Academic literature on the topic 'Market activity predictions'
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Journal articles on the topic "Market activity predictions"
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Abinzano, Isabel, Luis Muga, and Rafael Santamaria. "Hidden Power of Trading Activity: The FLB in Tennis Betting Exchanges." Journal of Sports Economics 20, no. 2 (September 22, 2017): 261–85. http://dx.doi.org/10.1177/1527002517731875.
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This article examines the impact of trading activity on the favorite–long shot bias (FLB) in tennis betting exchanges, using direct measures such as betting volume, average bet, and standard deviation of the odds. According to predictions based on disagreement models, odds mispricing is positively associated with trading volume but negatively associated with the presence of institutional bettors. The FLB is also positively related to the degree of uncertainty in the market. The existence of two simultaneous markets (a “main” and an “alternative” market) in this specific sports-betting environment has enabled us to observe that the relative amount of attention given to the favorite versus that given to the long shot is positively associated with the FLB. Finally, information is more rapidly incorporated into the odds in the market that receives more attention from bettors, an effect that is intensified by the arbitrage and hedging that occurs between the two markets.
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Jaiswal, Rupashi, Kunal Mahato, Pankaj Kapoor, and Sudipta Basu Pal. "A Comparative Analysis on Stock Price Prediction Model using DEEP LEARNING Technology." American Journal of Electronics & Communication 2, no. 3 (January 3, 2022): 12–19. http://dx.doi.org/10.15864/ajec.2303.
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In today's world, Artificial Intelligence and Deep Learning are getting popular regularly. The various applications areas of artificial intelligence are related to human activity. One of the general application areas of neural networks and artificial intelligence is prediction analysis. In this paper, the authors also have performed one comparative study based on artificial intelligence. Authors have performed stock market predictions using different models. In reality, stock markets are entirely volatile, so there is very much a requirement of good prediction analysis for judging the stocks prices and their ups and downs with time. The stock prices can easily be predicted using machine learning algorithms on data available in financial news, as this data can also change investors' interests. However, traditional prediction methods have become obsolete and do not provide accurate predictions over non-stationary time series data. This paper proposes a stock price prediction method that gives accurate results with the advancements in deep learning technologies.
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Dixit, Nandan, Chirag Patel, Mansi Bhavsar, Saumya Patel, Rakesh Rawal, and Hitesh Solanki. "QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP (QSAR) STUDY OF LIVER TOXIC DRUGS." International Association of Biologicals and Computational Digest 1, no. 1 (May 2, 2022): 63–71. http://dx.doi.org/10.56588/iabcd.v1i1.17.
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Drug-induced liver injury (DILI) is one of the most severe adverse effects (AEs) causing life-threatening conditions, such as acute liver failure. t has also been recognized as the single most common cause of safety- related post-market withdrawals or warnings Due to the nature and idiosyncrasy of clinical forms of DILI, attempts to develop new predictive approaches to evaluate the risk of a medication being a hepatotoxicant have been difficult. The FDA Adverse Event Reporting System (AERS) provides post-market data illustrating AE morbidity. A quantitative structure –activity relationship (QSAR) model for DILI prediction with satisfactory output is urgently needed. In this study, we documented a high-quality QSAR model for predicting the hepatotoxicity risk of DILI by integrating the use of eight effective classifiers and molecular descriptors given by the VlifeMds program. For the present QSAR study, data set of 99 compounds (withdrawn and approved drugs) collected from different databases were taken. Multiple linear regression and partial least square analysis methods had developed two dimensional QSAR models, and then validated for internal and external predictions. The 2D QSAR model developed was statistically important, and was highly predictive. The validation methods presented essential statistical parameters that proved the model's predictive ability. The developed 2D QSAR model revealed the significance of SsssCE-index, SsOHcount, SsssNcount and SdssPcount descriptors. These findings will prove to be an important guide for furtherdesigning and developing new hepatotoxicity activity.
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Padilla, Washington, Jesús García, and José Molina. "Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets." Sensors 19, no. 2 (January 12, 2019): 286. http://dx.doi.org/10.3390/s19020286.
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In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.
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TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (September 29, 2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.
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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.
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Fainmesser, Itay P., and Andrea Galeotti. "The Market for Online Influence." American Economic Journal: Microeconomics 13, no. 4 (November 1, 2021): 332–72. http://dx.doi.org/10.1257/mic.20200050.
