International Journal of Modeling, Simulation, and Scientific Computing

With the burgeoning prevalence of edge computing, there has been a significant surge in the utilization of content caching across the digital landscape, significantly streamlining and expediting short-term electronic forensic operations. This study pioneers an innovative caching model tailored specifically for short-lived electronic forensic evidence within edge computing environments. The novel framework integrates blockchain technology to ensure the secure storage of evidentiary data, bolstered by an advanced consensus mechanism rooted in the MerkleTree architecture. This design safeguards the consistency and immutability of stored information. By decentralizing the process, this approach empowers real-time, on-the-edge electronic forensic investigations, effectively mitigating any risk of tampering from all parties involved, such as forensic examiners, cloud service providers, and end-users. Empirical findings robustly substantiate the efficacy of this proposed model in safeguarding and maintaining the fidelity of electronic evidence records, thus guaranteeing both the integrity and promptness of forensic materials. This groundbreaking solution sets a new standard for trustworthy and expedient handling of digital forensics at the edge of networks.

Industrial Internet of Things (IIoT) advances the fault diagnosis of multi-tier edge computing systems into the realm of big data. However, a significant challenge arises in terms of privacy leakage due to the necessity for nodes in edge systems to share local private data. Additionally, a centralized architecture is susceptible to causing single-point failures in edge systems. Moreover, the fault data from nodes in IIoT edge systems are often nonindependent and nonidentically distributed (nonIID), posing difficulties for convergence. Furthermore, there is a lack of corresponding defenses to prevent malicious node fault attacks. To tackle these challenges, this work introduces a fault diagnosis framework and method for multi-tier edge computing systems based on blockchain smart contracts. The framework primarily adopts a decentralized model to ensure the privacy and security of fault data in multi-tier edge systems. Within this framework, a feature comparison loss function is designed to address the nonIID nature of faults. Additionally, a Byzantine fault-tolerant method is developed to prevent fault threats. Furthermore, we design an incentive method based on reputation to assess the rewards that nodes should receive. Experiments demonstrate that the proposed method achieves robust overall performance without compromising the privacy of local data.

With the rapid development of Internet of Things (IoT) and edge computing, a lot of edge devices are connected into the network, including the cameras that are used and applied in many scenarios, for example, the indoor environment. Now, in order to solve the problem of fast location of object indoors with low cost, the monocular vision based object location method is proposed in this paper. First of all, the pixel coordinates of the objects are obtained by target matching, and then the area of the object on the captured image is calculated for rough matching. Finally, the precise coordinates of the objects to be located are obtained through their center values and recognized by the fine matching. Experimental results indicate that the proposed method has better performance.

Sports exercise is very important for both physical and mental health, but improper exercise or equipment use may cause some security challenges. To address this issue, facing Internet of Things (IoT), currently, some video recognition algorithm-based smart security monitoring systems have been designed. However, existing video recognition algorithms usually assume that the data comes from the same collection device or follows the same distribution, resulting in ineffective handling of cross-camera or cross-device recognition problems. In this vein, this paper designed a transfer learning-based smart exercise monitoring security protection system, and proposed a new transfer learning-based video recognition framework, which consists of backbone network module, style transfer module, video feature aggregation module three parts and using this framework, two different models can be trained based on video face recognition dataset and video action recognition dataset, respectively, for identity recognition and action recognition. Experimental results show that the proposed framework can effectively handle video face recognition and video action recognition problems, which also demonstrates that our designed smart exercise monitoring security protection system can meet actual task requirements.

Static Chinese Sign Language Recognition (SCSLR) is an important field of research in human–computer interaction and assistive technology. Traditional SCSLR methods usually rely on computer vison sensors, which are susceptible to effects such as hand shapes, lighting conditions, and occlusions, resulting in low recognition accuracy. Additionally, sensor-based SCSLR methods cannot achieve high recognition accuracy due to limited hand gesture information. In this paper, we propose a multi-sensor fusion method, using a DE–XGBoost model, to fuse the information of hand gesture and finger curvature to achieve the SCSLR, which can overcome the recognition error problems caused by insufficient sign language information. In addition, we design and implement a prototype system, which consists of a smartphone and a smart glove, to evaluate our proposed method in comparison with support vector machine (SVM), XGBoost, gcForest, and artificial neural network (ANN). Experimental results show that our proposed method achieves a better performance in terms of accuracy, robustness, and real-time processing.

Hyperspectral image is gradually becoming the main medium in satellite observations. It provides rich spectral information of different channels to help explore various gas states. Its classification problem is among the most significant tasks on the hyperspectral image processing and application. But the classification often faces the difficulty of imbalanced data and differentiated intra-class/inter-class difference. A lot of deep learning methods have attempted to solve this problem by designing different kinds of networks, but they suffer from the query of whether the network is optimal. This paper provides an automatically framework to deal with the above problem. First of all, we introduced siamese framework to reduce the impact of imbalanced data and various intra-class/inter-class difference. Then, we designed a multi-branch search space with several convolutional operators, especially a gradient-based updating rules to search the architecture. After using ”time $+$ performance” criterion, we obtain the model on two public hyperspectral image dataset. The experimental results outperformed all other compared algorithms.

