A landfill is a site to dispose of materials that threaten the environment, such as leachate, poisons, or greenhouse gases. Leachate is a liquid that can contribute to water pollution if there is a leakage of flow into groundwater sources from landfill waste or other outside water sources. Leachate is a liquid accumulated from landfill waste and other outside water sources. To prevent this leachate from mixing with water resources when it flows into the soil, the behavior of Kenaf (Hibiscus Cannabinus) as a landfill liner system is investigated and understood thoroughly on how permeability properties can increase with the soil. In addition, the herbaceous plants known as Kenaf have beneficial mechanical properties, and they do not pose any threat to the health of human beings. Kenaf has significant potential for commercial usage because of the ease with which it can be cultivated and the low prices at which it can be harvested. In addition, Kenaf can contribute to infrastructure and innovation for economic growth and development (SDG 9.0), where it can reduce the usage of clay linear and simultaneously, increase the Permeability of the soil properties to resist the flow of leaches into the soil. This admixture is gaining popularity as a result of the fact that it has a relatively low cost and is simple to apply. In this investigation, Kenaf was used because of its reputation as a good absorbent product that can hold up to six times its weight and has a leaching capability of less than one percent. The results of laboratory work are presented to demonstrate that this material is suitable for usage in landfills to solve the leachate problem.
The start of the COVID-19 pandemic in early 2020 has caused trouble all over the world. This contagious disease is mostly spread by people, and it spreads much faster than other flu viruses that have been found before. Although vaccines have been discovered and are functional, it will still be the greatest challenge to conquer this disease. To effectively respond to this unprecedented crisis and save human lives from other infectious diseases in the future, it is crucial to better understand how the virus is transmitted from one host to another and how future zones of contagion can be anticipated. Several waves of infection have hit nations worldwide for almost the last four years, and governments have implemented necessary measures to tackle the spread of the virus. However, mathematical modeling has emerged as a powerful tool to inform decision-making, allowing for the prediction of COVID-19’s effects. In this research article, we investigate the impact of COVID-19 in Saudi Arabia using the three most commonly used mathematical models: the classic SIR (Susceptible-Infected-Recovered) model, the extended SEIR (Susceptible-Exposed-Infected-Recovered) model, and the advanced fractional-order models using freely available real recorded data for research. By incorporating actual data from Saudi Arabia and utilizing three simulation techniques, we strive to provide valuable insights into the dynamics of the pandemic and aid in the formulation of effective strategies to control its spread in Saudi Arabia.
Sustainability considerations play a crucial role in informing the modeling and fitting of medical and engineering data, ensuring the development of robust and environmentally conscious solutions. This paper delves into the investigation of a novel continuous distribution, aiming to provide a thorough understanding of its various fundamental mathematical and statistical properties. The analysis encompasses an exploration of survival functions, hazard rate functions, quantile, skewness, kurtosis, moments, mean time to failure, mean time to repair, insurance pricing principles, availability, and mean residual (past) lifetime functions. The proposed model demonstrates versatility in modeling both asymmetric and symmetric data across various kurtosis shapes. It can effectively handle outlier observations and accommodate different shapes of failure rates, including unimodal, bathtub, increasing, or decreasing patterns. This makes the proposed model suitable for modeling data in diverse fields. The maximum likelihood approach is employed to estimate model parameters using complete and upper recorded values. A simulation study is conducted to evaluate the performance of the estimators under different sample sizes for both complete and upper recorded values. To further demonstrate the flexibility and effectiveness of the new model, two datasets from medical and engineering domains are utilized for validation and testing purposes.
Developing countries' industrial growth necessitates innovative machine foundation design and resilient infrastructure. Meanwhile, soil properties play a significant role in the satisfactory design of machine foundations. The paper presents an experimental work to determine accurately the damping ratio and shear modulus by equations. The paper studies the effect of different parameters (embedded depth of foundation, shape of footing, and changing operating frequency of machine) on the damping ratio and shear modulus of saturated clay soil. Moreover, mathematical expressions are derived from statistical analysis to predict the damping ratio for rectangular and circular footing on saturated clay. The physical model results reveal that the damping ratio inside the soil will be increased with an increase in the depth of the point inside the soil for all cases. The damping ratio tends to decrease with an increase in operating frequency. The maximum damping ratio has occurred below a rectangular foundation with a 16.6 Hz operating frequency in saturated clay. The damping ratio is a function of many factors, such as embedment depth, degree of saturation, shape of foundation, and operation frequency of the machine.