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Recent developments in social media have morphed the age-old practice of paying influential individuals for product endorsements into a multibillion dollar industry, extending well beyond celebrity sponsorships. We develop a parsimonious model in which influencers trade off the increased revenue they obtain from paid endorsements with the negative impact that these have on their followers’ engagement and, therefore, on the price influencers receive from marketers. The model provides testable predictions that match suggestive evidence on pricing of paid endorsements, reveals a novel type of inefficiency that emerges in this market, and clarifies the role of search technology and advice transparency in shaping market activity. In particular, we show that recent policies that make paid endorsements more transparent can backfire, whereas an increase in the effectiveness of the search technology that matches followers to influencers has both direct and strategic positive welfare effects. (JEL D83, L82, L86, M31.)
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Quintero, Luis Eduardo, and Paula Restrepo. "Market Access and the Concentration of Economic Activity in a System of Declining Cities." REGION 5, no. 3 (December 28, 2018): 97–109. http://dx.doi.org/10.18335/region.v5i3.223.
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Market access has been widely used as a measure of agglomeration spillovers in models that seek to explain productivity, economic or population growth at the city level. Most results have shown that having higher market access is beneficial to these outcomes. These results, both theoretical and empirical, have been obtained in a context of population growth. This article examines the impact that market access has on a system of cities that has suffered a negative population shock. An extended version of the Brezis and Krugman (1997) model of life cycle of cities predicts that a system of cities experiencing population loss will see a relative reorganization of its population from small to larger cities, and that higher market potential will make this movement stronger. We test these predictions with a comprehensive sample of cities in Eastern Europe and Central Asia. We find that having higher market access - when operating in an environment of population decline - is detrimental to city population growth. This result is robust to different measures of market access that use population. Alternative measures that use economic size rather population are tested, and the result weaker. A possible explanation is that using NLs restricts the sample to only using larger cities.
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Fu, Xianshu, Xiaoping Yu, Zihong Ye, and Haifeng Cui. "Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics." Journal of Chemistry 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/379327.
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This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice (n=121) collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1plus 6000–6400 cm−1has the best correlation with TPC, while the range of 4400–5200 cm−1plus 6000–6400 cm−1is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1for TPC and 0.141 mg GAE g−1for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.
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Godard, John. "Strikes as Collective Voice: A Behavioral Analysis of Strike Activity." ILR Review 46, no. 1 (October 1992): 161–75. http://dx.doi.org/10.1177/001979399204600112.
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This paper outlines a “collective voice” approach for examining the behavioral determinants of variation in strike activity at the organizational level. The author argues that strikes should be viewed primarily as expressions of worker discontent rather than a result of imperfect or asymmetrical information. An analysis of survey data collected from 112 Canadian firms in 1980–81 indicates that managerial practices, operations size and technology, product market structure and conditions, union politics, and various other factors that influence the behavioral context of negotiations are significantly related to days lost due to strike activity. These findings are generally consistent with predictions from the collective voice approach.
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Wu, Ke, Spencer Wheatley, and Didier Sornette. "Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations." Royal Society Open Science 5, no. 9 (September 2018): 180381. http://dx.doi.org/10.1098/rsos.180381.
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We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being ‘entrenched incumbents’ and tokens as an ‘explosive immature ecosystem’, largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
Dissertations / Theses on the topic "Market activity predictions"
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Badenhorst, Dirk Jakobus Pretorius. "Improving the accuracy of prediction using singular spectrum analysis by incorporating internet activity." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80056.
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Thesis (MComm)--Stellenbosch University, 2013.ENGLISH ABSTRACT: Researchers and investors have been attempting to predict stock market activity for years. The possible financial gain that accurate predictions would offer lit a flame of greed and drive that would inspire all kinds of researchers. However, after many of these researchers have failed, they started to hypothesize that a goal such as this is not only improbable, but impossible. Previous predictions were based on historical data of the stock market activity itself and would often incorporate different types of auxiliary data. This auxiliary data ranged as far as imagination allowed in an attempt to find some correlation and some insight into the future, that could in turn lead to the figurative pot of gold. More often than not, the auxiliary data would not prove helpful. However, with the birth of the internet, endless amounts of new sources of auxiliary data presented itself. In this thesis I propose that the near in finite amount of data available on the internet could provide us with information that would improve stock market predictions. With this goal in mind, the different sources of information available on the internet are considered. Previous studies on similar topics presented possible ways in which we can measure internet activity, which might relate to stock market activity. These studies also gave some insights on the advantages and disadvantages of using some of these sources. These considerations are investigated in this thesis. Since a lot of this work is therefore based on the prediction of a time series, it was necessary to choose a prediction algorithm. Previously used linear methods seemed too simple for prediction of stock market activity and a new non-linear method, called Singular Spectrum Analysis, is therefore considered. A detailed study of this algorithm is done to ensure that it is an appropriate prediction methodology to use. Furthermore, since we will be including auxiliary information, multivariate extensions of this algorithm are considered as well. Some of the inaccuracies and inadequacies of these current multivariate extensions are studied and an alternative multivariate technique is proposed and tested. This alternative approach addresses the inadequacies of existing methods. With the appropriate methodology chosen and the appropriate sources of auxiliary information chosen, a concluding chapter is done on whether predictions that includes auxiliary information (obtained from the internet) improve on baseline predictions that are simply based on historical stock market data.
