Search results for: mutual price setting

Authors: Lipaz Shamoa-Nir, Irene Razpurker-Apfeld

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This research examined the impact of subliminal exposure to religious content on the mutual attitudes of majority group members (Jews) and minority group members (Muslims). Participants were subliminally exposed to religious concepts (e.g., Mezuzah, yarmulke or veil) and then they filled questionnaires assessing their stereotypes towards the out-group members. Each participant was primed with either in-group religious concepts, out-group concepts or neutral ones. The findings show that the Muslim participants were not influenced by the religious content to which they were exposed while the Jewish participants perceived the Muslims as less 'hostile' when subliminally exposed to religious concepts, regardless of concept type (out-group/in-group). This research highlights the influence of evoked religious content on out-group attitudes even when the perceiver is unaware of prime content. The power that exposure to content in a non-native language has in activating attitudes towards the out-group is also discussed.

Keywords: intergroup attitudes, stereotypes, majority-minority, religious out-group, implicit content, native language

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Authors: Eugene Okyere-Kwakye

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Knowledge sharing is a practice where individuals commonly exchange both tacit and explicit knowledge to jointly create a new knowledge. Knowledge management literature vividly express that knowledge sharing is the keystone and perhaps it is the most important aspect of knowledge management. To enhance the understanding of knowledge sharing domain, this study is aimed to investigate some factors that could influence employee’s attitude and behaviour to share their knowledge. The researchers employed the social exchange theory as a theoretical foundation for this study. Three essential factors namely: Trust, mutual reciprocity and perceived enjoyment that could influence knowledge sharing behaviour has been incorporated into a research model. To empirically validate this model, data was collected from one hundred and twenty respondents. The multiple regression analysis was employed to analyse the data. The results indicate that perceived enjoyment and trust have a significant influence on knowledge sharing. Surprisingly, mutual reciprocity did not influence knowledge sharing. The paper concludes by highlight the practical implications of the findings and areas for future research to consider.

Keywords: perceived enjoyment, trust, knowledge sharing, knowledge management

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Authors: Javed Y. Uppal

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The background of this study is the great degree of stress that the world is experiencing today, internationally among the countries, within a community among people, and even individually within one’s own self. The significance of the study is the attempt to find a solution of this stress in the philosophy of the olden times of Jalaluddin Rumi and comparatively recently of that of Allama Iqbal. The methodology adopted in this paper is firstly exploration of the perspectives of these philosophers that are being consolidated by a number of psychic and spiritual experts of today, who are being widely read but less followed. This paper further goes on presenting brief life sketches of Rumi and Iqbal. It expounds the key concepts proposed by them and the social change that was resulted in the times of the two above mentioned metaphysical philosophers. It is further amplified that with the recent advancements, in both metaphysics and the physical sciences, the gap between the two is closing down. Both Rumi and Iqbal emphasized their common essence. The old time's concepts, postulates, and philosophies are hence once again becoming valid. The findings of this paper are that the existence of human empathy, affection and mutual social attraction among humans is still valid. The positive inner belief system that dictates our thoughts and actions is vital. As a conclusion, empathy should enable us solving our problems collectively. We need to strengthen our inner communication system, to listen to the messages that come to our inner-selves. We need to get guidance and strength from them. We need to value common needs and purposes collectively to achieve results. Spiritual energy among us is to be harnessed and utilized. Connectivity is to be recognized to unify and strengthen ties among people. Mutual bonding at small and large group levels is to be employed for the survival of the disadvantaged, and sustainability of the empowering trends. With the above guidelines, hopefully, we can define a framework towards a brave and happy new humane world.

