International Journal of Modern Physics C

In today’s interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies.

As an important research topic in social network analysis, the influence maximization problem is aimed at identifying a set of influential nodes from a social network that can produce the maximum propagation cascade effect. Developing flattering policies that can balance both the solution quality and computational efficiency is one of the key concerns on this problem. To address the deficiency of the classical static memetic algorithms that only consider the quality of neighboring nodes but lack the adaptability to different network topologies, this paper proposes a hybrid dynamic memetic algorithm (HDMA) for the influence maximization problem. According to the proposed framework, each node in the network is first assigned a label to indicate its importance. Based on the local influence, the potential influential nodes are added to the candidate set by ranking the importance, which is adopted to improve the quality of the initial population and prune the search space. An adaptive roulette taboo strategy by combining the stochastic local searching method, greedy mechanism with the centrality metrics is constructed to enhance the adaptability of the algorithm as well as its accuracy. The results prove that the HDMA can adapt well to different network structures. Higher accuracy solutions are obtained in faster running time, which verifies the satisfying performance of the algorithm in balancing global search and local exploitation.

Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases.

Nonnearest-neighbor hop during random walks is observed in various real situations and their nontrivial roles are also confirmed. On the other hand, the reciprocity also can significantly impact dynamical processes on weighted networks. However, the roles of nonnearest-neighbor hop are less explored in the presence of reciprocity. Here, we quantify the effect of nonnearest-neighbor hop on random walks in the presence of reciprocity. Specifically, a class of directed weighted fractal scale-free networks is proposed to accommodate reciprocity. Subsequently, mixed random walk that includes both nearest-neighbor and next-nearest-neighbor jump is defined on the networks. Finally, a trap is placed at a node of the networks and the average trapping time (ATT) is derived analytically and exactly to determine the effect of next-nearest-neighbor hop on transport efficiency. The obtained closed form solution of ATT shows that (1) Dependent on the combination of values of parameters governing network evolution and reciprocity, ATT can grow linearly, sub-linearly or super-linearly with network size. (2) The parameter manipulating mixed random walk only modifies the prefactor of ATT, so the next-nearest-neighbor hop has no effect on the scaling of transport efficiency in the presence of reciprocity. (3) The weight parameter enters into both the prefactor and leading scaling of ATT, so reciprocity impacts the transport efficiency both qualitatively and quantitatively.

A cavity-flame holder is widely used for the injection of the fuel jet in a supersonic combustor due to high fuel residence time. This paper presents extensive results about the impacts of extruded annular multi-jets for improving fuel mixing inside the cavity flame holder. A three-dimensional model of the cavity with multiple extruded nozzles in different lengths and conditions has been examined in this study to investigate the flow and fuel mixing inside the cavity. In addition, the usage of internal air jets is investigated and extensive comparisons between the elongation of the extruded nozzle and the use of the inner air jets for better fuel distribution are done. The presented results show that the usage of both inner air jet and nozzle elongation significantly enhances the fuel distribution within the cavity flame-holder. The achieved finding confirms that the latter technique is more efficient for the fuel mixing in the combustor of a scramjet engine.

In this paper, a two-layer network model featuring inherent community structures is introduced to examine the interplay between awareness transmission and epidemic spreading. The model incorporates an unaware-aware-unaware (UAU) framework within the informational upper layer, shaped by community structures, and a susceptible-infected-susceptible (SIS) paradigm within the epidemic lower layer. The role of individual self-awareness is also considered, with the hypothesis that individuals spontaneously become aware of the disease when the local awareness rate in their vicinity exceeds a predetermined threshold. The epidemic threshold is delineated through the application of the microscopic Markov chain approach (MMCA). Findings indicate that the individual self-awareness mechanism impacts epidemic thresholds in a dual-phase manner, with distinct high and low threshold stages. It is observed that when the community structure remains static, fluctuations in the transmission probability of awareness — whether intra-community or inter-community — do not significantly influence the threshold or the ultimate scale of the epidemic. Analysis suggests that a denser community structure, along with increased inter-community connectivity, facilitates the spread of awareness and effectively suppresses the propagation of the epidemic.

In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved Archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the Archimedes optimization algorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm.

The Direct Simulation Monte Carlo (DSMC) algorithm is widely employed for simulating rarefied gas flows and is increasingly applied in near-continuum regimes for research and engineering purposes. However, its computational demands, notably load imbalance and extended simulation time, hinder widespread adoption. Addressing these challenges, this paper introduces the Two-Level parallel load balancing strategy. This novel approach combines thread-level and multi-process parallelism to enhance load balancing and reduce simulation time. Key features include a thread-level load-decoupling strategy implemented via OpenMP and a multi-process load balancing mechanism employing distributed memory via MPI. Building upon our previous $PartPlusColl$ [L. Li, W. Ren and B. Zhang, J. Aeronaut. Astronaut. Aviat. Ser. A 46, 88 (2014)] approach, the load balancing mechanism utilizes Stop At Risk (SAR) criteria for repartitioning with METIS. Additionally, a specialized data transmission mechanism utilizing MPI nonblocking communication minimizes global communication between processes. Validation and evaluation are performed using four hypersonic flow cases around a cylinder and sphere, demonstrating significant improvements. Notably, the proposed strategy achieves $30\%$ enhancement over the $PartPlusColl$ strategy under 512 CPU cores compared to 16 CPU cores, and reduces between-process communication time with $33.57\%$. These advancements contribute to enhancing the effectiveness of the DSMC algorithm in near-continuum aerodynamic simulations.