AFRIKAANSE OPSOMMING: Navorsers en beleggers is vir jare al opsoek na maniere om aandeelpryse meer akkuraat te voorspel. Die moontlike finansiële implikasies wat akkurate vooruitskattings kan inhou het 'n vlam van geldgierigheid en dryf wakker gemaak binne navorsers regoor die wêreld. Nadat baie van hierdie navorsers onsuksesvol was, het hulle begin vermoed dat so 'n doel nie net onwaarskynlik is nie, maar onmoontlik. Vorige vooruitskattings was bloot gebaseer op historiese aandeelprys data en sou soms verskillende tipes bykomende data inkorporeer. Die tipes data wat gebruik was het gestrek so ver soos wat die verbeelding toegelaat het, in 'n poging om korrelasie en inligting oor die toekoms te kry wat na die guurlike pot goud sou lei. Navorsers het gereeld gevind dat hierdie verskillende tipes bykomende inligting nie van veel hulp was nie, maar met die geboorte van die internet het 'n oneindige hoeveelheid nuwe bronne van bykomende inligting bekombaar geraak. In hierdie tesis stel ek dus voor dat die data beskikbaar op die internet dalk vir ons kan inligting gee wat verwant is aan toekomstige aandeelpryse. Met hierdie doel in die oog, is die verskillende bronne van inligting op die internet gebestudeer. Vorige studies op verwante werk het sekere spesifieke maniere voorgestel waarop ons internet aktiwiteit kan meet. Hierdie studies het ook insig gegee oor die voordele en die nadele wat sommige bronne inhou. Hierdie oorwegings word ook in hierdie tesis bespreek. Aangesien 'n groot gedeelte van hierdie tesis dus gebasseer word op die vooruitskatting van 'n tydreeks, is dit nodig om 'n toepaslike vooruitskattings algoritme te kies. Baie navorsers het verkies om eenvoudige lineêre metodes te gebruik. Hierdie metodes het egter te eenvoudig voorgekom en 'n relatiewe nuwe nie-lineêre metode (met die naam "Singular Spectrum Analysis") is oorweeg. 'n Deeglike studie van hierdie algoritme is gedoen om te verseker dat die metode van toepassing is op aandeelprys data. Verder, aangesien ons gebruik wou maak van bykomende inligting, is daar ook 'n studie gedoen op huidige multivariaat uitbreidings van hierdie algoritme en die probleme wat dit inhou. 'n Alternatiewe multivariaat metode is toe voorgestel en getoets wat hierdie probleme aanspreek. Met 'n gekose vooruitskattingsmetode en gekose bronne van bykomende data is 'n gevolgtrekkende hoofstuk geskryf oor of vooruitskattings, wat die bykomende internet data inkorporeer, werklik in staat is om te verbeter op die eenvoudige vooruitskattings, wat slegs gebaseer is op die historiese aandeelprys data.
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Williams, Neiliane. "Arbitrageur activity and market anticipation in predicting takeover success." Thesis, 2009. http://spectrum.library.concordia.ca/976400/1/MR63261.pdf.
