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Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

In this paper, by using subsuper solutions method, we study the existence of weak positive solutions for a new class of p,q Laplacian nonlinear elliptic system in bounded domains, when ax, bx,αx, and βx are sign-changing functions that maybe negative near the boundary, without assuming sign conditions on f0,g0,h0, and γ0.

The current paper introduces a realistic solution for energy demand in Makadi Bay, Red-Sea, Hurgada, Egypt using energy system crossbred of Renewable Wind Energy System (WES) and Photovoltaic System (PVS) in the presence of Battery Energy Storage (BES). A real measurement for wind speed was recorded through a year of 2017. Also, the sun irradiance and temperature were recorded through the same period, to be considered for the output power calculations from the proposed crossbred renewable energy system. The demand load data for the city was recoded as well as through the same period for evaluating the feasibility of the system if it can cover the city loads. Linear TORSCHE optimization technique has utilized to reach an optimum solution of the proposed crossbred renewable energy system. Individual configuration of PVS & WES in presence of BES have been studied and compared with the hybrid PV/WT. Furthermore, economic analysis has presented to prove the best economical system. The obtained results show that installing such hybrid system consists of WES, PVS and BES is cheaper than installing each one individually.

Hybrid optimization techniques have been extensively utilized for solving optimal power flow problems in distribution systems integrated with or without renewable energy systems, with load uncertainty. Particle swarm optimization (PSO) is integrated with Gray wolf optimizer (GWO) to create a hybrid algorithm, HPSOGWO. HPSOGWO is implemented to augment the optimal power flow solutions of IEEE-30 bus and IEEE-62 bus systems. Five objective functions are considered to investigate the power quality of the hybrid algorithm. The proposed algorithm strength is justified by a comparative study with each individual algorithm. The suggested algorithms provide different accuracy results in small and large scale distributed systems, which indicates their drawbacks in certain systems. The system is solved using MATLAB.

This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.

connected. Online machine learning is developing day by day. Many systems have been completely modified thanks to this development to help achieve more correct results. The Internet of Things allows us to develop a system that is able to operate without human intervention. In other words, the Internet of Things is a technology that has the ability to transmit data on the Internet without human-computer cooperation. The Internet of Things is used in different ways in most regions. This study aims to develop an automated attendance management system where attendance is recorded via mobile devices and the student's presence is limited to a specific place based on a specific network. Recent advances in wireless technologies have led to the development and growth of smart systems in everyday life. Nowadays, the Wi-Fi localization mechanism can cover a specific area so that any user connected to the Wi-Fi station can be recognized. We developed the design and implementation of class attendance monitoring system through Wi-Fi signal. The developed software system will be able to store, retrieve and deliver student information, such as attendance or absence, through a mobile device with server knowledge.

A vehicular ad-hoc network (VANET) was employed in commercial, road-safety, and entertainment applications due to its accessibility. Applications and services were shared with the service providers (SP) over mobile nodes to any destination without special infrastructure. The mobility pattern of the nodes was independent, and the acceleration remained unpredictable, which led to service failures and information drop-outs. Resuming communication requires the flooding of additional control messages, which exploits the network resource in a shorter period. This paper introduces the neighbor predictive adaptive handoff (NPAH) algorithm for ensuring seamless communication, regardless of the application service time. NPAH discovers weak communication links in the service, which persist through the least resource dependent distance-based neighbor discovery. The selected neighbors are characterized by distance and minimum cost exploitation using the Q-learning technique. The process of learning decides the handoff of a vehicle based on storage utilization and cost factors. The results demonstrated the effectiveness of the NPAH algorithm by achieving less packet loss, shorter outage times, and improving the delivery factor.

The development of emotional intelligence robotics in the learning environment plays valuable support for social interaction among students. Emotional intelligence robots should be scalable to recognize emotions, appear empathetic in learning situations, and enrich the confidence with students for active interaction. This paper presents some related issues about integrating emotional intelligence robotics in E-learning such as its role and outcomes to motivate interaction during education and discover the main aspects of the emotional intelligence between humans and robots. This paper aims to determine the design requirements of emotional robots. Besides, this paper proposed a framework of educational Robotics with Emotional Intelligence in Learning(EREIL). EREIL consists of three main units; student emotions discovery, student emotions representation, and EREIL-Student Communication (RSC). In addition, it introduces a perception of EREIL working. In the future, this paper tries to merge more sensor devices and machine learning algorithms to integrate face analysis with speech recognition. Besides, it can add a persuasion unit in the EREIL robot to convince students with better learning choices to their abilities.

