Venture capital, credit, and FinTech start-up formation: A cross-country study

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordGrowing FinTech entrepreneurship is a recent global phenomenon. Drawing on the national innovation systems framework, we examine how countries’ venture capital (VC) and credit markets differently affect FinTech entrepreneurship across countries. We argue that with their established and globally diffused norms and practices, VC investors—but not banks—require a critical mass of FinTech entrepreneurship in a country to more positively influence FinTech entrepreneurship. Moreover, we argue that VC and credit markets are substitutes, especially in countries with more FinTech entrepreneurship. Using quantile regressions on data from 53 countries, we find support for our hypotheses

RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system

Towards sustainable manufacturing by enabling optimum selection of life extension strategy for industrial equipment based on cost modelling

Sustainable manufacturing is of great importance in today’s world. In manufacturing, keep industrial equipment well-functioning is important because failure of equipment leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual value. This raises the need to adopt methods and means that help extending the life of equipment and reduce waste of material. This paper presents a digital toolkit of cost model to estimate and understand the costs to be incurred when applying life extension strategy for industrial equipment. It is meant to be integrated with other tools and methodologies to enable end-users to perform optimal decision-making regarding which life extension strategy (e.g., remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life or needs maintenance, taking into account criteria such as cost, machine performance, and energy consumption. The cost model developed integrates a combination of parametric costing and activity-based costing methods to per form cost estimation. It has been implemented in an Excel-based Macro platform. A case study with application scenarios has been conducted to demonstrate the application of the cost model to optimize life extension strategies for industrial equipment. Finally, conclusions on the developed cost model have been reported

Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing

Industrial equipment/machinery is an important element of manufacturing. They are used for producing objects that people need for everyday use. Therefore, there is a challenge to adopt effective maintenance strategies to keep them well-functioning and well-maintained in production lines. This will save energy and materials and contribute genuinely to the circular economy and creating value. Remanufacturing or refurbishment is one of the strategies to extend life of such industrial equipment. The paper presents an initial framework of cost estimation model based on combination of activity-based costing (ABC) and human expertise to assist the decision-making on best life extension strategy (e.g. remanufacturing, refurbishment, repair) for industrial equipment. Firstly, ABC cost model is developed to calculate cost of life extension strategy to be used as a benchmark strategy. Next, expert opinions are employed to modify data of benchmark strategy, which is then used to estimate costs of other life extension strategies. The developed cost model has been implemented in VBA-based Excel® platform. A case study with application examples has been used to demonstrate the results of the initial cost model developed and its applicability in estimating and analysing cost of applying life extension strategy for industrial equipment. Finally, conclusions on the developed cost model have been reported

DESIGN OF DIRECTIONAL MODULATION RADIO

In this thesis, Directional Modulation (DM) as a technique to enhance security in the physical layer (PHY) for wireless communication systems is discussed, and a multiple-input, multiple-output (MIMO) DM radio is designed using GNU Radio software. A DM processing block for a MIMO system with two transmit and two receive antennas based on the knowledge of the channel state information (CSI) is implemented and tested in GNU Radio. An attempt to demonstrate a physical DM communication system using GNU Radio and universal software radio peripheral (USRP) devices is described, the problems faced during the experimental setup are included, and the reasons it was not finally completed for real testing are explained. Bit Error Rate (BER) calculations are presented by simulating a MIMO DM QPSK communication system in the GNU Radio Companion (GRC) environment using the implemented DM block. The simulation results illustrate the capability of the MIMO DM system to achieve small BER at an intended receiver and large BER for potential eavesdroppers.Major, Hellenic ArmyApproved for public release. distribution is unlimite

Essays in financial innovation and sustainability

The scope of this dissertation is to observe three of the most prominent trends of this era and to present innovations that could inspire both academics and businesses. There is an urgent need to improve the traditional ways in which the markets operate to achieve financial growth while respecting the environment and the society. The third and fourth manuscript tackle directly the topic of sustainable development through the lenses of SMEs. The first manuscript offers evidence related to the development of FinTech entrepreneurship in a market. Through FinTech and sustainability might appear unrelated at first, their simultaneous development could support achievement of a sustainable economy. FinTech could foster the availability of green finance, which is necessary to the capital-intensive sustainability transformation (Vergara and Agudo, 2021). FinTech supports the sustainable development not only through green finance but also by providing financial resources to underrepresented groups, hence promoting financial inclusion (Arnert et al., 2020). Both FinTech and sustainability are relatively recent trends, hence this dissertation, which focuses on financial innovation and sustainability, oughts to provide evidence for both considering their increasing dependency and relevance.The dissertation was funded by the Academic Research Fund of Vlerick Business School and ABN AMRO Belgium - Partner of the Centre for Sustainable Finance of Vlerick Business Schoo

Κάπνισμα και καρκίνος του πνεύμονα

Κάπνισμα, τοξικές ουσίες παραγόμενες κατά το κάπνισμα, καρκίνος του πνεύμονα και θεραπευτική προσέγγιση.Smoking, toxic substances which are produced during smoking habit, lung cancer and therapeutic approach

Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns