International Journal of Modeling, Simulation, and Scientific Computing

Aiming at the navigation congestion of water conservancy projects, the shipowner’s psychological mechanism and behavior evolution are embedded into the dynamic model, which explores the evolution law using empirical data. The structural equation model (SEM) was constructed based on four variables: Shipowner personality, waiting time perception, congestion charging experience and anchorage environmental facilities (AEF). Taking the Three Gorges Project as an example, the influence relationship and degree are empirically tested. Then the dynamic evolution process was simulated by using the system dynamics (SD). The results suggest that the waiting time perception and congestion charge can weaken the shipowner’s intention, while the anchorage environment facilities can enhance the intention. The different personality characteristics will bring different effects, and the waiting time perception is the key intermediary. The psychological cost determines the dynamic evolution. Adjusting the factor can help the ship transfer between the navigable building and turning over the dam, realizing the balanced “double-channel” and alleviating navigation congestion.

We investigate the influence of critical parameters in epidemic modeling. Simulations using live scripts directly reveal the dependence of the dynamics of infected and susceptible people on e.g., infection rate, contact restrictions as well as vaccination rates. In particular, time delays reflecting the incubation time are particularly critical since they may, depending on the parameters representing a given situation, drive a system from stable to unstable behavior or vice versa. Analysis of the endemic states of an epidemic and visualization of the respective sensitivity to parameter changes may lead to a deeper understanding of the underlying interactions. In particular, the computer program may support the idea of making the complex interrelationships that occur during an epidemic accessible and understandable to a wide audience through user-friendly adjustable parameter settings and immediate updating of simulation results and figures.

New policies are commenced all over the globe to diminish the use of fossil fuels, which gives rise to the augmented utilization of solar energy (SE). The photovoltaic (PV) system’s performance is extremely environmental variables reliant. Long-range transmission of SE is incompetent as well as complex to carry in the PV system. It can be affected by disparate sorts of faults, which cause severe energy loss all through the system operation. Thus, it is vital to incessantly monitor the solar PV (SPV) system to detect as well classify the faults by preventing energy losses. The IoT applications in SE production engage sensor devices that are fixed to the generation, and transmission, together with distribution equipment. These devices assist in monitoring the operation of the SPV power plant (SPVPP) system remotely in real-time. Presenting a new algorithm that can perform fault detection and classification in a PV system to a higher level of accuracy is the major contribution of this work. Thus, this work designs as well as develops an IoT platform for carrying out analytical tasks that can analyze data generated as of IoT operating systems to detect as well as classify faults in the SPVPP. Initially, the data collected from the dataset is pre-processed in which data duplication is performed using Hadoop distributed file system (HDFS) and then the fault is detected from the pre-processed data using the cosine function based k-means clustering (CFKC) technique in the SPV system. Finally, the obtained fault data is fed into the optimized deep learning centered ENN (ODENN) method which classifies the faults. The proposed techniques detect as well as classify the faults effectively that are experimentally proved by means of comparing them with the prevailing techniques, namely ENN, ANN and SVM, along with KNN in terms of some quality measures. The obtained results for ODENN showed an accuracy of 98.99%, specificity of 97.6%, and a sensitivity of 97.02%.

In this paper, we introduce a coupled system of nonlocal fractional q-integro-differential equations. Under certain assumptions, we prove the existence and uniqueness of solutions for a coupled system of fractional q-integro-differential equations. We also study continuous dependence. We solve this system numerically using the finite–Simpson’s and cubic spline–Simpson’s methods. Finally, three examples are provided to demonstrate the efficacy of the methods employed.

In this paper, a numerical method for solving three-asset Black–Scholes partial differential equation is presented. The model is based on the Crank–Nicolson and the radial basis function methods. Also, the convergency of the proposed method is proved. Implementation of the proposed method is specially studied on cash-or-nothing option. The numerical results show the efficiency and high accuracy of the proposed method.

This paper examines the performance of a new probability model developed in terms of the surface area and velocity of an aero-structure, and presents some relevant statistical inferences. Several examples will compare the robustness of three kinds of point estimators developed for the lift coefficient. Four different types of confidence intervals for the lift coefficient are also developed, and it is shown that the shortest length confidence interval is an UMA confidence interval. Using the probability model, the correlation coefficient between lift and velocity, and the relevant regression slope are computed. The inferences and the results of this paper can be used particularly in the designing process of an aero-structure, especially when there is some uncertainty about its surface area; this provides some flexibilities for aerodynamicists in determining some of the unknown design characteristics.

