High Convergence Research Articles

Integrative supporting techniques for the taxonomy of Schistorchis carneus Lühe, 1906 (Digenea: Megaperidae) with perspective for the existence of species complexes

The morphology and host-parasite data of Schistorchis carneus Lühe, 1906 indicate low host specificity and geographical range variation, accompanied by a large scale of intraspecific variability and ambiguous molecular phylogeny, confusing in determining biodiversity extent and specificity to a particular host and/or locality. To address the challenging circumstances of records of S. carneus, a detailed morphological description, molecular characterization and species delimitation analyses were conducted using a combination of comparative morphology, molecular phylogeny, multivariate analyses, and host-parasite data. Several morphological features exhibited an extensive intra-variation, as well as a wide morphometric range in many measurements within and across records. Phylogenetic analyses showed that the classification of schistorchiines is highly correlated with the morphology/nature of the oral sucker and highlighted the limitations of ribosomal 28S rDNA and ITS2 rDNA in distinguishing morphologically close/related taxa. Species delimitation analyses reflected the critical importance of host distinction in schistorchiines recognition/differentiation, even those that are indistinct morphologically. The clustering of schistorchiines into certain groups was driven by host differences. Additionally, host variation typically corresponds to a distinct schistorchiine species, even if it is morphologically identical to another. Cluster analysis associated with host-parasite data revealed high morphometric convergence and significant diversity among Egyptian and Sri-Lankan records of S. carneus. It also confirmed the distinction between Indian records from the Bay of Bengal and those from the Gulf of Mannar (previously mentioned as Manaar), each in their own distant group. In conclusion, the records of S. carneus represent a repository for a group of closely related cryptic species. The restricted concept of S. carneus should include only records from the white-spotted puffer, Arothron hispidus (Linnaeus) and the stellate puffer, Arothron stellatus (Anonymous). The Australian specimens from the narrow-lined puffer, Arothron manilensis (Marion de Procé) and the Indian record from the lunartail puffer, Lagocephalus lunaris (Bloch & Schneider) likely represent a free-standing cryptic species with limited distribution, requiring further characterization.

Unveiling the effects of artificial intelligence and green technology convergence on carbon emissions: An explainable machine learning-based approach.

Green technology and artificial intelligence (AI) are playing a positive role in reducing carbon emissions. Technology convergence, as a typical form of technological innovation, can expedite the realization of low-carbon goals through the outcomes of AI and green technology convergence (e.g., the smart home system and smart transportation system). To investigate the mechanisms within AI and green technologies that affect carbon emissions, this study extracts convergence features from convergence attributes and convergence networks, based on panel data from Chinese prefecture-level cities spanning the period from 1997 to 2019. By combining the eXtreme Gradient Boosting (XGBoost) algorithm and the Shapley Additive Explanations (SHAP) value method, the study explains the individual effects and interaction effects of each feature on carbon emissions. The research findings reveal that technology convergence generality and innovation team scale have a significant impact on carbon emissions, with the latter exhibiting a U-shaped effect. Cities with high convergence network efficiency are found to influence suppressing carbon emissions positively. This study and its findings provide insights for policymakers to develop AI and green convergence technologies to reduce carbon emissions.

A MODIFIED NUMERICAL METHOD OF DETERMINING HYDRAULIC SYSTEM PARAMETERS FOR THE DEVELOPMENT OF MATHEMATICAL MODELS AND INFORMATION COMPLEXES OF COMPUTER SIMULATION MODELS FOR INDUSTRIAL CHEMICAL PRODUCTION