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

Studies of modified Korteweg-de Vries-type equations are of considerable mathematical interest due to the importance of their applications in various branches of mechanics and physics. In this article, using trilinear estimate in Bourgain spaces, we show the local well-posedness of the initial value problem associated with a coupled system consisting of modified Korteweg-de Vries equations for given data. Furthermore, we prove that the unique solution belongs to Gevrey space G &sigma, &times, G &sigma, in x and G 3 &sigma, G 3 &sigma, in t. This article is a continuation of recent studies reflected.

Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well. However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible athttp://www.biopred.org/enpred.

This paper deals with the global existence of solutions in a bounded domain for nonlinear viscoelastic Kirchhoff system with a time varying delay by using the energy and Faedo–Galerkin method with respect to the delay term weight condition in the feedback and the delay speed. Furthermore, by using some convex functions properties, we prove a uniform stability estimate.

This article proposes an information security management system (ISMS) model for automated data processing systems of critical applications (ADPS-CAs). The model allows users to carry out an assessment of the risk level for information security (IS) purposes and provides support for decision-making related to countering unauthorized access to the information circulating in an ADPS-CA. Further, this article analyzes the effect of a control parameter on the probability of launching an information integrity monitoring service (IMS) during a procedure that launches a typical ADPS-CA and its IS subsystem. The results present system quality indicators for protecting information from unauthorized access. The simulation was performed in relation to automated workstations as part of the ADPS-CA of a large enterprise.

The main purpose of this study is to investigate the effects of integration of online learning and assessment in synchronization form (OLASF) on students’ learning performance. The study seeks to evaluate how the synchronization content with immediate assessment can affect the knowledge performance of the students. An experimental design with digital formative assessment tools was used to fulfill the research purpose. Participants were 64 undergraduate students enrolled in is introduction to computer and programming (CSC 101) at Qassim University. The students from level five were divided into two-treatment groups: online learning & assessment instruction in Synchronization form using Nearpod, and PowerPoint instruction with traditional form. The experiment was completed within 6 weeks. Formative assessment test was used to access students learning performance after class. Descriptive statistics and a t-test were used to analyze the data. Our findings revealed that the use of OLASF was an intrinsic motivation and could be a promising way of enhancing students’ learning performance.

Social entrepreneurship has recently become a much-desired area of research for academia, practices, and policymaking. Natural or cognitive personal thoughtfulness like loving-kindness meditation (LKM) and compassion trigger individual intentions towards the social entrepreneurial venture. In this process of individual social entrepreneurial intention personality trait plays a very vital role, such as entrepreneurship resilience. For this study, a purposive sampling technique was incorporated and data was collected from 631 business and management sciences students. Data is analyzed by SPSS 23 and for the hypothesis testing, we used the bootstrap analysis of Hayes PROCESS v3.5. This study depicts that LKM has a positive significant impact on compassion and no significant impact on social entrepreneurship intentions while resilience strengthens the direct relationship of compassion with social entrepreneurship and the indirect relationship of LKM with social entrepreneurship via compassion. This study contributes to solving the economic and social problems over the globe especially by boosting the LKM and resilience traits so that the young graduate commence social entrepreneurship. This study helps the academician and policymakers to adopt strategies through which they can encourage youth to indulge in social entrepreneurial ventures solve the social problem and decrease unemployment.

This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features’ set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.

In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions, and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

The artificial neural network (ANN) has been successfully applied to many systems’ models with the objective of improving their performance. This study presents the ANN application to the automated voltage regulator (AVR) model of the synchronous machine. The studied system consists of the single-machine infinite bus (SMIB) power system. Data was extracted over a diverse range of conditions. Different fault types were also considered for collecting a large number of data to avoid overfitting. The multilayer feedforward neural network (MLFFNN) approach was applied to design the paradigm and system equations were solved using the MATLAB Simulink software.

