Hasil untuk "Applied mathematics. Quantitative methods"

Idriss El-Thalji

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing.

Daniel Barbosa dos Santos Silva, Claudia Editt Tornero Becerra, Amanda Duarte Feitosa et al.

Leadership in an organizational environment is responsible for enabling team members to achieve their daily objectives. Leadership has evolved continuously over time, demonstrating flexibility as a core aspect of its essence. In an organizational setting, the feedback between leaders and led has great significance, particularly for teams with shared goals. This study aims to analyze the leader’s behavior using the Johari Window tool from the subordinates’ perspective in a food company, providing feedback on the leader’s behavior to foster a more harmonious relationship. This study adopts an applied methodology with a descriptive objective and qualitative approach, utilizing the Johari Window framework to conduct a case study. Data collection involved administering questionnaires to gather insights into the behavior of both the sector leader and those being led. The results indicate that 60% of employees have different perceptions from the sector manager, concentrated in the “Blind” area, suggesting infrequent feedback exchanges. This could occur in situations in which behaviors are visible to others, but unnoticed by the leader. Therefore, communication is a critical element in the management of an organization. When practiced clearly and objectively, managers can follow paths that lead to effective and efficient decision-making.

Guantao Xuan, Huijie Jia, Yuanyuan Shao et al.

This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R2C of 0.9393, a validation set R2V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection.

Naila Nasreen, Jan Muhammad, Adil Jhangeer et al.

This work investigates the dynamics of optical waves to the generalized coupled nonlinear fractional Helmholtz equation with quintic and cubic nonlinear effects. The evolution of broad multicomponent self-trapping beams in Kerr-type nonlinear media is described by the coupled Helmholtz equation, which also take into account spatial dispersion due to non-paraxial effects. This phenomena is particularly relevant to the progressive miniaturization of optics, where the optical wavelength is similar to the beam width. It is essential to integrate non-Kerr terms, such as the self-steepening and the self frequency shift, into the coupled Helmholtz system in order to investigate the propagation of ultrashort optical pulses in the non-paraxial domain. For optical wave solutions, nonlinear ordinary equation of the governing model is achieved by applying the fractional transformation. The different types of the solutions like bright, dark, singular and mixed type solitons are extracted by applying advanced integration methods, namely modified Sardar subequation method and new Kudryashov method. These solutions provide very useful information on how the system operates. The applied approaches are highly efficient and have significant computational capability to efficiently tackle the nonlinear systems. Additionally, we include a diverse array of graphs to demonstrate the physical interpretation of the obtained solutions in relation to a number of significant parameters, thereby highlighting the impact of fractional derivatives. In the context of the proposed model, these visualizations assist with a comprehensive understanding of the solution’s behavior and characteristics. It is anticipated that these solutions may be significant in the study of wave propagation and related fields.

Pavan Kumar Reddy Muduganti, Aparna Podila, Pothanna Nalimela et al.

In this work, we have analysed the impact of a transverse magnetic field on the steady flow of an incompressible conducting couple stress fluid within a rectangular channel of uniform cross-section, incorporating suction or injection at the lateral walls. In order to get the velocity ‘w’ along the axis of the rectangular tube, we ignore the induced electric and magnetic fields. To find w, we apply the standard hyper stick and no slip boundary conditions. The velocity w and temperature θ were calculated using Fourier series. The velocity distribution compared for various magnetic parameter values by taking suction velocity zero and the results are in good agreement (99.99 %) with the existing results. The volumetric flow rate and skin friction are obtained and the effects of physical parameters like magnetic parameter, Reynolds number and couple stress parameter on this are studied through graphs.

Hajar Majjate, Youssra Bellarhmouch, Adil Jeghal et al.

Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.

Subin P. Joseph

A system of coupled Klein–Gordon nonlinear partial differential equations are considered in this paper and several new families of exact solutions for this system are derived. Since this system is highly nonlinear, exact solutions are very difficult to obtain. A rational ansatz form is assumed to derive the required exact solutions in terms of the doubly periodic functions. We derive all the possible real solutions having this ansatz form and which are functions of Jacobi elliptic functions. Particular solutions corresponding to each family of solutions are presented graphically.