Nowadays, verification and validation activities have become the prominent parameters to check the acceptability of a simulation model by the users for the intended purpose. This review paper aims to identify and analyze the recent development and challenges of simulation model verification and validation. To achieve this objective, a systematic review of the literature was carried out which mainly consisted of the methodological development of verification and validation process paralleled with simulation model development. Initially, a cumulative total of 980 records was found sourced from Google Scholar via an advanced search method, Science Direct, Web of science and from Scopus. Through a rigorous screening process about 72 sources or publications were included for analysis. The verification and validation techniques that have been developed so far were classified into five categories methodologically. From intensive analysis, it is found that researchers extensively scrutinize the traditional methods and graphical/ statistical tools, escalating interest in data-driven and automation techniques, and limited focus on agent-based and hybrid models. Though agent-based and hybrid models are increasingly vital in the realm of complex system simulations, their verification and validation processes remain relatively under-explored. Though reasonable efforts have been exerted on the verification and validation methods development, still academicians and researchers agreed on the lack of verification and validation methodology for the recently developed simulation model paradigms such as agent-based and hybrid models, autonomous robotics models, high fidelity and data-driven models, and real-time prediction models such as digital twins. As a challenge of verification and validation processes, lack of universal methodologies, lack of reliable real-world data for validation, inaccuracy of real-world data for the intended purpose, different world views by different individuals, and the rapid growth and complexity of simulation modeling are identified as the hindering factors of verification and validation process.

In [Liu L., Chen S., Feng L., Wang J., Zhang S., Chen Y., Si X., Zheng L., Analysis of the anomalous diffusion in comb structure with absorbing boundary conditions, J. Comput. Phys. 490:112315, 2023], an anomalous diffusion model in comb structure is simulated by finite difference method, which exhibits a low convergence rate in time compared to space. It inspires us to enhance temporal convergence accuracy for this interesting problem. In this paper, we first consider the finite difference method of normal diffusion equation in comb structure which can be viewed as a natural generalization of the anomalous diffusion case. Then we use finite difference method to solve the above anomalous diffusion problem. Unlike the existed work which employs L1 method to discrete the fractional derivative of the considered equation, the Crank–Nicolson method is used for integer-order problem and L2-1$\sigma$ method is used for fractional problem here. For these two proposed numerical schemes, their stability and convergence are discussed, respectively. For the fractional problem, the present convergence result shows that the accuracy in time has been obviously improved compared with the existed work. Finally, the obtained theoretical results are verified by some numerical tests.

In cloud computing, load balancing is crucial for effective resource management. Keeping servers from getting overworked entails dividing up incoming network traffic or computational tasks among several servers. Better resource management, increased throughput, and quicker reaction times result from this. Several heuristic and metaheuristic techniques have been used to disperse the load across the available virtual machines. Researchers have worked very hard to find a solution for the load balancing issue. This paper uses the following stages to develop a novel load-balancing model with optimization assistance: Virtual Machine (VM) classification, load balancing, and replica management are the three main processes. For the VM classification process, a modified version of the fuzzy clustering approach is suggested. For the load balancing procedure, the COOT Insisted Bald Eagle Search (COOTIBES) model is suggested. This optimization-assisted load balancing takes into account a number of limitations, including frequency, makespan, memory usage, resource utilization, and execution time. Additionally, the suggested COOTIBES algorithm manages replicas while taking load, put cost, and storage cost into account. Lastly, using various performance indicators, the suggested work’s performance is contrasted with traditional models. While the Inquisitive Genetic Algorithm with Grey Wolf Optimization Algorithm (IG-GWO), Ant Colony Optimization for Continuous Domains (ACOR), Life Choice-Based Optimizer (LCBO), Cat Swarm Optimization (CSO), Genetic Algorithm Combined with First Come First Serve + Genetic Algorithm Combined with Round Robin (GA-FCFS$+$GA-RR), Jellyfish Optimization (JFO), and Namib Beetle Optimization (NBO) offer bigger makespan values, the COOTIBES scheme gives a smaller makespan of 180 for a task count of 2000.

