Search results for: mathematical decomposition

Authors: Gilbert Makanda, Roelf Sypkens

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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.

Keywords: differential equations, knowledge acquisition, least squares nonlinear, dynamical systems

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Authors: Anurag Savarnya

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The reliability of the various parts of electric mining shovel has been assessed through the application of Weibull Model. The study was initiated to find reliability of components of electric mining shovel. The paper aims to optimize the reliability of components and increase the life cycle of component. A multilevel decomposition of the electric mining shovel was done and maintenance records were used to evaluate the failure data and appropriate system characterization was done to model the system in terms of reasonable number of components. The approach used develops a mathematical model to assess the reliability of the electric mining shovel components. The model can be used to predict reliability of components of the hydraulic mining shovel and system performance. Reliability is an inherent attribute to a system. When the life-cycle costs of a system are being analyzed, reliability plays an important role as a major driver of these costs and has considerable influence on system performance. It is an iterative process that begins with specification of reliability goals consistent with cost and performance objectives. The data were collected from an Indian open cast coal mine and the reliability of various components of the electric mining shovel has been assessed by following a Weibull Model.

Keywords: reliability, Weibull model, electric mining shovel

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Authors: Chusak Thanawattano, Roongroj Bhidayasiri

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This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively.

Keywords: tremor, Parkinson, essential tremor, empirical mode decomposition, quadratic discriminant, support vector machine, peak frequency, auto-regressive, spectrum estimation

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Authors: Ayse Ozturk

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The study extends the prior research on modeling competencies by positioning students’ cognitive and language resources as the fundamentals for pursuing their own inquiry and expression lines through mathematical modeling. This strategy aims to answer the question that guides this study, “How do students’ group approaches to modeling tasks affect their modeling competencies over a unit of instruction?” Six bilingual tenth-grade students worked on open-ended modeling problems along with the content focused on quantities over six weeks. Each group was found to have a unique cognitive approach for solving these problems. Three different problem-solving strategies affected how the groups’ modeling competencies changed. The results provide evidence that the discussion around groups’ solutions, coupled with their reflections, advances group interpreting and validating competencies in the mathematical modeling process

Keywords: cognition, collective learning, mathematical modeling competencies, problem-solving

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Authors: Martin Cermak, Stanislav Sysala

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In this paper we present the efficient parallel implementation of elastoplastic problems based on the TFETI (Total Finite Element Tearing and Interconnecting) domain decomposition method. This approach allow us to use parallel solution and compute this nonlinear problem on the supercomputers and decrease the solution time and compute problems with millions of DOFs. In our approach we consider an associated elastoplastic model with the von Mises plastic criterion and the combination of linear isotropic-kinematic hardening law. This model is discretized by the implicit Euler method in time and by the finite element method in space. We consider the system of nonlinear equations with a strongly semismooth and strongly monotone operator. The semismooth Newton method is applied to solve this nonlinear system. Corresponding linearized problems arising in the Newton iterations are solved in parallel by the above mentioned TFETI. The implementation of this problem is realized in our in-house MatSol packages developed in MATLAB.

Keywords: isotropic-kinematic hardening, TFETI, domain decomposition, parallel solution

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Authors: Siti Noor Farwina Anwar, Hailiza Kamarulhaili

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In this paper, we present the idea of implementing the Integer Sub-Decomposition (ISD) method on elliptic curves with j-invariant 1728. The ISD method was proposed in 2013 to compute scalar multiplication in elliptic curves, which remains to be the most expensive operation in Elliptic Curve Cryptography (ECC). However, the original ISD method only works on integer number field and solve integer scalar multiplication. By extending the method into the complex quadratic field, we are able to solve complex multiplication and implement the ISD method on elliptic curves with j-invariant 1728. The curve with j-invariant 1728 has a unique discriminant of the imaginary quadratic field. This unique discriminant of quadratic field yields a unique efficiently computable endomorphism, which later able to speed up the computations on this curve. However, the ISD method needs three endomorphisms to be accomplished. Hence, we choose all three endomorphisms to be from the same imaginary quadratic field as the curve itself, where the first endomorphism is the unique endomorphism yield from the discriminant of the imaginary quadratic field.