The paper introduces a modified numerical method for determining the parameters of hydraulic systems used in mathematical modeling and simulation-based computer systems for industrial chemical production. It explores the dynamics of fluids, gases, and their mixtures as they traverse pipelines equipped with valves, compressors, and other complex components. Traditional methods for solving nonlinear equation systems often face limitations, including sensitivity to initial approximations and the need for relaxation coefficient adjustments, which complicate simulation processes. The proposed method converts nonlinear equations into an optimization problem, us- ing the Nelder-Mead algorithm to minimize deviations between initial estimates and comput- ed parameters. This derivative-free approach employs a simplex geometric transformation, overcoming issues associated with iterative methods and delivering high accuracy, stable convergence, and computational efficiency. Its adaptability makes it suitable for handling in- tricate hydraulic systems. To validate the method, a practical example involving a pipeline system with multiple restricting devices is analyzed. The mathematical model includes equations for pressures and flow rates, supplemented by material balance constraints at branching points. Numerical ex- periments evaluate how input pressure influences system parameters, demonstrating the mod- el's robustness and precision. The results confirm that the method accurately simulates system parameters under steady-state conditions and identifies critical optimization points. This innovative approach offers significant advantages for designing and managing industrial chemical facilities. By simplifying model construction, enhancing reliability, and accelerating simulation processes, the method provides a powerful tool for optimizing the op- eration and control of complex technological systems.

Personality traits and their correlation with behavior in the bonnet macaque (Macaca radiata) in southern India

The purpose of our research was to identify the primary personality traits of bonnet macaques using an observer-based questionnaire and to assess the accuracy of this method in predicting behavior. Additionally, we examined the relationship between personality traits and variables such as sex, age, and rank. We rated 25 males and 38 females on 50 personality questionnaire items using a 7-point scale, with reliable ratings and good agreement among raters (the lowest of the calculated ICC (3:k) index ≥ 0.62). Behavioral observations of 38 females over two-years were analyzed. Exploratory factor analysis revealed five traits: Friendliness, Aggression, Playfulness, Fearfulness, and Opportunism. Every trait correlated with a certain number of observed behaviors. Our results suggest that the questionnaire-based method is sufficiently valid as it confirmed high behavioral convergence with Fearfulness, Playfulness and Aggressiveness. Further studies are needed to understand the functional value of behaviors associated with Friendliness and Opportunism. Our study confirmed a tolerant social style of bonnet macaques, characterized by similar Aggression levels in both males and females, and lack of difference in Aggression level between subadult and adult males. Moreover, we identified a species-characteristic trait Opportunism which, unlike in other studied macaques, did not involve aggression-related characteristics.

FCAE-YOLOv8n: a target detection method for immature grape clusters

ABSTRACT In order to quickly and accurately detect immature grape clusters in the complex environment of orchard, a new model FCAE-YOLOv8n was proposed with low hardware requirements, high target accuracy and fast model convergence. The model was improved based on the original YOLOv8n model. Firstly, the model possessed numerous parameters and was unsuitable for mobile terminal deployment. Faster Block module was used to replace Bottleneck module in C2f, which reduced model parameters. Secondly, complex similarly-colored backgrounds, numerous small-sized grape grains, and mutual occlusion among grape clusters contributed to its poor recognition. The CA mechanism was embedded in the model to improve its feature extraction ability. Finally, the EIoU loss function instead the CIoU loss function was used to accelerate the convergence of the bounding interval. FCAE-YOLOv8n was experimentally compared with different models using the self-build datasets. FCAE-YOLOv8n model achieved precision of 98.6%, recall rate of 97.1% and mAP@0.5:0.95 of 92.5%. It increases by 1.2%, 2.0%, and 3.4% respectively compared with the original YOLOv8n. The average detection speed of each image was increased. It achieved the rapid and precise recognition of immature grape clusters in complex orchards, which provided technical support for subsequent automated bagging.

Physical Modelling of Cross-Wedge Rolling of Ball Studs

The publication presents and reviews the results of a physical modelling study of the Cross Wedge Rolling process of ball-studs. The ball-studs was formed in a double system from C45 steel at 1050°C, while the physical modelling process was performed on a scale of 1:2.5 using Primo plasticine as the model material, which was formed at 5°C. For the real tests, steel tools were used, while for the model tests the tools were made of ABS plastic. The experimental tests were followed by measurements of the individual stud steps. The geometrical analysis of the specimens showed that the model material allowed the shape of the forging obtained in the rolling process to be accurately modelled. Based on the physical modelling studies of the Cross Wedge Rolling process of ball-studs, it was concluded that the ball-studs rolling process can be modelled using PRI MO plasticine. Based on the analysis of the physical modelling results obtained, it was concluded that physical modelling allows the Cross Wedge Rolling process to be modelled with a high convergence of the results obtained in real and physical tests.