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For decades, merger arbitrageurs have enjoyed significantly higher returns than those enjoyed by targets, on average. However, these returns are only enjoyed if the merger or acquisition ultimately occurs. An arbitrageur estimates several critical and interrelated factors before assuming any position. These factors are transaction risks, potential reward and the probability of event occurrence. The literature, thus far, has failed to establish a successful takeover success prediction model, which by definition, would use publicly available information at the time of the announcement. In this paper, we use a simple logistic model to test the ability of our four proposed takeover success prediction models. Our sample consists of the targets associated with the first or initial bids for corporate control in bidding contests between 1993 and 2005. We introduce two new variables! turnover and run-up as indicators of arbitrageur activity and market anticipation, respectively. Consistent with theory, turnover, when high enough to facilitate arbitrageur influence on deal outcomes without the dilution of their information advantage, is significant in predicting deal success. This relationship is strongest for seller-initiated turnover. In addition, we find that market anticipation is positively and significantly related to the probability of deal success.
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Malska, Joanna. "Does financial volatility help in explaining and predicting economic activity?" Master's thesis, 2017. http://hdl.handle.net/10362/26210.
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Driven by the difficulty to predict the last financial crisis and possible distortion of predictive power of the conventional financial indicators on economic activity, this thesis provides in-sample and out-of-sample analyses whether financial volatility helps in explaining and forecasting economic activity. Several measures of financial volatility were constructed, such as: realized volatility, volatility following a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process, common long-run component of volatility estimated by Dynamic Factor Model, Principal Component Analysis and cyclical components of financial volatilities filtered out with Baxter-King filter. I find that statistically there are measures of financial volatility that help in explaining economic activity. Moreover, out-of-sample analysis suggests that the model with term-spread and volatility of financial volatility (volatility of value-weighted returns of market portfolio volatility) performs best in forecasting economic activity. The inclusion of a volatility measure reduces the noise in estimated probabilities of expansions and leads to the lowest number of uncertain periods, i.e. periods for which probability of recession is between 16.86% (percentage of recessions in the sample) and 50%, an event that in some studies is already considered as a recession. Thus, a certain financial volatility measure improves forecasts from the conventional financial indicators, especially during less volatile times. Moreover, the most parsimonious measure of volatility predicts business cycles best. On the other hand, industrial production growth seems to be barely affected by financial volatility measures, which tend to be a better predictor for the direction of the future path of the economy than the actual growth rate.
Books on the topic "Market activity predictions"
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International Conference on Environmental Mutagens (7th 1997 Rome, Italy). Satellite meeting of the 7th International Conference on Environmental Mutagens (ICEM): Workshop : quantitative modeling approaches for understanding and predicting mutagenicity and carcinogenicity : Istituto Superiore di Sanità Rome, September 3-5, 1997 : abstract book. Roma: Istituto superiore di sanità, 1997.
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Vaughan-Williams, Leighton, and Donald S. Siegel, eds. The Oxford Handbook of the Economics of Gambling. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199797912.001.0001.
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In recent years, there has been a substantial rise in interest among academics and policymakers in the economics of gambling. A concomitant trend has been the implementation of major regulatory changes and modifications to the taxation of gambling markets in several nations. Examples include a fundamental change in the U.K. in 2001 from a turnover-based tax on betting operators to a tax based on gross profits, resulting in the effective abolition of taxation levied directly on bettors, followed in 2005 by extensive reforms to the gambling sector resulting from introduction of the Gambling Act. In the U.S., passage of the Unlawful Internet Gambling Enforcement Act of 2006 had profound implications for the global online gambling sector. There have also been numerous regulatory changes to gambling in Europe, Asia, and Australia. These changes and rising concern regarding revenue generated from this activity have heightened interest in understanding the economics of this sector. Despite growing interest in the economics of gambling, there is no comprehensive source of path-breaking research on this topic. The purpose of this handbook is to fill this gap. Specifically, we divide the handbook into sections on casinos, sports betting, racetrack betting, betting strategy, motivation, behaviour and decision-making in betting markets, prediction markets and political betting, and lotteries and gambling machines.
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Som, Lalita. State Capitalism. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849595.001.0001.
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The crises emanating from the Global Financial Crisis and the COVID-19 pandemic have underscored, the emergency role of the State and its smooth, seamless reactivation, for situations when private activity and markets are disrupted. In many countries, state-owned enterprises (SOEs) have been a crucial part in delivering on that effort as agents of the State. While SOEs are increasingly sought to play a role during emergency situations, evidence suggests that they misallocate capital and mismanage resources. This is indicative of the conflicts of interests in owning and regulating enterprises as well as between the commercial and non-commercial objectives of SOEs, crony capitalism, the private agenda of public officials, internal management of SOEs, the significant role played by state owned banks and financial institutions, and the conflicts that arise in the State’s primary role vs its ownership of enterprises. The eight country studies in this book provide answers to these key policy questions related to state capitalism. Generalizing from the results of multi-country studies to arrive at universally applicable predictions, prescriptions, and policy recommendations is inherently difficult. But the eight country studies highlight, from available research, the principal common characteristics of, and practices followed by, successful SOEs independently of country context. Among other conditions, the two most important conclusions that can be drawn from the country studies are that competition and regulation rather than ownership per se are key to efficiency.
Book chapters on the topic "Market activity predictions"
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Mishra, Partha Sarathi, and Satchidananda Dehuri. "Higher Order Neural Network for Financial Modeling and Simulation." In Advances in Computational Intelligence and Robotics, 440–66. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0063-6.ch018.
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Financial market creates a complex and ever changing environment in which population of investors are competing for profit. Predicting the future for financial gain is a difficult and challenging task, however at the same time it is a profitable activity. Hence, the ability to obtain the highly efficient financial model has become increasingly important in the competitive world. To cope with this, we consider functional link artificial neural networks (FLANNs) trained by particle swarm optimization (PSO) for stock index prediction (PSO-FLANN). Our strong experimental conviction confirms that the performance of PSO tuned FLANN model for the case of lower number of ahead prediction task is promising. In most cases LMS updated algorithm based FLANN model proved to be as good as or better than the RLS updated algorithm based FLANN but at the same time RLS updated FLANN model for the prediction of stock index system cannot be ignored.
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Elliott, Andrew C. A. "Taking a Gamble." In What are the Chances of That?, 125–42. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869023.003.0007.
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Gambling is an ancient human activity. We indulge ourselves by allowing ourselves to experience the dangers and thrills of chance in a somewhat controlled way. The history of lotteries and related games is explored. The chances of drawing various poker hands are laid out. The role of probability in horse racing is described, and how the odds quoted are not strictly statements of probability, but terms on which business is to be done. Political prediction betting markets give us a further interpretation of probability as a way of expressing strength of opinion in a quantifiable, albeit flawed way. Wagers encourage boasters to put their money where their mouth is, and so to quantify their degree of belief.
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Tytykalo, Volodymyr. "PROCESS MANAGEMENT OF ENTERPRISE DEVELOPMENT IN THE CONTEXT OF ECONOMIC POTENTIAL IMPROVEMENT." In Economics, management and administration in the coordinates of sustainable development. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-157-2-34.
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Transformation of economic actions in the modern market conditions causes a necessity of formation of essentially new approaches in the activity of the companies, as usual systems do not provide their effective development and reception of the necessary norm of profit. However, before choosing a certain trajectory of action to improve the functioning and the choice of a business model, it is necessary to consider possible before the definition and prediction factors of influence on the formation of economic potential of enterprises and accordingly design a successful structure of its components, the interaction between them, which will be a fundamental basis for a further development. It is the economic potential of the enterprise that accumulates both its competitive advantages and renewable abilities and reflects the opportunities to acquire new ones in time on the basis of intellectualization of management processes, improvement of the functional content and adaptation of employees’ competences.
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Kuosmanen, Petri, and Juuso Vataja. "Predicting Economic Activity with Financial Market Data in a Small Open Economy: Revisiting Stylized Facts During Economic Turbulence." In Macroeconomic Analysis and International Finance, 217–34. Emerald Group Publishing Limited, 2014. http://dx.doi.org/10.1108/s1571-038620140000023008.
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Jaén-Vargas, M., K. Reyes Leiva, F. Fernandes, S. B. Gonçalves, M. Tavares Silva, D. S. Lopes, and J. Serrano Olmedo. "A Deep Learning Approach to Recognize Human Activity Using Inertial Sensors and Motion Capture Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210196.
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Human Activity Recognition (HAR) plays an important role in behavior analysis, video surveillance, gestures recognition, gait analysis, and posture recognition. Given the recent progress of Artificial Intelligence (AI) applied to HAR, the inputs that are the data from wearable sensors can be treated as time-series from which movement events can be classified with high accuracy. In this study, a dataset of raw sensor data served as input to four different deep learning networks (DNN, CNN, LSTM, and CNN-LSTM). Differences in accuracy and learning time were then compared and evaluated for each model. An analysis of HAR was made based on an attempt to classify three activities: walking, sit-to-stand, and squatting. We also compared the performance of two different sensor data types: 3-axis linear acceleration measured from two inertial measurement units (IMUs) versus 3D acceleration of two retro-reflective markers from the high-end optoelectronic motion capture system (MOCAP). The dataset created from observations of ten subjects was preprocessed with labelling and sliding windows and then used as input to the four frameworks. The results indicate that, for HAR prediction, linear accelerations estimated using IMUs are as reliable as those measured using the MOCAP system. Also, the use of the hybrid CNN-LSTM framework for both methods resulted in higher accuracy (99%).