The traditional fault diagnosis approach for cranes often results in unclear control status, protracted fault location times, prolonged equipment downtime, and other related challenges. To address these issues, this paper proposes embedding the stacked denoising auto-encoder, multi-class support vector machine, and fuzzy Bayesian network (SDAE-MCSVM-FBN (SMF)) hidden cascade fault diagnosis method into a digital twin model of the crane. The approach utilizes the digital twin to monitor the crane’s state and identify virtual environment faults to make decisions to reduce the direct operation of complex equipment and enhance operational efficiency. This paper first constructs a digital twin model of the crane to describe its composition and interaction behavior within the system. Next, it solves the hidden cascade faults in cranes using the SMF method. Finally, this approach develops a prototype system for a digital twin of cranes with an example in a large steel mill used for verification and analysis purposes. Experimental results demonstrate that compared with traditional manual inspection methods, this paper’s proposed hidden cascade fault method reduces repair time by 24.5%–32.8% while decreasing downtime by 20.5%–32.4%. The outcome highlights the effectiveness of our diagnostic approach and demonstrates digital twin technology can minimize frequent maintenance requirements by personnel while protecting crane service life overall.

Artificial electric field algorithm (AEFA) inspired by Coulomb’s law of electrostatic force is a swarm intelligent optimization algorithm. It utilizes the electrostatic force to enable information transmission and evolution of individuals. The traditional AEFA has achieved some successful applications, but it still has problems with premature convergence and low search ability. To solve these problems, an AEFA with a variable spiral search strategy and an optimal solution mutation strategy (VOAEFA) is proposed. The variable spiral search strategy which includes spiral search and skip search can improve the global exploration capability and reduce the probability of the algorithm falling into local optima. The optimal solution mutation strategy can increase the diversity of population. To evaluate the performance of VOAEFA, it is compared with AEFA algorithms and other algorithms on 57 benchmark functions and three engineering design problems. The comparison results on benchmark functions indicate that VOAEFA is superior to its competitors. In addition, VOAEFA has the advantage of high accuracy in solving challenging problems.

Forecasting an election outcome is a challenging exercise because of the constantly varying and numerous political, social, and demographic factors. Unlike electoral data, no work explores the local structure in large-scale data since traditional approaches fail to capture complex patterns involving large sets of data, especially from diverse areas such as India. This research aims to present an improved, large-scale scale, and efficient prediction model for Indian General Elections through a Multiscale Dense Nested Parallel MobileNetV3 Mantis Search Attention Network, 3MDNPV3-SAN. The model is developed to overcome the shortcomings common to other techniques and incorporates several approaches. For data preprocessing, the novel Anisotropic Gaussian Filtering with Directionally Truncated First Derivative (AG2F2DT) is proposed to perform smoothing and eliminate noise while preserving significant directional information. To achieve the right feature selection for optimal modeling results, the proposed Group Teaching Optimization Algorithm (GTOA) is used, so as to retain and only use significant features in modeling. The main structure of the prediction process is based on two models: the 3MDNPV3-SAN model based on multiscale dense nests, the MobileNetV3 parallel framework, and a dynamic Mantis Search Attention Network for data region emphasis. The proposed model proves itself with 99.1% accuracy, 99.3% precision, 99.7% recall, 99.6% sensitivity, 0.1% error rate, 4% computation complexity, and 0.01% computational cost while outcompeting more conventional approaches due to the incorporation of multiscale interactions as well as dynamic attention. The work hence provides a scalable robust efficient model that can be used to analyze electoral phenomena and to support decisions and design in the social sciences. The methodology is in Python which means that it is practically applicable for large databases.

We present a comprehensive SEIRDI${}_{\mathrm{\text{CU}}}$ model incorporating six compartments to examine the progression of COVID-19 and evaluate control strategies. Our findings reveal that implementing partial restrictions with varying levels of stringency delays the infection peak by approximately 160 days, alleviating pressure on healthcare systems. Under a herd immunity approach with an 85% immunity threshold, at least 425 days (14 months) are required for a gradual return to normalcy. Accelerated vaccination significantly shortens this timeline, emphasizing the critical role of immunization in societal reopening. Additionally, we highlight the importance of rigorously assessing post-vaccine side effects to uphold ethical and scientific standards. These insights provide actionable guidance for policymakers to balance public health interventions with economic recovery.

The validity of the continuous susceptible-infected-recovered (SIR) is questioned. It is pointed out that the basic assumption used in the model equations is wrong. The classic SIR and susceptible-infected-recovered-susceptible (SIRS) models have the form of the state ordinary differential equation (ODE). These equations do not reflect the spatial distribution of individuals. Moreover, what is more important, the SIR model equations do not reflect the huge collective memory of the modeled system. Such issues as the probability distribution of the immunity period and other time intervals are not taken into account in the ODE SIR model. It is pointed out that the model results depend strongly on these distributions. A comparison to the agent-based model (ABM) and corresponding computer simulations is done. In the paper, the validity of SIR epidemics model is questioned, the deficiency and the basic assumptions of the SIR model are pointed out. This makes the existence of the corresponding differential equations doubtful.

Composites with various layings of unidirectional plies in the conditions of a plane stress state are considered. Using the criteria of maximum stresses and Tsai–Wu, their tensile and shear strength is investigated. The dependences of the failure stresses on the direction of loading in polar coordinates are constructed. For comparison, the results of calculations by the finite element method using the ANSYS Mechanical APDL 2020 R2 program are presented.