Shuo-Chang Tsai, Chih-Hsien Chen

The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using an abductive reasoning approach. We used literature reviews and semi-structured interviews to interview representatives of fund companies to see if they had adopted AI big data forecasting models to invest in stock selection. This study summarizes the big data stock market forecasts of the literature. According to the summary, the accuracy of the prediction models of these scholars ranged from 52% to 97%, with the prediction results of the models varying significantly. Interviews with 21 representatives of these fund companies revealed that the stock market forecast model of big data robo-advisors have not become a reference basis for fund investment candidates, mainly because of the unstable model prediction rate, and the lack of apparent relative advantages and observability, as well as being too complex. Thus, from the view of innovation diffusion, there is a lack of diffusion for the robo-advisor. Knowledge occurs when an individual is exposed to the existence of innovation, and gains some understanding of how it functions. Thereby, when investors become more familiar with neural network-like stock prediction models, this novel AI stock market forecasting model is expected to become another indicator of technical analysis in the future.

R. Prem Kumar, Sanjoy Basu, P.K. Santra et al.

In this paper, a mathematical model of the COVID-19 pandemic with lockdown that provides a more accurate representation of the infection rate has been analyzed. In this model, the total population is divided into six compartments: the susceptible class, lockdown class, exposed class, asymptomatic infected class, symptomatic infected class, and recovered class. The basic reproduction number (R0)is calculated using the next-generation matrix method and presented graphically based on different progression rates and effective contact rates of infective individuals. The COVID-19 epidemic model exhibits the disease-free equilibrium and endemic equilibrium. The local and global stability analysis has been done at the disease-free and endemic equilibrium based on R0. The stability analysis of the model shows that the disease-free equilibrium is both locally and globally stable when R0<1, and the endemic equilibrium is locally and globally stable when R0>1under some conditions. A control strategy including vaccination and treatment has been studied on this pandemic model with an objective functional to minimize. Finally, numerical simulation of the COVID-19 outbreak in India is carried out using MATLAB, highlighting the usefulness of the COVID-19 pandemic model and its mathematical analysis.

A. Ilhan

The present study aimed to investigate the effects of geometry instruction activities conducted in nature based on modeling, game-based, and cooperative learning methods on achievement, mathematical motivation, and visual mathematical literacy perceptions of third-grade elementary school students. The present study is a quantitative study conducted with a pre-test/post-test experimental design with a control group. The study was conducted with 61 students (35 students in the experimental group and 26 students in the control group). Modeling-, game-, and collaborative learning-based activities were conducted with the students in the experimental group. It was determined that the achievements of students who were instructed with modeling-based activities in geometry were high when compared to that of the students instructed with collaborative learning- and game-based methods, and those in the control group where no intervention was applied. This group was followed by the game-based and collaborative learning groups. Based on the variable of motivation, the mean motivation of the students in the modeling group was higher when compared to that of the students in the collaborative learning, game-based, and conventional instruction groups. This group was followed by the collaborative and game-based learning groups. Also, based on the visual mathematical literacy perception variable, the mean visual mathematics literacy perception of the students in the collaborative learning group was higher when compared to that of the students in the groups where the modeling, game-based, and conventional instruction methods were used. This group was followed by the modeling and game-based learning groups.

Xin-She Yang

Ahlem Teniou, A. Rhouati, J. Marty

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Rangan Gupta, Christian Pierdzioch

Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance of gold returns and its upside (good) and downside (bad) counterparts. We go beyond earlier research in that we do not focus exclusively on U.S.-based measures of uncertainty, and in that we account for international spillovers of uncertainty. Our results, based on the Lasso estimator, show that, across the various model configurations that we study, uncertainty has a more systematic effect on out-of-sample forecast accuracy than spillovers. Our results have important implications for investors in terms of, for example, pricing of related derivative securities and the development of portfolio-allocation strategies.

E. Berrezueta, J. Cuervas-Mons, Á. Rodríguez-Rey et al.

This paper introduces an assessment of the representation of shape parameter measurements on theoretical particles. The aim of the study was to establish a numerical method for estimating sphericity, roundness, and roughness on artificially designed particles and to evaluate their interdependence. The parameters studied included a fractal dimension (FD), solidity (So), Wadell’s roundness (Rw), a perimeter-area normalized ratio (¥), and sphericity (S). The methods of the work included: (a) the design of theoretical particles with different shapes, (b) the definition of optimal analysis conditions for automated measurements, (c) the quantification of particle parameters by computer vision-based image processing, and (d) the evaluation of interdependence between the parameters. The study established the minimum sizes required for analysis of the particle shape. These varied depending on the method used (150 pixels or 50 pixels). Evaluating the relationships between the parameters showed that FD and So are independent of S. Nevertheless, Rw and ¥ are clearly dependent on S and, thus, must be numerically corrected to Rwc and ¥c. FD, So, Rwc, and ¥c were used to establish, mathematically, a new regularity parameter (RBC) that reflects the degree of roundness of a particle. The process was applied to a case study and the evaluation of all parameters corroborated previous petrographic characterizations.

