Narrow Results

This chapter is on condition monitoring. The condition monitoring domain in which the data is visualized is discussed, and in particular the time, modal, frequency, and time–frequency domains. The generalized condition monitoring framework which includes the data acquisition device, data analysis device, feature selection Device and decision-making device is also presented. Techniques for using these decision-making devices are introduced. These are the finite element models, correlation based methods and computational intelligence methods.

The German mechanical engineering sector plays a key role as provider and user of digitally integrated products, processes and business models for Industry 4.0. However, it has not yet been empirically investigated to what extent these companies are able to independently develop digital products and services and what kind of competences they need to be able to do this successfully. An online survey of 335 German mechanical engineering companies and 15 deepening expert interviews shows that they mainly regard digitized service offerings such as predictive maintenance or condition monitoring as core competences and develop them in-house. The analysis shows that agile and design-oriented development methods can significantly improve the companies’ abilities to develop digitized products themselves. An interdisciplinary approach will become more and more important in the future, as digitally integrated innovations are increasingly taking place in the intersection of different disciplines.

Disrupted urban rail services are routinely experienced by rail passengers throughout the world. To minimize the impacts to passengers, transport providers will temporarily run rail replacement bus services that use buses to replace passenger trains. This paper presents a novel data-driven bussing optimization system that can efficiently, reliably, and dynamically determine the replacement bus timetables. The system first infers travel behaviour of train passengers via data mining, then it formulates the route selection problem as a multi-objective optimization problem which is solved in a meta-heuristic way. Finally, a demand-driven scheduling approach is developed to generate the replacement bus timetable. This data-driven bussing optimization system is jointly developed and implemented by University of Technology Sydney and Sydney Trains. Deployment results show that the system not only saved cost for the transport operators, but also significantly improved customer experience in New South Wales, Australia.

Multipole vectors and pseudo entropies provide powerful tools for a numerically fast and vivid investigation of possible statistically anisotropic, resp. non-Gaussian signs in CMB temperature fluctuations. After reviewing and linking these two conceptions we compare their application to data analysis using the Planck 2015 NILC full sky map.

Cosmic topological defects possibly formed during phase transitions in the very early universe and are theoretically well-motivated. Cosmic strings (CSs) would leave distinct imprints on CMB fluctuations. We apply certain topological and geometrical measures in a multi-scale edge-detection algorithm to CMB maps with contributions from CS network to assess the capability of the CS footprints. On the noiseless sky maps with an angular resolution of 0.9′, we show that our pipeline is capable of detecting CSs with Gμ as low as Gμ > 4.3 × 10^-10. We also explore two powerful tree-based machine learning algorithms to perform feature importance analysis. This analysis would improve future CS searches to focus on the most significant and informative features. Our machine-learning algorithm can detect CSs with Gμ > 2.1 × 10^-10 at 3σ level, for a noise-less experiment and angular resolution of 0.9′.

A stochastic background of gravitational waves can be described basically as the superposition of several unrelated events, of cosmological or astrophysical origin. This is a generic definition: depending on specific cases the background can show a rich statistical structure, both in the space and time domain, and in principle several physical information of great interest can be extracted from it. I will discuss the features of this kind of signal, giving some details about how detection and parameter estimation procedures can be designed to obtain optimal performances.

Solar, galactic and magnetospheric energetic particles can create a serious radiation danger during space activities and affect astronauts and equipment, up to and including loss. Anomalous changes in the dynamics of cosmic rays occur during periods of nonstationary solar phenomena and heliospheric disturbances. At the moment, the problem of identifying and forecasting anomalies in cosmic rays is open.

We propose an automated method for detecting and identifying non-periodic variations in cosmic rays, based on a combination of nonlinear adaptive schemes and deep learning. The Autoencoder paradigm and orthogonal package decompositions are used. The effectiveness of the method is confirmed by numerical calculations using data from neutron monitors of a network of ground stations.

The paper utilizes a Python crawler to obtain and organize data from the CSMAR database. The PDF links of annual reports are extracted using Xpath, and the tabular data in the annual reports are extracted by embedding the “pdf plumber” programming in Python. Then, by building a regression model, we analyzed the issue of the influence of capital structure on the corporate growth of A-share GEM-listed companies. The result indicates that the capital structure negatively affects corporate growth in the context of Chinese GEM. However, company size, profitability, and return on investment are positively correlated to corporate growth. The result also shows that industry and ownership of property do not affect the relationship between capital structure and corporate growth. Finally, this paper suggests some policy implications and suggestions from the perspective of managers of GEM companies, government regulators, and investors.