Vehicle Travel Time Prediction Based on SSA-LSTM

Highway travel time, defined as the time difference between a vehicle entering and exiting the highway, is primarily influenced by factors such as the highway's characteristics, current road conditions, and the driver's behavior. Travel time is a crucial parameter for assessing the operational state of a highway and serves as an important reference for both drivers when selecting routes and for traffic managers in implementing control measures. Therefore, predicting highway travel time is of significant importance. Long Short-Term Memory (LSTM) is a type of recurrent neural network, an improved version of the traditional recurrent neural network, known for its excellent performance in predicting sequential data, such as time-series data on highway travel times. In operations research, search optimization algorithms have been a key area of focus in conjunction with machine learning. The Sparrow Search Algorithm (SSA), a relatively new search optimization algorithm, boasts high convergence speed and accuracy, along with strong global search capabilities, reducing the risk of getting trapped in local optima. This study utilizes vehicle entry and exit data collected by the ETC system at the Jin-Gang Expressway toll stations to analyze the highway's basic information. Building upon the traditional LSTM model, we integrate the SSA to optimize the LSTM model parameters. By using SSA to search for the optimal parameters, we form a new SSA-LSTM model. Finally, we apply this model to predict travel time data, showing a 120% improvement in prediction accuracy compared to the original LSTM model.

Mathematical modeling of the operation of plate elements when working together with a soil base in conditions of flat deformation

Assessment of the forces arising in the structures of underground parts of structures, such as piles, foundation slabs and walls, as well as in the structures of the pit fence made of metal sheet piling and using the “wall in the ground” technology is a normative mandatory check for the first group of limit conditions. The work of such structures should be evaluated in conjunction with the soil base, the characteristics of which directly affect the final effort. The article provides a detailed mathematical description of the implementation of a three-node finite element describing a beam (plate in the conditions of a flat problem). At the end, a comparison of the calculation results of the test tasks with another computing software package is provided. High convergence of the results was noted. The results obtained can be used in the development of proprietary finite elements or as alternative calculation methods within the framework of scientific and technical support.

АНАЛІЗ НАПРУЖЕНОГО СТАНУ ШАРУ З ДВОМА ЦИЛІНДРИЧНИМИ ВРІЗАНИМИ ОПОРАМИ ТА ЦИЛІНДРИЧНИМИ ВТУЛКАМИ

The spatial problem of the theory of elasticity for a layer on cylindrical embedded supports with cylindrical sleeves (thick-walled pipes) located between each support and the layer is solved. Smooth contact conditions are set at the interface between the layer and the pipes. Stresses are specified on the surfaces of the layer, and displacements are specified on the inner surface of the pipe (rigidly conjugated to the support). The analytical and numerical solution of the problem is based on the Lamé equations written for the layer and each pipe. When the boundary conditions and the conditions of conjugation of the layer with the pipes are met, a system of integro-algebraic equations is created, which reduces to a system of linear algebraic equations. Each equation is written in its local coordinate system. For this purpose, the transition formulas of the generalized Fourier method are applied to the basic solutions of the Lamé equation. After solving the system of equations and finding the unknowns, the stress-strain state in the body of the layer and pipes was obtained. The reduction method was used to obtain numerical results. Fulfillment of the boundary conditions showed high convergence of the results, the accuracy of which depends on the order of the system of equations. The analysis of the stress-strain state of the layer and the pipe was carried out for different sleeve materials in places of stress concentration. The results indicate an increase in the stresses sφ and sz on cylindrical surfaces in the case of using polyamide bushings. The proposed method makes it possible to analyze the stress-strain state of a wide range of pipe structures. It also provides an opportunity to assess how changes in material and geometric parameters affect the stress distribution in such systems, which allows optimizing structures and ensuring their reliability. In the further development of this research topic, it is necessary to consider models where bushings are combined with other types of inhomogeneities (cavities, reinforcement) and other boundary conditions.

