Regular Model Research Articles

A novel hybrid optimization model to determine optimum water resources for water supply of residential areas

In this study, a new hybrid model was improved by combining heuristic and numerical optimization algorithms to decide on optimum water resources based on their costs in the water supply. The purpose of the hybrid model is to reach a best result in the shortest time by simultaneously searching global and local minimums. Therefore, the steepest descent (SD) algorithm (numerical optimization method) was embedded in the classical modified clonal selection algorithm (the classical modified Clonalg) (one of artificial immune systems, heuristic optimization technique). This hybridization allows the SD algorithm to search local minimums while the classical modified Clonalg is searching a global minimum. The hybrid optimization model was applied to the cost objective function depending on distances and piezometric head differences between the water resources and destination. A scenario consists of five hypothetical water resources and one residential area/settlement. Herein, the aim is to satisfy the water demand of the residential area with a minimum cost from the water resources. The cost objective function was also minimized by the regular model (the classical model) according to the scenario, and their results were compared. Both models were run ten times for testing their stabilities. According to the results, the hybrid model is better than the regular model in terms of run time and stability. The hybrid model found a minimum cost for the water supply in a shorter time (in half) in comparison with the regular model in all runs.

Adaptive Total-Variation and Nonconvex Low-Rank Model for Image Denoising

In recent years, image denoising methods based on total variational regularization have attracted extensive attention. However, the traditional total variational regularization method is an approximate solution based on convex method, and does not consider the particularity of the region with rich details. In this paper, the adaptive total-variation and nonconvex low-rank model for image denoising is proposed, which is a hybrid regularization model. First, the image is decomposed into sparse terms and low rank terms, and then the total variational regularization is used to denoise. At the same time, an adaptive coefficient based on gradient is constructed to adaptively judge the flat area and detail texture area, slow down the denoising intensity of detail area, and then play the role of preserving detail information. Finally, by constructing a nonconvex function, the optimal solution of the function is obtained by using the alternating minimization method. This method not only effectively removes the image noise, but also retains the detailed information of the image. The experimental results show the effectiveness of the proposed model, and SNR and SSIM of the denoised image are improved.

Image Restoration with Fractional-Order Total Variation Regularization and Group Sparsity

In this paper, we present a novel image denoising algorithm, specifically designed to effectively restore both the edges and texture of images. This is achieved through the use of an innovative model known as the overlapping group sparse fractional-order total variation regularization model (OGS-FOTVR). The OGS-FOTVR model ingeniously combines the benefits of the fractional-order (FO) variation domain with an overlapping group sparsity measure, which acts as its regularization component. This is further enhanced by the inclusion of the well-established L2-norm, which serves as the fidelity term. To simplify the model, we employ the alternating direction method of multipliers (ADMM), which breaks down the model into a series of more manageable sub-problems. Each of these sub-problems can then be addressed individually. However, the sub-problem involving the overlapping group sparse FO regularization presents a high level of complexity. To address this, we construct an alternative function for this sub-problem, utilizing the mean inequality principle. Subsequently, we employ the majorize-minimization (MM) algorithm to solve it. Empirical results strongly support the effectiveness of the OGS-FOTVR model, demonstrating its ability to accurately recover texture and edge information in images. Notably, the model performs better than several advanced variational alternatives, as indicated by superior performance metrics across three image datasets, PSNR, and SSIM.

Conductor-Discriminant Inequality for Hyperelliptic Curves in Odd Residue Characteristic

Abstract We prove an inequality between the conductor and the discriminant for all hyperelliptic curves defined over discretely valued fields $K$ with perfect residue field of characteristic not $2$. Specifically, if such a curve is given by $y^{2} = f(x)$ with $f(x) \in \mathcal{O}_{K}[x]$, and if $\mathcal{X}$ is its minimal regular model over $\mathcal{O}_{K}$, then the negative of the Artin conductor of $\mathcal{X}$ (and thus also the number of irreducible components of the special fiber of $\mathcal{X}$) is bounded above by the valuation of $\operatorname{disc}(f)$. There are no restrictions on genus of the curve or on the ramification of the splitting field of $f$. This generalizes earlier work of Ogg, Saito, Liu, and the second author.

Feature screening for multiple responses

Feature screening has been widely investigated in many literatures and quite a few procedures have been proposed. However, most of the existing methods are developed based on regularization strategy and model assumptions such as linear model and Gaussian distribution, which limit their application range. And also, they were mainly designed to deal with univariate response and cannot handle multiple responses situations. To tackle these issues, we introduce a new association measure for multiple responses and univariate predictor, called multiple explained variability (MEV), and further propose a feature screening procedure, named MEV-SIS, based on MEV. MEV-SIS removes the commonly used model assumptions and can conduct feature screening for multiple responses simultaneously. The asymptotic properties of MEV are deduced, and the sure screening property and ranking consistency property of MEV-SIS are obtained. Extensive simulation studies and real data application demonstrate the advantage of MEV-SIS over the existing screening procedures in sufficiency and robustness.

