Solution Of Differential Problem Research Articles

5G Communication Network and Federated Learning for College Accounting Informatization Teaching

At present, the social economy is entering the information age represented by computer, communication technology and network technology as the core, and the continuous development of modern information technology will certainly have a great impact on the teaching mode, content and methods of traditional accounting computerization. We aim to improve the existing higher vocational accounting teaching mode by building a multi-integrated teaching mode through federated learning based on 5G communication network as an environment for efficient information transfer. In addition, we develop a joint optimization strategy for priority-dependent task offloading, wireless bandwidth, and computational power in a distributed machine learning approach to ensure that more resources are allocated to users with higher priority while protecting user data privacy and reducing learning overhead. We have conducted extensive simulation experiments for both environments, and these simulation results demonstrate the effectiveness of our proposed solutions for different problems from different perspectives.

A Method for Inspiring Radical Innovative Design Based on Cross-Domain Knowledge Mining

The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device.

En el desafío de la predicción de la respuesta a terremotos mediante el enfoque de redes neuronales

Since the decade of the 1990 the neural networks algorithms have been used for compute approximate solutions for different problems in engineering.
 In the building behavior against loads is important to know its response. The behavior during the earthquakes and the estimation of the response is quite difficult to compute due to the nonlinearity of geometry and material properties. Neural networks approach is a powerful tool for computing the response of structures with an appropriate learning process from big data of structural components. Even if some material parameters are unknown, the learning on a neural network will be possible and will provide an estimation using collect information from experience and learning.
 To make a learning process in this paper, we present a simple algorithm of back propagation implemented in python programming language where the output shows the decrease of the error and how the response start to learn from the beginning until the end of the process. The results show good agreement between the learning data set and predicted response after the neural network learning.

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.

A Hybrid Approximation Method for Integer-Order Approximate Realization of Fractional-Order Derivative Operators

The use of fractional-order (FO) calculus for the solution of different problems in many fields has increased recently. However, the usage of FO system models in practice brings some difficulties. The FO operator, fractance device, is usually realized via several integer-order approximation methods, which have pros and cons in the aspect of operation frequency, time response and stability region. These methods may not meet all performance expectations. In this regard, author proposes an efficient hybrid integer-order approximation method for FO derivative operator without causing any additional difficulty in realization. The proposed method combines Matsuda and modified stability boundary locus (M-SBL) approximation methods. The advantage of each method is combined in a single hybrid function by considering root mean square error (RMSE) rates for step response. The performance of hybrid transfer function is analyzed in comparison with Matsuda, Oustaloup, continued fraction expansion (CFE) and M-SBL transfer functions for both frequency and time response. Analog realization of the proposed model is performed experimentally via partial fraction expansion method. Analog design is verified via both Multisim simulations and experimental results. The improvements due to the hybrid behavior and the consistency of experimental results with theoretical and simulation results demonstrate the practicality and usefulness of the hybrid model.

A Review of Peridynamics and its Applications; Part 2: Applications of Peridynamics to the Solution of Different Problems

A Review of Peridynamics and its Applications; Part 2: Applications of Peridynamics to the Solution of Different Problems

A NOVEL BI-OBJECTIVE MODEL FOR A MULTI-PERIOD MULTI-PRODUCT CLOSED-LOOP SUPPLY CHAIN

Closed-loop supply chain (CLSC) is a kind of supply chain which contains forward and backward flows of commodities within a logistics network. In the decision-making process of CLSC, locational, inventory control and transportation issues are addressed to deal with strategic, tactical and operational decisions. This paper utilizes a novel bi-objective mixed-integer linear programming (MILP) model to formulate a multi-period multi-product CLSC design problem considering aggregate cost minimization and service level maximization at the same time. To tackle the bi-objectiveness of the model, goal attainment method (GAM) is applied which is then executed by Gurobi Python API to test the applicability of the suggested model for three different scales (small, medium and large). It is demonstrated that the proposed methodology can find the optimal solutions for different problems in a maximum of 500 seconds. Finally, a set of sensitivity analyses is carried out on the main parameters in order to test the behaviors of the objective functions and suggest managerial insights as well as decision aids. The results reveal that the model is highly dependent on the demand parameter, that is, an increase in demand is closely related to an increase in the aggregate cost and a simultaneous downward trend in the service level.

