Search results for: skew inverse Laurent series rings

Authors: Jui-Pui Hung, Yu-Sheng Lai, Tzuo-Liang Luo, Kung-Da Wu, Yun-Ji Zhan

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This study presented the investigation of the influence of the tool holder interface stiffness on the dynamic characteristics of a spindle tool system. The interface stiffness was produced by drawbar force on the tool holder, which tends to affect the spindle dynamics. In order to assess the influence of interface stiffness on the vibration characteristic of spindle unit, we first created a three dimensional finite element model of a high speed spindle system integrated with tool holder. The key point for the creation of FEM model is the modeling of the rolling interface within the angular contact bearings and the tool holder interface. The former can be simulated by a introducing a series of spring elements between inner and outer rings. The contact stiffness was calculated according to Hertz contact theory and the preload applied on the bearings. The interface stiffness of the tool holder was identified through the experimental measurement and finite element modal analysis. Current results show that the dynamic stiffness was greatly influenced by the tool holder system. In addition, variations of modal damping, static stiffness and dynamic stiffness of the spindle tool system were greatly determined by the interface stiffness of the tool holder which was in turn dependent on the draw bar force applied on the tool holder. Overall, this study demonstrates that identification of the interface characteristics of spindle tool holder is of very importance for the refinement of the spindle tooling system to achieve the optimum machining performance.

Keywords: dynamic stiffness, spindle-tool holder, interface stiffness, drawbar force

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Authors: Yu T. Tsai, Jin H. Huang

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In this paper, the specific sound transmission loss (TL) of the laminated composite plate (LCP) with different material properties in each layer is investigated. The numerical method to obtain the TL of the LCP is proposed by using elastic plate theory. The transfer matrix approach is novelty presented for computational efficiency in solving the numerous layers of dynamic stiffness matrix (D-matrix) of the LCP. Besides the numerical simulations for calculating the TL of the LCP, the material properties inverse method is presented for the design of a laminated composite plate analogous to a metallic plate with a specified TL. As a result, it demonstrates that the proposed computational algorithm exhibits high efficiency with a small number of iterations for achieving the goal. This method can be effectively employed to design and develop tailor-made materials for various applications.

Keywords: sound transmission loss, laminated composite plate, transfer matrix approach, inverse problem, elastic plate theory, material properties

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Authors: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

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This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

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Authors: Marina Benito-Parejo, Raul Perez-Lopez, Miguel Herraiz, Carolina Guardiola-Albert, Cesar Martinez

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Persistency, long-term memory and randomness are intrinsic properties of time-series of earthquakes. The Rescaled Range Analysis (RS-Analysis) was introduced by Hurst in 1956 and modified by Mandelbrot and Wallis in 1964. This method represents a simple and elegant analysis which determines the range of variation of one natural property (the seismic energy released in this case) in a time interval. Despite the simplicity, there is complexity inherent in the property measured. The cumulative curve of the energy released in time is the well-known fractal geometry of a devil’s staircase. This geometry is used for determining the maximum and minimum value of the range, which is normalized by the standard deviation. The rescaled range obtained obeys a power-law with the time, and the exponent is the Hurst value. Depending on this value, time-series can be classified in long-term or short-term memory. Hence, an algorithm has been developed for compiling the RS-Analysis for time series of earthquakes by days. Completeness time distribution and locally stationarity of the time series are required. The interest of this analysis is their application for a complex seismic crisis where different earthquakes take place in clusters in a short period. Therefore, the Hurst exponent has been obtained for the seismic crisis of Alhoceima (Mediterranean Sea) of January-March, 2016, where at least five medium-sized earthquakes were triggered. According to the values obtained from the Hurst exponent for each cluster, a different mechanical origin can be detected, corroborated by the focal mechanisms calculated by the official institutions. Therefore, this type of analysis not only allows an approach to a greater understanding of a seismic series but also makes possible to discern different types of seismic origins.

Keywords: Alhoceima crisis, earthquake time series, Hurst exponent, rescaled range analysis

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Authors: Di Yao, Gunther Prokop, Kay Buttner

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Dynamic parameters, including the center of gravity, mass and inertia moments of vehicle, play an essential role in vehicle simulation, collision test and real-time control of vehicle active systems. To identify the important vehicle dynamic parameters, a systematic parameter identification procedure is studied in this work. In the first step of the procedure, a conceptual parallel manipulator (virtual test rig), which possesses three rotational degrees-of-freedom, is firstly proposed. To realize kinematic characteristics of the conceptual parallel manipulator, the kinematic analysis consists of inverse kinematic and singularity architecture is carried out. Based on the Euler's rotation equations for rigid body dynamics, the dynamic model of parallel manipulator and derivation of measurement matrix for parameter identification are presented subsequently. In order to reduce the sensitivity of parameter identification to measurement noise and other unexpected disturbances, a parameter optimization process of searching for optimal exciting trajectory of parallel manipulator is conducted in the following section. For this purpose, the 321-Euler-angles defined by parameterized finite-Fourier-series are primarily used to describe the general exciting trajectory of parallel manipulator. To minimize the condition number of measurement matrix for achieving better parameter identification accuracy, the unknown coefficients of parameterized finite-Fourier-series are estimated by employing an iterative algorithm based on MATLAB®. Meanwhile, the iterative algorithm will ensure the parallel manipulator still keeps in an achievable working status during the execution of optimal exciting trajectory. It is showed that the proposed procedure and methods in this work can effectively identify the vehicle dynamic parameters and could be an important application of parallel manipulator in the fields of parameter identification and test rig development.

Keywords: parameter identification, parallel manipulator, singularity architecture, dynamic modelling, exciting trajectory

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Authors: Manuela Gamba, Niki Mandolesi

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Teenagers today have little knowledge about how to move or play together. The adults who are part of sports culture must find an effective way to foster this essential ability. Our research in Italy uses a 'holistic model' based on fantasy literature to explore the relationships between the game identities and self-identities of young people and the achievement of psycho-motor, emotional and social well-being in the realms of sport and education. Physical activity projects were carried out in schools and extra-curricular associations in Rome, combining outdoor activities and distance learning. This holistic and malleable game model is inspired by fantasy accounts of the journeys taken in The Lord of Rings and Harry Potter books. We know that many have a lot of resistance to the idea of using fantasy and play as a pedagogical tool, but the results obtained in this experience are surprising. Our interventions and investigations focused on promoting self-esteem, awareness, a sense of belonging, social integration, cooperation, well-being, and informed decision making: a basis for healthy and effective citizenship. For teenagers, creative thinking is the right stimulus to involve and compare the story of characters to their own journey through social and self-reflective identity analysis. We observed how important it is to engage students emotionally as well as cognitively and that enabling them to play with identity through relationships with peers. There is a need today for a multidisciplinary synthesis of analog and digital values, especially in response to recent distance-living experiences. There is a need for a global reconceptualization of free time and nature in the human experience.

Keywords: awareness, creativity, identity, play

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Authors: Debasis Mondal, Chandan Datta, S. K. Sazim

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Quantum coherence is a key resource like entanglement and discord in quantum information theory. Wigner- Yanase skew information, which was shown to be the quantum part of the uncertainty, has recently been projected as an observable measure of quantum coherence. On the other hand, the quantum speed limit has been established as an important notion for developing the ultra-speed quantum computer and communication channel. Here, we show that both of these quantities are related. Thus, cast coherence as a resource to control the speed of quantum communication. In this work, we address three basic and fundamental questions. There have been rigorous attempts to achieve more and tighter evolution time bounds and to generalize them for mixed states. However, we are yet to know (i) what is the ultimate limit of quantum speed? (ii) Can we measure this speed of quantum evolution in the interferometry by measuring a physically realizable quantity? Most of the bounds in the literature are either not measurable in the interference experiments or not tight enough. As a result, cannot be effectively used in the experiments on quantum metrology, quantum thermodynamics, and quantum communication and especially in Unruh effect detection et cetera, where a small fluctuation in a parameter is needed to be detected. Therefore, a search for the tightest yet experimentally realisable bound is a need of the hour. It will be much more interesting if one can relate various properties of the states or operations, such as coherence, asymmetry, dimension, quantum correlations et cetera and QSL. Although, these understandings may help us to control and manipulate the speed of communication, apart from the particular cases like the Josephson junction and multipartite scenario, there has been a little advancement in this direction. Therefore, the third question we ask: (iii) Can we relate such quantities with QSL? In this paper, we address these fundamental questions and show that quantum coherence or asymmetry plays an important role in setting the QSL. An important question in the study of quantum speed limit may be how it behaves under classical mixing and partial elimination of states. This is because this may help us to choose properly a state or evolution operator to control the speed limit. In this paper, we try to address this question and show that the product of the time bound of the evolution and the quantum part of the uncertainty in energy or quantum coherence or asymmetry of the state with respect to the evolution operator decreases under classical mixing and partial elimination of states.