Robust facial expression recognition with Transformer Block Enhancement Module

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer’s excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks.

Data Anomaly Detection through Semisupervised Learning Aided by Customised Data Augmentation Techniques

Structural health monitoring (SHM) systems may suffer from multiple patterns of data anomalies. Anomaly detection is an essential preprocessing step prior to the use of monitoring data for structural condition assessment or other decision making. Deep learning techniques have been extensively used for automatic category classification by training the network with labelled data. However, because the SHM data are usually large in quantity, manually labelling these abnormal data is time consuming and labour intensive. This study develops a semisupervised learning-based data anomaly detection method using a small set of labelled data and massive unlabelled data. The MixMatch technique, which could mix labelled and unlabelled data using MixUp, is adopted to enhance the generalisation and robustness of the model. A unified loss function is defined to combine information from labelled and unlabelled data by incorporating consistency regularisation, entropy minimisation, and regular model regularisation items. In addition, customised data augmentation strategies for time series are investigated to further improve the model performance. The proposed method is applied to the SHM data from a real bridge for anomaly detection. Results demonstrate the superior performance of the developed method with very limited labelled data, greatly reducing the time and cost of labelling efforts compared with the traditional supervised learning methods.

Reducing impacts of artificial ponding in modeling salt marshes using a conductivity-formulated subgrid model

The landforms of salt marsh systems are rather complex, with meandering channel networks cutting through low-lying, extensive marsh platforms. Some small-scale topographic details are critical to flooding and draining processes but cannot be reflected in the Digital Elevation Model (DEM) due to limited measurement resolution and vegetation bias. Numerical models need a grid with high enough resolution to capture the small-scale connectivity to correctly model the small-scale flow processes, making the model computationally unaffordable. In this study, we derived a subgrid model, which couples two flow components, one is the regular subgrid model for simulating the subgrid-resolved flows, and the other is a conductivity-formulated porous flow model for modeling small-scale gut flows unresolved at the subgrid level. The model validation was performed using an idealized case with a narrow slot that was unresolved in the coupled flow model. Model results were compared with a slot-resolved regular subgrid model where only surface flows are solved and suggested that, with a proper model parameterization, the coupled flow model is capable of reproducing flooding and draining processes similar to the slot-resolved subgrid model of surface flow. The model was applied to simulating flooding and draining processes in a meso-tidal salt marsh. The model results demonstrate that the coupled subgrid model can be used to reduce the impacts of artificial ponding that usually occur in numerical simulations of salt marshes with numerous narrow gullies and creeks.

An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

By producing the required proteins, the process of gene expression establishes the physical properties of living things. Gene expression from DNA or RNA may be recorded using a variety of approaches. Regression analysis has evolved in prominence in the area of genetic research recently. Several of the genes in high dimensional gene expression information for statistical inference may not be related to their illnesses, which is one of the major problems. The ability of gene selection to enhance the outcomes of several techniques has been demonstrated. For censored survival data, the Cox proportional hazards regression model is the most widely used model. In order to identify important genes and achieve high classification accuracy, a new technique for selecting the tuning parameter is suggested in this study using an optimization algorithm. According to experimental findings, the suggested strategy performs much better than the two rival methods in terms of the area under the curve and the number of chosen genes. This study provides a comprehensive assessment of the latest work on performance evaluation of regression analysis in gene selection. In addition to its performance analysis, this research conducts a thorough assessment of the numerous efforts done on various extended models based on gene selection in recent years.

Critical evaluation and thermodynamic modeling of the Al–Li binary system

Al–Li alloys have extensive applications in aircraft and spacecraft due to their high mechanical performance and lightness in weight. In this work, the thermodynamic property and phase equilibria data of the Al–Li system available in the literature were critically reviewed and evaluated, thermodynamic assessments of the entire Al–Li system were then carried out based on the reliable experimental thermochemistry and phase equilibria data. The liquid phase was described using both the modified quasi-chemical model in pair approximation and the regular solution model. The compound energy formalism and the regular solution model were used to describe the Gibbs energies of the solid phases. The split four sublattice compound energy formalism was used to model the Gibbs free energies of the ordered AlLi (B32) and Al3Li (L12) superlattice structures. The reliable thermodynamic property and phase equilibria data can generally be represented by the currently obtained sets of self-consistent thermodynamic descriptions.

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model's force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.