Keywords: belief system, connectivity, human empathy, inner-self, mutual bonding, spiritual energy

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Authors: Daniil Karzanov

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This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

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Authors: Weiliang Gong, Lissa Gomes, Lucile Raymond, Hui Xu, Werner Lutze, Ian L. Pegg

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Alkali-activated materials, particularly geopolymers, have attracted much interest in academia. Commercial applications are on the rise, as well. Geopolymers are produced typically by a reaction of one or two aluminosilicates with an alkaline solution at room temperature. Fly ash is an important aluminosilicate source. However, using low-Ca fly ash, the byproduct of burning hard or black coal reacts and sets slowly at room temperature. The development of mechanical durability, e.g., compressive strength, is slow as well. The use of fly ashes with relatively high contents ( > 6%) of unburned carbon, i.e., high loss on ignition (LOI), is particularly disadvantageous as well. This paper will show to what extent these impediments can be mitigated by mixing the fly ash with one or two more aluminosilicate sources. The fly ash used here is generated at the Orlando power plant (Florida, USA). It is low in Ca ( < 1.5% CaO) and has a high LOI of > 6%. The additional aluminosilicate sources are metakaolin and blast furnace slag. Binary fly ash-metakaolin and ternary fly ash-metakaolin-slag geopolymers were prepared. Properties of geopolymer pastes before and after setting have been measured. Fresh mixtures of aluminosilicates with an alkaline solution were studied by Vicat needle penetration, rheology, and isothermal calorimetry up to initial setting and beyond. The hardened geopolymers were investigated by SEM/EDS and the compressive strength was measured. Initial setting (fluid to solid transition) was indicated by a rapid increase in yield stress and plastic viscosity. The rheological times of setting were always smaller than the Vicat times of setting. Both times of setting decreased with increasing replacement of fly ash with blast furnace slag in a ternary fly ash-metakaolin-slag geopolymer system. As expected, setting with only Orlando fly ash was the slowest. Replacing 20% fly ash with metakaolin shortened the set time. Replacing increasing fractions of fly ash in the binary system by blast furnace slag (up to 30%) shortened the time of setting even further. The 28-day compressive strength increased drastically from < 20 MPa to 90 MPa. The most interesting finding relates to the calorimetric measurements. The use of two or three aluminosilicates generated significantly more heat (20 to 65%) than the calculated from the weighted sum of the individual aluminosilicates. This synergetic heat contributes or may be responsible for most of the increase of compressive strength of our binary and ternary geopolymers. The synergetic heat effect may be also related to increased incorporation of calcium in sodium aluminosilicate hydrate to form a hybrid (N,C)A-S-H) gel. The time of setting will be correlated with heat release and maximum heat flow.

Keywords: alkali-activated materials, binary and ternary geopolymers, blends of fly ash, metakaolin and blast furnace slag, rheology, synergetic heats

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Natarajan Venkatachalam, Subrahmanya V. R. K. Rao, Vijay Karthikeyan Dhandapani, Swaminathan Saravanavel

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The quantum communication technology is an evolving design which connects multiple quantum enabled devices to internet for secret communication or sensitive information exchange. In future, the number of these compact quantum enabled devices will increase immensely making them an integral part of present communication systems. Therefore, safety and security of such devices is also a major concern for us. To ensure the customer sensitive information will not be eavesdropped or deciphered, we need a strong authentications and encryption mechanism. In this paper, we propose a mutual authentication scheme between these smart quantum devices and server based on the secure exchange of information through quantum channel which gives better solutions for symmetric key exchange issues. An important part of this work is to propose a secure mutual authentication protocol over the quantum channel. We show that our approach offers robust authentication protocol and further our solution is lightweight, scalable, cost-effective with optimized computational processing overheads.

Keywords: quantum cryptography, quantum key distribution, wireless quantum communication, authentication protocol, quantum enabled device, trusted third party

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Authors: Maha Mourad, Fady Todros

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The objective of this research is to find an explanatory model to define the most important variables and factors that affect the acceptance of Mobile Internet in the Egyptian market. A qualitative exploratory research was conducted to support the conceptual framework followed with a quantitative research in the form of a survey distributed among 411 respondents. It was clear that relative advantage, complexity, compatibility, perceived price level and perceived playfulness have a dominant role in influencing consumers to adopt mobile internet, while observability is correlated to the adoption but when measured with the other factors it lost its value. The perceived price level has a negative relationship with the adoption as well the compatibility.

Keywords: innovation, Egypt, communication technologies, diffusion, innovation adoption, emerging market

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Authors: Raja Lahiani

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This study is concerned with scrutinising translations into English and French of references to locations in the desert of pre-Islamic Arabia. These references are used in the Source Text (ST) within a poetic image. Reference is made to the names of three different mountains in Arabia, namely Qatan, Sitar, and Yadhbul. As these mountains are referred to in the context of the poet’s description of the density and expansion of the clouds, it is crucial to know that while Sitar and Yadhbul are close to each other, Qatan is far away from them. This distance was functional for the poet to describe the expansion of the clouds. This reflects the spacious place (desert) he handled, and the fact that it was possible for him to physically see what he described. The purpose of this image is for the poet to communicate the vastness of the space he managed to see as he was in a moment of contemplation. Thus, knowledge of this characteristic about the setting is capital for the receiver to understand the communicative function of the verse. A corpus of eighteen translations is gathered. These vary between verse and prose renderings. The methodology adopted in this research work is comparative. Comparison is conducted at both the synchronic and diachronic levels; every translation shall be compared to the ST and then to previous translations. The comparative work will prove at the end that the translators who target historical facts do not necessarily succeed in preserving the image of the ST. It also proves that the more recent the translation is, the deeper the translator’s awareness is the link between imagery, setting, and point of view. Since the late eighteenth century and until nowadays, pre-Islamic poetry has been translated into Western languages. Translators differ as to motives, sources, priorities and intellectual backgrounds. A translator's skopoi undoubtedly affect the way s/he handles aspects of the ST. When it comes to culture-specific aspects and details related to setting, the problem is even more complex. Setting is a very important factor that reveals a great deal of the culture of pre-Islamic Arabia as this is remote in place, historical framework and literary tradition from its translators. History is present in pre-Islamic poetry, which justifies the important literature that has been written to extract information and data from it. These are imbedded not only by signalling given facts, events, and meditations but also by means of references to specific locations and landmarks that used to exist at the time. Spatial setting is an integral part of a literary text as it places it within its historical context. The importance of the translator’s awareness of spatial anthropological data before indulging in the process of translation is tested. This is also crucial in measuring the effect of setting loss and setting gain in translation. The findings of this research would ultimately evaluate the extent to which a comparative methodology is reliable in investigating the role of spatial setting awareness in translation.

Keywords: historical context, translation, comparative literature, spatial setting

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Authors: V. Churkin, M. Lopatin

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The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second –95,3%.

Keywords: bass model, generalized bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Anyapak Prapannetivuth

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The purpose of this research is to investigate the marketing mix that is perceived to be important for the small sized hotels in Pattaya. Unlike previous studies, this research provides insights through a review of the marketing activities performed by the small sized hotels. Nine owners and marketing manager of small sized hotels and resorts, all local Chonburi people, were selected for an in-depth interview. A snowball sampling process was employed. The research suggests that seven marketing mixes (e.g. Product, Price, Place, Promotion, People, Physical Evidence and Process) were commonly used by these hotels, however, three types – People, price and physical evidence were considered most important by the owners.

Keywords: marketing mix, marketing tools, small sized hotels, pattaya

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Authors: Janya Sainui

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As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches.

Keywords: video summarization, key frame extraction, dependency measure, quadratic mutual information

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Authors: Abdellatif Amouri, Sami Boudabbous

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The current study provides insights on the importance of entrepreneurial coaching as a source of developing entrepreneurial intentions among entrepreneurs and a determinant factor of business creation process and growth. Coaching, which implies exchange of adequate information and a mutual understanding between entrepreneurs and their partners, requires a better mutual knowledge of the representations and the perceptions of ideas which are widely present in their dealings and transactions. Therefore, to analyze entrepreneurs’ perceptions of business creation, we addressed a survey questionnaire to a group of Tunisian entrepreneurs and experts in business creation to indicate their level of approval concerning the prominence of coaching. The factor analysis indicates that more than 60% of the respondents believe that each statement reflects an aspect of coaching, with no bias to its position in the entrepreneurial process. Therefore, the image drawn from our respondents’ perceptions is that an entrepreneur is rather "constructed" and "shaped" by multiple apprenticeships both before and during the entrepreneurial act, through an accompaniment process and within interactions with trainers, consultants or professionals in starting a business. Similarly, the results indicate that the poor support structures and lack of accompaniment procedures stand as an obstacle impeding the development of entrepreneurial intention among business creators.

Keywords: Entrepreneurial Behavior, Entrepreneurial Coaching, Entrepreneurial Intention, Perceptions, Venture Creation

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Authors: Amit Das

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Background and significance of the study: Generally investors and market forecasters use financial statement for investigation while it awakens contribute to investing. The main vicinity of financial accounting and reporting practices recommends a few basic financial performance indicators, namely, return on capital employed, return on assets and earnings per share, which is associated considerably with share prices. It is principally true in case of Indian pharmaceutical companies also. Share investing is intriguing a financial risk in addition to investors look for those financial evaluations which have noteworthy shock on share price. A crucial intention of financial statement analysis and reporting is to offer information which is helpful predominantly to exterior clients in creating credit as well as investment choices. Sound financial performance attracts the investors automatically and it will increase the share price of the respective companies. Keeping in view of this, this research work investigates the impact of financial performance indicators on share price of pharmaceutical companies in India which is listed in the Bombay Stock Exchange. Methodology: This research work is based on secondary data collected from moneycontrol database on September 28, 2015 of top 101 pharmaceutical companies in India. Since this study selects four financial performance indicators purposively and availability in the database, that is, earnings per share, return on capital employed, return on assets and net profits as independent variables and one dependent variable, share price of 101 pharmaceutical companies. While analysing the data, correlation statistics, multiple regression technique and appropriate test of significance have been used. Major findings: Correlation statistics show that four financial performance indicators of 101 pharmaceutical companies are associated positively and negatively with its share price and it is very much significant that more than 80 companies’ financial performances are related positively. Multiple correlation test results indicate that financial performance indicators are highly related with share prices of the selected pharmaceutical companies. Furthermore, multiple regression test results illustrate that when financial performances are good, share prices have been increased steadily in the Bombay stock exchange and all results are statistically significant. It is more important to note that sensitivity indices were changed slightly through financial performance indicators of selected pharmaceutical companies in India. Concluding statements: The share prices of pharmaceutical companies depend on the sound financial performances. It is very clear that share prices are changed with the movement of two important financial performance indicators, that is, earnings per share and return on assets. Since 101 pharmaceutical companies are listed in the Bombay stock exchange and Sensex are changed with this, it is obvious that Government of India has to take important decisions regarding production and exports of pharmaceutical products so that financial performance of all the pharmaceutical companies are improved and its share price are increased positively.

Keywords: financial performance indicators, share prices, pharmaceutical companies, India

