Continuous Model Research Articles

A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset.

Characteristics of Damage to Brown Rice Kernels under Single and Continuous Mechanical Compression Conditions.

During the rice milling process, single and continuous compression occurs between brown rice and the processing parts. When the external load exceeds the yield limit of brown rice, brown rice kernels are damaged; with an increase in compression deformation or the extent of compression, the amount of damage to the kernels expands and accumulates, ultimately leading to the fracture and breakage of kernels. In order to investigate the mechanical compression damage characteristics of brown rice kernels under real-world working conditions, this study constructs an elastic-plastic compression model and a continuous damage model of brown rice kernels based on Hertz theory and continuous damage theory; the accuracy of this model is verified through experiments, and the relevant processing critical parameters are calculated. In this study, three varieties of brown rice kernels are taken as the research object, and mechanical compression tests are carried out using a texture apparatus; finally, the test data are analysed and calculated by combining them with the theoretical model to obtain the relevant critical parameters of damage. The results of the single compression crushing test of brown rice kernels showed that the maximum destructive forces Fc in the single compression of Hunan Early indica 45, Hunan Glutinous 28, and Southern Japonica 518 kernels were 134.77 ± 11.20 N, 115.64 ± 4.35 N, and 115.84 ± 5.89 N, respectively; the maximum crushing deformations αc in the single compression crushing test were 0.51 ± 0.04 mm, 0.43 ± 0.01 mm, and 0.48 ± 0.17 mm, respectively; and the critical average deformations αs of elasticity-plasticity deformation were 0.224 mm, 0.267 mm, and 0.280 mm, respectively. The results of the continuous compression crushing test of brown rice kernels showed that the critical deformations αd of successive compression damage formation were 0.224 mm, 0.267 mm, and 0.280 mm, and the deformation ratios δ of compression damage were 12.24%, 14.35%, and 12.84%. From the test results, it can be seen that the continuous application of compression load does not result in the crushing of kernels if the compression deformation is less than αd during mechanical compression. The continuous application of compressive loads can lead to fragmentation of the kernels if the compressive deformation exceeds αd; the larger the compression variant, the less compression is required for crushing. If the compression deformation exceeds αc, then a single compressive load can directly fragment the kernels. Therefore, the load employed during rice milling should be based on the variety of brown rice used in order to prevent brown rice deformation, which should be less than αd, and the maximum load should not exceed Fc. The results of this study provide a theoretical reference for the structure and parameter optimisation of a rice milling machine.

Cybersecurity threats in banking: Unsupervised fraud detection analysis

Customers all over the world now enjoy remarkable levels of accessibility and convenience thanks to the digital transformation of the banking industry. However, technology has also brought up new difficulties, including cybersecurity. The incapacity of conventional rule-based fraud detection strategies to keep up with the rapid evolution of cyber threats has generated interest in flexible and efficient approaches like unsupervised learning. The potential of unsupervised learning to improve fraud detection in the banking sector is examined in this article. The article addresses the disadvantages of traditional methods, the benefits of unsupervised learning, and how cybersecurity measures may be affected. A thorough framework for putting unsupervised fraud detection strategies into practice, including data preprocessing, feature engineering, isolation forest implementation, thresholding, and assessment, is provided in the methodology section. To further improve anomaly detection frameworks, future efforts propose integrating advanced machine learning techniques, dynamic thresholding, enhanced feature engineering, and continuous model monitoring. In summary, this essay offers useful insights on using modern machine learning algorithms to reduce cybersecurity threats and ensure the security of digital transactions within the banking industry.