The Belt and Road Initiative (B&R) aims to unite countries and regions along the route to form a community with shared destiny and to achieve mutual benefits. As a macroeconomic barometer, the stock market can reflect the fluctuations of the real economy and predict the macroeconomic development trend. To timely identify the opportunities and challenges brought by the B&R to the stock markets of the countries along the route, this paper utilizes a Python crawler to obtain stock price indexes of eight countries and analyzes the fluctuations of the indexes. This paper divides the data into two parts, namely before and after the countries joined the B&R initiative, and uses GARCH-VaR models to calculate the stock price index risk of each country using WinRATS. The empirical results show that the impact of the B&R on stock price index risk varies across the eight countries. In addition, the price volatility of CSI 300 after the countries participated in the B&R initiative is much lower than that before the participation, and the stock price index risk of some sample countries is also reduced. Finally, the paper makes some suggestions from the perspective of the government and foreign investors.

With the advent of the era of big data, the influence of the Internet finance and the digital economy is becoming stronger and stronger. At the same time, data analysis and cloud computing provide a powerful digital and scientific basis for the formulation and improvement of current social policies. The proportion of pension fund expenditures has always been in the first place in the social insurance fund expenditure. It is of great practical significance to statistically analyze the impact of pension fund expenditures on residents’ consumption in Guangdong Province. This paper selects six variables, such as urban and rural pension fund expenditures and residents’ consumption level, in Guangdong Province from 2010 to 2019, uses the statistical software Stata for data analysis, and establishes the OLS multiple regression model. At the same time, the empirical analysis shows the relationship between urban and rural pension fund expenditures and residents’ consumption levels. The data shows that the OLS model has passed the 1% significance level and has a good fitting effect. The modeling result with a high fitting degree further confirms the conclusion that increasing the expenditures of urban and rural pension funds can improve residents’ consumption level, which provides scientific and solid digital support for the development of the “Internet plus pension industry” in Guangdong Province.

The purpose of this paper is to study the actual economic development of cities in Heilongjiang Province since the occurrence of COVID-19. The Stata software is adopted to carry out descriptive statistics, correlation analysis, regression analysis, and factor analysis on the data of the existing statistical yearbook of Heilongjiang Province in 2021, and then various empirical analysis results are obtained. Through correlation analysis, it can be seen that the regional GDP of each city in Heilongjiang Province has the highest correlation with public financial revenue and total loans. Through regression analysis, it can be seen that the regional GDP has the highest correlation with the gross output value of agriculture, forestry, animal husbandry, fishery, and public finance revenue. It can be seen from the principal component factor analysis that the comprehensive strength of Harbin is the strongest among all cities in the province. Finally, according to the results of various empirical analyses, I give my suggestions. First, give play to the radiation and driving role of Harbin’s modern metropolitan area. Second, build the backbone of the rise of the eastern city cluster industry. Finally, consolidate the achievements of urban green economy development in ecological areas.

From the perspective of the relationship between China’s new energy development, energy transition, and geopolitics, this paper analyzes the important role of energy strategic layout in the country’s rise. Powered by the blossoming of China’s new energy, the increasing degree of intensification, and the government’s promotion of new energy, the whole industry has put forward higher requirements for the efficiency and intelligence of production equipment. It has drawn the concerns of many investors over time. For growth companies, stakeholders are more interested in whether the company’s share price is in line with the fundamentals. Thus, this article applies the Schwartz-Moon model in determining the proximity of the company value to the market value at the end of 2020. It was found that the estimated firm value was 0.5% below the market value at the time, suggesting that the Schwartz-Moon model is a good fit for the market’s perception of the case firm’s fundamentals. The traditional DCF valuation method was used as a comparative study tool which was outshone by the Schwartz-Moon model with more accurate valuation results. The results provide insights into the applicability of the Schwartz-Moon valuation model for booming new energy-related companies while demonstrating the limitations of traditional valuation methods, providing enlightenment for valuing new energy firms using data analysis for further research.

It has been two and a half years since the outbreak of COVID-19, and it is now nearly a year since it entered the post-COVID era globally. During this year, most countries have relaxed, or even lifted, their COVID prevention policies. And will those differences change investors’ attitudes? To explore this topic, this paper uses a time series model to examine whether stock investor behavior has changed under the regular epidemic control compared to the initial era by analyzing Boeing’s stock price since the start of COVID combined with the data of daily new cases globally. Previous studies have shown a downward trend in stock prices following a pandemic outbreak at the beginning of the epidemic, and a few months later, their stock price started to surge again. The conclusion of this paper is that the returns on Boeing stock prices in the post-COVID era are generally repeating history, with investors not gaining much experience from previous investments. However, investor panic has been less severe than at the beginning of the outbreak.