Conference papers on the topic "Market activity predictions"
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Roy, Ranjan Kumar, Koyel Ghosh, and Apurbalal Senapati. "Stock Price Prediction: LSTM Based Model." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.19.
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Stock price prediction is a critical field used by most business people and common or retail people who tried to increase their money by value with respect to time. People will either gain money or loss their entire life savings in stock market activity. It is a chaos system. Building an accurate model is complex as variation in price depends on multiple factors such as news, social media data, and fundamentals, production of the company, government bonds, historical price and country's economics factor. Prediction model which considers only one factor might not be accurate. Hence incorporating multiple factors news, social media data and historical price might increase the model's accuracy. This paper tried to incorporate the issue when someone implements it as per the model outcome. It cannot give the proper result when someone implements it in real life since capital market data is very sensitive and news-driven. To avoid such a situation, we use the hedging concept when implemented.
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Shan Wei, Jarrett Yeo, and Yeo Chai Kiat. "CalixBoost: A Stock Market Index Predictor using Gradient Boosting Machines Ensemble." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121009.
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The potential of machine learning has sustained the interest of both academia and industry in stock market prediction over the past decade. This paper aims to integrate modern techniques such as Gradient Boosting Machines (GBMs) into a novel ensemble called CalixBoost which is a resource-efficient and accurate stock index predictor. Data comprising macro-economic metrics and technical financial indicators, as well as sentiment analysis of social media using a simple and fast but effective rule-based model are used in this paper. Other techniques include model tuning with Bayesian Optimization, temporal consistency analysis for invariant feature selection over random trial-and-error, feature importance and inter-feature relationships analysis using a unified game theory approach using Shapley values. Lastly, the model will be evaluated using a novel holdout method, viz. on two separate test datasets whose datapoints are collected under (i) normal economic activity and (ii) during a black swan (financial downturn). The experimental results show that our model outperforms previous methods and can achieve a good prediction performance with 84.88% accuracy, 0.0956 RMSE, 0.0573 MAE and 4.19% MAPE.
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Araújo, Felipe Rocha de, Denis Lima Rosário, Kassio Machado, Eduardo Coelho Cerqueira, and Leandro Villas. "TEMMUS: A Mobility Predictor based on Temporal Markov Model with User Similarity." In XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc.2019.7389.
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Location-Based Social Networks (LBSN) data contains spatial, temporal, and social features of user activity, providing valuable information that is currently available on large-scale and low-cost fashion via traditional data collection methods. In this way, LBSN data enables to predict user mobility based on spatial, temporal, and social features, which can be used in several areas, such as device-to-device (D2D) communication, caching, and others. In addition, a Temporal Markov Chain (TMC) is a stochastic model used to model randomly changing systems, such as mobility prediction based on the spatiotemporal factor such as location and day of the week. In this paper, we introduce the Temporal Markov Model with User Similarity (TEMMUS) mobility prediction model. TEMMUS considers a TMC of variable order based on the day of the week (weekday or weekend) and the user similarity to predict the user's future location. The results highlight a higher accuracy of TEMMUS compared to three state-of-the-art Markov Model predictors.
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Ruijtenbeek, Rob, Victor Thijssen, Eva Schaake, Liesbeth Houkes, Rik de Wijn, Michel van de Heuvel, Robert-Jan van Suylen, et al. "Abstract 4113: Kinase activity based biomarkers: Identification of prognostic and erlotinib response prediction markers in NSCLC patients." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-4113.
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Holowsko, Nicholas, and Christopher McComb. "Multi-Objective Model-Based Optimization of Pilot Decision Making for Urban Air Mobility." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69819.
