Transformation Rules Research Articles

Investigation of Spatio-Temporal Simulation of Mining Subsidence and Its Determinants Utilizing the RF-CA Model

It is important to carry out timely scientific assessments of surface subsidence in coal resource cities for ecological environmental protection. Traditional subsidence simulation methods cannot quantitatively describe the driving factors that contribute to or ignore the dynamic connections of subsidence across time and space. Thus, a novel spatio-temporal subsidence simulation model is proposed that couples random forest (RF) and cellular automaton (CA) models, which are used to quantify the contributions of driving factors and simulate the spatio-temporal dynamic changes in subsidence. The RF algorithm is first utilized to clarify the contributions of the driving factors to subsidence and to formulate transformation rules for simulation. Then, a spatio-temporal simulation of subsidence is accomplished by combining it with the CA model. Finally, the method is validated based on the Yongcheng coalfield. The results show that the depth–thickness ratio (0.242), distance to the working face (0.159), distance to buildings (0.150), and lithology (0.147) play main roles in the development of subsidence. Meanwhile, the model can effectively simulate the spatio-temporal changes in mining subsidence. The simulation results were evaluated using 2021 subsidence data as the basis data; the simulation’s overall accuracy (OA) was 0.83, and the Kappa coefficient (KC) was 0.71. This method can obtain a more realistic representation of the spatio-temporal distribution of subsidence while considering the driving factors, which provides technological support for land-use planning and ecological and environmental protection in coal resource cities.

ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction

The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. Therefore, computational models that achieve high accuracies in predicting Caco-2 permeability are crucial for enhancing the efficiency of oral drug development. In this study, we conducted an in-depth analysis of the characteristics of an augmented Caco-2 permeability dataset, and evaluated a diverse range of machine learning algorithms in combination with different molecular representations. The results indicated that XGBoost generally provided better predictions than comparable models for the test sets. In addition, we investigated the transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets. Our findings, based on the Shanghai Qilu’s in-house dataset, showed that the boosting models retained a degree of predictive efficacy when applied to industry data. Furthermore, Y-randomization test and applicability domain analysis were employed to assess the robustness and generalizability of these models. Matched Molecular Pair Analysis (MMPA) was utilized to extract chemical transformation rules. We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability.Scientific contributionA comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. The transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets was also investigated. Matched molecular pair analysis was carried out to provide reasonable suggestions for researchers to improve the Caco-2 permeability of compounds.Graphical

Tri-vector deformations with external fluxes

We extend the formalism of tri-vector deformations to the full SL(5) exceptional field theory with no truncation assumed thus covering 11D backgrounds of any form. We derive explicit transformation rules for 11D supergravity component fields and prove that these generate solutions given the same algebraic conditions hold: generalized Yang–Baxter equation and the unimodularity condition.

Culture and Sketching: Comparative Analysis of UK and Chinese Designers

ABSTRACTThis research investigates the influence of culture on sketching by comparing UK and Chinese designers. We employed a novel dual‐method approach: machine learning algorithms analyzed a dataset of 2,090 digitized sketches from student designers, while protocol analysis based on shape transformation rules examined the sketching processes of professional designers from both cultures. The machine learning analysis classified sketches by extracting region‐ and shape‐based features, revealing distinctive visual attributes between the two cultures. Concurrently, protocol studies of professional designers' sketches uncovered patterns in their sketching processes. By integrating computational analyses of student sketches with protocol studies of professional designers, we identified significant cultural differences in both visual attributes and sketching processes. The paper discusses potential factors contributing to these disparities, drawing on insights from cross‐cultural comparisons of cognitive styles. This comprehensive mixed‐methods analysis provides new insights into the complex interplay between culture, cognition, and design.

All You Need to Know About Allometric Scaling: An Integrative Review on the Theoretical Basis, Empirical Evidence, and Application in Human Pharmacology.

Scaling approaches are used to describe or predict clearance for paediatric or obese populations from normal-weight adult values. Theoretical allometry assumes the existence of a universal bodyweight-based scaling relationship. Although theoretical allometry is highly disputed, it is commonly applied in pharmacological data analyses and clinical practice. The aim of the current review is to (1) increase pharmacologists' understanding of theoretical allometry to better understand the (implicit) assumptions and (dis)advantages and (2) highlight important methodological considerations with the application of this methodology. Theoretical allometry originated in an empirical, and later debated, observation by Kleiber of a scaling exponent of 0.75 between basal metabolic rate and body mass of mammals. The mathematical framework of West, Brown, and Enquist provides one possible explanation for this value. To date, multiple key assumptions of this framework have been disputed or disproven, and an increasing body of evidence is emerging against the existence of one universal allometric exponent. The promise of ease and universality of use that comes with theoretical approaches may be the reason they are so strongly sought after and defended. However, ecologists have suggested that the theory should move from a 'Newtonian approach', in which physical explanations are sought for a universal law and variability is of minor importance, to a 'Darwinian approach', in which variability is considered of primary importance for which evolutionary explanations can be found. No scientific support was found for the application of allometry for within-species scaling, so the application of basal metabolic rate-based scaling principles to clearance scaling remains unsubstantiated. Recent insights from physiologically based modelling approaches emphasise the interplay between drugs with different properties and physiological variables that underlie drug clearance, which drives the variability in the allometric scaling exponent in the field of pharmacology. To deal with this variability, drug-specific or patient-specific adaptations to theoretical allometric scaling are proposed, that introduce empiric elements and reduce the universality of the theory. The use of allometric scaling with an exponent of 0.75 may hold empirical merit for paediatric populations, except for the youngest individuals (aged ≤ 5 years). Nevertheless, biological interpretations and extrapolation potential attributed to models based on 0.75 allometric scaling are theoretically unfounded, and merits of the empirical application of this function should, as for all models, always be supported by appropriate model validation procedures. In this respect, it is not the value of the allometric exponent but the description and prediction of individual clearance values and drug concentrations that are of primary interest.

Notes on rate equations in nonlinear continuum mechanics

This paper gives an introduction to rate equations in nonlinear continuum mechanics which should obey specific transformation rules. Emphasis is placed on the geometrical nature of the operations involved in order to clarify the different concepts. This paper is particularly concerned with common classes of constitutive equations based on corotational stress rates and their proper implementation in time for solving initial boundary value problems (IBVPs). Hypoelastic simple shear is considered as an example application for the derived theory and algorithms.

Study on high temperature tensile constitutive behavior and deformation mechanism of Ti-47.5Al-2.5 V-1.0Cr-0.2Zr alloy

The high temperature tensile test of Ti-47.5Al-2.5 V-1.0Cr-0.2Zr alloy was carried out by electronic universal testing machine under the condition of 750–900 °C/10−5–10−3 s−1. The hot tensile stress-strain curves were analyzed, and the constitutive model under hot tensile conditions was established. The microstructure transformation rule and deformation mechanism during tensile process were determined. The results show that the hot tensile curve is longer in the steady-state flow stage corresponding to lower strain rate and higher tensile temperature, the true stress declines and the elongation at break increases. The constitutive equation was established based on the tensile curve data, and the corresponding thermal activation energy was 310.3 kJ/mol. As the rise of tensile temperature and the decline of strain rate, the proportion about cross-layer fracture in the tensile fracture morphology decreases, the number of dimples increases, more lamellar structures change into recrystallized structures, and the softening effect of the alloy is more obvious. The dislocation deformation mechanism mainly includes the existence of dislocation slip and climb, dislocation intersection and dislocation ring or dislocation network formation. Twinning deformation is also another important mechanism of high temperature tensile deformation of TiAl alloy. Twins are formed in parallel with each other, so that the deformation can be further carried out. The dislocations and twins in the deformed microstructure will provide nucleation conditions for recrystallized grains. The dynamic recrystallization(DRX) grains are preferentially formed around the grain boundary and the vicinity of dislocation and twin is also preferred nucleation site for DRX. The DRX behavior is the main softening mechanism, and DRX size and volume fraction corresponding to lower tensile rate and higher tensile temperature are larger.

Effects of scale, interface, and salt ratio on phase change characteristics of mesoporous complex nitrate for thermal energy storage