Education, Prevention, and Treatment of Helicobacter pylori Infection by General Practitioners

General practitioners play a key role in the education and prevention of Helicobacter pylori infection. Through extensive knowledge dissemination, they effectively enhance public awareness of this bacteria and popularize relevant prevention and control methods. At the practical level, general practitioners combine rich clinical experience and professional knowledge to develop personalized treatment plans for patients and ensure the continuity and safety of treatment effects through meticulous follow-up work. In addition, they extend the prevention and treatment knowledge to the family and community, helping residents build comprehensive guidelines through health education and practical guidance. The leading role of general practitioners not only optimizes the prevention and treatment process of Helicobacter pylori infection but also closes the distance between doctors and patients, improving the overall quality of primary care. This people-oriented and continuous service model has profound significance for improving public health standards and promoting the development of primary care in the future.

Prediction of the hydraulic conductivity function for unsaturated soils over the entire suction range

The hydraulic conductivity function (HCF) refers to in this paper is the variation of hydraulic conductivity over the entire suction range from 0 to 10<sup>6</sup> kPa. Most of the available HCF models in the literature are derived based on capillary theory and have limitations in the explanation of HCF in the high suction range. Recently, several models have been proposed for accounting for the thin film flow dominated by the adsorbed water in the high suction range. However, these models require measured HCF data for describing the thin film flow, which is time-consuming. In this study, a mathematically continuous and simple model is proposed for predicting the HCF. The model has physically meaningful parameters for predicting the HCF. In this model, the residual suction estimated from the soil-water characteristic curve (SWCC) is used to distinguish the capillary and adsorption-dominated regions, and a correction operator that considers the thin film flow is introduced. The performance of the model was validated using the measured and published experimental data for various soils. In addition, the proposed model is successfully applied in a numerical investigation of the hydraulic behavior of a capillary barrier system, highlighting the role of the thin film flow.

Dynamics of a Conformable Fractional Order Generalized Richards Growth Model on Star Network with N=20 Nodes

In this study, we analyze dynamical behavior of the conformable fractional order Richards growth model. Before examining the analysis of the dynamical behavior of the fractional continuous time model, the model is reduced to the system of difference equations via utilizing piecewise constant functions. An algebraic condition that ensures the stability of the positive fixed point of the system is obtained. With the center manifold theory, the existence of a Neimark-Sacker bifurcation at the fixed point of the discrete-time system is proven and the direction of this bifurcation is determined. In addition, the discrete dynamical system is also studied on the star network with N=20 nodes. Analysis complex dynamics of Richards growth model into coupled dynamical network shows that the complex star network with N=20 nodes also exhibits Neimark-Sacker bifurcation about the fixed point concerning with parameter c. Numerical simulations are performed to demonstrate the stability, bifurcations and dynamic transition of the coupled network.

A Novel Three-Segment Model to Describe the Entire Soil–Water Characteristic Curve

This study aims to accurately describe the soil–water characteristic curve (SWCC) across the full range from saturation to oven dryness. We propose a smooth, continuous three-segmented SWCC model that divides the saturation range into wet, air-dried, and oven-dried segments. The two model junction points are anchored at matric suctions of 104.5 and 106.5 cm, respectively. The soil water content at 104.5 cm represents the maximum soil hygroscopy, reflecting the maximum water content in air-dried soil, while the soil water content at 106.5 cm characterizes the minimum soil water content. This imbues the junction points with specific physical significance regarding soil moisture content and matric potential. The model was tested with the water retention data of nine soils across the SWCC and compared with three existing SWCC models based on the adjusted coefficient of determination (adjR2) and root mean square error (RMSE). The results indicated that the proposed model accurately described the entire SWCC. The three-segmented model yielded an adjR2 of >0.99 and an RMSE of ≤0.022 cm3 cm−3, outperforming other models. We also introduce a new method for predicting soil water data in air-dried and oven-dried segments. The results showed that the predicted soil water content values were accurate.