Keywords: completely positive trace preserving maps, quantum coherence, quantum speed limit, Wigner-Yanase Skew information

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Authors: Abiodun Ayodele Sanyaolu

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The research was carried out to investigate the time series analysis on quarterly production of fruit juice at the National Horticultural Research Institute Ibadan from 2010 to 2018. Documentary method of data collection was used, and the method of least square and moving average were used in the analysis. From the calculation and the graph, it was glaring that there was increase, decrease, and uniform movements in both the graph of the original data and the tabulated quarter values of the original data. Time series analysis was used to detect the trend in the highest number of fruit juice and it appears to be good over a period of time and the methods used to forecast are additive and multiplicative models. Since it was observed that the production of fruit juice is usually high in January of every year, it is strongly advised that National Horticultural Research Institute should make more provision for fruit juice storage outside this period of the year.

Keywords: fruit juice, least square, multiplicative models, time series

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Authors: Hanan Eltayeb, Reem Al Refaie

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To be able to connect and relate to shows spoken in a foreign language, advanced learners must understand not only linguistics inferences but also cultural, metacognitive, and pragmatic connotations in colloquial Egyptian TV series. These connotations are needed to both understand the different facets of the dramas put before them, and they’re also consistently grown and formulated through watching these shows. The inferences have become a staple in the Egyptian colloquial culture over the years, making their way into day-to-day conversations as Egyptians use them to speak, relate, joke, and connect with each other, without having known one another from previous times. As for advanced learners, they need to understand these inferences not only to watch these shows, but also to be able to converse with Egyptians on a level that surpasses the formal, or standard. When faced with some of the somewhat recent shows on the Egyptian screens, learners faced challenges in understanding pragmatics, cultural, and religious background of the target language and consequently not able to interact effectively with a native speaker in real-life situations. This study aims to enhance the linguistic and cultural proficiency of learners through studying two genres of TV Colloquial Egyptian series. Study samples derived from two recent comedian and social Egyptian series ('The Seventh Neighbor' سابع جار, and 'Nelly and Sherihan' نيللي و شريهان). When learners watch such series, they are usually faced with a problem understanding inferences that have to do with social, religious, and political events that are addressed in the series. Using discourse analysis of the sematic, semantic, pragmatic, cultural, and linguistic characteristics of the target language, some major deductions were highlighted and repeated, showing a pattern in both. The research paper concludes that there are many sets of lingual and para-lingual phrases, idioms, and proverbs to be acquired and used effectively by teaching these series. The strategies adopted in the study can be applied to different types of media, like movies, TV shows, and even cartoons, to enhance student proficiency.