Liubang Han, Kuosheng Jiang, Qidong Wang et al.

High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

Fabian Böttcher, A. Casasso, G. Götzl et al.

Abstract This paper proposes a method to assess the potential for thermal use of groundwater and its integration in spatial energy planning. The procedure can be adapted to local regulatory and operational limits, thus estimating legally and technically achievable flow rates and subsequently, the thermal power that can be exchanged with the aquifer through a well doublet. The constraints applied to flow rates are i ) a drawdown threshold in the extraction well, i i ) a limit for the groundwater rise in the injection well and i i i ) a threshold to avoid the hydraulic breakthrough between the two wells. For the spatial assessment, the hydraulic influence on neighbouring well doublets is simulated with the maximum flow rates before the hydraulic breakthrough occurs. The Thermal Aquifer Potential (TAP) method combines mathematical relations derived through non-linear regression analysis using results from numerical parameter studies. A demonstration of the TAP method is provided with the potential assessment in Munich, Germany. The results are compared with monitoring data from existing open-loop systems, which prove that conservative peak extraction estimates are achieved.

Yuriy Zhernovyi, Bohdan Kopytko

Jaume Garcia-Amorós, Dolores Velasco

Liquid single crystal elastomers (LSCEs) containing carbazole fluorogenic components alter their luminescence when they are stretched along the director direction. The differential luminescent behavior arises from the distinct interaction between the carbazole fluorophores and their local environment before and after the application of the mechanical input. Indeed, the uniaxial deformation of the material, along its anisotropic direction, forces a closer mesogen–fluorophore interaction, which leads to the quenching of the carbazole luminescence. Importantly, this intermolecular interaction is intimately related to the intrinsic order present in the LSCE. As a result, the amount of light emitted by the material in the form of fluorescence diminishes upon deformation. Thus, the application of mechanical stimuli to liquid-crystalline elastomers furnishes to two interconvertible states for the system with distinct optical properties (with either different emission color or fluorescence intensity). The initial state of the material is completely restored once the applied force is removed. In this way, this kind of macromolecular system can transduce mechanical events into detectable and processable optical signals, thus, having great potential as optical force sensors. In this context, the realization of the distinct structural factors that govern the interactions established between the mesogenic and fluorogenic units at the supramolecular level upon deformation is essential for the development of efficient LSCE-based force sensors. In fact, not only the density of carbazole units and their connection to the main polymer backbone, but also the presence of long range molecular order in the system and the type of mesophase exhibited by the LSCE are key factors for the conception of efficient force sensors based on these self-organized polymer networks. In this review, we present a comprehensive and systematic description of the different features that control the mechanoluminescent behavior of fluorescent liquid-crystalline elastomers and will guide the future design of LSCE-based force sensors with improved performances.

Michael I. Argyros, Gus I. Argyros, Ioannis K. Argyros et al.

A new technique is developed to extend the convergence ball of Newton’s algorithm with projections for solving generalized equations with constraints on the multidimensional Euclidean space. This goal is achieved by locating a more precise region than in earlier studies containing the solution on which the Lipschitz constants are smaller than the ones used in previous studies. These advances are obtained without additional conditions. This technique can be used to extend the usage of other iterative algorithms. Numerical experiments are used to demonstrate the superiority of the new results.

Gerald Gamrath, Timo Berthold, Domenico Salvagnin

Dual degeneracy, i.e., the presence of multiple optimal bases to a linear programming (LP) problem, heavily affects the solution process of mixed integer programming (MIP) solvers. Different optimal bases lead to different cuts being generated, different branching decisions being taken and different solutions being found by primal heuristics. Nevertheless, only a few methods have been published that either avoid or exploit dual degeneracy. The aim of the present paper is to conduct a thorough computational study on the presence of dual degeneracy for the instances of well-known public MIP instance collections. How many instances are affected by dual degeneracy? How degenerate are the affected models? How does branching affect degeneracy: Does it increase or decrease by fixing variables? Can we identify different types of degenerate MIPs? As a tool to answer these questions, we introduce a new measure for dual degeneracy: the variable–constraint ratio of the optimal face. It provides an estimate for the likelihood that a basic variable can be pivoted out of the basis. Furthermore, we study how the so-called cloud intervals—the projections of the optimal face of the LP relaxations onto the individual variables—evolve during tree search and the implications for reducing the set of branching candidates.