Buckling and post-buckling of anisogrid lattice-core sandwich plates with nanocomposite skins

This research offers an in-depth examination of the buckling and post-buckling responses observed in sandwich plates distinguished by the presence of an anisogrid lattice core and face layers composed of functionally graded carbon nanotube-reinforced composites (FG-CNTRC). To model this structure, a global continuous model for the lattice core is employed, while the displacement field is approximated using the First-Order Shear Deformation Theory (FSDT). Geometrically nonlinear strain relations based on the von-Kármán assumptions are incorporated to accurately capture the structural response. The homogenization of the FG-CNTRC skins is achieved through the modified rule of mixture, enabling estimations of effective material properties. To separate pre-buckling and post-buckling phases, we implement the adjacent-equilibrium criterion. Solving the linear (pertaining to buckling) and nonlinear (related to post-buckling) equations is based on the Generalized Differential Quadrature (GDQ) technique alongside an iterative method grounded in the displacement control strategy. Our analysis explores the influence of core and skin characteristics on the stability of these sandwich plates. By examining critical buckling thresholds, post-buckling equilibrium paths, and the underlying mechanisms driving these responses, we contribute insights into the structural stability and performance of such composite structures. This study not only enhances our understanding of buckling behavior in anisogrid lattice core sandwich plates but also holds promise for diverse engineering applications where structural stability is paramount.

Preserving Linguistic Diversity in the Digital Age: A Scalable Model for Cultural Heritage Continuity

In the face of the rapid erosion of both tangible and intangible cultural heritage globally, the urgency for effective, wide-ranging preservation methods has never been greater. Traditional approaches in cultural preservation often focus narrowly on specific niches, overlooking the broader cultural tapestry, particularly the preservation of everyday cultural elements. This article addresses this critical gap by advocating for a comprehensive, scalable model for cultural preservation that leverages machine learning and big data analytics. This model aims to document and archive a diverse range of cultural artifacts, encompassing both extraordinary and mundane aspects of heritage. A central issue highlighted in the article is the dominance of English, especially in popular culture, and its role in reshaping cognitive patterns, leading to the loss of traditional languages and cultures. This trend, favoring orality over literacy, amplifies the need for the preservation actions proposed. The article underscores the importance of establishing a more meaningful and comprehensive lingua franca as the first step in this endeavor. This approach is not just about safeguarding languages but also about preserving the nuanced ways in which different linguistic paradigms shape our understanding of the world. The paper explores how language evolution, translation nuances, and the unique perspectives inherent in various linguistic paradigms contribute to a rich cultural diversity. It also examines the impact of technology and social media on traditional cultural norms and the critical need for deliberate efforts to reinforce endangered languages and practices. The proposed model aims to bridge the gap between past and present cultural expressions, using technology to reinforce mother-tongue contexts and enhance the appreciation of diverse human identities and worldviews. Through this, the article presents a compelling case for a more inclusive and holistic approach to preserving the global tapestry of cultural heritage.

Investigation of the bond properties between hybrid fiberreinforced concrete and BFRP bars after exposure to high temperature

This research investigated the bond properties at high temperatures and with hybrid fiber addition between basalt fiber reinforced polymer (BFRP) bars and hybrid fiber-reinforced concrete (HFRC). The tensile strength and appearance morphology of BFRP bars were analyzed. Used scanning electron microscopy (SEM) to examine the microscopic morphology and used the bond-slip constitutive model to fit the bond-slip curves. When BFRP bars after exposed to 300 °C, their tensile strength dropped by 42%. With the increasing temperature, the bond strength and stiffness of normal concrete (NC) specimens decreased by 27.3 % and 67.5 %, respectively, while HFRC specimens decreased by 20.8 % and 55 %, respectively. Hybrid fibers increased the bond strength and stiffness of HFRC specimens by 27.1 % and 49.1 %, respectively. The best fitting models were the Malvar model and the Continuous Curve model.