Keywords: Egyptian series, culture, linguistic competence, pragmatics, semantics, social

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Authors: Oyku Yenen

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The notion of biopolitics, as studied by such intellectuals as Foucault, Agamben, and Negri, is an important guide for comprehending the current understanding of politics. While Foucault evaluates biopolitics as a survival policy, Giorgio Agamben, German legist, identifies the theory with death. Agamben claims the fact we can all considered to be homo sacer who are abandoned by the law, left in the field of exception, and whose killing does not require punishment. Agamben defines the person who is tried by the public for committing a crime but is not allowed to be sacrificed and whose killing is not considered a crime, as 'homo sacer'. This study analyzes how the concept of 'homo sacer' is made visible in TV series such as Çemberimde Gül Oya (Cagan Irmak, 2005-2005), Hatırla Sevgili (Ummu Burhan, 2006-2008), Bu Kalp Seni Unutur Mu? (Aydin Bulut, 2009-1010) all of which portray the period Turkey's 1980 military coup, within the framework of Agamben's thoughts and notions about biopolitics. When the main plots of these abovementioned TV series, which constitute the universe of this study, are scrutinized closely, they lay out the understanding of politics that has existed throughout history and prevails today. Although there is a large number of TV series on the coup of 1980, these three series are the only main productions that specifically focused on the event itself. Our final analysis will reveal that the concepts of homo sacer, bare life, exception, camp have been embodied in different ways in these three series. In these three series, which all deal with similar subjects using differing perspectives, the dominant understanding of politics is clearly conveyed to the audience. In all three series, the reigning power always decides on the exceptions, those who will live, those who will die, and those who will be ignored by law. Such characters as Mehmet, Sinan, Yıldız, Deniz, Defne, all of which we come across in these series, are on trial as a criminals of thought and are subjected to various forms of torture while isolated in an area where they are virtually deprived of law. Their citizenship rights are revoked. All of them are left alone with their bare lives (zoe).

Keywords: bare life, biopolitics, homo sacer, sovereign power, state of exception

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Authors: Ana Clara Santos, Maria Manuela Portela, Bettina Schaefli

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This work assesses the performance of an analytical model framework to generate daily flow duration curves, FDCs, based on climatic characteristics of the catchments and on their streamflow recession coefficients. According to the analytical model framework, precipitation is considered to be a stochastic process, modeled as a marked Poisson process, and recession is considered to be deterministic, with parameters that can be computed based on different models. The analytical model framework was tested for three case studies with different hydrological regimes located in Switzerland: pluvial, snow-dominated and glacier. For that purpose, five time intervals were analyzed (the four meteorological seasons and the civil year) and two developments of the model were tested: one considering a linear recession model and the other adopting a nonlinear recession model. Those developments were combined with recession coefficients obtained from two different approaches: forward and inverse estimation. The performance of the analytical framework when considering forward parameter estimation is poor in comparison with the inverse estimation for both, linear and nonlinear models. For the pluvial catchment, the inverse estimation shows exceptional good results, especially for the nonlinear model, clearing suggesting that the model has the ability to describe FDCs. For the snow-dominated and glacier catchments the seasonal results are better than the annual ones suggesting that the model can describe streamflows in those conditions and that future efforts should focus on improving and combining seasonal curves instead of considering single annual ones.

Keywords: analytical streamflow distribution, stochastic process, linear and non-linear recession, hydrological modelling, daily discharges

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Authors: Murray L. Ireland, Kevin J. Worrall, Rebecca Mackenzie, Thaleia Flessa, Euan McGookin, Douglas Thomson

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Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing input residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances.

Keywords: fault detection, ground robot, inverse simulation, rover

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Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao

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The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: reliability, optimization, meta-heuristic, genetic algorithm, redundancy

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Boris Melnikov, Ye Zhang, Dmitrii Chaikovskii

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We continue to consider one of the cybernetic methods in computational biology related to the study of DNA chains. Namely, we are considering the problem of reconstructing the not fully filled distance matrix of DNA chains. When applied in a programming context, it is revealed that with a modern computer of average capabilities, creating even a small-sized distance matrix for mitochondrial DNA sequences is quite time-consuming with standard algorithms. As the size of the matrix grows larger, the computational effort required increases significantly, potentially spanning several weeks to months of non-stop computer processing. Hence, calculating the distance matrix on conventional computers is hardly feasible, and supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains; then, we published algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. In this paper, we propose an algorithm for restoring the distance matrix for DNA chains, and the primary focus is on enhancing the algorithms that shape the greedy function within the branches and boundaries method framework.

