In this paper, we present an investigation into the stability of equilibrium points and synchronization within a finite time frame for fractional-order Lengyel–Epstein reaction-diffusion systems. Initially, we utilize Lyapunov theory and multiple criteria to examine the finite-time stability of equilibrium points. Following this analysis, we design efficient, interdependent linear controllers. By applying a Lyapunov function, we define new adequate conditions to ensure finite-time synchronization within a specified time interval. Finally, we provide two illustrative examples to demonstrate the effectiveness and practicality of our proposed method and validate the theoretical outcomes.

Cybercriminals continuously devise new and more sophisticated ways to attack their targets’ security and cyberattacks are on the rise. One of the earliest and most vulnerable network services is the Domain Name System (DNS), which has had several security issues that have been repeatedly exploited over time. Building a strong Intrusion Detection System (IDS) that guards against unwanted access to network resources is essential to identify DNS attacks in the network and safeguard data. Recently, a number of interesting approaches have been developed as a cure-all for intrusion detection, but constructing a safe DNS system remains difficult because attackers frequently alter their tactics to move around the system’s security measures. In this study, we provide a self-learning model that detects the new attacks on DNS using machine learning classifiers. Support Vector Machine (SVM), K-nearest neighbor, Naive Bayes, and Decision Tree are used in the proposed model to classify data as intrusive or normal. The UNSW_NB15 dataset is used to assess the model performance. Data are pre-processed to eliminate irrelevant attributes from the dataset given that the dimensions of the data affect the success of an IDS. Empirical findings show that SVM and Decision Tree have the best performance for all the classifiers, with an accuracy rate of 99.99%. The performance of Naive Bayes is 99.89% for all attack types, which is the lowest of all the classifiers.

Assessing the void fraction in diverse multiphase flows across industries, including petrochemical, oil, and chemical sectors, is crucial. There are multiple techniques available for this objective. The capacitive sensor has gained significant popularity among these methods and has been extensively utilized. Fluid properties have a substantial impact on the performance of capacitance sensors. Factors such as density, pressure, and temperature can introduce significant errors in void fraction measurements. One approach to address this issue is a meticulous and laborious routine calibration process. In the current study, an artificial neural network (ANN) was developed to accurately Assess the proportion of gas in a biphasic fluid motion, irrespective of variations in the fluid phase form or variations, eliminating the need for frequent recalibration. To achieve this objective, novel combined capacitance-based sensors were specifically designed. The sensors were simulated by employing the COMSOL Multiphysics application. The simulation encompassed five distinct liquids: oil, diesel fuel, gasoline, crude oil, and water. The input for training a multilayer perceptron network (MLP) came from data gathered through COMSOL Multiphysics, simulations for estimating the Percentage of gas content of an annular two-phase fluid with a specific liquid form. The MATLAB software was utilized to construct and model the proposed neural network. The utilization of the novel and precise apparatus for measuring the intended MLP model demonstrated the ability to prognosticate the volume percentage with a mean absolute error (MAE) of 0.004.

This study aims to measure the influence of university incubators on promoting the culture of entrepreneurship and to what extent these incubators guide and motivate students, teaching staff, and university employees to achieve entrepreneurial achievements in various disciplines and fields. The questionnaire was used as a tool to collect the required data. The study population consists of students, teaching staff, administrators, and employees from almost all Jordanian universities, which are estimated to be more than tens of thousands. We distributed the questionnaire among 14 universities, and only 310 responses were collected. Based on the results of the questionnaire that were statistically analyzed, presented, and processed we believe that business incubators are like any existing project. Universities must pay attention to all matters that may lead to their success, including attention to all administrative, financial, and marketing matters, in addition to paying attention to all work they carry out, including the mechanism of helping small projects. It is necessary to introduce an entrepreneurship course within the compulsory requirements at the university level and for first or second-year students, because of its importance in spreading the culture of entrepreneurship among students. We hope that our recommendations will change the horizons of the university community and intellectual level and convert graduate students from “job seekers” into “job creators” which strongly contribute to solving unemployment and poverty problems.

The demand for the telecommunication services, such as IP telephony, has increased dramatically during the COVID-19 pandemic lockdown. IP telephony should be enhanced to provide the expected quality. One of the issues that should be investigated in IP telephony is bandwidth utilization. IP telephony produces very small speech samples attached to a large packet header. The header of the IP telephony consumes a considerable share of the bandwidth allotted to the IP telephony. This wastes the network's bandwidth and influences the IP telephony quality. This paper proposes a mechanism (called Smallerize) that reduces the bandwidth consumed by both the speech sample and the header. This is achieved by assembling numerous IP telephony packets in one header and use the header's fields to carry the speech sample. Several metrics have been used to measure the achievement Smallerize mechanism. The number of calls has been increased by 245.1% compared to the typical mechanism. The bandwidth saving has also reached 68% with the G.28 codec. Therefore, Smallerize is a possible mechanism to enhance bandwidth utilization of the IP telephony.

Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency.