scholarly journals Bitcoin Cost Prediction using Deep Neural Network Technique

2019 ◽  
pp. 96-101
Keyword(s):  
Market Price ◽  
Real Life ◽  
Cost Prediction ◽  
Historical Trends ◽  
Data Set ◽  
Price Prediction ◽  
Crypto Currency ◽  
Long Term Prediction ◽  
The Impact

The accusative of this paper is to predict the bitcoin price accurately by taking various parameters into consideration which affects the bitcoin value. Here multi-layer perceptron algorithms under deep learning are used to predict the price of crypto-currency. Many researchers have analysed the crypto-currency features in many ways such as, market price prediction, the impact of cryptocurrency in real life. It has the ability to make long-term prediction of the exchange price in crypto-currencies particularly in US dollar, based on historical trends. The bitcoin cost prediction is done based on the data set which consists of 13 features relating to the crypto-currency price recorded daily over the period of particular range.

Long term variations of the hydrochemical composition of deep thermal ground water in the Lower Bavarian Molasse Basin – Causes and Perspectives

2021 ◽  
Author(s):  
Annette Dietmaier ◽  
Thomas Baumann
Keyword(s):  
Time Series ◽  
Real Life ◽  
Data Series ◽  
Fluid Composition ◽  
Molasse Basin ◽  
Deep Groundwater ◽  
Data Set ◽  
Groundwater Exploitation ◽  
Groundwater Aquifers

<p>The European Water Framework Directive (WFD) commits EU member states to achieve a good qualitative and quantitative status of all their water bodies.  WFD provides a list of actions to be taken to achieve the goal of good status.  However, this list disregards the specific conditions under which deep (> 400 m b.g.l.) groundwater aquifers form and exist.  In particular, deep groundwater fluid composition is influenced by interaction with the rock matrix and other geofluids, and may assume a bad status without anthropogenic influences. Thus, a new concept with directions of monitoring and modelling this specific kind of aquifers is needed. Their status evaluation must be based on the effects induced by their exploitation. Here, we analyze long-term real-life production data series to detect changes in the hydrochemical deep groundwater characteristics which might be triggered by balneological and geothermal exploitation. We aim to use these insights to design a set of criteria with which the status of deep groundwater aquifers can be quantitatively and qualitatively determined. Our analysis is based on a unique long-term hydrochemical data set, taken from 8 balneological and geothermal sites in the molasse basin of Lower Bavaria, Germany, and Upper Austria. It is focused on a predefined set of annual hydrochemical concentration values. The data range dates back to 1937. Our methods include developing threshold corridors, within which a good status can be assumed, and developing cluster analyses, correlation, and piper diagram analyses. We observed strong fluctuations in the hydrochemical characteristics of the molasse basin deep groundwater during the last decades. Special interest is put on fluctuations that seem to have a clear start and end date, and to be correlated with other exploitation activities in the region. For example, during the period between 1990 and 2020, bicarbonate and sodium values displayed a clear increase, followed by a distinct dip to below-average values and a subsequent return to average values at site F. During the same time, these values showed striking irregularities at site B. Furthermore, we observed fluctuations in several locations, which come close to disqualifying quality thresholds, commonly used in German balneology. Our preliminary results prove the importance of using long-term (multiple decades) time series analysis to better inform quality and quantity assessments for deep groundwater bodies: most fluctuations would stay undetected within a < 5 year time series window, but become a distinct irregularity when viewed in the context of multiple decades. In the next steps, a quality assessment matrix and threshold corridors will be developed, which take into account methods to identify these fluctuations. This will ultimately aid in assessing the sustainability of deep groundwater exploitation and reservoir management for balneological and geothermal uses.</p>

scholarly journals Effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan

2018 ◽  
Vol 78 (5) ◽  
pp. 592-610 ◽  
Author(s):  
Abbas Ali Chandio ◽  
Yuansheng Jiang ◽  
Feng Wei ◽  
Xu Guangshun
Keyword(s):  
Wheat Yield ◽  
Considerable Proportion ◽  
Small Scale ◽  
Small Farms ◽  
Short Term ◽  
Agricultural Credit ◽  
Data Set ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan. Design/methodology/approach The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach. Findings This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years. Research limitations/implications In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well. Originality/value This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.

scholarly journals High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy

2018 ◽  
Vol 120 (3) ◽  
pp. 1451-1460 ◽  
Author(s):  
Sigge Weisdorf ◽  
Sirin W. Gangstad ◽  
Jonas Duun-Henriksen ◽  
Karina S. S. Mosholt ◽  
Troels W. Kjær
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Temporal Dynamics ◽  
Real Life ◽  
High Similarity ◽  
Time Frequency ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
The Impact

Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevant waveforms and patterns. We found high similarity between the subcutaneous EEG channels and nearby temporal scalp channels for most investigated electroencephalographic events. In particular, the temporal dynamics of one typical temporal lobe seizure in one patient were similar in scalp and subcutaneous recordings in regard to frequency distribution and morphology. Signal similarity is strongly related to the distance between the subcutaneous and scalp electrodes. On the basis of these limited data, we conclude that subcutaneous EEG recordings are very similar to scalp recordings in both time and time-frequency domains, if the distance between them is small. As many electroencephalographic events are local/regional, the positioning of the subcutaneous electrodes should be considered carefully to reflect the relevant clinical question. The impact of implantation depth of the subcutaneous electrode on recording quality should be investigated further. NEW & NOTEWORTHY This study is the first publication comparing the detection of clinically relevant, pathological EEG features from a subcutaneous recording system designed for out-patient ultra-long-term use to gold standard scalp EEG recordings. Our study shows that subcutaneous channels are very similar to comparable scalp channels, but also point out some issues yet to be resolved.

scholarly journals Assessment of impact of climate change on water resources: a long term analysis of the Great Lakes of North America

2008 ◽  
Vol 12 (1) ◽  
pp. 239-255 ◽  
Author(s):  
E. McBean ◽  
H. Motiee
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Water Resources ◽  
North America ◽  
Great Lakes ◽  
Great Lakes Basin ◽  
Historical Trends ◽  
Global Circulation Models ◽  
The Impact

Abstract. In the threshold of the appearance of global warming from theory to reality, extensive research has focused on predicting the impact of potential climate change on water resources using results from Global Circulation Models (GCMs). This research carries this further by statistical analyses of long term meteorological and hydrological data. Seventy years of historical trends in precipitation, temperature, and streamflows in the Great Lakes of North America are developed using long term regression analyses and Mann-Kendall statistics. The results generated by the two statistical procedures are in agreement and demonstrate that many of these variables are experiencing statistically significant increases over a seven-decade period. The trend lines of streamflows in the three rivers of St. Clair, Niagara and St. Lawrence, and precipitation levels over four of the five Great Lakes, show statistically significant increases in flows and precipitation. Further, precipitation rates as predicted using fitted regression lines are compared with scenarios from GCMs and demonstrate similar forecast predictions for Lake Superior. Trend projections from historical data are higher than GCM predictions for Lakes Michigan/Huron. Significant variability in predictions, as developed from alternative GCMs, is noted. Given the general agreement as derived from very different procedures, predictions extrapolated from historical trends and from GCMs, there is evidence that hydrologic changes particularly for the precipitation in the Great Lakes Basin may be demonstrating influences arising from global warming and climate change.

scholarly journals Disposal and Recycle Economic Assessment for Aircraft and Engine End of Life Solution Evaluation

2020 ◽  
Vol 10 (2) ◽  
pp. 522
Author(s):  
Xiaojia Zhao ◽  
Wim J.C. Verhagen ◽  
Richard Curran
Keyword(s):  
End Of Life ◽  
Real Life ◽  
Economic Assessment ◽  
Aircraft Engine ◽  
Economic Indicator ◽  
Data Set ◽  
Core Concepts ◽  
Increasing Demand ◽  
R Process ◽  
The Impact

The present study proposes an economic indicator to support the evaluation of aircraft End of Life (EoL) strategies in view of the increasing demand with regards to aircraft decommissioning. This indicator can be used to evaluate an economic performance and to facilitate the trade-off studies among different strategies. First, Disposal and Recycle (D&R) scenarios related to stakeholders are investigated to identify the core concepts for the economic evaluation. Next, we extracted the aircraft D&R process from various real-life practices. In order to obtain the economic measure for the engineering process, a method of estimating the D&R cost and values are developed by integrating product, process and cost properties. This analysis is demonstrated on an averaged data set and two EoL aircraft cases. In addition, sensitivity analysis is performed to evaluate the impact of the D&R cost, residual value, and salvage value. Results show that the disassembly and dismantling of an aircraft engine possesses relatively more economic gains than that for the aircraft. The main factors influencing the proposed D&R economic indicator are the salvage value and D&R cost for economically efficient D&R cases. In addition, delaying the disposal and recycle process for EoL aircraft can lead to economically unfavorable solutions. The economic indicator combined with the evaluation methods is widely applicable for evaluations of engineering products EoL solutions, and implies a significant contribution of this research to decision making for such complex systems in terms sustainable policy.