Markov-Modulated On–Off Processes in IP Traffic Modeling

This paper deals with the modeling of real IP flows using Markov-modulated On–Off processes. In the first section of the paper, we summarize the knowledge found so far about the Markov modulated On–Off regular process model, which has already been published in our previous papers. For the sake of completeness, we also summarize the well-known facts regarding the Bernoulli process. In the second section, we deal with the continuation of modeling using the Markov-modulated On–Off Bernoulli process. Our own derivation of the hitherto-unknown probability distribution of time spaces (tail distribution) is completely new. For its derivation, we used the tail distribution generating function, and then, using its derivation, we calculated the hitherto-unknown moments of the distribution (mean, variation, and third initial moment). This knowledge will allow us to create a new numerical procedure for estimating MMBP parameters from measured IP traffic. Finally, we present a formula for the sizing of network resources for a given flow using effective bandwidth with respect to QoS based on a given level of IP traffic.

Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging

Objective. Here, we propose a dynamic residual Kaczmarz (DRK) method as an improved reconstruction method for magnetic particle imaging (MPI) to achieve a better reconstruction quality from high-noise signals. Approach. Based on the Kaczmarz (KZ) method, we introduced a residual vector to select parts of the low-noise equations for reconstruction. In each iteration, a low-noise subset was formulated based on the residual vector. Thus, the reconstruction converged to an accurate result with less noise. Main Results. To evaluate the performance of the proposed method, it was compared with classical Kaczmarz-type methods and state-of-the-art regularization models. The numerical simulation results demonstrate that the DRK method can achieve better reconstruction quality than all other comparison methods at similar noise levels. It can acquire a signal-to-background ratio (SBR) that is five times higher than that of classical Kaczmarz-type methods at a 5 dB noise level. Furthermore, the DRK method can acquire up to 0.7 structural similarity (SSIM) indicators at a 5 dB noise level when combined with the non-negative fused Least absolute shrinkage and selection operator (LASSO) regularization model. In addition, a real experiment based on the OpenMPI data set validated that the proposed DRK method can be applied to real data and perform well. Significance. The experimental results demonstrate that the proposed DRK method can significantly improve the reconstruction quality of MPI when the signals contain high noise. It has the potential to be applied to MPI instruments that contain high signal noise, such as human-sized MPI instruments. It is beneficial for expanding the biomedical applications of MPI technology.

Real-complex hybrid modal response spectrum method for seismically base-isolated structures

In this study, a novel real-complex hybrid modal response spectrum method (RCHM-RSM) for seismically base-isolated (BI) structures is proposed, which only applies the modal analysis to a superstructure, avoiding the complex calculation. In contrast to the complex modal response spectrum method (CM-RSM), the RCHM-RSM is more reliable and easier to understand. Additionally, analytical expressions of the structural modal matrices are developed for seismically BI structures. A regular model and an irregular model of BI structures are developed to investigate the impact of the proposed method on the solution accuracy. The results show that the proposed method demonstrated its advantages of simple calculations and a high accuracy for two models. The maximum relative deviation of the superstructure story displacement and inter-story are 12.32% and 12.52% for the irregular BI structure, respectively, by comparing them with the response history analysis (RHA). Additionally, the forced decoupling response spectrum method (FD-RSM) significantly underestimates the superstructure response, the maximum relative deviation of the superstructure story displacement and inter-story displacement are 26.56% and 31.56%. The numerical results obtained also confirm that using the Bernoulli-Euler model to idealize the superstructure significantly underestimates the response of the superstructure, and the roof displacement is underestimated by 53%.

Hubungan Penerapan Model Reguler Pendidikan Kepramukaan dengan Hasil Belajar Kognitif Siswa SD

This study aims to determine the relationship of applying the regular model of scouting education (x) with students' cognitive learning outcomes (y) at sd negeri 01 kota bengkulu. This type of research is quantitative research. The population of this study amounted to 41 student scout members who followed the regular model of sampling total sampling technique. Data collection techniques used questionnaires, recapitulation of weekly repeat values ​​in cognitive and documentation aspects. Research instruments were tested using validity and reliability. While prerequisite analysis test using normality test, homogeneity test, and hypothesis test. Data analysis technique is done by using "product moment correlation" formula. This technique is used to determine the significance of independent variable relation and dependent variable by rhitung count with rtabel with 5% significant level. Based on the results of data analysis diproleh value rhitung = > 0.698 rtabel = 0.308 which means there is a relationship of the regular modeling modeling of scouting education with students' cognitive learning outcomes with very strong interpretation. Furthermore, from the significance test results obtained t count 6.087 ≥ ttabel = 1.664 which means there is a significant relationship between variables, with a contribution of 48.72% between the application of regular model of scouting education with cognitive learning outcomes of elementary school students 01 kota bengkulu. Based on this, it can be concluded that ha accepted, where there is a relationship of application of regular model of scouting education with cognitive learning outcomes at state elementary school 01 kota bengkulu

Using expression parsing and algebraic operations to generate test sequences.