Developing clean and sustainable energy technologies has become crucial in light of the growing concern worldwide regarding energy security and climate change. Thermal energy storage technology has emerged as one of the key technologies for achieving sustainable energy development. Research on developing and improving mesoporous composite phase change materials (CPCM) with mixed nitrates as the phase change core material has gained immense attention. The objective is to thoroughly understand the transformation rules governing the phase change characteristics of mesoporous complex nitrate. This study integrates molecular dynamics simulation with an experimental approach to investigate the impact of scale, interface, and the mixing ratio of salts on the phase change properties of CPCM, exploring the competitive relationships among these factors. The results indicated that the phase change temperatures of the mixed nitrate follow the order T (9:1) > T (4:6) > T (6:4) > T (5:5) depending on the ratio of NaNO3 and KNO3. The latent heat associated with the phase change of the CPCM increases with the content of NaNO3. When comparing different matrix materials with the same ratio of mixed salts, the CPCM with Al2O3 as the matrix exhibits the highest phase change temperature and latent heat. For the same ratio of mixed salts, the phase change temperature initially increases and then decreases with the increase of mesopores scale, while the latent heat increases with the enlargement of scale. Thus, the interfacial effect significantly impacts the phase change temperature. When considering the impact of interfacial effects and scale effects on phase change temperature, it was evident that the interfacial effect was dominant in the scale ranges of 3–5 nm and 7.5–10 nm, whereas the scale effect is dominant in the 5–7.5 nm. We found that the ratio of salts played a dominant role in the latent heat for CPCM compared to scale and interfacial effects.

From single-objective to multi-objective reinforcement learning-based model transformation

AbstractModel-driven optimization allows to directly apply domain-specific modeling languages to define models which are subsequently optimized by applying a predefined set of model transformation rules. Objectives guide the optimization processes which can range from one single objective formulation resulting in one single solution to a set of objectives that necessitates the identification of a Pareto-optimal set of solutions. In recent years, a multitude of reinforcement learning approaches has been proposed that support both optimization cases and competitive results for various problem instances have been reported. However, their application to the field of model-driven optimization has not gained much attention yet, especially when compared to the extensive application of meta-heuristic search approaches such as genetic algorithms. Thus, there is a lack of knowledge about the applicability and performance of reinforcement learning for model-driven optimization. We therefore present in this paper a general framework for applying reinforcement learning to model-driven optimization problems. In particular, we show how a catalog of different reinforcement learning algorithms can be integrated with existing model-driven optimization approaches that use a transformation rule application encoding. We exemplify this integration by presenting a dedicated reinforcement learning extension for MOMoT. We build on this tool support and investigate several case studies for validating the applicability of reinforcement learning for model-driven optimization and compare the performance against a genetic algorithm. The results show clear advantages of using RL for single-objective problems, especially for cases where the transformation steps are highly dependent on each other. For multi-objective problems, the results are more diverse and case-specific, which further motivates the usage of model-driven optimization to utilize different approaches to find the best solutions.

Triply Bifurcations and Metric Universalities in 1D Trimodal Maps

The famous Feigenbaum’s constants such as scaling factor and convergence rate can be measured and recomputed in unimodal maps, one of the methods is by the n-tupling bifurcations to chaos and parameter calculation algorithm which are called star transformation and word-lifting technique respectively. In the paper, we presented a kind of simple triply star transformation rule and the corresponding proof of admissible three super-stable kneading sequences (TSSKS), a few of TSSKS to chaos by triply bifurcations and the metric universalities are investigated.

Synergistic influence of gyrotactic microorganisms and bimolecular reaction on bidirectional tangent hyperbolic fluid with Nield boundary conditions: A biomathematical model

In biomedical engineering, the behavior of gyrotactic microorganisms with non-Newtonian fluids such as tangent hyperbolic fluids improve the design of targeted drug delivery systems. In this system control over microorganism movement is essential. The present study deals with the synergistic influence of gyrotactic microorganisms and bimolecular reactions on the bidirectional flow of tangent hyperbolic fluids under Nield boundary conditions. Further, the flow characteristic of the non-Newtonian fluid is enhanced by incorporating the impact of thermal radiation, heat sources, Brownian motion, and thermophoresis. The presentation of these phenomena is vital for an extensive range of applications, including industrial processes, biomedical engineering, and environmental management. The analysis employs advanced mathematical modeling which needs suitable transformation rules to get the non-dimensional form and further numerical simulation is presented with the assistance of the “shooting-based fourth-order Runge–Kutta technique”. The results are depicted for the several contributing factors via the built- in-house function bvp4c in “MATLAB”. The authentication of the study with the prior research is a benchmark to precede further research in this direction. However, the outstanding results are; the fluid velocity is controlled by increasing non-Newtonian Weissenberg number whereas the velocity slip shows dual characteristics on the axial velocity distribution. Further, the motile microorganism profile is controlled by the enhanced bioconvection Lewis number.