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Abstract The emergence of an Urban Air Mobility (UAM) market utilizing fleets of vertical takeoff and land (VTOL) aircraft has the potential to shorten commutes, alleviate traffic congestion, and transform the way people interact with cities. Both industry and academic effort has been aimed towards predicting and simulating fleet activity for the purpose of informing vehicle design decisions and infrastructure planning activities. However, little effort has been targeted at analyzing aircraft fleet performance and sensitivity to pilot decision making, infrastructure availability, and changing vehicle characteristics. In this work, we utilize an existing proprietary, industry developed, discrete-event simulation tool that calculates air vehicle fleet performance with user specified vehicle and demand parameters. Building on this simulation, we then apply an optimization and sensitivity analysis, identifying the relative importance of different vehicle parameters, network attributes, decision making policies, and demand characteristics as they ultimately relate to objective functions that aim to maximize vehicle utilization and minimize the number of deadhead trips. The result of this analysis will inform critical requirements of the UAM market and highlight the importance of effective vehicle scheduling in a UAM scenario.
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Baratta, Mirko, Andrea E. Catania, and Francesco C. Pesce. "CNG Injector Nozzle Design and Flow Prediction." In ASME 2010 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/icef2010-35104.
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In the last few years, a great research effort has been made for developing and enhancing Direct Injection (DI) compressed natural gas (CNG) engines. A number of research projects has been promoted by the European Community (EC) in this field with the objectives of finding new solutions for the automotive market and also of encouraging a fruitful knowledge exchange among car manufacturers and technical universities. The present paper concerns part of the research activity that has been carried out at Politecnico di Torino (PT) within the EC VII Framework Program (FP) InGAS Integrated Project (IP). The target of the work was to support the design phase of a new injector for CNG direct injection, paying specific attention to the nozzle configuration and also to its behavior under different conditions and over runtime. The needle design was carried out with the aims of enhancing the injector reliability and reducing the injector internal friction, which usually causes injector wear due to the lack of lubrication effect with respect to liquid-fuel injectors. The new needle design concept which was considered in the present research project was oriented to maximize the contact area between the needle and its cartridge so as to reduce needle wear. For this reason, the injector feeding part was realized by means of two series of holes. The design was assisted by 3D numerical simulations which indicated the best feeding-hole number and geometry to obtain a maximum mass-flow rate. For this investigation, the needle was kept at its maximum lift and the feeding pressure was gradually increased up to the design rail pressure. The results indicated that the hole number remarkably influences the flow losses along the internal flow path and, in turn, the resultant mass-flow rate. These effects, along with the flow field characteristics inside the injector, are examined and discussed in detail throughout the paper.
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Wang, Chenli, and Hohyun Lee. "Economical and Non-Invasive Residential Human Presence Sensing via Temperature Measurement." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88211.
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Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO2 sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.
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Chao, Manuel Arias, Darrel S. Lilley, Peter Mathé, and Volker Schloßhauer. "Calibration and Uncertainty Quantification of Gas Turbine Performance Models." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42392.
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Calibration and uncertainty quantification for gas turbine (GT) performance models is a key activity for GT manufacturers. The adjustment between the numerical model and measured GT data is obtained with a calibration technique. Since both, the calibration parameters and the measurement data are uncertain the calibration process is intrinsically stochastic. Traditional approaches for calibration of a numerical GT model are deterministic. Therefore, quantification of the remaining uncertainty of the calibrated GT model is not clearly derived. However, there is the business need to provide the probability of the GT performance predictions at tested or untested conditions. Furthermore, a GT performance prediction might be required for a new GT model when no test data for this model are available yet. In this case, quantification of the uncertainty of the baseline GT, upon which the new development is based on, and propagation of the design uncertainty for the new GT is required for risk assessment and decision making reasons. By using as a benchmark a GT model, the calibration problem is discussed and several possible model calibration methodologies are presented. Uncertainty quantification based on both a conventional least squares method and a Bayesian approach will be presented and discussed. For the general nonlinear model a fully Bayesian approach is conducted, and the posterior of the calibration problem is computed based on a Markov Chain Monte Carlo simulation using a Metropolis-Hastings sampling scheme. When considering the calibration parameters dependent on operating conditions, a novel formulation of the GT calibration problem is presented in terms of a Gaussian process regression problem.
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Tahiri, Andliena, Kathrine Roe, Christian Busch, Per Eystein Lonning, Anne H. Ree, Vessela N. Kristensen, and Juergen Geisler. "Abstract 863: Tyrosine kinase activity profiling of metastatic malignant melanoma: Identification of possible therapeutic targets and markers predicting response to therapy." In Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/1538-7445.am2012-863.
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Zhou, Joe, Gordon Craig, Beez Hazen, and James D. Hart. "An Integrated Engineering Model for Prediction of Strain Demands in Pipelines Subject to Frost Heave." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10053.