Evaporation characteristics of nanofuel droplets: A review

Increasing greenhouse gas emissions due to an uncontrolled increase in fuel consumption has become a worldwide challenge during the last decades. Meanwhile, replacing green energy with traditional fuel energy is always a solution to this problem. However, the transition between these two energy resources is a little time-consuming because of changing the infrastructures. Nanotechnology provides promising solutions for different problems in different fields, including the fuel industry. Various studies show the improvement of the toxic and greenhouse emissions by adding nanoparticles into the pure fuel. The nanoparticles change the thermophysical properties of the pure flue. Subsequently, it changes the evaporation behavior of the sprayed droplet in the combustion chamber, leading to faster ignition and enhancing the burning rate. The present review focused on the effect of additive nanoparticles on the evaporation behavior of fuel droplets and its underlying mechanism. The impact of different parameters, including the nanoparticle type, concentration, size, the ambient temperature, on the evaporation characteristics is presented.

Routing in Elastic Optical Networks Based on Deep Reinforcement Learning

Modern optical network schemes consist of eighty channels involving fixed grids and offering speeds of up to 400 Gbps per channel rendering the allocated bandwidths insufficient during certain timeframes and underutilized during others. These speeds will become inefficient when compared to the accelerated growth of traditional traffic, which threatens to be tripled by 2025. In order to exploit the capacity of elastic optical networks, efficient algorithms are essential. This article assesses Deep reinforcement learning RMCSA algorithms, focused on providing high availability to the allocation of routing, spectrum and modulation resources over a given network topology, in order to determine how to allocate physical resources in optical transport networks and increase capacity while accounting for traffic variability and demands. Reinforcement learning is currently used in many scientific fields for the solution of different problems. In optical networks, this work will use this method to determine the best path and resources for each type of traffic-related demand. Simulations are performed by increasing volumes of variable traffic over a fourteen-node topology. The results show a significant 40% improvement in blocking probability for 22 links with 100 frequency slots compared to current transmission systems. They also reflect low latency, higher speeds, and high availability, which can deliver better quality of service for the end user.

Evaluation of Oversampling Methods (OVER, SMOTE, and ROSE) in Classifying Soil Liquefaction Dataset based on SVM, RF, and Naïve Bayes

Class imbalanced datasets are prevalent in real-world applications, including engineering, medical domain, financial sector, and others. Machine learning (ML)-based prediction models have successfully demonstrated the applicability of various algorithms for the solution of different problems. However, their application for the soil liquefaction issue considering the class imbalance situation is limited. This paper presents the prediction results of random forest (RF), support vector machine (SVM), and naïve bayes (NB) algorithms with different training sample sizes for soil liquefaction. The effect of oversampling methods, namely simple oversampling (OVER), random oversampling examples (ROSE), and synthetic minority oversampling technique (SMOTE), on the prediction performance of classification algorithms is also investigated. Performance results are evaluated by means of some metrics, including Accuracy, Kappa, Precision, Recall, and F-measure. The results concluded the effectiveness of applying oversampling methods on imbalanced data before the modeling phase. All of the oversampling methods helped to enhance the overall performances of the classification models. It is also observed that the SMOTE exhibited slightly better performance than other considered oversampling methods. Furthermore, the SVM model outperformed compared to RF and NB models when all algorithms were trained by the SMOTE algorithm.

Nonexistence Results for Higher Order Fractional Differential Inequalities with Nonlinearities Involving Caputo Fractional Derivative

Higher order fractional differential equations are important tools to deal with precise models of materials with hereditary and memory effects. Moreover, fractional differential inequalities are useful to establish the properties of solutions of different problems in biomathematics and flow phenomena. In the present work, we are concerned with the nonexistence of global solutions to a higher order fractional differential inequality with a nonlinearity involving Caputo fractional derivative. Namely, using nonlinear capacity estimates, we obtain sufficient conditions for which we have no global solutions. The a priori estimates of the structure of solutions are obtained by a precise analysis of the integral form of the inequality with appropriate choice of test function.

Role of Nasal Instillation with Ayurvedic Polyherbal Formulation in Melasma: A Case Study

Background: External beauty of human being is mainly dependent upon the quality of skin. Melasma is common hyperpigmentation disorder in the facial skin of the Indian women. It is the cause of common socio-cosmetic stigma along with hindrance to the success of more or less all professions. Allopathic management of melasma is having higher recurrence rate with mild to moderate adverse effect. This case study is to establish a scientific data to the Ayurvedic cosmetology in the management of melasma. Case summary: A 25 year old female, known case of melasma since last 5 years attended OPD of Panchakarma dept. Rishikul campus. After assessing the nidan and samprapti, she was planned for nasya with kumkumadi tailam along with some herbomineral compound. Allopathic medication which she had been taking for the same problem was reduced in tapering dose and after 1 week she was completely on Ayurvedic management. During the total study period (56 days) the patient was assessed for five times. Patient had shown significant improvement in symptoms like darkness, area involvement, itching, swelling, dryness related to the patches. Subjective and Objective parameters of the study is observed and assessed with the criteria as per the modern cosmetology. Conclusion: Ayurveda, in the field of cosmetology is a promising aspect of practice. This single case study has shown highly significant result with combined management of Ayurvedic internal medication with panchakarma therapy. It surely can lead to find out solution of different problems in the field of cosmetology with Ayurvedic/panchakarma treatment.