Asynchronous temporal Boolean Networks (ATBNs) are more complex in structure and have different linear expressions compared to traditional synchronous Boolean networks. The complexity depends on the number of updated nodes. The main contribution of this paper is to propose results of synchronization and anti-synchronization problems of ATBNs. First, this paper abstracts two Temporal Boolean Networks (TBNs) into a mathematical model, and provides a linear expression of the network by means of the semi-tensor product (STP) tool. Second, based on the asynchronous updating scheme of temporal Boolean networks, we investigate the synchronization (including complete synchronization and anti-synchronization) of ATBNs, and provide two results to guarantee the synchronization and anti-synchronization. Finally, we provide two examples to verify the correctness of our theorems and corollaries.

The aim of this paper is to investigate the ventilated cavitating flow behavior, through experimental and numerical methods. The experiments are performed for a test model placed in a water tunnel and a high-speed camera is used to image the formation and collapse of the supercavity. For the numerical simulations, Ansys CFX commercial software is employed to solve the Reynolds-averaged Navier–Stokes (RANS) equations and additional transport equation for the liquid volume fraction in the unsteady condition. Both the experimental observation and numerical prediction show a hysteresis behavior for ventilated supercavity in which the value of the air entrainment coefficient required to maintain the supercavity in the formation process is less than the amount required to form it. The good agreement observed between the numerical prediction and experimental data, revealed the accuracy and capability of the numerical scheme. Also, the numerical simulation shows that the variations in the supercavity length, in addition to the air entrainment, are a function of the air leakage regime from the closure region of supercavity.

In this work we propose a simple model for the emergence of drug dealers. For this purpose, we built a compartmental model considering four subpopulations, namely susceptibles, passive supporters, drug dealers and arrested drug dealers. The target is to study the influence of the passive supporters on the long-time prevalence of drug dealers. Passive supporters are people who are passively consenting to the drug trafficking cause. First we consider the model on a fully connected network, in such a way that we can write a rate equation for each subpopulation. Our analytical and numerical results show that the emergence of drug dealers is a consequence of the rapid increase in the number of passive supporters. Such increase is associated with a nonequilibrium active-absorbing phase transition. After that, we consider the model on a two-dimensional square lattice, in order to compare the results in the presence of a simple social network with the previous results. The Monte Carlo simulation results suggest a similar behavior in comparison with the fully connected network case, but the location of the critical point of the transition is distinct, due to the neighbors’ correlations introduced by the presence of the lattice.

This paper is dedicated to the comparison of discrete velocity models (DVM) used for the simulation of heat transfer and fluid flows in the lattice Boltzmann method (LBM). In this study, the discrete velocity models of $D_2{Q}_9$ and $D_2{Q}_8$ in which particle velocity vectors are eight are used in simulating four benchmark problems. An enhancement in the stability of the single relaxation time (SRT) model for problems is facilitated by solving the continuous Boltzmann equation, and its application in efficiently addressing conductive heat transfer/fluid flow benchmark problems is expanded by utilizing the finite difference methods paired with the lattice Boltzmann method (FDLBM). To generalize the paper’s conclusion more accurately, the LBM with two-relaxation time (TRT-LBM) is included in all comparisons. A D2Q5 scheme is also considered to understand how it would address heat transfer problems compared to the ones presented. Moreover, an improvement in the accuracy of the FDLBM for some cases is assisted through the integration of the finite differentiation mechanism with the Runge–Kutta method. A series of unsteady and steady problems were numerically solved to validate the introduced methods. The outcomes affirmed that the proposed FDLBM algorithm procures more stable and robust results compared to those derived from the DVMs of SRT-LBM devoid of filtering techniques. Notably, the FDLBM method achieves this increased accuracy without introducing additional complexity to the SRT model by resorting to nonuniform grids. The decision to exclude more complex methods stems from their requirement for refined computational grids. To ascertain the numerical stability and accuracy of the devised algorithm, a comparative analysis was performed with analytical results or other numerical methods. The simulations underscored the efficacy of the current method, showcasing commendable harmony with analytical solutions and other numerical strategies.

Network dismantling, which aims at finding the optimal node sequence whose removal will dismantle the whole network effectively, remains as a hot topic in the research area of complex networks and systems. In this study, an improved network dismantling strategy based on ant-colony optimization algorithm is proposed. Through large quantities of numerical simulations, compared with several classical network dismantling methods, the effectiveness of the proposed strategy is verified. Current results can provide a new perspective for us to comprehend the complexity and optimization of complex networked systems.

Evolutionary game theory provides a platform to investigate the emergence of cooperation in population consisting of selfish agents. In this work, we study evolutionary games on networks in which agents cooperate or defect according to Q-learning algorithms with extended state space. Extended state space provides agents two types of information, local environment information based on the cooperation level in agents’ neighborhood and personal information based on the last action of agents. Through numerical simulations, we find that rich information on local environment tends to improve cooperation in the population no matter whether personal information is present or not. Moreover, we show that, for the same local environment information, the introduction of personal information may improve cooperation except for the situations with low amount of local environment information where personal information deteriorates cooperation in bad-condition environment. For the same total information, the absence of personal information promotes cooperation in bad-condition environment while the presence of personal information promotes cooperation in good-condition environment. By investigating the distributions and temporal behaviors of Q-values, we present explanations for the above statements. This work suggests an effective way of extending the state space in evolutionary games incorporated with Q-learning algorithm to enhance cooperation.