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Long distance pipelines are actively pursued by the industry to transport natural gas from remote arctic regions to markets. A chilled gas pipeline is one of the options to minimize the environmental impact resulting from operation of such pipelines. When a chilled gas pipeline crosses discontinuous permafrost areas, differential frost heave can occur. The result is pipe being subjected to potentially high strains, primarily in the axial direction. Reliable prediction of strain demands is one of the key components for a strain-based design process and it is essential for both ensuring pipeline integrity and facilitating life-cycle cost optimization for the design and maintenance of pipelines. The prediction of strain demands resulting from frost heave of chilled gas pipelines involves three fundamental engineering analysis processes. They are gas hydraulic analysis, geothermal analysis and pipeline structural analysis. Not only are these three processes complex, they are also mutually interdependent. To reliably predict strain demands and fully capture the interactions among these processes, TransCanada Pipelines Ltd. (TransCanada) and its partners developed an integrated engineering model on the basis of three well established programs for the three individual engineering processes. This paper will briefly review the integrated model for strain demand prediction.
Reports on the topic "Market activity predictions"
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Elizur, Abigail, Amir Sagi, Gideon Hulata, Clive Jones, and Wayne Knibb. Improving Crustacean Aquaculture Production Efficiencies through Development of Monosex Populations Using Endocrine and Molecular Manipulations. United States Department of Agriculture, June 2010. http://dx.doi.org/10.32747/2010.7613890.bard.
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Background Most of Australian prawn aquaculture production is based on P. monodon. However, the Australian industry is under intense competition from lower priced overseas imports. The availability of all-female monosex populations, by virtue of their large size and associated premium prize, will offer competitive advantage to the industry which desperately needs to counteract competitors within this market. As for the redclaw production in Israel, although it is at its infancy, the growers realized that the production of males is extremely advantageous and that such management strategy will change the economic assumptions and performances of this aquaculture to attract many more growers. Original objectives (as in original proposal) Investigating the sex inheritance mechanism in the tiger prawn. Identification of genes expressed uniquely in the androgenic gland (AG) of prawns and crayfish. The above genes and/or their products will be used to localize the AG in the prawn and manipulate the AG activity in both species. Production of monosex populations through AG manipulation. In the prawn, production of all-female populations and in the crayfish, all-male populations. Achievements In the crayfish, the AG cDNA library was further screened and a third AG specific transcript, designated Cq-AG3, had been identified. Simultaneously the two AG specific genes, which were previously identified, were further characterized. Tissue specificity of one of those genes, termed Cq-AG2, was demonstrated by northern blot hybridization and RNA in-situ hybridization. Bioinformatics prediction, which suggested a 42 amino acid long signal anchor at the N-terminus of the deduced Cq-AG2, was confirmed by immunolocalization of a recombinant protein. Cq-IAG's functionality was demonstrated by dsRNA in-vivo injections to intersex crayfish. Cq-IAGsilencing induced dramatic sex-related alterations, including male feature feminization, reduced sperm production, extensive testicular apoptosis, induction of the vitellogeningene expression and accumulation of yolk proteins in the ovaries. In the prawn, the AG was identified and a cDNA library was created. The putative P. monodonAG hormone encoding gene (Pm-IAG) was identified, isolated and characterized for time of expression and histological localization. Implantation of the AG into prawn post larvae (PL) and juveniles resulted in phenotypic transformation which included the appearance of appendix masculina and enlarged petasma. The transformation however did not result in sex change or the creation of neo males thus the population genetics stage to be executed with Prof. Hulata did not materialized. Repeated AG implantation is currently being trialed. Major conclusions and Implications, both scientific and agricultural Cq-IAG's involvement in male sexual differentiation had been demonstrated and it is strongly suggested that this gene encodes an AG hormone in this crayfish. A thorough screening of the AG cDNA library shows Cq-IAG is the prominent transcript within the library. However, the identification of two additional transcripts hints that Cq-IAG is not the only gene mediating the AG effects. The successful gene silencing of Cq-IAG, if performed at earlier developmental stages, might accomplish full and functional sex reversal which will enable the production of all-male crayfish populations. Pm-IAG is likely to play a similar role in prawns. It is possible that repeated administration of the AG into prawn will lead to the desired full sex reversal, so that WZ neo males, crossed with WZ females can result in WW females, which will form the basis for monosex all-female population.
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Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Minz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598153.bard.
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Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report