Keywords: DNA chains, distance matrix, optimization problem, restoring algorithm, greedy algorithm, heuristics

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: L. Mallesha, P. Mallu, B. Veeresh

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In the present study, 2, 6-diflurobenzohydrazide and 4-fluorophenylisothiocyanate were used as the starting materials to synthesize 5-phenyl-N3-(4-fluorophenyl)-4H-1, 2, 4-triazole-3, 4-diamine. Further, compound 5-phenyl-N3-(4-fluorophenyl)-4H-1, 2, 4-triazole-3,4-diamine reacted with fluoro substituted benzaldehydes to yield a series of Schiff bases. All the final compounds were characterized using IR, 1H NMR, 13C NMR, MS and elemental analyses. New compounds were evaluated for their antiproliferative effect using the MTT assay method against four human cancer cell lines (K562, COLO-205, MDA-MB231, and IMR-32) for the time period of 24 h. Among the series, few compounds showed good activity on all cell lines, whereas the other compounds in the series exhibited moderate activity.

Keywords: Schiff bases, MTT assay, antiproliferative activity, human cancer cell lines, 1, 2, 4-triazoles

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Authors: Yashas Mattur, Jensen Coonradt

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Muon flux, the rate of muons reaching Earth from the atmosphere, is impacted by various factors such as air pressure, temperature, and humidity. However, the influence of concentrations of PM₂.₅ (particulate matter with diameters 2.5 mm or smaller) on muon detection rates remains unexplored. During the summer of 2023, smoke from Canadian wildfires (containing significant amounts of particulate matter) blew over regions in the Northern US, introducing huge fluctuations in PM₂.₅ concentrations, thus inspiring our experiment to investigate the correlation of PM₂.₅ concentrations and muon rates. To investigate this correlation, muon collision rates were measured and analyzed alongside PM₂.₅ concentration data over the periods of both light and heavy smoke. Other confounding variables, including temperature, humidity, and atmospheric pressure, were also considered. The results reveal a statistically significant inverse correlation between muon flux and PM₂.₅ concentrations, indicating that particulate matter has an impact on the rate of muons reaching the earth’s surface.

Keywords: Muon Flux, atmospheric effects on muons, PM₂.₅, airborne particulate matter

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Authors: Pranas Baltrėnas, Dainius Paliulis

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Cyclones are widely used for separating particles from gas in energy production objects. Efficiency of normal centrifugal air cleaning devices ranges from 85 to 90%, but weakness of many cyclones is low collection efficiency of particles less than 10 μm in diameter. Many factors have impact on cyclone efficiency – humidity, temperature, gas (air) composition, airflow velocity and etc. Many scientists evaluated only effect of origin and size of PM on cyclone efficiency. Effect of gas (air) composition and temperature on cyclone efficiency still demands contributions. Complex experimental research on efficiency of cylindrical eight-channel system with adjustable half-rings for removing fine dispersive particles (< 20 μm) was carried out. The impact of gaseous smoke components on removal of wood ashes was analyzed. Gaseous components, present in the smoke mixture, with the dynamic viscosity lower than that of same temperature air, decrease the d50 value, simultaneously increasing the overall particulate matter removal efficiency in the cyclone, i.e. this effect is attributed to CO2 and CO, while O2 and NO have the opposite effect. Air temperature influences the d50 value, an increase in air temperature yields an increase in d50 value, i.e. the overall particulate matter removal efficiency declines, the reason for this being an increasing dynamic air viscosity. At 120 °C temperature the d50 value is approximately 11.8 % higher than at air temperature of 20 °C. With an increase in smoke (gas) temperature from 20 °C to 50 °C, the aerodynamic resistance in a 1-tier eight-channel cylindrical cyclone drops from 1605 to 1380 Pa, from 1660 to 1420 Pa in a 2-tier eight-channel cylindrical cyclone, from 1715 to 1450 Pa in a 3-tier eight-channel cylindrical cyclone. The reason for a decline in aerodynamic resistance is the declining gas density. The aim of the paper is to analyze the impact of gaseous smoke components on the eight–channel cyclone with tangential inlet.

