Search results for: series compensation

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: 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: Chong Jiang, Liang Zhao, Hua Jian, Xiaoguang Wang

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The belief that there is a strong relationship between reading narrative and morality has generally become the basic assumption of scholars, philosophers, critics, and cultural critics. The virtuality constructed by literary novels inspires readers to regard the narrative as a thinking experiment, creating the distance between readers and events so that they can freely and morally experience the positions of different roles. Therefore, the virtual narrative combined with literary characteristics is always considered as a "moral laboratory." Well-established findings revealed that people show less lying and deceptive behaviors in the morning than in the afternoon, called the morning morality effect. As a limited self-regulation resource, morality will be constantly depleted with the change of time rhythm under the influence of the morning morality effect. It can also be compensated and restored in various ways, such as eating, sleeping, etc. As a common form of entertainment in modern society, literary novel reading gives people more virtual experience and emotional catharsis, just as a relaxing afternoon tea that helps people break away from fast-paced work, restore physical strength, and relieve stress in a short period of leisure. In this paper, inspired by the compensation control theory, we wonder whether reading literary novels in the digital environment could replenish a kind of spiritual energy for self-regulation to compensate for people's moral loss in the afternoon. Based on this assumption, we leverage the social annotation text content generated by readers in digital reading to represent the readers' reading attention. We then recognized the semantics and calculated the readers' moral motivation expressed in the annotations and investigated the fine-grained dynamics of the moral motivation changing in each time slot within 24 hours of a day. Comprehensively comparing the division of different time intervals, sufficient experiments showed that the moral motivation reflected in the annotations in the afternoon is significantly higher than that in the morning. The results robustly verified the hypothesis that reading compensates for moral motivation, which we called the moral afternoon tea effect. Moreover, we quantitatively identified that such moral compensation can last until 14:00 in the afternoon and 21:00 in the evening. In addition, it is interesting to find that the division of time intervals of different units impacts the identification of moral rhythms. Dividing the time intervals by four-hour time slot brings more insights of moral rhythms compared with that of three-hour and six-hour time slot.

Keywords: digital reading, social annotation, moral motivation, morning morality effect, control compensation

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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: 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: 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: J. Niemisto, A. Nissinen, S. Soimakallio

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Aviation causes carbon dioxide (CO2) emissions and other climate forcers which increase the contribution of aviation on climate change. Aviation industry and number of air travellers are constantly increasing. Aviation industry has an ambitious goal to strongly cut net CO2 emissions. Modern fleet, alternative jet fuels technologies and route optimisation are important technological tools in the emission reduction. Faster approaches are needed as well. Emission trade systems, voluntary carbon offset compensation schemes and taxation are already in operation. Global scenarios of aviation industry and its greenhouse gas emissions and other climate forcers are discussed in this review study based on literature and other published data. The focus is on the aviation in Nordic countries, but also European and global situation are considered. Different emission reduction technologies and compensation modes are examined. In addition, the role of aviation in a single passenger’s (a Finnish consumer) annual carbon footprint is analysed and a comparison of available emission calculators and carbon offset systems is performed. Long-haul fights have a significant role in a single consumer´s and company´s carbon footprint, but remarkable change in global emission level would need a huge change in attitudes towards flying.

Keywords: aviation, climate change, emissions, environment

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Authors: Chikae Isobe, Toshihiko Souma, Yoshiya Furukawa

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In Japan, an organization is sometimes asked for responsibility and apology toward the organization for the annoying behavior of employees, even though the behavior is not relevant to the organization. Our studies have repeatedly shown that it is important for organizational evaluation to organization propose compensatory behavior for such annoying behavior, even though the behavior is not relevant to the organization. In this study, it was examined how such an organizational response (apology) was likely to evaluate by members of the organization who were not related to the annoying behavior. Three independent variables were manipulated that is organization emotion (guilt and shame), compensation (proposal or not), and the relation between organization and the annoying behavior (relate or not). And the effects of organizational identity (high and low) were also examined. We conducted an online survey for 240 participants through a crowdsourcing company. Participants were asked to imagine a situation in which an incident in which some people in your company did not return an important document that they borrowed privately (vs. at work) became the topic of discussion, and the company responded. For the analysis,189 data (111 males and 78 females, mean age = 40.6) were selected. The results of ANOVA of 2 by2 on organizational appraisal, perceived organizational responsibility, and so on were conducted. Organization appraisal by members was also higher when the organization proposed compensatory behavior. In addition, when the annoying behavior was related to their work (than no related), for those who were high in organization identity (than low), organization appraisal was high. The interaction between relatedness and organizational identity was significant. Differences in relatedness between the organization and annoying behavior were significant in those with low organizational identity but not in those with high organizational identity. When the organization stated not taking compensatory action, members were more likely to perceive the organization as responsible for the annoying behavior. However, the interaction results indicated this tendency was limited to when the annoying behavior was not related to the organization. Furthermore, it tended to be perceived as responsible for the organization when the organization made a statement that felt shame for the annoying behavior not related to the organization and would compensate for the annoying behavior. These results indicate that even members of the organization do not consider the organization's compensatory actions to be unjustified. In addition, because those with high organizational identity perceived the organization to be responsible when it showed strong remorse (shame and compensation), they would be a tendency to make judgments that are consistent with organizational judgments. It would be considered that the Japanese have the norm that even if the organization is not at fault for a member's disruptive behavior, it should respond to it.