We propose the definition of quasi-n-normed spaces and prove some new results on fixed points theory related to weak contractions in this framework. We prove the existence and uniqueness of fixed point for (φ,ψ)-generalized weak contractions and (φ,ψ)-generalized weak C-contractions in quasi n-normed spaces. The obtained results extend some known theorems for nonlinear contractive functions on quasi n-normed spaces. In addition, we demonstrate an application of obtained results to Integral Equation.
The COVID-19 pandemic has radically altered the worldwide learning environments, setting the stage for Electronic Learning (E-Learning) advancement, where remote learning is facilitated through digital tools. The key stakeholders (professors, teaching assistants, and students) face bottlenecks as they shift to online education providing blended learning. New learning tools based on Natural Language Processing (NLP) are provided. Designing a chatbot is one of the solutions to deal with this issue. Chatbots are simple computer programs that attempt to simulate human conversation using Artificial Intelligence (AI) and NLP. They allow learners to have a standardized learning environment. In this paper, we have set up a chatbot application named Scientific Data Management BOT (SDMBOT) for handling E-Learning activities specifically for Scientific Data Management (SDM) courses based on AI and NLP techniques using Dialogflow Framework, a Google development platform for building NLP-based human-computer interface solutions. SDMBOT was trained on a dataset that was specifically created based on course content. The web or mobile app, via which our built chatbot is available, is used for student interaction. Students can ask the chatbot questions concerning SDM, and the chatbot will process the message and respond to the user by displaying the proper result. The accuracy of the SDM chatbot, which is calculated by using a confusion matrix indicated that our chatbot is 74 % accurate.
This paper aims to develop a fuzzy system to control the level and temperature of a fluid in a mixing tank by dynamically adjusting the flow rates of cold and hot liquids. The system involves a temperature and level loop, with the control variables being the rates of cold water and hot water flow, while also considering the outlet flow from the reservoir as a disruptive factor. The main goal of this control strategy is to maintain the liquid level and temperature within specified reference values. The system is a multivariable process that uses a fuzzy control system to develop a technique that, based on the state variables, can adapt the controller to achieve optimal control performance. In the development of this application, some linear controllers were tested, each implemented around specific regions within the phase plane. The designed controllers are then employed in conjunction with the fuzzy system to evaluate the system’s state variables. Therefore, an interpolation of these linear controllers is conducted, allowing for their application in a nonlinear system, with the aim of enhancing control performance near the boundaries between regions. The results show that the fuzzy approach is promising and provides convergence to the reference values of the liquid level and temperature in the mixing tank.
This paper studied the perception of European option which is geared towards valuation of financial assets at a prescribed time in the future. In particular, the analytic formula of Black-Scholes model was considered for share prices of Fidelity and Access banks which gave closed form prices of call options. The explicit price on the variations of maturity days is found accordingly. The closed form prices of both banks were compared. The simulation results show that: the share price determines the value of call option prices. An increase on the maturity days dominantly increases the value of call option for both Fidelity Bank, Access Bank and their future merger Bank. Merging of the two banks improved the value of call option prices. Fidelity bank has a good maximum value of call option prices during the period of investments, Kolmogorov ?Smirnov (KS) test shows that the two call option prices (Fidelity and Access) do not come from a common distribution, the normality test of both banks are not significant. The results presents to the Banks management, a basis for taking vital decisions, depending on the levels of their investments.
Inthisstudy,weareinterestedininvestigatingtheoscillatorybehaviorofsolutionstoageneralclassoffunctionaldifferential equations. We consider a neutral-type equation with multiple delays. We first test some monotonic properties of positive solutions to the studied equation. Then, we use some techniques to obtain criteria that guarantee the oscillation of all solutions. We obtain three different forms of oscillation criteria and compare them in terms of efficiency by applying them to a special case of the studied equation. The results obtained are an extension and generalization of previous results in the literature.
This paper addresses the generalized Euler polynomial matrix E (α) (x) and the Euler matrix E .Taking into account some properties of Euler polynomials and numbers, we deduce product formulae for E (α) (x) and define the inverse matrix of E .We establish some explicit expressions for the Euler polynomial matrix E (x), which involves the generalized Pascal, Fibonacci and Lucas matrices, respectively.From these formulae, we get some new interesting identities involving Fibonacci and Lucas numbers.Also, we provide some factorizations of the Euler polynomial matrix in terms of Stirling matrices, as well as a connection between the shifted Euler matrices and Vandermonde matrices.
The particle that moving on a circular path under a certain constraint is studied using Lagrangian mechanics (Euler Lagrange equation). The action function is obtained by integrating the Lagrangian through time interval ; from this function we can calculate the wave function , the behavior for the action function and the wave function is described through illustrative graphs.