WhatsApp is a widely used social media app, growing in popularity across the Middle East, and the most popular in Saudi Arabia. In this paper, we investigate the usage of WhatsApp as an educational support tool in a Saudi university. An online survey was constructed to ascertain how students and staff feel about and utilize WhatsApp as part of their daily studies. It also aimed to gather their thoughts on other platforms offered by the university such as Blackboard and email. The survey was tested and the results analyzed for frequency distributions, mean score, and standard deviation. Our results from nearly 200 student and staff members reveals that WhatsApp is heavily utilized for a variety of educational support tasks and greatly preferred over the other platforms. We propose that WhatsApp has good potential to support not only student coordination, information dissemination and simple enquiries but also to support formal teaching and out-of-class learning.

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.

Our main concern in this paper, is to prove the stability combined well-posedness results of solutions for a linear Timoshenko’s beam laminated with thermoplastic and past history together with distributed delay terms for the both cases nonequal and equal speeds of wave propagation. By using of some of the usual basic theorems and well-known methods in functional analysis.

The present research aims to investigate the efficiency and effectiveness of e-learning systems adoption and implementation in context to the Saudi educational sector with specific focus on Saudi universities. It investigates the impact of the system on academic staffs teaching online courses. Adopting quantitative research approach, the IS-Impact Measurement model was used to design the questionnaire to collect the primary data. It was used to find out the effectiveness of the current systems based on the perspectives of academic staff with experience teaching online courses for a year at least. The total outcomes of the whole research ended up proposing a business model to enhance and modify e-learning environments for Saudi universities with specific focus on their quality by assessing and understanding of the impact employees have within their roles.

Optimal homotopy asymptotic method (OHAM) is prolifically implemented to find the optimal solutions of fractional order heat- and wave-like equations. We inspect the competence of the method by examining fractional order time dependent partial differential equations. It is observed that OHAM is a prevailing and convergent method for the solutions of linear and nonlinear fractional order time dependent partial differential problems. The numerical results rendering that the applied method is explicit, effective and easy to use, for handling more general fractional order heat- and wave-like models.

Despite the considerable potential for e-learning to improve learning outcomes, particularly for female students and students who need to rely on distance learning, feedback from current users of e-learning systems in the Kingdom of Saudi Arabia (KSA) suggests a relatively low level of satisfaction. This study adopts a mixed-methods approach in investigating the underlying reasons for this situation. Results indicate that students are not unhappy with the information technology infrastructure or with other technical aspects. However, many students report a low level of interaction between them and their fellow students. When interactions do occur, students indicate overwhelmingly that they find such interactions useful. Together, these findings suggest that greater student interaction, which could be fostered through the use of collaborative learning, is likely to be both popular with students and beneficial to their learning outcomes.

Optimal homotopy asymptotic method (OHAM) is proposed to solve linear and nonlinear systems of second-order boundary value problems. OHAM yields exact solutions in just single iteration depending upon the choice of selecting some part of or complete forcing function. Otherwise, it delivers numerical solutions in excellent agreement with exact solutions. Moreover, this procedure does not entail any discretization, linearization, or small perturbations and therefore reduces the computations a lot. Some examples are presented to establish the strength and applicability of this method. The results reveal that the method is very effective, straightforward, and simple to handle systems of boundary value problems.

Despite the considerable potential for e-learning to improve learning outcomes, particularly for female students and students who need to rely on distance learning, feedback from current users of e-learning systems in the Kingdom of Saudi Arabia (KSA) suggests a relatively low level of satisfaction. This study adopts a mixed-methods approach in investigating the underlying reasons for this situation. Results indicate that students are not unhappy with the information technology infrastructure or with other technical aspects. However, many students report a low level of interaction between them and their fellow students. When interactions do occur, students indicate overwhelmingly that they find such interactions useful. Together, these findings suggest that greater student interaction, which could be fostered through the use of collaborative learning, is likely to be both popular with students and beneficial to their learning outcomes.