Osteoporosis Disease Detection using Optimized Elman Recurrent Neural Network based on Hybrid Bacterial Colony Optimization and Tabu Search Algorithm

Bone loss and fragility are indications of osteoporosis, a condition caused by calcium deficiency. The detection of osteoporosis is a significant and difficult diagnostic endeavor. Elman recurrent neural network (ERNN) is a well-known medical disease detection method due to its modeling sequential data and capturing temporal dependencies. ERNN training can be computationally costly and necessitates precise adjustment of hyperparameters. In this research, optimized ERNN is used to predict osteoporosis diseases to achieve high detection accuracy and to improve the global convergence rate. The new hybrid method is used to optimize the hyperparameters of ERNN based on the bacterial colony optimization (BCO) and tabu search (TS) algorithm, which is called IBCO-ERNN. The hybrid technique can efficiently explore the solution space by combining BCO's global exploration capabilities and TS's local exploitation capability, perhaps leading to better solutions to hyperparameter optimization problems. The hybrid BCO-TS strategy trains the ERNN model to prevent local optima and improve convergence rate. The experimental results demonstrated that the proposed IBCO-ERNN obtained high accuracy and fast convergence compared to other detection methods.

Ultra-local model based prescribed performance adaptive control of overhead cranes under uncertainties

In this paper, a model-free adaptive control scheme is proposed to address the problem of system performance variations due to inaccurate models of overhead cranes, and external disturbances. First, an adaptive fast integral terminal sliding mode controller with a prespecified performance setting is proposed to accelerate the convergence of the sliding mode surface. In addition, an improved form of performance function is introduced to ensure the high accuracy convergence of state errors, while effectively enhance response performance and state tracking accuracy. Second, a prespecified performance nonlinear disturbance observer is introduced to estimate the lumped dynamics part of the ultra-local model, which can guarantee effective model-free properties. Finally, the stability of the closed-loop control scheme is analyzed by means of Lyapunov stability theory, and the effectiveness and superiority of the proposed method is proved by means of numerical simulation results.

Semi-Implicit Numerical Integration of Boundary Value Problems

The numerical solution to boundary differential problems is a crucial task in modern applied mathematics. Usually, implicit integration methods are applied to solve this class of problems due to their high numerical stability and convergence. The known shortcoming of implicit algorithms is high computational costs, which can become unacceptable in the case of numerous right-hand side function calls, which are typical when solving boundary problems via the shooting method. Meanwhile, recently semi-implicit numerical integrators have gained major interest from scholars, providing an efficient trade-off between computational costs, stability, and precision. However, the application of semi-implicit methods to solving boundary problems has not been investigated in detail. In this paper, we aim to fill this gap by constructing a semi-implicit boundary problem solver and comparing the performance of explicit, semi-implicit, semi-explicit, and implicit methods using a set of linear and nonlinear test boundary problems. The novel blinking solver concept is introduced to overcome the main shortcoming of the semi-implicit schemes, namely, the low convergence on exponential solutions. The numerical stability of the blinking semi-implicit solver is investigated and compared with existing methods by plotting the stability regions. The performance plots for investigated methods are obtained as a dependence between global truncation error and estimated computation time. The experimental results confirm the assumption that semi-implicit numerical methods can significantly outperform both explicit and implicit solvers while solving boundary problems, especially in the proposed blinking modification. The results of this study can be efficiently used to create software for solving boundary problems, including partial derivative equations. Constructing semi-implicit numerical methods of higher-accuracy orders is also of interest for further research.

Fast symplectic integrator for Nesterov-type acceleration method

In this paper, explicit stable integrators based on symplectic and contact geometries are proposed for a family of non-autonomous ordinarily differential equations (ODEs) found in improving convergence rate of Nesterov’s accelerated gradient method. Symplectic geometry is known to be suitable for describing Hamiltonian mechanics, and contact geometry is known as an odd-dimensional counterpart of symplectic geometry. Moreover, a procedure, called symplectization, is a known way to construct a symplectic manifold from a contact manifold, yielding autonomous Hamiltonian systems from contact ones. It is found in this paper that a previously investigated non-autonomous ODEs can be written as a contact Hamiltonian system family. Then, by developing and applying a symplectization of non-autonomous contact Hamiltonian vector fields expressing the non-autonomous ODEs, novel symplectic integrators are derived. Because the proposed symplectic integrators preserve hidden symplectic and contact structures in the ODEs, they are expected to be more stable than the Runge–Kutta method. Numerical experiments demonstrate that, as expected, the second-order symplectic integrator is stable and high convergence rates are achieved.

