• Journal of English Language & Literature
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• v.57 no.4
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• pp.529-552
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• 2011

My essay aims at reading Ginsberg's "Howl" and "Kaddish" with the concept of 'shaman-prophet-poet' to illustrate the dynamic relationship between his poetics and radical politics. Throughout his widely-ranging career, Ginsberg represents himself as a poet-prophet and commands a typical rhetoric of revelation as a way of decentering Cold War orthodoxies. While well aware of the oppressive and pervasive power of the dominant post-war ideologies, he adopts 'madness' to oppose conventional political, social, and religious institutions; by way of entering into the madness of this world and actively engaging himself as a victim, he can finally heal both himself and the world. This dual function of poet characterizes his rhetoric of revelation, but it doesn't appeal to the mainstream of American critical ideology where the post-structural approach to language and subject gives a skeptical look at any account of active human agency and humanistic belief in the possibility of language. In "Howl" and "Kaddish," Ginsburg persuades the reader of the truth of his own vision through the convincing and realistic portraits of his contemporaries as well as his own mother and family. Different from his visionary predecessors such as Emerson and Whitman, Ginsberg knew the difficulty of a negotiation between history and divine vision, and attempted to imbricate his family, friends, and even the larger social and political units within his visionary experience in order to avoid naive idealism, escapism, or solipsism. Furthermore, he deconstructs the Logos of Western prophecy and replaces it with the groundless identity and the nontheistic epistemology of Buddhism, which, in turn, leads to emptying his powerful language of absolutist meaning and prevents his prophecy from becoming re-reified as divine essentialism. Ginsberg's idea of poet and poem revitalizes the skeptical view on language and literary representation of our contemporary critical community which is unwilling to engage the experimental scope of his radical prophecy.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.155-174
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• 2021

This study noted the emergence of the Resell investment within the fashion market, among emerging investment techniques. Worldwide, the market size is growing rapidly, and currently, there is a craze taking place throughout Korea. Therefore, we would like to use shoe data from StockX, the representative site of Resell, to present basic guidelines to consumers and to break down barriers to entry into the Resell market. Moreover, it showed the current status of the Resell craze, which was based on information from various media outlets, and then presented the current status and research model of the Resell market through prior research. Raw data was collected and analyzed using the XGBoost algorithm and the Prophet model. Analysis showed that the factors that affect the Resell market were identified, and the shoes suitable for the Resell market were also identified. Furthermore, historical data on shoes allowed us to predict future prices, thereby predicting future profitability. Through this study, the market will allow unfamiliar consumers to actively participate in the market with the given information. It also provides a variety of vital information regarding Resell investments, thus. forming a fundamental guideline for the market and further contributing to addressing entry barriers.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4561-4565
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• 2012

The aim of the present study is to find out the influence of rational-emotive behavior therapy (REBT) on pain intensity among cancer patients in India and Iran. The study followed a quasi-experimental, pre-post test, carried out with a sample of 88 cancer patients, aged 21-52 years, referred to the Baharat cancer hospital of Mysore in India and Shahidzade hospital of Behbahan in Iran. They were randomly assigned to the experimental (n=India 21; Iran 22) and control (n=India 22; Iran 23) groups. Pain was measured with the McGill Pain Questionnaire-MPQ (1975), the intervention by REBT has given to the experimental group for 45 days (ten sessions) and at the end of intervention, the pain of patients was again evaluated. Concerning to hypothesis of the study, two independent sample T test and three ways mixed ANOVA is used to analyze the data. Results showed that the experimental group in post test had less pain than the control group, but there were no statistically significant differences between Indian and Iranian patients in pain perception. With respect the outcome of study, it has realized that REBT can be used in hospitals and other psychological clinics to reduce the pain of cancer patients.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.329-337
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• 2021

This research aims to expose the Islamic perspective on the concept of justice on the Mudarabah contract's profit-sharing ratio. In certain verses in Al-Qur'an, Islamic values in Muamalah, the rules dictated by the Qur'an and its practices, and explanations rendered (more commonly known as Sunnah) by the Prophet Muhammad (pbuh) and Sahabah (the companions of the Prophet Muhammad), and Fiqh Axiom (rules) in Muamalah (Islamic jurisprudence), are used as the instruments of sharia to achieve the study objective. Islamic monetary establishments in Indonesia are still not in full consistency with the Shariah principles, significantly as far as satisfying equity and justice by Islamic banks in mudarabah contract (with clients). The ignominy is the nisbah (ratio) between the capital proprietor and the capital director. There are models or propositions to decide the benefit (profit)-sharing proportion. Nevertheless, none of them explains or specifies the possibility of equity/justice in the profit-sharing ratio. This research utilizes an explorative and subjective methodology that contributes to the philosophical premise of deciding the profit-sharing fairness. The elements of a just ratio for the Mudharabah contract are mutual willingness, the existence of negotiation, and the level of advantages and risks of the labor.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.317-322
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• 2021

The modern technological era has brought about the Semantic Web. Ontologies are essential to achieve the vision of the Semantic Web. Ontologies enable machines to understand data. The Arabic Language currently does not have a significant presence on the Web. To achieve a comparable level of Arabic access to other important languages, further work is needed to build Arabic ontologies. A goal is to design and create a robust Arabic ontology that represents the concepts from a large and significant subset of Arabic. We use a source of Hadiths (prophet saying and deeds) from Riyadh As-Saliheen. Preliminary results are very promising.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.357-361
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• 2022

The paper demonstrates how the Islamic governments in the Islamic history derived the authority for regulations and laws from the Qur'ān and the Sunna (sayings of the Prophet). These two laws are sovereign over public policy. Then, it shows the obstacles that prevented modern Muslim countries from formulating public policy based on Sharia law.

• Mass Spectrometry Letters
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• v.5 no.1
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• pp.16-23
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• 2014

Liquid chromatography based mass spectrometry (LC-MS) is a key technology for analyzing highly complex and dynamic proteome samples. With highly accurate and sensitive LC-MS analysis of complex proteome samples, efficient data processing is another critical issue to obtain more information from LC-MS data. A typical proteomic data processing starts with protein database search engine which assigns peptide sequences to MS/MS spectra and finds proteins. Although several search engines, such as SEQUEST and MASCOT, have been widely used, there is no unique standard way to interpret MS/MS spectra of peptides. Each search engine has pros and cons depending on types of mass spectrometers and physicochemical properties of peptides. In this study, we describe a novel data process pipeline which identifies more peptides and proteins by correcting precursor ion mass numbers and unifying multi search engines results. The pipeline utilizes two open-source software, iPE-MMR for mass number correction, and iProphet to combine several search results. The integrated pipeline identified 25% more proteins in mouse epididymal adipose tissue compared with the conventional method. Also the pipeline was validated using control and colitis induced colon tissue. The results of the present study shows that the integrated pipeline can efficiently identify increased number of proteins compared to the conventional method which can be a breakthrough in identification of a potential biomarker candidate.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.