Keywords: efficiently computable endomorphism, elliptic scalar multiplication, j-invariant 1728, quadratic field

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Authors: Hanbey Hazar, Hakan Gul

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In this study; piston, exhaust, and suction valves of a diesel engine were coated in 300 mm thickness with Tungsten Carbide (WC) by using the HVOF coating method. Mathematical modeling of a coated and uncoated (standardized) engine was performed by using ANN (Artificial Neural Networks). The purpose was to decrease the number of repetitions of tests and reduce the test cost through mathematical modeling of engines by using ANN. The results obtained from the tests were entered in ANN and therefore engines' values at all speeds were estimated. Results obtained from the tests were compared with those obtained from ANN and they were observed to be compatible. It was also observed that, with thermal barrier coating, hydrocarbon (HC), carbon monoxide (CO), and smoke density values of the diesel engine decreased; but nitrogen oxides (NOx) increased. Furthermore, it was determined that results obtained through mathematical modeling by means of ANN reduced the number of test repetitions. Therefore, it was understood that time, fuel and labor could be saved in this way.

Keywords: Artificial Neural Network, Diesel Engine, Mathematical Modelling, Thermal Barrier Coating

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Authors: Jina Lee

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Methane gas is a common byproduct in any process of rot and degradation of organic matter. This gas, when decomposition occurs, is emitted directly into the atmosphere. Methane is the simplest alkane hydrocarbon that exists. Its chemical formula is CH₄. This means that there are four atoms of hydrogen and one of carbon, which is linked by covalent bonds. Methane is found in nature in the form of gas at normal temperatures and pressures. In addition, it is colorless and odorless, despite being produced by the rot of plants. It is a non-toxic gas, and the only real danger is that of burns if it were to ignite. There are several ways to generate methane gas in homes, and the amount of methane gas generated by the decomposition of organic matter varies depending on the type of matter in question. An experiment was designed to measure the efficiency, such as a relationship between the amount of raw material and the amount of gas generated, of three different mixtures of organic matter: 1. food remains of home; 2. animal waste (excrement) 3. equal parts mixing of food debris and animal waste. The results allowed us to conclude which of the three mixtures is the one that grants the highest efficiency in methane gas generation and which would be the most suitable for methane gas generation systems for homes in order to occupy less space generating an equal amount of gas.

Keywords: alternative energy source, energy conversion, methane gas conversion system, waste management

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Authors: Nikhil Padhye

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Recently a new phenomenon for bonding of polymeric films in solid-state, at ambient temperatures well below the glass transition temperature of the polymer, has been reported. This is achieved by bulk plastic compression of polymeric films held in contact. Here we analyze the process of cold-rolling of polymeric films via finite element simulations and illustrate a flexible and modular experimental rolling-apparatus that can achieve bonding of polymeric films through cold-rolling. Firstly, the classical theory of rolling a rigid-plastic thin-strip is utilized to estimate various deformation fields such as strain-rates, velocities, loads etc. in rolling the polymeric films at the specified feed-rates and desired levels of thickness-reduction(s). Predicted magnitudes of slow strain-rates, particularly at ambient temperatures during rolling, and moderate levels of plastic deformation (at which Bauschinger effect can be neglected for the particular class of polymeric materials studied here), greatly simplifies the task of material modeling and allows us to deploy a computationally efficient, yet accurate, finite deformation rate-independent elastic-plastic material behavior model (with inclusion of isotropic-hardening) for analyzing the rolling of these polymeric films. The interfacial behavior between the roller and polymer surfaces is modeled using Coulombic friction; consistent with the rate-independent behavior. The finite deformation elastic-plastic material behavior based on (i) the additive decomposition of stretching tensor (D = De + Dp, i.e. a hypoelastic formulation) with incrementally objective time integration and, (ii) multiplicative decomposition of deformation gradient (F = FeFp) into elastic and plastic parts, are programmed and carried out for cold-rolling within ABAQUS Explicit. Predictions from both the formulations, i.e., hypoelastic and multiplicative decomposition, exhibit a close match. We find that no specialized hyperlastic/visco-plastic model is required to describe the behavior of the blend of polymeric films, under the conditions described here, thereby speeding up the computation process .

Keywords: Polymer Plasticity, Bonding, Deformation Induced Mobility, Rolling

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Authors: Alexander N. Savostyanov, Tatiana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Tatiana A. Golovko, Yulia V. Kovas

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EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16-20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources.