It has become a popular trend to build software’s regular expression or extended regular expression models in order to generate test sequences from these models. Such test sequences tend to have promising test coverage and fault detection capability. During this process, one critical step is expression parsing based on algebraic operations. However, the parsing can be very challenging as different algebraic systems have different algebraic operators and algebraic operations. Besides, the parsing difficulty continues to grow as software complexity increases. To address the above challenges, this paper proposes a general expression parsing framework for test sequence generation. The proposed framework consists of three stages, expression decomposition, algebraic operations, and subexpression combination. To implement the framework, an expression parsing algorithm based on abstract syntax tree is developed. Case studies based on 117 expressions collected from the literature over the past 30 years as well as 13 software systems are conducted to evaluate the effectiveness of the proposed algorithm. The results indicate that our algorithm is superior to three existing and commonly used algorithms with respect to expression parsing and software fault detection.

Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach

ABSTRACT The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs’ physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.

Quaternion and Biquaternion Methods and Regular Models of Analytical Mechanics (Review)

The work is of a survey analytical nature. The first part of the work presents quaternion and biquaternion methods for describing motion, models of the theory of finite displacements and regular kinematics of a rigid body based on the use of four-dimensional real and dual Euler (Rodrigues–Hamilton) parameters. These models, in contrast to the classical models of kinematics in Euler–Krylov angles and their dual counterparts, do not have division-by-zero features and do not contain trigonometric functions, which increases the efficiency of analytical research and numerical solution of problems in mechanics, inertial navigation, and motion control. The problem of regularization of differential equations of the perturbed spatial two-body problem, which underlies celestial mechanics and space flight mechanics (astrodynamics), is discussed using the Euler parameters, four-dimensional Kustaanheimo–Stiefel variables, and Hamilton quaternions: the problem of eliminating singularities (division by zero), which are generated by the Newtonian gravitational forces acting on a celestial or cosmic body and which complicate the analytical and numerical study of the motion of a body near gravitating bodies or its motion along highly elongated orbits. The history of the regularization problem and the regular Kustaanheim–Stiefel equations, which have found wide application in celestial mechanics and astrodynamics, are presented. We present the quaternion methods of regularization, which have a number of advantages over Kustaanheimo–Stiefel matrix regularization, and various regular quaternion equations of the perturbed spatial two-body problem (for both absolute and relative motion). The results of a comparative study of the accuracy of numerical integration of various forms of regularized equations of celestial mechanics and astrodynamics in Kustaanheimo–Stiefel variables and Newtonian equations in Cartesian coordinates are presented, showing that the accuracy of numerical integration of regularized equations in Kustaanheimo–Stiefel variables is much higher (by several orders of magnitude) than the accuracy of numerical integration Newtonian equations.

A non–convex total generalized variation model for image denoising

We propose an effective regularization model based on second–order total generalized variation for image restoration with mixed Poisson–Gaussian noise. An efficient alternating minimization algorithm is employed to solve the considered model. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

Large Decrease in the Melting Point of Benzoquinones via High-n Eutectic Mixing Predicted by a Regular Solution Model.

Decreasing the melting point (Tm) of a mixture is of interest in cryopreservatives, molten salts, and battery electrolytes. One general strategy to decrease Tm, exemplified by deep eutectic solvents, is to mix components with favorable (negative) enthalpic interactions. We demonstrate a complementary strategy to decrease Tm by mixing many components with neutral or slightly positive enthalpic interactions, using the number of components (n) to increase the entropy of mixing and decrease Tm. In theory, under certain conditions this approach could achieve an arbitrarily low Tm. Furthermore, if the components are small redox-active molecules, such as the benzoquinones studied here, this approach could lead to high energy density flow battery electrolytes. Finding the eutectic composition of a high-n mixture can be challenging due to the large compositional space yet is essential for ensuring the existence of a purely liquid phase. We reformulate and apply fundamental thermodynamic equations to describe high-n eutectic mixtures of small redox-active molecules (benzoquinones and hydroquinones). We illustrate a novel application of this theory by tuning the entropy of melting, rather than the enthalpy, in systems highly relevant to energy storage. We demonstrate with differential scanning calorimetry measurements that 1,4-benzoquinone derivatives exhibit eutectic mixing that decreases their Tm, despite slightly positive enthalpies of mixing (0-5 kJ/mol). By rigorously investigating all 21 binary mixtures of a set of seven 1,4-benzoquinone derivatives with alkyl substituents (Tm's between 44 and 120 °C), we find that the eutectic melting point of a mixture of all seven achieves a large decrease in Tm to -6 °C. We further determine that the regular solution model shows improvement over an ideal solution model in predicting the eutectic properties for this newly investigated type of mixture composed of many small redox-active organic molecules.