With the rapid growth of the use of e-learning systems around the globe, assessing the success and impact of such systems is becoming increasingly important. This paper presents findings from a study of the impact of e-learning systems on university students in the Kingdom of Saudi Arabia. It is asserted that gauging the impact of e-learning systems on learners is central to the development of suitable and effective e-learning systems. Students from two different universities in the KSA were surveyed to capture their perceptions regarding their current e-learning systems.The assessment framework is based on the IS Success/Impact Measurement framework, which has been successfully applied to similar studies on e-Learning, e-Health, and e-Government. This paper reports on the impacts that the e-learning systems have had on student participant's performance with regard to the depth of learning, customization of learning pace, student productivity, and student satisfaction. The conclusion of the study isthat the use of e-learning systems shows a positive impact on student learning. This paper provides information that will be of interest to e-learning system designers and developers.

This paper presents findings from a study evaluating eLearning systems in the Kingdom of Saudi Arabia (KSA). E-Learning Systems are a technological development that have reformed and restructured the delivery and interaction of students and teachers with course materials and related resources. E-Learning systems have been widely used in developed countries and have recently become more popular in many developing countries. To date, however, little attention has been paid to the issue of measuring the impact of eLearning on academic staff at higher educational institutions in the Kingdom of Saudi Arabia. Responding to this gap in the literature, this study investigates the impact of e-Learning systems in higher education institutions in this context. A survey based on the IS Success/Impact Measurement Framework was executed in a sample population of faculty members using e-Learning in two top universities in the KSA. The findings of this study show positive academics’ attitudes towards eLearning systems in higher education, helping faculty members to improve their job performance, and educational organizations to provide better and new products and services to users.

This paper describes the Cellular weighted Graph Neuron (CwGN) scheme, which demonstrates real-time transformation-invariant pattern recognition for resource constraint networks such as wireless sensor networks (WSN). This scheme can recognise patterns from a variety of perspectives in a fully distributed manner, which makes it highly responsive, scalable, and energy efficient. Learning operations are decoupled and reduced to relatively simple processing elements to achieve an economical parallelism. We show that by using only local computation, i.e., between adjacent nodes, we can fully describe patterns by their boundaries and then recognise the transformations of these patterns. Our scheme solves the invariant pattern recognition for the first time in a fully distributed manner within a predictable single cycle learning duration which allows the use of the scheme within resource-constrained networks such as wireless sensor networks for event-detection. Theoretical analysis and experimental results demonstrate online recognition of transformed patterns with competitive accuracy and a low energy budget.

Fuzzy logic control became the most applicable advanced control technique that used worldwide to control many electric devices. The study presents a new design of fuzzy logic controller (FLC) with triangular membership function and centroid defuzzification method to control the SVC thyristor firing angel. The static (SVC) provides damping signal to electromechanical modes of oscillation and regulate the voltages of the power systems. The proposed scheme is validated using sample single machine connected to infinite bus power system equipped with (SVC). A full nonlinear model of the studied system is developed and analysed. The total stability and control quality assessment of the studied power system has been derived and simulated. The time simulations, and studied system performance index have been investigated for the studied system to indicate the effect of the proposed SVC based on the proposed FLC on damping power systems oscillations and improve the post fault performance. Rung-Kutta modified by Gill is employed to solve the set of the nonlinear differential equations, and the iterative prediction-error minimization method used to identify the effect of change in studied system operation conditions on the system stability. The Mat lab software is used to solve and simulate the studied system equations. The simulation results show that the proposed SVC based FLC provide better control quality, damping oscillations very fast with low overshoot and undershoot and provides better post fault dynamic performance.

The IS-Impact Measurement Model, developed by Gable, Sedera and Chan in 2008, represents the to-date and expected stream of net profits from a given information system (IS), as perceived by all major user classes. Although this model has been stringently validated in previous studies, its generalizability and verified effectiveness are enhanced through this new application in e-learning. This paper focuses on the re- validation of the findings of the IS-Impact Model in two universities in the Kingdom of Saudi Arabia (KSA). Among the users of 2 universities e-learning systems, 528 students were recruited. A formative validation measurement with SmartPLS, a graphical structural equation modeling tool was used to analyse the collected data. On the basis of the SmartPLS results, as well as with the aid of data-supported IS impact measurements and dimensions, we confirmed the validity of the IS-Impact Model for assessing the effect of e-learning systems in KSA universities. The newly constructed model is more understandable, its use was proved as robust and applicable to various circumstances.