Keywords: EEG, brain activity, lexical and numerical error-recognition tasks, mathematical and trait anxiety

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Authors: Jeffrey A. Amelse

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Many corporations aspire to become Net Zero Carbon Carbon Dioxide by 2035-2050. This paper examines what it will take to achieve those goals. Achieving Net Zero CO₂ requires an understanding of where energy is produced and consumed, the magnitude of CO₂ generation, and proper understanding of the Carbon Cycle. The latter leads to the distinction between CO₂ and biomass carbon sequestration. Short reviews are provided for prior technologies proposed for reducing CO₂ emissions from fossil fuels or substitution by renewable energy, to focus on their limitations and to show that none offer a complete solution. Of these, CO₂ sequestration is poised to have the largest impact. It will just cost money, scale-up is a huge challenge, and it will not be a complete solution. CO₂ sequestration is still in the demonstration and semi-commercial scale. Transportation accounts for only about 30% of total U.S. energy demand, and renewables account for only a small fraction of that sector. Yet, bioethanol production consumes 40% of U.S. corn crop, and biodiesel consumes 30% of U.S. soybeans. It is unrealistic to believe that biofuels can completely displace fossil fuels in the transportation market. Bioethanol is traced through its Carbon Cycle and shown to be both energy inefficient and inefficient use of biomass carbon. Both biofuels and CO₂ sequestration reduce future CO₂ emissions from continued use of fossil fuels. They will not remove CO₂ already in the atmosphere. Planting more trees has been proposed as a way to reduce atmospheric CO₂. Trees are a temporary solution. When they complete their Carbon Cycle, they die and release their carbon as CO₂ to the atmosphere. Thus, planting more trees is just 'kicking the can down the road.' The only way to permanently remove CO₂ already in the atmosphere is to break the Carbon Cycle by growing biomass from atmospheric CO₂ and sequestering biomass carbon. Sequestering tree leaves is proposed as a solution. Unlike wood, leaves have a short Carbon Cycle time constant. They renew and decompose every year. Allometric equations from the USDA indicate that theoretically, sequestrating only a fraction of the world’s tree leaves can get the world to Net Zero CO₂ without disturbing the underlying forests. How can tree leaves be permanently sequestered? It may be as simple as rethinking how landfills are designed to discourage instead of encouraging decomposition. In traditional landfills, municipal waste undergoes rapid initial aerobic decomposition to CO₂, followed by slow anaerobic decomposition to methane and CO₂. The latter can take hundreds to thousands of years. The first step in anaerobic decomposition is hydrolysis of cellulose to release sugars, which those who have worked on cellulosic ethanol know is challenging for a number of reasons. The key to permanent leaf sequestration may be keeping the landfills dry and exploiting known inhibitors for anaerobic bacteria.

Keywords: carbon dioxide, net zero, sequestration, biomass, leaves

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Authors: Chiou-Yng Lee, Wen-Yo Lee, Chieh-Tsai Wu, Cheng-Chen Yang

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Shifted polynomial basis (SPB) is a variation of polynomial basis representation. SPB has potential for efficient bit-level and digit-level implementations of multiplication over binary extension fields with subquadratic space complexity. For efficient implementation of pairing computation with large finite fields, this paper presents a new SPB multiplication algorithm based on Karatsuba schemes, and used that to derive a novel scalable multiplier architecture. Analytical results show that the proposed multiplier provides a trade-off between space and time complexities. Our proposed multiplier is modular, regular, and suitable for very-large-scale integration (VLSI) implementations. It involves less area complexity compared to the multipliers based on traditional decomposition methods. It is therefore, more suitable for efficient hardware implementation of pairing based cryptography and elliptic curve cryptography (ECC) in constraint driven applications.

