfer in existing meta-learning methods. As applications, we demonstrate the efficacy of our proposed approach on PDE-based datasets and a real-world material modeling problem, illustrating that our method can handle complex and nonlinear physical response learning tasks while greatly improving the sampling efficiency in unseen tasks.

Towards Material Digitization with a Dual-scale Optical System

Existing devices for measuring material appearance in spatially-varying samples are limited to a single scale, either micro or mesoscopic. This is a practical limitation when the material has a complex multi-scale structure. In this paper, we present a system and methods to digitize materials at two scales, designed to include high-resolution data in spatially-varying representations at larger scales. We design and build a hemispherical light dome able to digitize flat material samples up to 11x11cm. We estimate geometric properties, anisotropic reflectance and transmittance at the microscopic level using polarized directional lighting with a single orthogonal camera. Then, we propagate this structured information to the mesoscale, using a neural network trained with the data acquired by the device and image-to-image translation methods. To maximize the compatibility of our digitization, we leverage standard BSDF models commonly adopted in the industry. Through extensive experiments, we demonstrate the precision of our device and the quality of our digitization process using a set of challenging real-world material samples and validation scenes. Further, we demonstrate the optical resolution and potential of our device for acquiring more complex material representations by capturing microscopic attributes which affect the global appearance: we characterize the properties of textile materials such as the yarn twist or the shape of individual fly-out fibers. We also release the SEDDIDOME dataset of materials, including raw data captured by the machine and optimized parameteres.

Improving Students' Speaking Skill by Using Task Based Learning Technique at First Semester of English Education Study Program STAIN Bengkalis

This research aims to explain the extent to which task based learning (TBL) technique can improve the students’ speaking skill and what factors that influence the changes of the students’ speaking skill by implementing TBL technique. This research is a classroom action research (CAR), which was conducted in two cycles. The instruments that were used to collect the data were speaking test, observation sheet, field notes, and interview guide. The data were analyzed quantitatively and qualitatively. The findings reveal that the implementation of TBL technique can improve the students’ speaking skill at first semester of English Education Study Program STAIN Bengkalis. It was showed from the students’ average score. The mean score before conducting the research was 2.62, the mean score of cycle 1 was 2.98, and the mean score of cycle 2 was 3.38. Besides, this research also found the factors that influence the changes of the students’ speaking skill by implementing TBL technique namely, real world material, interaction, and lecturer’s role. Therefore, it can be concluded that TBL technique is effective in teaching speaking. While, real world material, interaction, and lecturer’s role influence the improvement of the students’ speaking skill at first semester of TBI.

EEG decoding reveals neural predictions for naturalistic material behaviors.

Material properties like softness or stickiness determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment towards increased processing of unexpected material behaviors.Significance Statement:In the real world, we can make accurate predictions about how an object's material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (such as a glass shattering when falling to the ground) or unexpected material behaviors (such as a glass melting upon impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.

Fractional magnetoresistance oscillations in spin-triplet superconducting rings

Half-quantum vortices in spin-triplet superconductors are predicted to host Majorana zero modes and may provide a viable platform for topological quantum computation. Recent works also suggested that, in thin mesoscopic rings, the superconducting pairing symmetry can be probed via Little-Parks-like magnetoresistance oscillations of periodicity Φ0 = h/2e that persist below the critical temperature. Here we use the London limit of Ginzburg-Landau theory to study these magnetoresistance oscillations resulting from thermal vortex tunneling in spin-triplet superconducting rings. For a range of temperatures in the presence of disorder, we find magnetoresistance oscillations with an emergent fractional periodicity Φ0/n, where the integer n ≥ 3 is entirely determined by the ratio of the spin and charge superfluid densities. These fractional oscillations can unambiguously confirm the spin-triplet nature of superconductivity and directly reveal the tunneling of half-quantum vortices in real-world candidate materials.