: In this paper, we proposed an optimal control of the COVID-19 transmission dynamics. First, we investigated system features such as solution boundedness, positivity, disease-free and endemic equilibrium, and the local and global stability of equilibrium points. Besides, a disease-free equilibrium point is globally asymptotically stable if the basic reproduction number is less than one, and an endemic equilibrium point exists otherwise. Secondly, we have shown the sensitivity analysis of the basic reproduction number. Also the model is then fitted using COVID-19 infected reported in Ethiopia from February 1,2023 to March 2,2023. The values of model parameters are then estimated from the data reported using the least square method together with the the MATLAB software. Moreover, the optimal corruption minimization strategies are determined using three controls strategies, namely prevention, vaccination and treatment. The existence of the optimal controls and characterization is established using Pontryagin’s Maximum Principle. Finally, based on analysis of optimality system, the combination of the prevention and treatment of infected is the most optimal and least cost strategy to minimize the burden of the disease.
In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating the clique structures. The results obtained from well known data sets confirm the compatibility of clique clustering algorithm with dimensionality reduction.
In this paper, we propose new polynomial discrete logistic equations based on the classical logistic map, which exhibit chaotic behavior as control parameters vary. We also explore versions with fractional derivatives. Using the chaotic sequence generated by these equations, we develop an encryption scheme for text. The scheme relies on initial conditions, control parameters, and a transformation of text characters into values between 0 and 1, followed by a transformation to discrete chaotic values for transmission
In this paper, a parallel-series system is improved. All components assume independent and identically distributed. The Lindley distribution with three parameters is assumed to be a lifetime distribution for the components. Four methods are used to improve the performance of the parallel-series system. The γ-fractiles and equivalence factors are derived. Finally, numerical results are discussed.
This study examines how innovation (INN) influences the relationship between supply chain management and information technology in Jordan. 211 employees of Jordanian industrial enterprises who work in the Operations Department provided information for the study, which examines this subject. The findings indicate a close connection between information technology and supply chain management. Innovation also dramatically modifies the interaction between supply chain management and information technology. Management help may be the subject of future research.
Purpose: The authors observe the effect of exploring the reality of Intellectual Capital (IC) and its impact on the financial performance of Jordanian industrial firms in Amman Stock Exchange. This empirical research explores the effect of intellectual capital on financial performance using data from 36 Jordanian industrial firms listed in Amman Stock Exchange for the period 2016-2020. The Value-Added Intellectual coefficient (VAIC) was adopted to measure the intellectual capital, while the return on assets (ROA), return on equity (ROE), and earnings per share (EPS) were adopted as measures of the companys financial performance. The effect of IC was tested by using statistical analysis, dependent on the data obtained from annual financial statements. The results showed that the IC has a significant and positive effect on profitability due to its significant effect on ROA and EPS. However, it has not been proven that IC affects the ROE. This research extends the research on IC and aims to enrich studies in this field, especially in the Jordanian market. It reflects the reality of Intellectual Capital and its impact on industrial firms’ performance in Jordan as an example of developing countries
The fluctuating and disorganized state of todays global markets is the result of several factors. COVID-19 is an illustration. Supply chain managers should re-evaluate their competitive strategy and leverage big data analytics in light of the rising volatility in demand and supply, rivalry among supply chain partners, and the requirement to deliver tailored goods and services (BDA). Supply chain firms require sophisticated BDA processes and procedures to provide useful insights from big data to better decision-making and supply chain operations, as many leaders in the sector have acknowledged the necessity for improving with data" (SCO). This research gives theoretical justification for the influence that BDA has on SCO.
This study analyzes residential attractiveness in small Moroccan cities using statistical models. Net migration rates are commonly used to assess attractiveness. The study estimated net migration rates for each city and employed a structural econometric model with logistic regression to identify influential variables that affect the net migration rate. These variables were then used in a predictive model with an artificial neural network algorithm. The logistic model revealed insights, highlighting the complexity of residential attractiveness influenced by factors like job supply, accessibility, and housing conditions. The artificial neural network model provided accurate predictions (over 80%), aiding policymakers in decision-making and prospective analyses.
This paper proposes a novel hybrid framework for ECG signal classification and privacy preservation. The framework includes two phases: the first phase uses LSTM+CNN with attention gate for ECG classification, while the second phase utilizes adaptive least signal bit with neutrosophic for hiding important data during transmission. The proposed framework converts data into three sets of degrees (true, false, and intermediate) using neutrosophic and passes them to an embedding layer. In the sender part, the framework hides important data in ECG signal as true and false degrees, using the intermediate set as a shared dynamic key between sender and receiver. The receiver can reconstruct the important data using the shared dynamic key or the intermediate set. The proposed framework is more robust against attacks compared to other methods.
