SCIENTIFIC BASES FOR STOCK MARKET FIASCO FORECASTING TECHNOLOGY WITH USE OF INFORMATION SPACE ENTROPY

The article contains a theoretical study and description of general algorithm for predicting a stock market fiasco caused by non-financial and other factors. Market fiasco is considered as non-periodical, sudden and random event which can arise due to the many latent reasons. Methods of technical and fundamental analysis are useless to solve this problem, therefore, the use of systems analysis methods is proposed. The author’s idea is the numerical calculation of search queries entropy as a part of global information space. Decrease in the Renyi’s entropy, associated with rapid grow search queries, containing key terms from the subject area, indicates the possible stock market fiasco in the near future. This article presents an algorithm for the dynamic calculation of Renyi’s entropy, allowing predict rare events which are not reflected in statistical data (or frequency of their realizations is too small). The method and algorithm can be realized in trade information systems and decision-making systems in economic sphere.

Observational Constraints on the Solar Dynamo and the Hunt for Precursors to Solar Flares

While it is believed magnetic spots on solar-like stars originate in their convection zones, constraining the exact location of their generation has not been hitherto possible. Based on theoretical magnetohydrodynamic considerations and an analysis of helioseismic data on solar torsional oscillation, here we demonstrate that dynamic Lorentz forces and an equivalent Lenz’s law for magnetic induction conspire together to reveal where sunspots - strong magnetic field concentrations observed on the Sun’s surface - originate. Our results illuminate physical processes at the heart of solar-stellar magnetic cycles and indicate that stellar magnetic spots are generated in the lower half of their convection zones. The Sun’s axisymmetric flows - differential rotation and meridional flow, govern the dynamics of the solar magnetic cycle and serve as vital inputs for numerical models of the solar cycle. A variety of methods are used to measure these flows, each with its own strengths and weaknesses. Measurements based on cross-correlating images of the surface magnetic field (magnetograms) have been made since the 1970s. Measuring these flows precisely with this method requires advanced numerical techniques which are capable of detecting movements of less than the pixel size in images of the Sun. We have identified several systematic errors which influence previous measurements of these flows and propose numerical techniques which can minimize these errors. Our analysis of magnetograms from the Michelson Doppler Imager (MDI) on the ESA/NASA Solar and Heliospheric Observatory (SOHO) and Helioseismic and Magnetic Imager (HMI) on the NASA Solar Dynamics Observatory (SDO) shows long-term variations in the meridional flow and differential rotation from 1996 to 2019 which has implications for solar cycle prediction. We also introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in the Spaceweather HMI Active Region Patch (SHARP) data series obtained from HMI onboard SDO. Our dataset includes a cross-checked NOAA solar flare catalog which immediately facilitates solar flare prediction efforts, for the first time using time series in a detailed, quantitative manner. We discuss methods used for data collection, cleaning and pre-processing of the active region and flare data; and we further describe a novel data integration and sampling methodology. This dataset covers 4,075 MVTS of active regions between May 2010 and August 2018 matched to over 10,000 flare reports. Potential directions toward expansion of the time series, either horizontally - by adding more prediction specific parameters, or vertically - by generalizing it in order to predict other solar eruptions, are also indicated. The purpose of this dataset is two fold. First, it serves as a mine for precursors to solar flares and it also serves as a benchmark which facilitates the comparison of different solar flare prediction algorithms with both operational (research-to-operations), and basic research (operations-to-research), benefits potentially following in the future

'Some tactical problems in digital simulation' for the next 10 years

In his influential 1963 paper ‘Some Tactical Problems in Digital Simulation’, Conway identified important issues that became the pillars of research in simulation analysis methodology. Naturally these ‘problems’ were a product of the applications of interest at the time, as well as the state of simulation and computing, much of which has changed dramatically. In light of those changes, we attempt to identify the tactical problems that might occupy simulation researchers for the next 10 years

Scientific understanding through big data: From ignorance to insights to understanding

Here I argue that scientists can achieve some understanding of both the products of big data implementation as well as of the target phenomenon to which they are expected to refer --even when these products were obtained through essentially epistemically opaque processes. The general aim of the paper is to provide a road map for how this is done; going from the use of big data to epistemic opacity (Sec. 2), from epistemic opacity to ignorance (Sec. 3), from ignorance to insights (Sec. 4), and finally, from insights to understanding (Sec. 5, 6)

Last mile logistics innovations in the courier-express-parcel sector due to the COVID-19 pandemic

The development of the e-commerce market worldwide, which was already dynamic, was accelerated by the SARS-CoV-2 virus. Millions of incoming orders required analogue support from the CEP sector (courier-express-parcels sector) to provide the desired "customer experience". In the context of whether the habit of shopping in virtual reality will become permanent, it is worth considering what shape the logistics services will take in the last mile after the pandemic? Or, will customers return to shopping in the real world? A subject for these considerations was an analysis of the impact of the SARS-CoV-2 virus pandemic on the technologization of last mile logistics services, resulting in an increase in the level of "customer experience", with Poland as an example. The research methods used were participant observations and critical analysis of collected materials. The obtained results made it possible to conduct a descriptive and explanatory nomothetic study based on an Internet questionnaire. The authors formulated a diagnosis about the possibilities of using the potential of customer experience for the development of enterprises based on technologization of last mile deliveries. The recommendations can be used by scientists and managers in the CEP industry to redefine business models based on the technology of logistics customer service processes

Daydreaming factories

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

This paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.Ministerio de Ciencia e Innovación” (Spain) (Grant PID2020-112495RB-C21MCIN/AEI/10.13039/501100011033) and from the I+D+i FEDER 2020 project B-TIC-42-UGR20 “Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía.”Next Generation EU” Margaritas Salas aids

Blockchain Adoption:A Study of Cognitive Factors Underpinning Decision Making

The literature so far has been focused on technological sophistication rather than the aspects of blockchain adoption that can hinder or facilitate the use of the technology. To address this gap this paper aims to study the cognitive factors underpinning adoption decision-making moderated by user characteristics. Using a cross-sectional research design, the study recruited 506 respondents to participate and test the relationships hypothesised in the research model. The results of the analysis demonstrated that perceived threat vulnerability, response cost, response efficacy and self-efficacy determine adoption intention. These factors have varying effects on intention depending on users' subjective knowledge, objective knowledge and innovativeness. This evidence contributes to the understanding of users’ perspectives on blockchain adoption, which has been under-researched so far. The findings shed light on the cognitive factors motivating blockchain-based technology use and the individual characteristics of users who are likely to adopt the technology in the context of data privacy and security. In turn, these findings can inform practitioners about the aspects of user behaviour that should be considered while developing and marketing the technology