Small-scale districts identification of Boletus bainiugan from Yunnan province of China based on residual convolutional neural network continuous classification models

Small-scale districts identification of Boletus bainiugan from Yunnan province of China based on residual convolutional neural network continuous classification models

Prediction model for behavioral intention to use E-HRM through awareness in Jordanian five-star hotels

Using web-based technologies in various HR procedures is the concept of Electronic Human Resource Management or E-HRM. It makes a big difference to the efficiency of the business by providing several options for getting things done quickly. Nevertheless, there has been scant investigation into this field, and its maximum potential remains untapped, despite its meteoric rise. The present research also suggests adding the Technology Acceptance Model (TAM) to the E-HRM continuity model to find out what factors affect workers' behavioral intention to use E-HRM systems, and how awareness plays a role in this. In order to help advance E-HRM as a field, this study aims to expand the theoretical bounds of existing research. 400 prospective employees were scouted from Jordanian five-star hotels as part of the convenience sampling procedure used to get the data. Starting in January 2022, the data was collected over one month. We used a Structural Equation Model (SEM) with Smart PLS on the dataset to evaluate our hypothesis. The study found that behavioral intention to use E-HRM is significantly influenced by perceived usefulness, perceived ease of use, trust, and subjective norms. The results only backed up two of the four hypotheses on the moderator effect. Hotel workers will be better able to handle high levels of stress and conduct their jobs more efficiently if the industry adopts E-HRM technology.

Iterative Token Evaluation and Refinement for Real-World Super-resolution

Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative Adversarial Networks (GANs) or continuous diffusion models present their own issues including GANs being difficult to train while continuous diffusion models requiring numerous inference steps. In this paper, we propose an Iterative Token Evaluation and Refinement (ITER) framework for RWSR, which utilizes a discrete diffusion model operating in the discrete token representation space, i.e., indexes of features extracted from a VQGAN codebook pre-trained with high-quality (HQ) images. We show that ITER is easier to train than GANs and more efficient than continuous diffusion models. Specifically, we divide RWSR into two sub-tasks, i.e., distortion removal and texture generation. Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network. In particular, we propose to include a token evaluation network in the discrete diffusion process. It learns to evaluate which tokens are good restorations and helps to improve the iterative refinement results. Moreover, the evaluation network can first check status of the distortion removal output and then adaptively select total refinement steps needed, thereby maintaining a good balance between distortion removal and texture generation. Extensive experimental results show that ITER is easy to train and performs well within just 8 iterative steps.

CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design

Engineering design methods aim to generate new designs that meet desired performance requirements. Past work has directly introduced conditional Generative Adversarial Networks (cGANs) into this field and achieved promising results in single-point design problems(one performance requirement under one working condition). However, these methods assume that the performance requirements are distributed in categorical space, which is not reasonable in these scenarios. Although Continuous conditional GANs (CcGANs) introduce Vicinal Risk Minimization (VRM) to reduce the performance loss caused by this assumption, they still face the following challenges: 1) CcGANs can not handle multi-point design problems (multiple performance requirements under multiple working conditions). 2) Their training process is time-consuming due to the high computational complexity of the vicinal loss. To address these issues, A Continuous conditional Diffusion Probabilistic Model (CcDPM) is proposed, which the first time introduces the diffusion model into the engineering design area and VRM into the diffusion model. CcDPM adopts a novel sampling method called multi-point design sampling to deal with multi-point design problems. Moreover, the k-d tree is used in the training process of CcDPM to shorten the calculation time of vicinal loss and speed up the training process by 2-300 times in our experiments. Experiments on a synthetic problem and three real-world design problems demonstrate that CcDPM outperforms the state-of-the-art GAN models.

Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors

In this work, we focus on high-dimensional single index models with non-Gaussian sensing vectors and generative priors. More specifically, our goal is to estimate the underlying signal from i.i.d. realizations of the semi-parameterized single index model, where the underlying signal is contained in (up to a constant scaling) the range of a Lipschitz continuous generative model with bounded low-dimensional inputs, the sensing vector follows a non-Gaussian distribution, the noise is a random variable that is independent of the sensing vector, and the unknown non-linear link function is differentiable. Using the first- and second-order Stein's identity, we introduce efficient algorithms to obtain estimated vectors that achieve the near-optimal statistical rate. Experimental results on image datasets are provided to support our theory.