Keywords: digit-serial systolic multiplier, elliptic curve cryptography (ECC), Karatsuba algorithm (KA), shifted polynomial basis (SPB), pairing computation

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Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally non-stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, local mean decomposition, rolling element bearing, vibration analysis

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Zoltan Sebestyen, Eszter Barta-Rajnai, Emma Jakab, Zsuzsanna Czegeny

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Extraction of the energy content of lignocellulosic biomass is one of the possible pathways to reduce the greenhouse gas emission derived from the burning of the fossil fuels. The application of the bioenergy can mitigate the energy dependency of a country from the foreign natural gas and the petroleum. The diversity of the plant materials makes difficult the utilization of the raw biomass in power plants. This problem can be overcome by the application of thermochemical techniques. Pyrolysis is the thermal decomposition of the raw materials under inert atmosphere at high temperatures, which produces pyrolysis gas, biooil and charcoal. The energy content of these products can be exploited by further utilization. The differences in the chemical and physical properties of the raw biomass materials can be reduced by the use of torrefaction. Torrefaction is a promising mild thermal pretreatment method performed at temperatures between 200 and 300 °C in an inert atmosphere. The goal of the pretreatment from a chemical point of view is the removal of water and the acidic groups of hemicelluloses or the whole hemicellulose fraction with minor degradation of cellulose and lignin in the biomass. Thus, the stability of biomass against biodegradation increases, while its energy density increases. The volume of the raw materials decreases so the expenses of the transportation and the storage are reduced as well. Biooil is the major product during pyrolysis and an important by-product during torrefaction of biomass. The composition of biooil mostly depends on the quality of the raw materials and the applied temperature. In this work, thermoanalytical techniques have been used to study the qualitative and quantitative composition of the pyrolysis and torrefaction oils of a woody (black locust) and two herbaceous samples (rape straw and wheat straw). The biooil contains C5 and C6 anhydrosugar molecules, as well as aromatic compounds originating from hemicellulose, cellulose, and lignin, respectively. In this study, special emphasis was placed on the formation of the lignin monomeric products. The structure of the lignin fraction is different in the wood and in the herbaceous plants. According to the thermoanalytical studies the decomposition of lignin starts above 200 °C and ends at about 500 °C. The lignin monomers are present among the components of the torrefaction oil even at relatively low temperatures. We established that the concentration and the composition of the lignin products vary significantly with the applied temperature indicating that different decomposition mechanisms dominate at low and high temperatures. The evolutions of decomposition products as well as the thermal stability of the samples were measured by thermogravimetry/mass spectrometry (TG/MS). The differences in the structure of the lignin products of woody and herbaceous samples were characterized by the method of pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). As a statistical method, principal component analysis (PCA) has been used to find correlation between the composition of lignin products of the biooil and the applied temperatures.

Keywords: pyrolysis, torrefaction, biooil, lignin

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Authors: V. S. Klimash, Ye Min Thu

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Thyristor rectifiers, inverters grid-tied, and AC voltage regulators are widely used in industry, and on electrified transport, they have a lot in common both in the power circuit and in the control system. They have a common mathematical structure and switching processes. At the same time, the rectifier, but the inverter units and thyristor regulators of alternating voltage are considered separately both theoretically and practically. They are written about in different books as completely different devices. The aim of this work is to combine them into one class based on the unity of the equations describing electromagnetic processes, and then, to show this unity on the mathematical model and experimental setup. Based on research from mathematics to the product, a conclusion is made about the methodology for the rapid conduct of research and experimental design work, preparation for production and serial production of converters with a unified bundle. In recent years, there has been a transition from thyristor circuits and transistor in modular design. Showing the example of thyristor rectifiers and AC voltage regulators, we can conclude that there is a unity of mathematical structures and grid-tied thyristor converters.

Keywords: direct current, alternating current, rectifier, AC voltage regulator, generalized mathematical model

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Authors: B. Donkova, M. Nedyalkova, D. Mehandjiev