Keywords: appraisal for organization, annoying behavior, group shame and guilt, compensation, organizational apologies

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

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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: K. K. A. Bakhti, D. Mottet, J. Froger, I. Laffont

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Background: After a cerebrovascular accident (or stroke), many patients use excessive trunk movements to move their paretic hand towards a target (while the elbow is maintained flexed) even though they can use the upper-limb when the trunk is restrained. This phenomenon is labelled learned non-use and is known to be detrimental to neuroplasticity and recovery. Objective: The aim of this study is to quantify learned non-use of the paretic upper limb during a hand reaching task using 3D movement analysis. Methods: Thirty-four participants post supratentorial stroke were asked to reach a cone placed in front of them at 80% of their arm length. The reaching movement was repeated 5 times with the paretic hand, and then 5 times with the less-impaired hand. This sequence was first performed with the trunk free, then with the trunk restrained. Learned non-use of the upper-limb (LNUUL) was obtained from the difference of the amount of trunk compensation between the free trunk condition and the restrained trunk condition. Results: LNUUL was significantly higher for the paretic hand, with individual values ranging from 1% to 43%, and one-half of the patients with an LNUUL higher than 15%. Conclusions: Quantification of LNUUL can be used to objectively diagnose patients who need trunk rehabilitation. It can be also used for monitoring the rehabilitation progress. Quantification of LNUUL may guide upper-limb rehabilitation towards more optimal motor recovery avoiding maladaptive trunk compensation and its consequences on neuroplasticity.

Keywords: learned non-use, rehabilitation, stroke, upper limb

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

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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: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Tso-Yen Mao, De-Yen Liu, Chun-Feng Huang

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Purpose: This research intends to investigate the reliability and validity of ventilatory threshold (VT) and respiratory compensation point (RCP) determined by skeletal muscle hemodynamic status. Methods: One hundred healthy male (age: 22±3 yrs; height: 173.1±6.0 cm; weight: 67.1±10.5 kg) performed graded cycling exercise test which ventilatory and skeletal muscle hemodynamic data were collected simultaneously. VT and RCP were determined by combined V-slope (VE vs. VCO2) and ventilatory efficiency (VE/VO2 vs. VE/VCO2) methods. Pearson correlation, paired t-test, and Bland-Altman plots were used to analyze reliability, validity, and similarities. Statistical significance was set at α =. 05. Results: There are high test-retest correlations of VT and RCP in ventilatory or near-infrared spectroscopy (NIRS) methods (VT vs. VTNIRS: 0.95 vs. 0.94; RCP vs. RCPNIRS: 0.93 vs. 0.93, p<. 05). There are high coefficient of determination at the first timing point of O2Hb decreased (R2 = 0.88, p<. 05) with VT, and high coefficient of determination at the second timing point of O2Hb declined (R2 = 0.89, p< .05) with RCP. VO2 of VT and RCP are not significantly different between ventilatory and NIRS methods (p>. 05). Conclusion: Using NIRS method to determine VT and RCP is reliable and valid in male individuals during graded exercise. Non-invasive skeletal muscle hemodynamics monitor also can be used for controlling training intensity in the future.

Keywords: anaerobic threshold, exercise intensity, hemodynamic, NIRS

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Authors: N. Ravikumar, S. Krishnan, B. Yokeshkumar

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In present days, the power quality issues are increases due to non-linear loads like fridge, AC, washing machines, induction motor, etc. This power quality issues will affects the output voltages, output current, and output power of the total performance of the generator. This paper explains how to test the generator using the Fluke 435 II series power quality analyser. This Fluke 435 II series power quality analyser is used to measure the voltage, current, power, energy, total harmonic distortion (THD), current harmonics, voltage harmonics, power factor, and frequency. The Fluke 435 II series power quality analyser have several advantages. They are i) it will records output in analog and digital format. ii) the fluke analyzer will records at every 0.25 sec. iii) it will also measure all the electrical parameter at a time.

Keywords: THD, harmonics, power quality, TNEB, Fluke 435

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Authors: Narges Hamidipour

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VOD, which stands for "video on demand", is one kind of watching movies and series on web platforms that, by using them, individuals can access lots of video content by paying abonnement. The first platform in Iran was funded in 2014, and in the last 10 years, it has become the main part of the movie and series industry. There are 374 VOD platforms in Iran, but just three of them are in the mainstream. However, in these years, they have been developed and famed in different ways. This article focuses on the reasons for this development in the past years. For the framework, "digital economy", "media industries," and "political economy" have been used with the interview method. In this research, some experts in SATRA (regulatory organization of inclusive audio and video media in Iran), owners or managers of VODs and some others who directly have been in the system conveyed their opinions. By the way, some documents and analysis statistics are invoked to reach complete results.

Keywords: digital economy, political economy, VOD, interview, iran

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Authors: Ali Ben Abbes, Imed Riadh Farah

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Due to the considerable advances in Multi-Temporal Satellite Images (MTSI), remote sensing application became more accurate. Recently, many advances in modeling MTSI are developed using various models. The purpose of this article is to present an overview of studies using Hidden Markov Model (HMM). First of all, we provide a background of using HMM and their applications in this context. A comparison of the different works is discussed, and possible areas and challenges are highlighted. Secondly, we discussed the difference on vegetation monitoring as well as urban growth. Nevertheless, most research efforts have been used only stationary data. From another point of view, in this paper, we describe a new non-stationarity HMM, that is defined with a set of parts of the time series e.g. seasonal, trend and random. In addition, a new approach giving more accurate results and improve the applicability of the HMM in modeling a non-stationary data series. In order to assess the performance of the HMM, different experiments are carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern region of Tunisia and Landsat time series of tres Cantos-Madrid in Spain.

Keywords: multi-temporal satellite image, HMM , nonstationarity, vegetation, urban

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Authors: Sandra Regina Cavalcante, Rodolfo A. G. Vilela

Abstract:

Introduction: Work accidents and occupational diseases are a big problem for public health around the world and the main health problem of workers with high social and economic costs. Brazil has shown progress over the last years, with the development of the regulatory system to improve safety and quality of life in the workplace. However, the situation is far from acceptable, because the occurrences remain high and there is a great gap between legislation and reality, generated by the low level of voluntary compliance with the law. Brazilian laws provide procedural legal instruments for both, to compensate the damage caused to the worker's health and to prevent future injuries. In the Judiciary, the prevention idea is in the collective action, effected through Brazilian Class Actions. Inhibitory guardianships may impose both, improvements to the working environment, as well as determine the interruption of activity or a ban on the machine that put workers at risk. Both the Labor Prosecution and trade unions have to stand to promote this type of action, providing payment of compensation for collective moral damage. Objectives: To verify how class actions (known as ‘public civil actions’), regulated in Brazilian legal system to protect diffuse, collective and homogeneous rights, are being used to protect workers' health and safety. Methods: The author identified and evaluated decisions of Brazilian Superior Court of Labor involving collective actions and work accidents. The timeframe chosen was December 2015. The online jurisprudence database was consulted in page available for public consultation on the court website. The categorization of the data was made considering the result (court application was rejected or accepted), the request type, the amount of compensation and the author of the cause, besides knowing the reasoning used by the judges. Results: The High Court issued 21,948 decisions in December 2015, with 1448 judgments (6.6%) about work accidents and only 20 (0.09%) on collective action. After analyzing these 20 decisions, it was found that the judgments granted compensation for collective moral damage (85%) and/or obligation to make, that is, changes to improve prevention and safety (71%). The processes have been filed mainly by the Labor Prosecutor (83%), and also appeared lawsuits filed by unions (17%). The compensation for collective moral damage had average of 250,000 reais (about US$65,000), but it should be noted that there is a great range of values found, also are several situations repaired by this compensation. This is the last instance resource for this kind of lawsuit and all decisions were well founded and received partially the request made for working environment protection. Conclusions: When triggered, the labor court system provides the requested collective protection in class action. The values of convictions arbitrated in collective actions are significant and indicate that it creates social and economic repercussions, stimulating employers to improve the working environment conditions of their companies. It is necessary to intensify the use of collective actions, however, because they are more efficient for prevention than reparatory individual lawsuits, but it has been underutilized, mainly by Unions.

Keywords: Brazilian Class Action, collective action, work accident penalization, workplace accident prevention, workplace protection law

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Authors: K. Subbaiah, C. V. Jayakumar

Abstract:

Friction stir welding is a solid state welding process. Friction stir welding process eliminates the defects found in fusion welding processes. It is environmentally friend process. 5000 and 6000 series aluminum alloys are widely used in the transportation industries. The Al-Mg-Mn (5000) and Al-Mg-Si (6000) alloys are preferably offer best combination of use in Marine construction. The medium strength and high corrosion resistant 5000 series alloys are the aluminum alloys, which are found maximum utility in the world. In this review, the tool pin profile, process parameters such as hardness, yield strength and tensile strength, and microstructural evolution of friction stir welding of Al-Mg-Mn alloys (5000 Series) have been discussed.

Keywords: Al-Mg-Mn alloys, friction stir welding, tool pin profile, microstructure and mechanical properties

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Authors: Tamas Madl

Abstract:

Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Smita Sonker

Abstract:

Various investigators have determined the degree of approximation of conjugate signals (functions) of functions belonging to different classes Lipα, Lip(α,p), Lip(ξ(t),p), W(Lr,ξ(t), (β ≥ 0)) by matrix summability means, lower triangular matrix operator, product means (i.e. (C,1)(E,1), (C,1)(E,q), (E,q)(C,1) (N,p,q)(E,1), and (E,q)(N,pn) of their conjugate trigonometric Fourier series. In this paper, we shall determine the degree of approximation of 2π-periodic function conjugate functions of f belonging to the function classes Lipα and W(Lr; ξ(t); (β ≥ 0)) by (C1.T) -means of their conjugate trigonometric Fourier series. On the other hand, we shall review above-mentioned work in the light of Lenski.

Keywords: signals, trigonometric fourier approximation, class W(L^r, \xi(t), conjugate fourier series

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Authors: Ilaria Lucrezia Amerise

Abstract:

Electricity markets throughout the world have undergone substantial changes. Accurate, reliable, clear and comprehensible modeling and forecasting of different variables (loads and prices in the first instance) have achieved increasing importance. In this paper, we describe the actual state of the art focusing on reg-SARMA methods, which have proven to be flexible enough to accommodate the electricity price/load behavior satisfactory. More specifically, we will discuss: 1) The dichotomy between point and interval forecasts; 2) The difficult choice between stochastic (e.g. climatic variation) and non-deterministic predictors (e.g. calendar variables); 3) The confrontation between modelling a single aggregate time series or creating separated and potentially different models of sub-series. The noteworthy point that we would like to make it emerge is that prices and loads require different approaches that appear irreconcilable even though must be made reconcilable for the interests and activities of energy companies.

Keywords: interval forecasts, time series, electricity prices, reg-SARIMA methods

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Authors: Yo-Sheng Lin, Run-Chi Liu, Chien-Chu Ji, Chih-Chung Chen, Chien-Chin Wang

Abstract:

A W-band double-balanced mixer for direct up-conversion using standard 90 nm CMOS technology is reported. The mixer comprises an enhanced double-balanced Gilbert cell with PMOS negative resistance compensation for conversion gain (CG) enhancement and current injection for power consumption reduction and linearity improvement, a Marchand balun for converting the single LO input signal to differential signal, another Marchand balun for converting the differential RF output signal to single signal, and an output buffer amplifier for loading effect suppression, power consumption reduction and CG enhancement. The mixer consumes low power of 6.9 mW and achieves LO-port input reflection coefficient of -17.8~ -38.7 dB and RF-port input reflection coefficient of -16.8~ -27.9 dB for frequencies of 90~100 GHz. The mixer achieves maximum CG of 3.6 dB at 95 GHz, and CG of 2.1±1.5 dB for frequencies of 91.9~99.4 GHz. That is, the corresponding 3 dB CG bandwidth is 7.5 GHz. In addition, the mixer achieves LO-RF isolation of 36.8 dB at 94 GHz. To the authors’ knowledge, the CG, LO-RF isolation and power dissipation results are the best data ever reported for a 94 GHz CMOS/BiCMOS up-conversion mixer.

Keywords: CMOS, W-band, up-conversion mixer, conversion gain, negative resistance compensation, output buffer amplifier

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Authors: Ramesh K. Sunkaria, Puneeta Marwaha

Abstract:

Nonlinear methods of heart rate variability (HRV) analysis are becoming more popular. It has been observed that complexity measures quantify the regularity and uncertainty of cardiovascular RR-interval time series. In the present work, SampEn has been evaluated in healthy Normal Sinus Rhythm (NSR) male and female subjects for different data lengths and tolerance level r. It is demonstrated that SampEn is small for higher values of tolerance r. Also SampEn value of healthy female group is higher than that of healthy male group for short data length and with increase in data length both groups overlap each other and it is difficult to distinguish them. The SampEn gives inaccurate results by assigning higher value to female group, because male subject have more complex HRV pattern than that of female subjects. Therefore, this traditional algorithm exhibits higher complexity for healthy female subjects than for healthy male subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series and the hidden spatial and temporal fluctuations remains unexplored.

Keywords: heart rate variability, normal sinus rhythm group, RR interval time series, sample entropy

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

Abstract:

Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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