Keywords: cyclone, adjustable half-rings, particulate matter, efficiency, gaseous compounds, smoke

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

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Authors: Andres F. Ramirez, Carlos F. Valencia

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The increasing interest in renewable energies strategies application and the path for diminishing the use of carbon related energy sources have encouraged the development of novel strategies for integration of solar energy into the electricity network. A correct inclusion of the fluctuating energy output of a photovoltaic (PV) energy system into an electric grid requires improvements in the forecasting and simulation methodologies for solar energy potential, and the understanding not only of the mean value of the series but the associated underlying stochastic process. We present a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature bivariate time series based on copula functions to represent the cross-dependence and temporal structure of the data. We explore the advantages of using this nonlinear time series method over traditional approaches that use a transformation of the data to normal distributions as an intermediate step. The use of copulas gives flexibility to represent the serial variability of the real data on the simulation and allows having more control on the desired properties of the data. We use discrete zero mass density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time dependent distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, generates a significant improvement over state of the art strategies. These results will help to better understand the fluctuating nature of solar energy forecasting, the underlying stochastic process, and quantify the potential of a photovoltaic (PV) energy generating system integration into a country electricity network. Experimental analysis and real data application substantiate the usage and convenience of the proposed methodology to forecast solar irradiance time series and solar energy across northern hemisphere, southern hemisphere, and equatorial zones.

Keywords: copula autoregressive, solar irradiance forecasting, solar energy forecasting, time series generation

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Authors: Krish Jhurani

Abstract:

The research paper presents an in-depth analysis of symmetry properties in linear algebraic systems under the operation of non-canonical scalar multiplication structures, specifically semirings, and near-rings. The objective is to unveil the profound alterations that occur in traditional linear algebraic structures when we replace conventional field multiplication with these non-canonical operations. In the methodology, we first establish the theoretical foundations of non-canonical scalar multiplication, followed by a meticulous investigation into the resulting symmetry properties, focusing on eigenvectors, eigenspaces, and invariant subspaces. The methodology involves a combination of rigorous mathematical proofs and derivations, supplemented by illustrative examples that exhibit these discovered symmetry properties in tangible mathematical scenarios. The core findings uncover unique symmetry attributes. For linear algebraic systems with semiring scalar multiplication, we reveal eigenvectors and eigenvalues. Systems operating under near-ring scalar multiplication disclose unique invariant subspaces. These discoveries drastically broaden the traditional landscape of symmetry properties in linear algebraic systems. With the application of these findings, potential practical implications span across various fields such as physics, coding theory, and cryptography. They could enhance error detection and correction codes, devise more secure cryptographic algorithms, and even influence theoretical physics. This expansion of applicability accentuates the significance of the presented research. The research paper thus contributes to the mathematical community by bringing forth perspectives on linear algebraic systems and their symmetry properties through the lens of non-canonical scalar multiplication, coupled with an exploration of practical applications.

Keywords: eigenspaces, eigenvectors, invariant subspaces, near-rings, non-canonical scalar multiplication, semirings, symmetry properties

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Authors: Marina Franulovic, Robert Basan, Bozidar Krizan

Abstract:

Constitutive modelling of material behaviour is becoming increasingly important in prediction of possible failures in highly loaded engineering components, and consequently, optimization of their design. In order to account for large number of phenomena that occur in the material during operation, such as kinematic hardening effect in low cycle fatigue behaviour of steels, complex nonlinear material models are used ever more frequently, despite of the complexity of determination of their parameters. As a method for the determination of these parameters, genetic algorithm is good choice because of its capability to provide very good approximation of the solution in systems with large number of unknown variables. For the application of genetic algorithm to parameter identification, inverse analysis must be primarily defined. It is used as a tool to fine-tune calculated stress-strain values with experimental ones. In order to choose proper objective function for inverse analysis among already existent and newly developed functions, the research is performed to investigate its influence on material behaviour modelling.