Stress Recovery Techniques for the Modified Local Green’s Function Method

The Modified Local Green’s Function Method (MLGFM) is an integral method hybrid of the Finite Element Method (FEM) and the Boundary Element Method (BEM). The method uses the FEM to create discrete projections of the Green’s functions that will be used as fundamental solutions in BEM formulation. The MLGFM has the advantage of presenting high convergence for the displacements in the domain, inherited from the FEM, and for the normal stress on the boundary, inherited from the BEM. Despite these advantages, the accuracy of the recovered stresses in the domain has not been studied in previous works. As the MLGFM is a hybrid of the FEM and BEM, techniques used in both methods will be explored in this paper to study the advantages and disadvantages of each one. The techniques that will be used here are the Least Squares procedure, the Zienkiewicz and Zhu (ZZ) recovery, both widely used in FEM, and the integral form derived from the fundamental solution used in the BEM. The techniques will be analyzed in terms of errors and computational cost of each one.

Spatial Iterative Learning Torque Control of Robotic Exoskeletons for High Accuracy and Rapid Convergence Assistance

Spatial Iterative Learning Torque Control of Robotic Exoskeletons for High Accuracy and Rapid Convergence Assistance

Medical Image Classification Using DL-based Feature Extraction in IoMT

Aim:: Recent advances in Artificial Intelligence (AI) and the addition of Deep Learning (DL) have made it possible to analyse both real-time and historical data from the Internet of Things (IoT). Recently, IoT technology has been implemented in healthcare schemes as IoMT to aid in medical diagnoses. Medical image classification is useful for predicting and identifying serious diseases at an early stage, which is crucial in the diagnostic process. Background:: When it comes to managing, treating, and preventing illness, medical photographs are an essential element of a patient’s health record. However, it is a difficult issue in computer-based diagnostics to classify images using efficient characteristics. Objective:: The study aimed to develop a deep learning-based classification model for feature extraction. Methods:: Levy flight optimization is employed to pick the weight for the classification model optimally. At the end of the day, the optimal weight led to a better classification result and a higher degree of precision when analyzing medical photos for disease. Result:: We tested the proposed results in MATLAB and compared them with conventional methods of classification. The suggested model’s best results include 97.71% accuracy on a brain dataset and 97.2% accuracy on an Alzheimer’s disease dataset. Conclusion:: The proposed algorithm’s high rate of convergence proves that it can successfully balance the exploration and exploitation phases by avoiding capturing in local optimization and classifying thresholds rapidly. In light of the need for improved accuracy, precision, and computational speed in clinical picture classification, a novel approach based on soft sets has been presented.

Quadrature Solution for Fractional Benjamin–Bona–Mahony–Burger Equations

In this work, we present various novelty methods by employing the fractional differential quadrature technique to solve the time and space fractional nonlinear Benjamin–Bona–Mahony equation and the Benjamin–Bona–Mahony–Burger equation. The novelty of these methods is based on the generalized Caputo sense, classical differential quadrature method, and discrete singular convolution methods based on two different kernels. Also, the solution strategy is to apply perturbation analysis or an iterative method to reduce the problem to a series of linear initial boundary value problems. Consequently, we apply these suggested techniques to reduce the nonlinear fractional PDEs into ordinary differential equations. Hence, to validate the suggested techniques, a solution to this problem was obtained by designing a MATLAB code for each method. Also, we compare this solution with the exact ones. Furthermore, more figures and tables have been investigated to illustrate the high accuracy and rapid convergence of these novel techniques. From the obtained solutions, it was found that the suggested techniques are easily applicable and effective, which can help in the study of the other higher-D nonlinear fractional PDEs emerging in mathematical physics.

