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In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

The data revolution transforms operations, innovation, and society through artificial intelligence and advanced analytics. Data-driven innovations (DDI) have the most potential to tackle global challenges, including poverty, healthcare, climate actions, disaster management, gender inequality, peace and justice and others. This paper identifies the sources of DDI capabilities to address various global challenges. The findings show three major foundations of DDI capabilities: market orientation, infrastructure orientation, and talent orientation. Theoretically, these findings highlight the role of dynamic DDI capabilities to sense, seize and transform global challenges. Practically, we present guidelines for developing DDI in an agile and efficient manner that is fair and inclusive. [ABSTRACT FROM AUTHOR]

Businesses nowadays often feel impelled to rush and implement Big Data and Artificial Intelligence initiatives in their organizations without clarity on their business problems, nor the appropriate methodologies for extracting actionable insights from the data. In contrast, this paper presents a process that starts with an articulated business problem instead of a "data rush". The presented process, dubbed the Business-Driven Data-Supported (BDDS) process, is rigorously anchored in concepts from Theory of Constraints and Information Theory. BDDS guides businesses in solving their problems by stating observed performance gaps, uncovering their underlying root cause, formulating the "right question", utilizing only the "right data", and choosing the "right methodology" to extract the "right information" from the data, leading to the "right actionable insights." Also provided is a prescriptive framework, dubbed the Data-to-Information-Extraction-Methodology (DIEM), for selecting the "right methodology", based on the understanding level of relevant system dependencies and the availability of relevant data. The BDDS process is illustrated by an example from the healthcare industry, and the efficacy and applicability of the DIEM framework are supported by eleven case studies from a broad range of industries. [ABSTRACT FROM AUTHOR]

This article discusses recent advances in mathematical methods for finance. It highlights the impact of developments in financial markets, technology, and societal challenges on the field of Mathematical Finance. The article also mentions the emergence of new financial products, regulatory frameworks, and trading infrastructures, as well as the revolutionary changes brought about by artificial intelligence and machine learning techniques in numerical methods for finance. Additionally, it acknowledges the growing importance of new types of risks, such as climate-related and cyber-risks, in the financial industry. The article introduces a special issue on Recent Advances in Mathematical Methods for Finance, which provides a comprehensive overview of the latest developments in the field. The special issue includes 44 papers that underwent a rigorous peer review process, covering both theoretical and applied research in mathematical finance. The authors express their gratitude to the editor and staff for the opportunity to edit the special issue and to the referees and authors for their contributions. They hope that this collection of papers will stimulate further research in the field. [Extracted from the article]

The Optimal Multivariate Mixture Problem (OMMP) consists of finding an optimal mixture which, starting from a set of elements (items) described by a set of variables (features), is as close as possible to an ideal solution. This problem has numerous applications spanning various fields, including food science, agriculture, chemistry, materials science, medicine, and pharmaceuticals. The OMMP is a class of optimization problems that can be addressed using traditional Operations Research (OR) approaches. However, it can also be effectively tackled using meta-heuristic techniques within Artificial Intelligence (AI). This paper aims to present an Artificial Intelligence perspective. It proposes a Genetic Algorithm (GA) for Optimal Multivariate Mixture (GA-OMM), a novel improved version of a GA whose modified genetic operators prove to improve the exploration efficiency. Here, the algorithm is described in its general framework, and a test case 8-items 5-features is conducted to evaluate efficiency by exploring various combinations of hyperparameters. Test cases are also set up for the previous version, as well as a linear programming (LP) approach. The data experiments indicate that the proposed GA is efficient, converges towards the global optimum, consistently outperforms its predecessor, and delivers highly competitive results. In particular, GA-OMM shows an average fitness of GA-OMMP/LP and standard deviation with an order of magnitude ranging between 10-8\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$10^{-8}$$\end{document} to 10-4\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$10^{-4}$$\end{document}. Moreover, it consistently outperforms its predecessor, which exhibits similar values around 10-3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$10^{-3}$$\end{document} [ABSTRACT FROM AUTHOR]