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Applications powered by artificial intelligence (AI) and machine learning (ML) have become a crucial factor for success in modern sales organizations. This chapter investigates how Salesforce achieves scalable AI for businesses of all sizes and explores sales applications of AI and machine learning that are most common across industries. It is divided into three sections. The first section gives an introduction to AI and machine learning. The second section shows how data and automated machine learning models provide the foundation for AI applications and explains how Salesforce achieves scalable AI and machine learning for business applications. The third section demonstrates how AI applications impact the modern sales organization and the work of sales representatives. AI does not replace humans; it allows sales organizations to better engage with prospects and customers. Sales representatives using AI outperform their counterparts that rely purely on traditional methods.

The purpose of the present paper is to quantify and analyze the strain-rate dependence of the yield stress for both unfilled acrylonitrile-butadiene-styrene (ABS) and short carbon fiber-reinforced ABS (CF-ABS) materials, fabricated via material extrusion additive manufacturing (ME-AM). Two distinct and opposite infill orientation angles were used to attain anisotropy effects.

Tensile test samples were printed with two different infill orientation angles. Uniaxial tensile tests were performed at five different constant linear strain rates. Apparent densities were measured to compensate for the voided structure. Scanning electron microscope fractography images were analyzed. An Eyring-type flow rule was evaluated for predicting the strain-rate-dependent yield stress.

Anisotropy was detected not only for the yield stresses but also for its strain-rate dependence. The short carbon fiber-filled material exhibited higher anisotropy than neat ABS material using the same ME-AM processing parameters. It seems that fiber and molecular orientation influence the strain-rate dependence. The Eyring-type flow rule can adequately describe the yield kinetics of ME-AM components, showing thermorheologically simple behavior.

A polymer’s viscoelastic behavior is paramount to be able to predict a component’s ultimate failure behavior. The results in this manuscript are important initial findings that can help to further develop predictive numerical tools for ME-AM technology. This is especially relevant because of the inherent anisotropy that ME-AM polymer components show. Furthermore, short carbon fiber-filled ABS enhanced anisotropy effects during ME-AM, which have not been measured previously.

The purpose of this paper is to identify and disentangle the home bias in equity crowdfunding to better understand irrational decision making of investors.

A first choice-based conjoint (CBC) experiment with 217 participants was conducted in central Europe in order to single out different factors contributing to an apparent home bias of capital providers.

The authors find that investors show an avoidance of foreign currency, while payment methods seem to have no considerable influence on the decision making. Furthermore, participants significantly decided against national legislation in favor for EU legislation.

This study predominantly helps to gain deeper insights into influencing factors in crowdfunding markets with a special concern on a cross-border context. For capital-seeking companies, the home bias of potential investor has to be taken into account, when designing a crowdfunding campaign. For legislators, the apparent influence of the legal framework should serve as a wake-up call to consolidate the regulation of crowdfunding in the EU.

This is the first CBC experiment application in a cross-border crowdfunding context. It provides deeper understanding of the importance of geographical proximity between crowdfunding projects and investors and calls for further research to examine how such an effect could be diminished.

This study aims to examine the Financial Action Task Force’s recommendations on virtual currencies (VCs) and how Pakistan has responded to them.

Qualitative document and jurisprudence analysis techniques were used to achieve the study’s goal.

According to this study, VCs are modern FinTech that no jurisdiction can ignore. However, Pakistan has not adopted regulations to govern VCs but comprehensively prohibits their use. It is primarily due to the apathy of various regimes and regulators. Furthermore, the geographical location, undocumented economy and rampant corruption could facilitate the abuse of VCs for money laundering.

This study has provided a significant overview for developing regulations for VCs in Pakistan and other developing jurisdictions with the same characteristics.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.