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Prama Mukhopadhyay

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Starting from transport to food, today’s world platform economy and digital markets have taken over almost every sphere of consumers’ lives. Sellers and buyers are getting connected through platforms, which is acting as an intermediary. It has made consumer’s life easier in terms of time, price, choice and other factors. Having said that, there are several concerns regarding platforms. There are competition law concerns like unfair pricing, deep discounting by the platforms which affect the consumer welfare. Apart from that, the biggest problem is lack of transparency with respect to the business models, how it operates, price calculation, etc. In most of the cases, consumers are unaware of how their personal data are being used. In most of the cases, they are unaware of how algorithm uses their personal data to determine the price of the product or even to show the relevant products using their previous searches. Using personal or non-personal data without consumer’s consent is a huge legal concern. In addition to this, another major issue lies with the question of liability. If a dispute arises, who will be responsible? The seller or the platform? For example, if someone ordered food through a food delivery app and the food was bad, in this situation who will be liable: the restaurant or the food delivery platform? In this paper, the researcher tries to examine the legal concern related to platform economy from the consumer protection and consumer welfare perspectives. The paper analyses the cases from different jurisdictions and approach taken by the judiciaries. The author compares the existing legislation of EU, US and other Asian Countries and tries to highlight the best practices.

Keywords: competition, consumer, data, platform

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Shinji Ukita, Naohiro Nakamura, Yuji Miyazu

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In considering the dynamic characteristics of structure, natural frequency and damping ratio are useful indicator. When performing dynamic design, it's necessary to select an appropriate initial damping model and hysteretic model. In the linear region, the setting of initial damping model influences the response, and in the nonlinear region, the combination of initial damping model and hysteretic model influences the response. However, the dynamic characteristics of structure in the nonlinear region remain unclear. In this paper, we studied the effect of setting of initial damping model and hysteretic model on the dynamic characteristics of structure. On initial damping model setting, Initial stiffness proportional, Tangent stiffness proportional, and Rayleigh-type were used. On hysteretic model setting, TAKEDA model and Normal-trilinear model were used. As a study method, dynamic analysis was performed using a lumped mass model of base-fixed. During analysis, the maximum acceleration of input earthquake motion was gradually increased from 1 to 600 gal. The dynamic characteristics were calculated using the ARX model. Then, the characteristics of 1st and 2nd natural frequency and 1st damping ratio were evaluated. Input earthquake motion was simulated wave that the Building Center of Japan has published. On the building model, an RC building with 30×30m planes on each floor was assumed. The story height was 3m and the maximum height was 18m. Unit weight for each floor was 1.0t/m2. The building natural period was set to 0.36sec, and the initial stiffness of each floor was calculated by assuming the 1st mode to be an inverted triangle. First, we investigated the difference of the dynamic characteristics depending on the difference of initial damping model setting. With the increase in the maximum acceleration of the input earthquake motions, the 1st and 2nd natural frequency decreased, and the 1st damping ratio increased. Then, in the natural frequency, the difference due to initial damping model setting was small, but in the damping ratio, a significant difference was observed (Initial stiffness proportional≒Rayleigh type>Tangent stiffness proportional). The acceleration and the displacement of the earthquake response were largest in the tangent stiffness proportional. In the range where the acceleration response increased, the damping ratio was constant. In the range where the acceleration response was constant, the damping ratio increased. Next, we investigated the difference of the dynamic characteristics depending on the difference of hysteretic model setting. With the increase in the maximum acceleration of the input earthquake motions, the natural frequency decreased in TAKEDA model, but in Normal-trilinear model, the natural frequency didn’t change. The damping ratio in TAKEDA model was higher than that in Normal-trilinear model, although, both in TAKEDA model and Normal-trilinear model, the damping ratio increased. In conclusion, in initial damping model setting, the tangent stiffness proportional was evaluated the most. In the hysteretic model setting, TAKEDA model was more appreciated than the Normal-trilinear model in the nonlinear region. Our results would provide useful indicator on dynamic design.

Keywords: initial damping model, damping ratio, dynamic analysis, hysteretic model, natural frequency

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Authors: Cinthia C. Costa, Heloisa L. Burnquist, Joaquim J. M. Guilhoto

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This study identified the impact of special agricultural safeguards (SSG) for the global market of meat and for the Brazilian economy. The tariff lines subject to SSG were selected and the period of analysis was 1995 (when the rules about the SSGs were established) to 2015 (more recent period for which there are notifications). The value of additional tariff was calculated for each of the most important tariff lines. The import volume and the price elasticities for imports were used to estimate the impacts of each additional tariff estimated on imports. Finally, the effect of Brazilian exports of meat without SSG taxes was calculated as well as its impact in the country’s economy by using an input-output matrix. The most important markets that applied SSGs were the U.S. for beef and European Union for poultry. However, the additional tariffs could be estimated in only two of the sixteen years that the U.S. applied SSGs on beef imports, suggesting that its use has been enforced when the average annual price has been higher than the trigger price level. The results indicated that the value of the bovine and poultry meat that could not be exported by Brazil due to SSGs to both markets (EU and the U.S.) was equivalent to BRL 804 million. The impact of this loss in trade was about: BRL 3.7 billion of the economy’s production value (at 2015 prices) and almost BRL 2 billion of the Brazilian Gross Domestic Product (GDP).

Keywords: beef, poultry meat, SSG tariff, input-output matrix, Brazil

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Authors: Tarah Bouaricha

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The impact of worst natural disasters is analysed in terms of insured losses which happened between 2010 and 2014 on S&P insurance index. Event study analysis is used to test whether natural disasters impact insurance index stock market price. There is no negative impact on insurance stock market price around the disasters event. To analyse the reaction of insurance stock market, normal returns (NR), abnormal returns (AR), cumulative abnormal returns (CAR), cumulative average abnormal returns (CAAR) and a parametric test on AR and on CAR are used.

Keywords: study event, natural disasters, insurance, reinsurance, stock market

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Authors: Anwar Hasan Abdullah Othman, Ahamed Kameel, Hasanuddeen Abdul Aziz

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In this study, an attempt has been made to investigate the relationship specifically the causal relation between fund unit prices of Islamic equity unit trust fund which measure by fund NAV and the selected macro-economic variables of Malaysian economy by using VECM causality test and Granger causality test. Monthly data has been used from Jan, 2006 to Dec, 2012 for all the variables. The findings of the study showed that industrial production index, political election and financial crisis are the only variables having unidirectional causal relationship with fund unit price. However, the global oil prices is having bidirectional causality with fund NAV. Thus, it is concluded that the equity unit trust fund industry in Malaysia is an inefficient market with respect to the industrial production index, global oil prices, political election and financial crisis. However, the market is approaching towards informational efficiency at least with respect to four macroeconomic variables, treasury bill rate, money supply, foreign exchange rate and corruption index.

Keywords: fund unit price, unit trust industry, Malaysia, macroeconomic variables, causality

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Abdorreza Rabiee, Shahla Mohammad Hoseini Mirzaei

Abstract:

The advantages of distributed generations (DGs) based on renewable energy sources (RESs) leads to high penetration level of DGs in distribution network. With incorporation of DGs in distribution systems, the system reliability and security, as well as voltage profile, is improved. However, the protection of such systems is still challenging. In this paper, at first, the related papers are reviewed and then a practical scheme is proposed for coordination of OCRs in distribution system with DGs. The coordination problem is formulated as a nonlinear programming (NLP) optimization problem with the object function of minimizing total operating time of OCRs. The proposed method is studied based on a simple test system. The optimization problem is solved by General Algebraic Modeling System (GAMS) to calculate the optimal time dial setting (TDS) and also pickup current setting of OCRs. The results show the effectiveness of the proposed method and its applicability.