Utilization of Chebyshev collocation approach for differential, differential-difference and integro-differential equations

Numerical solutions to differential and integral equations have been a very active field of research. The necessary tools to solving differential equations can add much fuel for acceleration of scientific development. Therefore, it is quite challenging for scientists, especially mathematicians, to work on the solution methodologies for differential equations. In this study, we have discussed the use of Chebyshev collocation method for solving a large variety of problems including various types of integral equations, integro-differential equations, and differential-difference equations. The method under consideration relies on a trial solution which is a linear combination of basic functions derived from Chebyshev polynomials. The trial solution is then used to obtain a residue function whose vanishing weighted residual can be used to obtain a system of algebraic equations which gives the values of constants used in the trial solution. Plugging the constants back into the trial solution, we obtain an approximate solution to the problem at hand. We have provided the comparison between exact and approximate solutions for different problems and found an exceptionally good agreement in results. The comparison has been provided through plots and tables to portray the accurateness of Chebyshev collocation method. The behaviour of the solutions is also displayed with the help of the graphs.

Habilidades del pensamiento crítico en estudiantes de educación secundaria de Lima, Perú

En un mundo globalizado, donde la tecnología se desarrolla aceleradamente, es fundamental que los estudiantes de los diferentes niveles educativos desarrollen habilidades del pensamiento crítico como el razonamiento, análisis, síntesis, evaluación y toma de decisiones adecuadas para la solución de diferentes problemas, asimismo será de capaz de responder con éxito a los retos que plantea el siglo XXI. Frente a lo expuesto surge la necesidad de investigar sobre esta variable, de ahí que el objetivo del presente estudio fue describir los niveles de las habilidades del pensamiento crítico en estudiantes de Lima Metropolitana. La investigación fue desarrollada dentro del enfoque cuantitativo, tipo de investigación básica, diseño no experimental de carácter descriptivo, se trabajó con una muestra intencionada de 110 estudiantes de educación secundaria de la ciudad de Lima, Perú. Se recogió la información con el instrumento de pensamiento crítico basado en los planteamientos teóricos de Priestley (2015), el cual se dividió en veinte ítems, tres dimensiones, quince indicadores y un puntaje vigesimal. Los resultados descriptivos indicaron que el 80,0% de participantes se ubicaron en el nivel de inicio y proceso, es decir en camino de desarrollar las habilidades cognitivas del pensamiento crítico y un 20,0% logró aplicar de manera satisfactoria y destacada las capacidades propias de este tipo de pensamiento.

Analytical Solution of System of Volterra Integral Equations Using OHAM

In this work, a reliable technique is used for the solution of a system of Volterra integral equations (VIEs), called optimal homotopy asymptotic method (OHAM). The proposed technique is successfully applied for the solution of different problems, and comparison is made with the relaxed Monto Carlo method (RMCM) and hat basis function method (HBFM). The comparisons show that the present technique is more suitable and reliable for the solution of a system of VIEs. The presented technique uses auxiliary function containing auxiliary constants, which control the convergence. Moreover, OHAM does not require discretization like other numerical methods and is also free from small or large parameter.

Simulating Distributed and Global Consciousness Under Spatial Grasp Paradigm

The paper is investigating the possibility of using developed and tested in different countries Spatial Grasp model and technology (SGT) for simulating global awareness and consciousness in distributed dynamic systems, with potential applications in intelligent system management, industrial development, space research, security, and defense. The main technology component, Spatial Grasp Language (SGL), allows us to obtain powerful and compact spatial solutions of different problems by directly expressing their top semantics while hiding traditional system organization and management routines inside efficient networked implementation. The paper describes in SGL a traditional organization of two opposing swarms, called “chasers” and “targets”, randomly operating on expected area. It then enriches the chasers swarm with global awareness and a sort of migrating consciousness, further strengthened by external super-consciousness capability, which allows it to drastically improve performance and make important nonlocal decisions, while putting it to a superior position over the opposing targets swarm. Despite simplicity of the shown practical example, it gives hope for the use of SGT for simulation of much broader and complex areas linked with consciousness like, for example, brain’s bimolecular processes and the basic structure of the universe. The developed networking technology can be readily implemented even in traditional university environments, as was done in the past for its previous versions in different countries under the author’s supervision.