Soft Actor-critic-based Distributed Routing Scheme for Edge Computing Integrated with Dynamic IoT Networks

The rapid expansion of the Internet of Things (IoT) necessitates advanced routing schemes capable of meeting the stringent demands for low latency and high accuracy, which are critical for applications such as autonomous vehicles and telemedicine. Traditional edge computing methods often struggle with elevated latency, rendering them unsuitable for time-sensitive applications. Additionally, many reinforcement learning (RL) algorithms require action space discretization, which can introduce biases and dimensionality challenges. This paper introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme for edge computing, specifically designed to address these limitations. By integrating RL with Maximum Entropy principles and employing a decentralized approach, the proposed model enhances network performance, reduces delays, and effectively manages multi-optimality criteria. The distributed routing scheme operates independently of a centralized controller, allowing routers to make autonomous decisions and adapt seamlessly to changes in the network. This is accomplished through a Markov Decision Process (MDP) that optimizes routing paths based on various factors, including node depth, energy consumption, and transmission probability. The methodology encompasses local training phases for individual nodes, followed by federated training to refine the model across the network. Experimental results conducted on topologies of varying scales demonstrate the model’s efficacy in achieving high accuracy and efficient convergence, particularly in dynamic IoT environments. These findings underscore the potential of the proposed SAC-based distributed routing scheme as a robust solution for enhancing routing efficiency and reliability in the evolving landscape of IoT applications. IMPACT STATEMENT The rapid expansion of Internet of Things (IoT) applications demands advanced routing solutions to ensure low latency and high accuracy, crucial for sectors like autonomous vehicles and telemedicine. Traditional edge computing methods struggle with elevated latency, while many reinforcement learning (RL) algorithms face challenges with action space discretization, leading to biases and dimensionality issues. This study introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme to address these limitations. Integrating Maximum Entropy principles with RL enhances exploration and decision-making stability. The decentralized approach allows routers to make autonomous, real-time decisions based on local network conditions, optimizing routing paths through a Markov Decision Process (MDP). Experimental results from various simulated IoT network topologies show the model’s superior performance in reducing delays and maintaining bandwidth. This research paves the way for more reliable, low-latency IoT applications, significantly enhancing routing efficiency and network adaptability in dynamic IoT environments.

A Harmonic Response Topology Optimization Method Based on Hybrid Cellular Automata

Abstract Hybrid cellular automaton (HCA) method is a topology optimization method with high convergence efficiency. Harmonic response topology optimization problem has been widely researched and common frequency-domain methods have been applied to solve this problem. Despite this, little work has so far been undertaken to utilize the HCA method to improve the calculation efficiency of harmonic response topology optimization. In this article, we present a methodology that applies the HCA method to solve harmonic response topology optimization problem. Mode displacement method (MDM), mode acceleration method (MAM), and full method (FM) are common frequency-domain methods that are utilized as harmonic response analysis methods in this study. The examples under rotating loads demonstrate that feasibility of this methodology at one specific frequency excitation and multiple frequencies excitation. This article presents and implements a high convergence efficiency method for harmonic response topology optimization, and this method can converge in 20–30 iterations. This method can extend the application range of the HCA method and improve the optimization efficiency of harmonic response topology optimization.

Making plaque assessment easier – a validation study of simplified versions of the Marginal Plaque Index

BackgroundThe assessment of plaque indices may be time-consuming and error-prone. Simplification of these indices may increase their utility without compromising their validity. The aim of this study was to evaluate the validity of two simplified versions of the Marginal Plaque Index (MPI).MethodsTwo simplified versions of the MPI as well as the Plaque Control Record (PCR) were derived from full-scale MPI assessments in two studies with four age groups (N = 42 10-year-olds; N = 24 15 year-olds; N = 53 university students (18y-33y); N = 66 parents (32y-57y). Correlations with the Turesky modification of the Quigley-Hein Index (TQHI) and the Papillary Bleeding Index (PBI) were calculated.ResultsThe indices derived from the MPI showed high convergence with each other (all r ≥ 0.94) and with the TQHI (r ≥ 0.80). The concurrent validity of the MPI with the PBI was equal to that of the TQHI in all age groups. The simplified versions of the MPI and the PCR show a lower convergent validity with the PBI than the MPI within parents (p < 0.05). In the other age groups, their convergent validity was equal to that of the MPI.DiscussionSimplification of the MPI does not affect its convergent validity with other plaque indices but may reduce its concurrent validity with the PBI in middle-aged adults.