The colonial exports pattern, institutions and current economic performance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tania El Kallab ◽  
Cristina Terra
Keyword(s):  
Human Capital ◽  
Economic Development ◽  
Resource Curse ◽  
Data Set ◽  
Content Type ◽  
Long Run ◽  
French Colonies ◽  
Colonial Institutions ◽  
The Impact

PurposeThis paper explores the role of colonial heritage on long-term economic development from a resource-curse perspective. The authors investigate the impact of colonial exports on long-term economic development through two channels: (1) a direct impact of the economic dependency on natural resources and (2) an indirect impact via its effect on colonial institutions, which persisted over time and influenced current economic development.Design/methodology/approachTo address this issue, the authors use an original data set on French bilateral trade from 1880 to 1912. The authors use partial least square structural equation modeling (PLS-SEM) in the empirical analysis, so that the authors are able to construct latent variables (LVs) for variables that are not directly observable, such as the quality of institutions.FindingsThe authors find that exports of primary goods to France had a negative impact on colonial institutions and that for French colonies, this impact was driven by minerals exports. Despite its impact on colonial institutions, exports of French colonies had no significant indirect impact on their current institutions. The authors find no significant direct impact of colonial trade on current development for French colonies. Finally, colonial exports of manufactured products had no significant impact on colonial institutions among French colonies and a positive impact among non-French ones.Research limitations/implicationsResearch implications regarding the findings of this paper are, namely, that the relative poor performance within French colonies today cannot be attributed to the extraction of raw materials a century ago. However, human capital and institutional development, instead of exports, are more relatively important for long-term growth. Some limitations in trying to determine the simultaneous relationship among colonial trade, institutions and economic performance are the relation between colonial trade and the extent of extraction from the colonizer, which is hard to quantify, as well as its precise mechanism.Practical implicationsSince the initial institutions set in those former colonies presented a strong persistence in the long run, their governments should focus now on building sound and inclusive political and economic institutions, as well as on investing in human capital in order to foster long-term growth. Once a comprehensive set of institutional and human resources are put in place, the quality and quantity of exports might create a positive spillover on the short-run growth.Social implicationsOne social implication that can be retrieved from this study is the ever-lasting effect of both human capital investment and introduction of inclusive political and economic institutions on the long-run impact of growth.Originality/valueThe paper uses an original primary data set from archival sources to explore the role of colonial heritage on long-term economic development from a resource-curse perspective. It applies a relatively new model partial least squares path modeling (PLS-PM) that allows the construction of LVs for variables that are not directly observable, as well as channeling the impact on growth through both direct and indirect channels. Finally, it allows for the simultaneous multigroup analysis across different colonial groups.

scholarly journals Validation of SIE, Sies, GITMO Operational Criteria for the Definition of Fitness in Elderly Patients Affected with Acute Myeloid Leukemia: A Six-Years Retrospective Real-Life Experience

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2150-2150
Author(s):  
Raffaele Palmieri ◽  
Giovangiacinto Paterno ◽  
Eleonora De Bellis ◽  
Elisa Buzzatti ◽  
Valentina Rossi ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Myeloid Leukemia ◽  
Real Life ◽  
Italian Society ◽  
Intensive Chemotherapy ◽  
Advisory Committees ◽  
Actual Treatment ◽  
Operational Criteria ◽  
The Impact