Keywords: distributed generation, DG, distribution network, over current relay, OCR, protection coordination, pickup current, time dial setting, TDS

Procedia PDF Downloads 108

Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

Abstract:

Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

Procedia PDF Downloads 236

Authors: Sakineh Abbasi Bourondaragh

Abstract:

The aim of this research is effectiveness of integrative behavioral couples therapy on the communication patterns of couples applying for divorce. We selected (N=20) reports from Tabriz Family Judicial Complex (FJC) of couples which have conflict in their marital relationships. All of reports were released during 2012. First, they were randomly divided into two experimental and control groups and all the couples were given pre-test. They participated in twelve therapy sessions. Then the experimental group was exposed to an experimental intervention, but the control group was not received experimental intervention. The subjects were treated. At the end of treatment, a post-test was performed about subjects (each of two groups).The results showed that integrative behavioral couple therapy could increase and improve communication patterns. The findings also showed that integrative behavioral couples therapy had increased mutual constructive pattern and decreased demand/withdraw pattern and mutual avoidance pattern of CPQ sub-scale. Steady change indicator showed that the difference is clinically meaningful.

Keywords: integrative behavioral couple therapy, communication patterns, cognitive sciences, Family Judicial Complex

Procedia PDF Downloads 297

Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

Abstract:

In urban context, urban nodes such as amenity or hazard will certainly affect house price, while classic hedonic analysis will employ distance variables measured from each urban nodes. However, effects from distances to facilities on house prices generally do not represent the true price of the property. Distance variables measured on the same surface are suffering a problem called multicollinearity, which is usually presented as magnitude variance and mean value in regression, errors caused by instability. In this paper, we provided a theoretical framework to identify and gather the data with less bias, and also provided specific sampling method on locating the sample region to avoid the spatial multicollinerity problem in three distance variable’s case.

Keywords: hedonic regression, urban node, distance variables, multicollinerity, collinearity

Procedia PDF Downloads 441

Authors: Andrei-Marius Sandu

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This study aims to analyze the bovine meat and milk sector for the period 2007-2013. For the period analyzed, it is known that Romania has benefited from EU funding through the National Rural Development Programme 2007-2013. In this programme, there were measures that addressed exclusively the animal husbandry sector in Romania. This paper presents data on bovine production of meat, milk and livestock in Romania, but also data on the price and impact the European Funds implementation had on them.

Keywords: European funds, measures, national rural development programme, price

Procedia PDF Downloads 395

Authors: Ira Joseph

Abstract:

The LNG paradox addresses the issue of how the most expensive form of gas supply, which is LNG, will grow in an end user market where demand is most competitive, which is power generation. In this case, LNG demand growth is under siege from two entirely different directions. At one end is price; it will be extremely difficult for gas to replace coal in Asia due to the low price of coal and the age of the generation plants. Asia's coal fleet, on average, is less than two decades old and will need significant financial incentives to retire before its state lifespan. While gas would cut emissions in half relative to coal, it would also more than double the price of the fuel source for power generation, which puts it in a precarious position. In most countries in Asia other than China, this cost increase, particularly from imports, is simply not realistic when it is also necessary to focus on economic growth and social welfare. On the other end, renewables are growing at an exponential rate for three reasons. One is that prices are dropping. Two is that policy incentives are driving deployment, and three is that China is forcing renewables infrastructure into the market to take a political seat at the global energy table with Saudi Arabia, the US, and Russia. Plus, more renewables will lower import growth of oil and gas in China, if not end it altogether. Renewables are the predator at the gate of gas demand in power generation and in every year that passes, renewables cut into demand growth projections for gas; in particular, the type of gas that is most expensive, which is LNG. Gas does have a role in the future, particularly within a domestic market. Once it crosses borders in the form of LNG or even pipeline gas, it quickly becomes a premium fuel and must be marketed and used this way. Our research shows that gas will be able to compete with batteries as an intermittency and storage tool and does offer a method to harmonize with renewables as part of the energy transition. As a baseload fuel, however, the role of gas, particularly, will be limited by cost once it needs to cross a border. Gas converted into blue or green hydrogen or ammonia is also an option for storage depending on the location. While this role is much reduced from the primary baseload role that gas once aspired to land, it still offers a credible option for decades to come.

Keywords: natural gas, LNG, demand, price, intermittency, storage, renewables

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

Procedia PDF Downloads 106