Null hypothesis significance testing (NHST) has been a fixture of psychological research for decades. Despite much debate about the appropriateness, suitability, and philosophy of NHST, the field still has yet to move away from the practice. One factor in the unwillingness or inability to move away from NHST is the lack of alternative practices available. In the current chapter, we suggest several possibilities for analyzing and reporting results without reliance on NHST.

Technology-enabled feedback encompasses many significant features including AI. It also allows students greater flexibility in providing feedback, and the university/institute enables them to make data-driven decisions as the collected data are free from bias and data analyses are accurate. There are different technology-centric feedback collections such as Open edX Insight, speech-to-text, Kahoot, Dialogflow, and so on. These technology-centric feedback collections mean collecting more realistic data from the students. Both qualitative and quantitative data could be analyzed by applying technology-centric data analyses tools. AMOS, LABLEAU, SAS, SPSS, Stata, Statista, XLSTAT, and ROOT are a few of the quantitative data analyses software, and Atlas, Airtable, Coda, Condens, MaxQDA, Notion, and NVivo are a few of the qualitative data analyses software. To make the students’ feedback and survey more successful we recommend sharing survey methods/survey questions with other educational institutions, conducting broader surveys, broad topics to ask questions on, technology-based analysis of qualitative feedback comments, and including skills development-focused questions.

One of the most relevant concepts in statistics is data variability or dispersion. We can find a wide number of studies related to the measurement of dispersion for quantitative data, however, the measurement of dispersion for qualitative data is being poorly developed despite the increasing weight in the Science of linguistic terms to manage information. In fact, only a few measures can be found within a qualitative framework, and their properties have not received much attention. In this chapter, we stress this terrible theoretical lack, exploring a desirable set of properties to be accomplished by an ordinal dispersion measure that allows a structured view of most existing ordinal dispersion measures and a better understanding of ordinal dispersion.

This chapter examines the evaluation of economic and financial viability of marketing cooperatives (MCs) and Investor Owned Firms (IOFs). The analysis covers the periods from 1993-98 for MCs and from 1994-98 for IOFs. The data is based on the financial characteristics of 10 MCs and 2 IOFs established and operating in Crete (the largest Greek island) and 8 food processing and marketing companies operating in Greece, but chosen exclusively for their vast familiarity to Greek consumers. The assessment procedure includes data analysis techniques in combination with a multicriteria analysis method (PROMETHEE II). The analysis results in an overall ranking of the examined firms' performance. It further indicates the strengths and weaknesses of the involved firms with regard to their financial behavior, thus contributing to the identification of market imperfections of the examined firms. Therefore, relevant conclusions are drawn concerning the revision of corporate strategies.

Modern gait analysis results in large quantities of correlated data. A current challenge in the field is the development of appropriate data analysis techniques for the representation and interpretation of these data. Knee osteoarthritis is a common debilitating disease of the musculoskeletal system that has been the focus of many gait studies in recent years. Various data analysis techniques have been used to extract pathological information from gait data in these studies. The following review discusses the successes and limitations of many of these analysis techniques in the attempt to understand the biomechanics of knee osteoarthritis.

Decision support for planning and improving software development projects is a crucial success factor. The special characteristics of software development aggregate these tasks in contrast to the planning of many other processes, such as production processes. Process simulation can be used to support decisions on process alternatives on the basis of existing knowledge. Thereby, new development knowledge can be gained faster and more cost effective.

This chapter gives a short introduction to experimental software engineering, describes simulation approaches within that area, and introduces a method for systematically developing discrete-event software process simulation models. Advanced simulation modeling techniques will point out key problems and possible solutions, including the use of visualization techniques for better simulation result interpretation.

The analysis of genomic data from DNA microarray can produce a valuable information on the biological relevance of genes and on correlations among them. In the last few years some biclustering techniques have been proposed and applied to this analysis. Biclustering is a learning task for finding clusters of samples possessing similar characteristics together with features creating these similarities. When applied to genomic data it can allow us to identify genes with similar behavior with respect to different conditions. In this paper a new approach to the biclustering problem will be introduced extending the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. Some results on oligonucleotide microarray data sets will be presented and compared with those obtained using other biclustering methods.