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Sparingly soluble manganese oxalate is an appropriate precursor for the preparation of nanosized manganese oxides, which have a wide range of technological application. During the precipitation of manganese oxalate, three crystal forms could be obtained – α-MnC₂O₄.2H₂O (SG C2/c), γ-MnC₂O₄.2H₂O (SG P212121) and orthorhombic MnC₂O₄.3H₂O (SG Pcca). The thermolysis of α-MnC₂O₄.2H₂O has been extensively studied during the years, while the literature data for the other two forms has been quite scarce. The aim of the present communication is to highlight the influence of the initial crystal structure on the decomposition mechanism of these three forms, their magnetic properties, the structure of the anhydrous oxalates, as well as the nature of the obtained oxides. For the characterization of the samples XRD, SEM, DTA, TG, DSC, nitrogen adsorption, and in situ magnetic measurements were used. The dehydration proceeds in one step with α-MnC₂O₄.2H2O and γ-MnC₂O₄.2H₂O, and in three steps with MnC₂O₄.3H2O. The values of dehydration enthalpy are 97, 149 and 132 kJ/mol, respectively, and the last two were reported for the first time, best to our knowledge. The magnetic measurements show that at room temperature all samples are antiferomagnetic, however during the dehydration of α-MnC₂O₄.2H₂O the exchange interaction is preserved, for MnC₂O₄.3H₂O it changes to ferromagnetic above 35°C, and for γ-MnC₂O₄.2H₂O it changes twice from antiferomagnetic to ferromagnetic above 70°C. The experimental results for magnetic properties are in accordance with the computational results obtained with Wien2k code. The difference in the initial crystal structure of the forms used determines different changes in the specific surface area during dehydration and different extent of Mn(II) oxidation during decomposition in the air; both being highest at α-MnC₂O₄.2H₂O. The isothermal decomposition of the different oxalate forms shows that the type and physicochemical properties of the oxides, obtained at the same annealing temperature depend on the precursor used. Based on the results from the non-isothermal and isothermal experiments, and from different methods used for characterization of the sample, a comparison of the nature, mechanism and peculiarities of the thermolysis of the different crystal forms of manganese oxalate was made, which clearly reveals the influence of the initial crystal structure. Acknowledgment: 'Science and Education for Smart Growth', project BG05M2OP001-2.009-0028, COST Action MP1306 'Modern Tools for Spectroscopy on Advanced Materials', and project DCOST-01/18 (Bulgarian Science Fund).

Keywords: crystal structure, magnetic properties, manganese oxalate, thermal behavior

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Authors: Anton Serov, Alan Maslani, Michal Hlina, Vladimir Kopecky, Milan Hrabovsky

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Recycling of organic waste is an increasingly hot topic in recent years. This issue becomes even more interesting if the raw material for the fuel production can be obtained as the result of that recycling. A process of high-temperature decomposition of a lignite (a non-hydrolysable complex organic compound) was studied on the plasma gasification reactor PLASGAS, where water-stabilized plasma torch was used as a source of high enthalpy plasma. The plasma torch power was 120 kW and allowed heating of the reactor to more than 1000 °C. The material feeding rate in the gasification reactor was selected 30 and 60 kg per hour that could be compared with small industrial production. An efficiency estimation of the thermal decomposition process was done. A balance of the torch energy distribution was studied as well as an influence of the lignite particle size and an addition of methane (CH4) in a reaction volume on the syngas composition (H2+CO). It was found that the ratio H2:CO had values in the range of 1,5 to 2,5 depending on the experimental conditions. The recycling process occurred at atmospheric pressure that was one of the important benefits because of the lack of expensive vacuum pump systems. The work was supported by the Grant Agency of the Czech Republic under the project GA15-19444S.

Keywords: atmospheric pressure, lignite, plasma treatment, water-stabilized plasma torch

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

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Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of the biomass platform, comprising a potential pool of hydrogen energy that stands as a distinct energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need for in-situ H₂ production, which plays a key role in the hydrogenation reactions of biomass into higher-value components. It is reported elsewhere in the literature that catalytic decomposition of FA is usually performed in poorly designed setups using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. Our work suggests an approach that integrates designing a distinct catalyst featuring magnetic properties with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for the dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H₂ gas from FA. Using an ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under an inert medium. Through a distinct approach, FA is charged into the reactor via a high-pressure positive displacement pump at steady-state conditions. The produced gas (H₂+CO₂) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The uniqueness of this work lies in designing a very responsive catalyst, pumping a consistent amount of FA into a sealed reactor running at steady-state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at a lower temperature range (35-50°C) yielded more gas, while the catalyst loading and Pd doping wt.% were found to be the most significant factors with P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

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Authors: Farhad Alizadeh, Alireza Faregh Gharamaleki, Mojtaba Jalilzadeh, Houshang Gholami, Ali Akhoundzadeh

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This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.

Keywords: river stage-discharge process, LSSVM, discrete wavelet transform, Ensemble Empirical Decomposition Mode, multi-station modeling

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Authors: T. G. Kassem, A. K. Adunchezor, J. P. Chollom

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This paper describes a mathematical model developed to predict the dynamics of Hepatitis B virus (HBV) infection and to evaluate the potential impact of vaccination and treatment on its dynamics. We used a compartmental model expressed by a set of differential equations based on the characteristic of HBV transmission. With these, we find the threshold quantity R0, then find the local asymptotic stability of disease free equilibrium and endemic equilibrium. Furthermore, we find the global stability of the disease free and endemic equilibrium.

Keywords: hepatitis B virus, epidemiology, vaccination, mathematical model

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Authors: Pritika Ramharack, Mahmoud E. S. Soliman

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The re-emerging Zika virus is an arthropod-borne virus that has been described to have explosive potential as a worldwide pandemic. The initial transmission of the virus was through a mosquito vector, however, evolving modes of transmission has allowed the spread of the disease over continents. The virus already been linked to irreversible chronic central nervous system (CNS) conditions. The concerns of the scientific and clinical community are the consequences of Zika viral mutations, thus suggesting the urgent need for viral inhibitors. There have been large strides in vaccine development against the virus but there are still no FDA-approved drugs available. Rapid rational drug design and discovery research is fundamental in the production of potent inhibitors against the virus that will not just mask the virus, but destroy it completely. In silico drug design allows for this prompt screening of potential leads, thus decreasing the consumption of precious time and resources. This study demonstrates an optimized and proven screening technique in the discovery of two potential small molecule inhibitors of Zika virus Methyltransferase and RNA-dependent RNA polymerase. This in silico “per-residue energy decomposition pharmacophore” virtual screening approach will be critical in aiding scientists in the discovery of not only effective inhibitors of Zika viral targets, but also a wide range of anti-viral agents.

Keywords: NS5 protein inhibitors, per-residue decomposition, pharmacophore model, virtual screening, Zika virus

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Authors: In-Geun Lim, Sung-Woong Ra

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As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically.

Keywords: facility detection, satellite image, object, mathematical morphology

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Authors: Aroob Binhimd, Lyan Sayoti, Rana Almansour

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In recent years, artificial intelligence has grown drastically. This has led to the growth of educational programs to help students in solving educational problems and assist them in understanding certain topics. The purpose of this report is to investigate the Mathway application. Mathway is a mathematics software that teaches students how to solve and handle mathematical issues. The app allows students to insert questions manually on the platform or take a picture of the question, and then they get an answer to this mathematical question. It helps students enhance their performance in mathematics. This app can also be used to verify or check if their answers are correct. The report will include a questionnaire to collect data and analyze the users of this application.

Keywords: artificial intelligence, Mathway, mathematics, mathematical problems

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Authors: S. Jursová, P. Pustějovská, S. Brožová, J. Bilík

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The paper deals with possibilities of interpretation of iron ore reducibility tests. It presents a mathematical model developed at Centre ENET, VŠB–Technical University of Ostrava, Czech Republic for an evaluation of metallurgical material of blast furnace feedstock such as iron ore, sinter or pellets. According to the data from the test, the model predicts its usage in blast furnace technology and its effects on production parameters of shaft aggregate. At the beginning, the paper sums up the general concept and experience in mathematical modelling of iron ore reduction. It presents basic equation for the calculation and the main parts of the developed model. In the experimental part, there is an example of usage of the mathematical model. The paper describes the usage of data for some predictive calculation. There are presented material, method of carried test of iron ore reducibility. Then there are graphically interpreted effects of used material on carbon consumption, rate of direct reduction and the whole reduction process.

Keywords: blast furnace technology, iron ore reduction, mathematical model, prediction of iron ore reduction

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Authors: Amir Shafiee Kisomi, Mehrdad Mofidi