Introduction:Acute Myeloid Leukemia (AML) predominantly affects the older population, with a median age at diagnosis of 67 years (yrs). In the last decades, Overall Survival (OS) has not changed meaningfully for these patients (pts). This worse outcome is explained by the poor-risk biological profile of the disease but also by the scarce propensity in administering curative treatments in this age category. Despite this general attitude, there is consensus that age alone should not be representative of the functional profile of older pts and that making decisions based on the sole age parameter can compromise possible therapeutic attempts. Therefore, a panel of experts from SIE (Italian Society of Hematology),SIES (Italian Society of Experimental Hematology) and GITMO (Italian Group for Bone Marrow Transplantation) summarized a list of operational criteria to be used in the process of treatment allocation, identifying 3 fitness categories of patients to address to differentiated strategies: 1)Fit pts (FP), eligible to intensive chemotherapy (IC) with the aim to achieve complete remission (CR); 2)Unfit pts (UP), eligible to non-intensive chemotherapy (NIC) with the aim to prolong survival; 3)Frail pts (FP) for whom, since the natural course of disease cannot be altered, supportive therapy (ST) is the best option in the attempt to preserve an acceptable quality of life (Ferrara et. al, Leukemia 2013). Aim: We retrospectively applied the operational SIE, SIES, GITMO criteria to a series of 180 consecutive non-APL AML pts diagnosed at our institution from 2013 to 2018 to investigate (1) the degree of concordance between the "operational criteria derived categories" and the actual treatment received; (2) the impact of this evaluation on long-term OS. Methods: We analyzed 180 consecutive pts with AML (median age 66 yrs, range 21-91) diagnosed at our institution from January 2013 to December 2018. We mainly focused on 125 pts older than 60 yrs (median 70 yrs, range 61-91). For the purpose of comparison, 55 younger pts, submitted to IC (51/55, 93%), were also analyzed. Results: SIE, SIES, GITMO operational criteria were retrospectively applied through medical files backtracking. One-hundred-48 out of 181 pts were stratified according to ELN 2010 as follow: 24 (16%) low risk, 59 (40%) intermediate-I, 25 (17%) intermediate-II and 40 (27%) high risk. This risk stratification did not differ between younger and older pts, suggesting that risk distribution may not be always an age-related factor. Overall, according to physician choice, 98 pts were submitted to IC, 40 to NIC, 42 to ST (54%, 22%, 24%, respectively). When focusing on pts older than 60 yrs, 47/125 were submitted to IC, 39/125 to NIC, 39/125 to ST (38% vs. 31% vs. 31%, respectively). A high concordance between treatment actually given and the one derived from the application of the "operational criteria" (165/180, 92%) was observed. The highest concordance was observed for the association of ST with FrP (39/40, 98%), whereas the associations IC with FP (96/107, 90%) and NIC with UP (30/33, 91%) showed a lower level of concordance. Overall, by applying the "operational criteria" the 3 categories differed significantly in terms of OS: 15,3 mos (range 0,4-78) for FP, 8,6 mos (range 2,2-47,9) for UP and 1 mos (range 0,1-29,9) for FrP, respectively (p<0.001). For pts older than 60 yrs, OS was 9,1 mos (range 0,4-78), 9,2 mos (range 2,2-47,9) and 1 mos (range 0,1-29,9) for FP, UP and FrP, respectively (p<0.001) (Fig. 1A). According to the treatment actually received, OS of pts older than 60 yrs was 6,9 mos (range 0,4-78) for IC, 11.0 mos (range 2,2-47,9) for NIC and 1 mos (range 0,1-29,9) for ST, respectively (p<0.001) (Fig. 1B). Conclusions: In our real-life analysis, we confirmed that SIE, SIES, GITMO "operational criteria" represent a powerful tool to discriminate categories of older pts with different outcome.In pts older than 60 years, it was observed a high degree of concordance (>90%) between the "operational criteria" and the actual treatment delivered. However, long-term OS was longer when plotted according to the 3 fitness categories rather than to treatment intensity stratification (Fig.1 A and B). Such a discrepancy can be explained by a higher incidence of toxicity/early mortality in the IC group and also by a suboptimal application of treatment selection criteria. Reproducibility of operational criteria applications in a prospective fashion is currently underway. Disclosures Venditti: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy. Buccisano:Janssen: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

scholarly journals Stock Market Prediction Using Twitter Sentiment Analysis

2021 ◽  
pp. 265-270
Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Future Price ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Media Platform ◽  
The Impact

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

scholarly journals Autonomous PEV Charging Scheduling Using Deep-Q Network and Dyna-Q Reinforcement Learning

2021 ◽  
Author(s):  
Fan Wang
Keyword(s):  
Reinforcement Learning ◽  
Transition Probabilities ◽  
Real Life ◽  
Future Price ◽  
Traffic Condition ◽  
Data Set ◽  
User Behaviour ◽  
Model Free ◽  
Behaviour Data

This paper proposes a demand response method that aims to reduce the long-term charging cost of a plug-in electric vehicle (PEV) while overcoming obstacles such as the stochastic nature of the user’s driving be- haviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities and solved using deep reinforcement learning (RL) techniques. Existing methods using machine learning either requires initial user behaviour data, or converges far too slowly. This method does not require any initial data on the PEV owner’s driving behaviour and shows improvement on learning speed. A combination of both model-based and model-free learning called Dyna-Q algorithm is utilized. Every time a real experience is obtained, the model is updated and the RL agent will learn from both real data set and “imagined” experience from the model. Due to the vast amount of state space, a table-look up method is impractical and a value approximation method using deep neural networks is employed for estimating the long-term expected reward of all state-action pairs. An average of historical price is used to predict future price. Three different user behaviour without any initial PEV owner behaviour data are simulated. A purely model-free DQN method is shown to run out of battery during trips very often, and is impractical for real life charging scenarios. Simulation results demonstrate the effectiveness of the proposed approach and its ability to reach an optimal policy quicker while avoiding state of charge (SOC) depleting during trips when compared to existing PEV charging schemes for all three different users profiles.

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