Characteristics of induced magnetic field on the time-dependent MHD nanofluid flow through parallel plates

Abstract The heat transfer characteristics of an unsteady magnetohydrodynamic flow through non-conducting infinite vertical parallel plates are presented in this investigation. The flow is subjected to an induced magnetic field, and the base fluid water contains carbon nanotubes (CNTs), in particular multi-wall carbon nanotubes, to present the behaviour of the nanofluid. The aim is to examine the effect of the applied magnetization and CNT concentration on the heat transport performance of the system. However, suitable transformation rules are adopted for the re-designing of the proposed design model into its non-dimensional form. This transformed system is then solved analytically following the standard transformations. The influence of key parameters, including the Hartmann number (Ha), the angle of inclination of the magnetic field, thermal buoyancy, heat source, and the concentration of CNTs in the nanofluid, on the flow phenomena is analysed. The consequences reveal that the occurrence of the inclined magnetic field affects the flow and heat transfer characteristics significantly. Additionally, the introduction of CNTs to the nanofluid enhances the heat transfer performance due to their unique thermal properties. The study demonstrates that enhanced Ha and CNT concentration augments the heat transfer rate.

Modern tensor–spinor symbolic algebra algorithms and computing non-closure geometry and holoraumy in 11D, 𝒩=1 supergravity

The Supersymmetry Genomics project aims to classify supermultiplets by properties like adinkras and holoraumy. The project’s protocol is: (1) set up the SUSY transformation rules (and action) with unknown numerical coefficients, (2) solve those coefficients and (3) compute desired properties, e.g. non-closure geometry (the non-closure functions in the anticommutator of supercovariant derivatives) and holoraumy (the commutator of supercovariant derivatives). This paper provides the first broadly applicable computer algorithms for completing these computations, comprising a new Cadabra module we call “SusyPy”. We provide a significant extension of the available tensor arithmetic/canonicalization to include spinor-indexed expressions and NW–SE convention, and we provide a new Fierz expansion algorithm for spinor-indexed expressions. On top of this new tensor–spinor symbolic algebra, we have built algorithms for solving for the coefficients of a multiplet and its action, and for computing holoraumy, for both off-shell and on-shell [Formula: see text] multiplets of any dimension. We apply our tools to assimilate linearized 11D, [Formula: see text] supergravity into the Genomics project. We demonstrate solving the 11D, [Formula: see text] multiplet, as in Cremmer, Julia and Scherk, in a few lines of code, and we provide a comprehensive picture of the multiplet’s non-closure geometry. Further, we provide the first-ever computation of the holoraumy of 11D, [Formula: see text] supergravity.

Smart Grids Excellence – Cybersecurity in Empowering Digital Transformation

This paper is intended to elaborate on the 4th Industrial Revolution (Industry 4.0) that will alter and shift technologies and the utilities sector. Alongside advanced Cybersecurity technologies to be aligned with the need for more resilience and secured environments that empower digital transformation. Where we explore the drivers and applications of Cybersecurity considering 4thIR as an engine of digital transformation, that is governed and measured by complying with digital transformation rules, flowing from modern technology to the evolution where the quality of life is measured by its speed, energy, and security. As such, Industrial Control Systems (ICS) are a solid infrastructure for such a digital transformation. As the acceleration of cyberattacks are becoming more sophisticated, a well Cybersecurity strategies must be applied. Furthermore, we propose advanced Cybersecurity solutions for the smart grid that can be addressed by the efficiency of the Next Generation (SOC) and the utilization of Distributed Ledger Technology (DLT).

Implementation of a non-standard system for simplifying Peirce-Webb functions

The object of research are models of optimal logic circuits based on universal Peirce-Webb functions. The problem solved is the efficiency of the technique for simplifying the Peirce-Webb functions. The extension of the non-standard system to the simplification of Peirce-Webb functions makes it possible to discover new rules of equivalent transformations of Boolean functions, and to complete the simplification procedure in one step. A feature of the simplification of functions in the Peirce-Webb basis by a non-standard system is fixing the digital project at the level of abstraction, followed by the application of the mechanism of logical synthesis to generate the corresponding equivalent at the level of gates of the logic circuit. The result of the transformation of the terms of the binary matrix in the end is some combinatorial system, metadata that can explain other data, for example, determine the minimum function for another logical basis. The interpretation of the result consists in the use of combinatorial properties of binary structures of functions in the Peirce-Webb basis and binary structures of functions in the basic basis. These properties do not depend on the selected logical basis, which makes it possible to carry out equivalent transformations on binary matrices of Peirce-Webb functions according to the rules of the algebra of the main basis. It has been experimentally confirmed that a non-standard system enables: – to reduce the algorithmic complexity of simplifying the Peirce-Webb functions; – to increase the performance of the simplification of Peirce-Webb functions by 200–300 %; – to demonstrate the visibility of the process of simplifying functions. In terms of application, the non-standard system of simplifying the Peirce-Webb functions could ensure the transfer of innovations to material production: from conducting fundamental research, expanding the capabilities of digital component design technology to organizing serial or mass production of novelties.