KGDM: A Diffusion Model to Capture Multiple Relation Semantics for Knowledge Graph Embedding

Knowledge graph embedding (KGE) is an efficient and scalable method for knowledge graph completion. However, most existing KGE methods suffer from the challenge of multiple relation semantics, which often degrades their performance. This is because most KGE methods learn fixed continuous vectors for entities (relations) and make deterministic entity predictions to complete the knowledge graph, which hardly captures multiple relation semantics. To tackle this issue, previous works try to learn complex probabilistic embeddings instead of fixed embeddings but suffer from heavy computational complexity. In contrast, this paper proposes a simple yet efficient framework namely the Knowledge Graph Diffusion Model (KGDM) to capture the multiple relation semantics in prediction. Its key idea is to cast the problem of entity prediction into conditional entity generation. Specifically, KGDM estimates the probabilistic distribution of target entities in prediction through Denoising Diffusion Probabilistic Models (DDPM). To bridge the gap between continuous diffusion models and discrete KGs, two learnable embedding functions are defined to map entities and relation to continuous vectors. To consider connectivity patterns of KGs, a Conditional Entity Denoiser model is introduced to generate target entities conditioned on given entities and relations. Extensive experiments demonstrate that KGDM significantly outperforms existing state-of-the-art methods in three benchmark datasets.

Continuous-Time Graph Representation with Sequential Survival Process

Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GraSSP: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.

Bifurcation of finger-like structures in traveling waves of epithelial tissues spreading

We consider a continuous active polar fluid model for the spreading of epithelial monolayers introduced by R. Alert, C. Blanch-Mercader, and J. Casademunt, 2019. The corresponding free boundary problem possesses flat front traveling wave solutions. Linear stability of these solutions under periodic perturbations is considered. It is shown that the solutions are stable for short-wave perturbations while exhibiting long-wave instability under certain conditions on the model parameters (if the traction force is sufficiently strong). Then, considering the prescribed period as the bifurcation parameter, we establish the emergence of nontrivial traveling wave solutions with a finger-like periodic structure (pattern). We also construct asymptotic expansions of the solutions in the vicinity of the bifurcation point and study their stability. We show that, depending on the value of the contractility coefficient, the bifurcation can be a subcritical or a supercritical pitchfork.

A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field

This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production history, including carbonates from the Asmari and Bangestan Formations. A fracture intensity map was generated based on the interpretation of image logs from 28 wells drilled within the field. Mud loss data were collected and mapped based on the geostatistical Gaussian Random Function Simulation (GRFS) algorithm. Maximum curvature maps were generated based on Asmari structural surface maps. Comparing the results shows a good agreement between the curvature map, fault distribution model, mud loss map, fracture intensity map, and productivity index. The results of image log interpretations led to the identification of four classes of open fractures, including major open fractures, medium open fractures, minor open fractures, and hairline fractures. Using the azimuth and dip data of the four fracture sets mentioned above, the fracture intensity log was generated as a continuous log for each well with available image log data. For this purpose, the fracture intensity log and a continuous fracture network (CFN) model were generated. The continuous fracture network model was used to generate a 3D discrete fracture network (DFN) for the Asmari Formation. Finally, a 3D upscaled model of fracture dip and azimuth, fracture porosity, fracture permeability, fracture length, fracture aperture, and the sigma parameter (the connectivity index between matrix and fracture) were obtained. The results of this study can illuminate the modeling of intricate reservoirs and the associated production challenges, providing insights not only during the initial production phase but also in the application of advanced oil recovery methods, such as thermal recovery.

Universal fluctuations of global geometrical measurements in planar clusters.

We characterize universal features of the sample-to-sample fluctuations of global geometrical observables, such as the area, width, length, and center-of-mass position, in random growing planar clusters. Our examples are taken from simulations of both continuous and discrete models of kinetically rough interfaces, including several universality classes, such as Kardar-Parisi-Zhang. We mostly focus on the scaling behavior with time of the sample-to-sample deviation for those global magnitudes, but we have also characterized their histograms and correlations.