A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities

Generally, the emergence of Internet of Things enabled applications inspired the world during the last few years, providing state-of-the-art and novel-based solutions for different problems. This evolutionary field is mainly lead by wireless sensor network, radio frequency identification, and smart mobile technologies. Among others, the IoT plays a key role in the form of smart medical devices and wearables, with the ability to collect varied and longitudinal patient-generated health data, and at the same time also offering preliminary diagnosis options. In terms of efforts made for helping the patients using IoT-based solutions, experts exploit capabilities of the machine learning algorithms to provide efficient solutions in hemorrhage diagnosis. To reduce the death rates and propose accurate treatment, this paper presents a smart IoT-based application using machine learning algorithms for the human brain hemorrhage diagnosis. Based on the computerized tomography scan images for intracranial dataset, the support vector machine and feedforward neural network have been applied for the classification purposes. Overall, classification results of 80.67% and 86.7% are calculated for the support vector machine and feedforward neural network, respectively. It is concluded from the resultant analysis that the feedforward neural network outperforms in classifying intracranial images. The output generated from the classification tool gives information about the type of brain hemorrhage that ultimately helps in validating expert’s diagnosis and is treated as a learning tool for trainee radiologists to minimize the errors in the available systems.

Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis

Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings of GOA and handle continuous optimization problems more efficiently. For this purpose, two strategies, including orthogonal learning and chaotic exploitation, are introduced into the conventional GOA to find a more stable trade-off between the exploration and exploitation cores. Adding orthogonal learning to GOA can enhance the diversity of agents, whereas a chaotic exploitation strategy can update the position of grasshoppers within a limited local region. To confirm the efficacy of GOA, we compared it with a variety of famous classical meta-heuristic algorithms performed on 30 IEEE CEC2017 benchmark functions. Also, it is applied to feature selection cases, and three structural design problems are employed to validate its efficacy in terms of different metrics. The experimental results illustrate that the above tactics can mitigate the deficiencies of GOA, and the improved variant can reach high-quality solutions for different problems.

Unbalanced data processing using oversampling: Machine Learning

Nowadays, the DL algorithms show good results when used in the solution of different problems which present similar characteristics as the great amount of data and high dimensionality. However, one of the main challenges that currently arises is the classification of high dimensionality databases, with very few samples and high-class imbalance. Biomedical databases of gene expression microarrays present the characteristics mentioned above, presenting problems of class imbalance, with few samples and high dimensionality. The problem of class imbalance arises when the set of samples belonging to one class is much larger than the set of samples of the other class or classes. This problem has been identified as one of the main challenges of the algorithms applied in the context of Big Data. The objective of this research is the study of genetic expression databases, using conventional methods of sub and oversampling for the balance of classes such as RUS, ROS and SMOTE. The databases were modified by applying an increase in their imbalance and in another case generating artificial noise.

Simulating distributed and global consciousness under spatial grasp paradigm

The interest to what is often called awareness, consciousness, also self-consciousness, has grown enormously in recent years, with many theories and publications trying to explain what this can actually mean and where can be located. The paper is investigating the possibility of using developed and tested in different countries Spatial Grasp model and technology (SGT) for simulation of different ideas and concepts related to global awareness and consciousness in distributed dynamic systems, with potential applications in intelligent system management, industrial development, space research, security, and defense. The main technology component, Spatial Grasp Language (SGL), allows us to obtain powerful and compact spatial solutions of different problems by directly expressing their top semantics while hiding traditional system organization and management routines inside networked implementation. The paper describes in SGL a traditional organization of two opposing swarms, called “chasers” and “targets”, randomly operating in expected area. It then enriches the chasers swarm with global awareness and a sort of migrating consciousness, further strengthened by external super-consciousness capability, which allows it to drastically improve performance and make important nonlocal decisions, while moving to superior position over opposing targets swarm. Despite simplicity of the shown practical example, it gives us hope for the use of SGT for simulation of much broader and complex areas linked with consciousness like, for example, brain's biomolecular processes and basic structure of the universe. The developed networking technology can be implemented even in traditional university environments, as was done in the past for its previous versions in different countries.