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Nowadays, fuel cells as a promising alternative for power source have been widely studied owing to their security, high energy density, low operation temperatures, renewable capability and low environmental pollutant emission. The nanoparticles of core-shell type could be widely described in a combination of a shell (outer layer material) and a core (inner material), and their characteristics are greatly conditional on dimensions and composition of the core and shell. In addition, the change in the constituting materials or the ratio of core to the shell can create their special noble characteristics. In this study, a fast technique for the fabrication of a Pd-Co/G/GCE modified electrode is offered. Thermal decomposition reaction of cobalt (II) formate salt over the surface of graphene/glassy carbon electrode (G/GCE) is utilized for the synthesis of Co nanoparticles. The nanoparticles of Pd-Co decorated on the graphene are created based on the following method: (1) Thermal decomposition reaction of cobalt (II) formate salt and (2) the galvanic replacement process Co by Pd2+. The physical and electrochemical performances of the as-prepared Pd-Co/G electrocatalyst are studied by Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDS), Cyclic Voltammetry (CV), and Chronoamperometry (CHA). Galvanic replacement method is utilized as a facile and spontaneous approach for growth of Pd nanostructures. The Pd-Co/G is used as an anode catalyst for ethanol oxidation in alkaline media. The Pd-Co/G not only delivered much higher current density (262.3 mAcm-2) compared to the Pd/C (32.1 mAcm-2) catalyst, but also demonstrated a negative shift of the onset oxidation potential (-0.480 vs -0.460 mV) in the forward sweep. Moreover, the novel Pd-Co/G electrocatalyst represents large electrochemically active surface area (ECSA), lower apparent activation energy (Ea), higher levels of durability and poisoning tolerance compared to the Pd/C catalyst. The paper demonstrates that the catalytic activity and stability of Pd-Co/G electrocatalyst are higher than those of the Pd/C electrocatalyst toward ethanol oxidation in alkaline media.

Keywords: thermal decomposition, nanostructures, galvanic replacement, electrocatalyst, ethanol oxidation, alkaline media

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Authors: William Adzawla

Abstract:

In the midst of climate variability and change, the role of gender in ensuring food production remains vital. Therefore, this study analysed the gendered productivity among maize farmers, and the effects of climate change and variability as well as livelihood diversification on gendered productivity gap. This involved a total of 619 farmers selected through a multistage sampling procedure. The data was analysed using Oaxaca Blinder decomposition model. From the result, there is a significant productivity gap of 58.8% and 44.8% between male and female heads, and between male heads and female spouses, respectively. About 87.47% and 98.08% of the variations in gendered productivity were explained by resource endowment. While livelihood diversification significantly influenced gendered productivity through endowment and coefficient effect, climate variables significantly affect productivity gap through only coefficient effects. The study concluded that there is a substantial gendered productivity gap among farmers and this is particularly due to differences in endowment. Generally, there is a high potential of reducing gendered productivity gaps through the provision of equal diversification opportunities and reducing females’ vulnerability to climate change. Among the livelihood activities, off-farm activities such as agro-processing and shea butter processing should be promoted. Similarly, the adoption of on-farm adaptation strategies should be promoted among the farmers.

Keywords: climate change and variability, gender, livelihood diversification, oaxaca-blinder decomposition, productivity gap

Procedia PDF Downloads 145

Authors: M. S. Lu, Y. P. Tsai, H. Shu, K. F. Chen, L. L. Lai

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This study used sodium hypochlorite/sodium dodecyl sulfate method to successfully extract polyhydroxyalkanoates (PHA) from the wasted sludge of a husbandry industry wastewater treatment plant. We investigated the optimum operational conditions of three key factors with respect to effectively extract PHAs from husbandry industry wastewater sludge, including the sodium hypochlorite concentration, liquid-solid ratio, and reaction time. The experimental results showed the optimum operational conditions for polyhydroxyalkanoate recovery as follows: (1) being digested by the sodium hypochlorite/sodium dodecyl sulfate solution with 15% (v/v) of hypochlorite concentration, (2) being operated at the condition of 1.25 mLmg-1 of liquid-solid ratio, and (3) being reacted for more than 60 min. Under these conditions, the content of the recovered PHAs was about 53.2±0.66 mgPHAs/gVSS, and the purity of the recovered PHAs was about 78.5±6.91 wt%. The recovered PHAs were further used to produce biodegradable plastics for decomposition test buried in soils. The decomposition test showed 66.5% of the biodegradable plastics produced in the study remained after being buried in soils for 49 days. The cost for extracting PHAs is about 10.3 US$/kgPHAs and is lower than those produced by pure culture methods (12-15 US$/kgPHAs).

Keywords: biodegradable plastic, biopolymers, polyhydroxyalkanoates (PHAs), waste sludge

Procedia PDF Downloads 321