Symbolic equation solving via reinforcement learning

Machine-learning methods are rapidly being adopted in a wide variety of social, economic, and scientific contexts, yet they are notorious for struggling with exact mathematics. A typical example is computer algebra, which includes tasks like simplifying mathematical terms, calculating formal derivatives, or finding exact solutions of algebraic equations. Traditional software packages for these purposes are commonly based on a huge database of rules for how a specific operation (e.g., differentiation) transforms a certain term (e.g., sine function) into another one (e.g., cosine function). These rules have usually needed to be discovered and subsequently programmed by humans. Efforts to automate this process by machine-learning approaches are faced with challenges like the singular nature of solutions to mathematical problems, when approximations are unacceptable, as well as hallucination effects leading to flawed reasoning. We propose a novel deep-learning interface involving a reinforcement-learning agent that operates a symbolic stack calculator to explore mathematical relations. By construction, this system is capable of exact transformations and immune to hallucination. Using the paradigmatic example of solving linear equations in symbolic form, we demonstrate how our reinforcement-learning agent autonomously discovers elementary transformation rules and step-by-step solutions.

Polyvector deformations of Type IIB backgrounds

We develop a formalism of poly-vector deformations for Type IIB backgrounds with a block diagonal metric and non-vanishing self-dual 5-form RR field strength. Making use of the embedding of the Type IIB theory into the E6(6) exceptional field theory we derive explicit transformation rules for four-vector deformations. We prove that the algebraic condition following from the Type IIB realization of exceptional Drinfeld algebras is sufficient for the transformation to generate a solution.

Model-Driven Approach to Cloud-Portability Issue

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as “vendor lock-in”. Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers.

Calcium Carbonate as Dephosphorization Agent in Direct Reduction Roasting of High-Phosphorus Oolitic Iron Ore: Reaction Behavior, Iron Recovery, and Dephosphorization Mechanism

Calcium carbonate, renowned for its affordability and potent dephosphorization capabilities, finds widespread use as a dephosphorization agent in the direct reduction roasting of high-phosphorus oolitic hematite (HPOIO). However, its precise impact on iron recovery and the dephosphorization of iron minerals with phosphorus within HPOIO, particularly the mineral transformation rule and dephosphorization mechanism, remains inadequately understood. This study delves into the nuanced effects of calcium carbonate on iron recovery and dephosphorization through direct reduction roasting and magnetic separation. A direct reduction iron (DRI) boasting 95.57% iron content, 93.94% iron recovery, 0.08% phosphorus content, and an impressive 92.08% dephosphorization is achieved. This study underscores how the addition of calcium carbonate facilitates the generation of apatite from phosphorus in iron minerals and catalyzes the formation of gehlenite by reacting with silicon dioxide and alumina, inhibiting apatite reduction. Furthermore, it increases the liquid phase, enhancing the dissociation of metallic iron monomers during the grinding procedure, thus facilitating efficient dephosphorization.

Integration WGCNA with LC-MS data for evaluating the processing status and transformation rules of Ligustri Lucidi Fructus: A novel strategy for evaluating the processing technology of traditional Chinese medicines

Ligustri Lucidi Fructus (LLF) is a traditional Chinese medicine (TCM) to treat hepatopathy and osteopathy. Wine-processed LLF (WLLF) was much more widely used than raw LLF (RLLF) in clinical practice, however, there is no consensus on processing time. To investigate the processing status of WLLF and transformation rules during processing, a UHPLC-Q-Orbitrap-MS method combined with data-independent acquisition (DIA) mode was firstly established and 227 compounds were identified or tentatively identified. Subsequently, a novel strategy using integration weighted gene co-expression network analysis (WGCNA) with LC-MS data was proposed. A total of 73 differential metabolites were screened out between RLLF and WLLF (wine steaming for 18 h). Meanwhile, the concentration of 11 differential compounds for WLLF was quantified. Finally, correlations between compounds were analyzed by WGCNA and the top five compounds negatively correlated with salidroside were validated, revealing that G13, specnuezhenide, oleuropein, acteoside, and neonuzhenide could be transformed into salidroside and its analogues during processing, respectively. The results indicated that our proposed strategy could be effectively employed to evaluate the processing status of TCMs.