Keywords: genetic algorithm, kinematic hardening, material model, objective function

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

Abstract:

Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

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Authors: Chimezie P. Nnanwa, Thomas C. Urama, Patrick O. Ezepue

Abstract:

In this paper we use Random Matrix Theory to analyze the eigen-structure of the empirical correlations of 82 stocks which are consistently traded in the Nigerian Stock Exchange (NSE) over a 4-year study period 3 August 2009 to 26 August 2013. We apply the Marchenko-Pastur distribution of eigenvalues of a purely random matrix to investigate the presence of investment-pertinent information contained in the empirical correlation matrix of the selected stocks. We use hypothesised standard normal distribution of eigenvector components from RMT to assess deviations of the empirical eigenvectors to this distribution for different eigenvalues. We also use the Inverse Participation Ratio to measure the deviation of eigenvectors of the empirical correlation matrix from RMT results. These preliminary results on the dynamics of asset price correlations in the NSE are important for improving risk-return trade-offs associated with Markowitz’s portfolio optimization in the stock exchange, which is pursued in future work.

Keywords: correlation matrix, eigenvalue and eigenvector, inverse participation ratio, portfolio optimization, random matrix theory

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Authors: Noraizah Abu Bakar, Norhayati Alais, Nurdiana Azizan, Fatimah Alwi, Muhammad Zaky Razaly

Abstract:

This is a concise research module with the Islamic values concept proposed to a group of researches, potential researchers, PhD and Master Scholars to prepare themselves for their studies. The intention of designing this module is to help and guide Malaysian citizens to undergone their postgraduate’s studies. This is aligned with the 10th Malaysian plan- MyBrain 15. MyBrain 15 is a financial aid to Malaysian citizens to pursue PhD and Master programs. The program becomes one of Ministry of Education Strategic Plan to ensure by year 2013, there will be 60,000 PhD scholars in Malaysia. This module is suitable for the social science researchers; however it can be useful tool for science technology researchers such as Engineering and Information Technology disciplines too. The module consists of eight (8) series that provides a proper flow of information in doing research with the Islamic Value Application provided in each of the series. This module is designed to produce future researchers with a comprehensive knowledge of humankind and the hereafter. The uniqueness about this research module is designed based on Islamic values concept. Researchers were able to understand the proper research process and simultaneously be able to open their minds to understand Islam more closely. Application of Islamic values in each series could trigger a broader idea for researchers to examine in greater depth of knowledge related to humanities.

Keywords: Eight Series Research Module, Islamic Values concept, Teaching Methodology, Flow of Information, Epistemology of research

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Authors: Deepika Saini, Sandeep Jain, Ajay Kumar

Abstract:

The quinoline scaffold is one of the most widely studied in the discovery of derivatives with various heterocyclic moieties due to its potential antimalarial activities. In the present study, a chalcone series of quinoline derivatives clubbed with pyrazole were synthesized to evaluate their antimalarial property by in vitro schizont maturation inhibition assay against both chloroquine sensitive, 3D7 and chloroquine resistant, RKL9 strain of Plasmodium falciparum. Further, top five compounds were studied for in vivo preclinical study for antimalarial potential against P. berghei in Swiss albino mice. To understand the mechanism of synthesized analogues, they were screened computationally by molecular docking techniques. Compounds were docked into the active site of a protein receptor, Plasmodium falciparum Cysteine Protease Falcipain-2. The compounds were successfully synthesized, and structural confirmation was performed by FTIR, 1H-NMR, mass spectrometry and elemental analysis. In vitro study suggested that the compounds 5b, 5g, 5l, 5s and 5u possessed best antimalarial activity and further tested for in vivo screening. Compound 5u (CH₃ on both rings) with EC₅₀ 0.313 & 0.801 µg/ml against CQ-S & CQ-R strains of P. falciparum respectively and 78.01% suppression of parasitemia. The molecular docking studies of the compounds helped in understanding the mechanism of action against falcipain-2. The present study reveals the binding signatures of the synthesized ligands within the active site of the protein, and it explains the results from in vitro study in their EC₅₀ values and percentage parasitemia.

Keywords: antimalarial activity, chalcone, docking, quinoline

Procedia PDF Downloads 377