Predicting engineering students’ academic performance is crucial for teachers to implement teaching interventions. How to use students’ behavior records to predict grades during school has attracted extensive attention from researchers. Various machine learning methods have been proposed by previous researchers to predict engineering students’ academic performance through their learning records. However, one of the major challenges now is that many researchers ignore the problem of reduced accuracy of prediction models due to imbalanced data. Simultaneously, a single classifier is susceptible to data changes and has an unstable classification performance. To this end, this paper proposes the RBF-Naive Bayes (RBFNB) prediction model to predict students’ academic performance, which integrates Radial Basis Function (RBF) Network algorithm and Naive Bayes algorithm. The method of RBF Network is applied to predict the academic performance of students and generate prediction results. Then, the method of Naive Bayes is adopted to correct the previous prediction results for improving the accuracy of the prediction. The proposed RBFNB prediction model was applied to the data set about the engineering students’ academic performance from a university and the Kalboard 360 public data set, respectively.

Defects are frequent in software systems, and they can cause a lot of issues for users. Despite the fact that many studies have been conducted on employing software product metrics to determine defect-prone modules, defect prediction techniques are still worth investigating. Hence, the aim of this work is to provide a unique Software Defect Prediction (SDP) approach that includes four steps like “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) detection.” At first, the input data are given to the pre-processing step, as well as in the feature extraction step; the “statistical features, raw features, higher-order statistical features as well as proposed entropy features” are extracted from the pre-processed data. In addition, the retrieved features are sent into a feature selection step, wherein the appropriate features are selected utilizing a modified chi-square scheme. In the detection step, a hybrid Long Short-Term Memory (LSTM) and Deep Belief Network (DBN) classifiers are used to predict the defects. To provide a more accurate detection, the weights of both DBN and LSTM are optimally tuned via a Self Improved Social Ski-Driver Optimization (SISSDO) algorithm. This proposed SDP model is a beneficial practice for enhancing software quality and reliability. Moreover, the results of the adopted technique are assessed to traditional techniques on the basis of various measures. In particular, the accuracy of the suggested approach for dataset 3 is 5.80%, 6.52%, 5.07%, 7.97%, 5.80%, 9.42%, 9.42%, 10.15%, 2.17%, and 3.62% better than the extant HC+ALO, HC+SMO, HC+CMBO, HC+SSD, RNN, CNN, NN, Bi-LSTM, HC+SPFCNN, and HC+CWAR approaches, correspondingly. Moreover, the computation time of the suggested approach is 17.05%, 5.78%, 1.31%, and 50.53% better than the existing HC+ALO, HC+SMO, HC+CMBO, and HC+SSD approaches, correspondingly.

For the shortcomings of the analytical calculation method and the design of few-piece main-auxiliary parabolic leaf springs, based on the theory of material mechanics, through analyzing the actual structural characteristics of the few-piece main-auxiliary parabolic leaf spring with root diagonal segment, an analytical calculation model of the deformation and stress for the few piece main-auxiliary parabolic leaf spring with root diagonal segment was established. The reliability of the analytical calculation model was verified by using the leaf spring mechanical characteristics test platform. The results show that the maximum deviation between the analytical calculation values of the deformation and stress obtained by the calculation model and the test value is only 4.76% and 6.06%, respectively, which can effectively characterize the mechanical characteristics of the actual few-piece main-auxiliary parabolic leaf spring. The research can quickly and accurately realize the stiffness check, strength check, and forward design of the few-piece main-auxiliary parabolic leaf spring with root diagonal segment, which provides an effective theoretical basis and technical reference for the parabolic leaf spring design.

With the deepening of the country’s digital transformation and the development of big data artificial intelligence software definition and other technologies, customers’ demand for IT services is constantly increasing. In the electric power industry, the front-end knowledge base for traditional customer service lacks self-learning ability, the accumulated customer service data in the middle cannot be analyzed and utilized effectively, the back-end quality inspection was not fully covered and lacked emotion recognition, etc. Apply artificial intelligence, information and communication technology and big data technology to power ICT customer service. It is proposed to construct a network model by combining conditional random airport with the long- and short-term memory neural network, and to realize the intelligent customer service model combined with human–computer interaction by constructing an intelligent knowledge base and the information system fault association analysis model, so as to improve the extraction performance of context information and establish a knowledge base question answering system in the field of power service. Through the comparative analysis of experimental data, the proposed model has achieved good results and can be effectively applied to the power industry to improve customer service satisfaction.

Cyber–physical systems (CPSs) can be treated as an emerging technology that has the ability to handle the physical process and computational view of interlinked systems. At the same time, the high-performing processing capability provides assurance of CPS applications in real time. Besides, task scheduling is considered as the Nondeterministic Polynomial (NP)-hard problem and optimal allocation of tasks is important for the CPS environment. The primary concept of the optimum energy-based scheduling approach searches for the physical host allocation vector to the allotted virtual machine with an aim of reducing energy utilization. The multiple processor packet scheduling technique defined that every task in the system is already divided into processors by the task allocating scheme and every process can execute on the distinct or identical single processor scheduling technique. With this motivation, this paper presents a new quantum invasive weed optimization-based energy-aware scheduling (QIWO-EATS) technique for the CPS environment. The goal of the QIWO-EATS technique is to assign $n$ autonomous tasks to $m$ dissimilar resources, and thereby the whole task completion duration gets reduced and resources are completely used. The proposed model has been simulated using the MATLAB tool. The experimental results highlighted the better outcomes of the QIWO-EATS technique over the recent approaches in terms of several evaluation metrics.

The occurrence of botnets over the network is crucial as it shows advent effect on various applications like finance, cyber-security, and healthcare application. Botnets are refined and more dangerous in their functionality over the network model. Most of the prevailing models and flow- and rule-based models feel challenging to predict the bot functionalities in a preventive manner. Therefore, the modeling of efficient and automated botnet detection approaches is highly essential. This research concentrates on modeling a novel botnet detection approach based on the recursively analyzing the flow of features of the network nodes spatially and temporally where the attack samples are intra-dependent time-series data. The hierarchical structural design of the network helps to integrate various levels of feature information and learns the spatial and temporal information automatically among the adjacent network connection. This process is carried out by the proposed architectural model known as Recursively Learning Long Short-Term Memory over spatial and temporal (${\mathrm{\text{RL}}}^2\text{TM-st})$. Thus, the bot activities are detected by recursively analyzing the limited number of nodes. The ${\mathrm{\text{RL}}}^2\text{TM-st})$ model is modeled to improve the efficiency of the network by eliminating unnecessary activities. The proposed model is validated using the online accessible CTU-13 dataset and benchmarked against the prevailing classification approaches for botnet detection. The simulation is done in a MATLAB environment, and the outcomes work efficiently and evaluated with prevailing models to project the significance of the ${\mathrm{\text{RL}}}^2\text{TM-st}$ model.

In recent years, web application frameworks have been widely practised by many developers to increase programming productivity as the frameworks are more flexible, rapidly built using CRUD operation, MVC-based, secure and most of them are published under an open-source license which will reduce the final cost of development. Although the CRUD automation in the web application framework boosts the development process, there are many important aspects of a web application absent from the CRUD output. Therefore, this multivocal literature review investigates the record management aspects that are required in modern WA and the perceived benefit of integrating the record management aspect into CRUD operation. The study extracted 284 publications from respectable scientific resources and the grey resources literature created by WA development practitioners outside academic mediums. After a detailed review process, only 14 scientific primary studies and 13 gray studies were considered for this review based on defined inclusion and exclusion criteria. The review shows that the most important aspects required in WA are search, role-based access control, retention, appraisal, search, audit trail, digital archiving, sharing, reporting, inactive files management and several other features. These important aspects have been analyzed and characterized according to its function and features. The method and procedure for integrating the specified aspect into CRUD operation are identified and discussed. Integrating and implementing the specified record management features into CRUD operation will boost the WA development productivity by producing more features as a standard output with integrated record management functions.

The Internet of Things (IoT) is a new technology that has drawn interest from both industry and academia in the modern digital era. The IoT provides a platform for any device to connect and share data over the Internet. Because of the diverse IoT environment and resource-constrained IoT devices, this nascent technology has numerous security challenges. Due to the lack of fine-grained access control and flexibility, traditional cryptographic algorithms such as private key encryption and public-key encryption are not suitable for providing access control mechanisms. ABE (Attribute-Based Encryption) is a new cryptographic approach that not only provides security but also fine-grained access control. This paper provides a comprehensive survey of ABE and its various forms, such as key policy ABE and ciphertext policy ABE, based on access structure, multi-authority, and hierarchy features. Further access structures used in ABE and applications of the ABE technique in IoT are also discussed. A comparison of different ABE schemes is presented regarding features, efficiency, and security. In addition, numerous schemes are analyzed to uncover obstacles, issues, and gaps that need to be investigated further.