Abstract
Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.

Abstract
This chapter aims to discuss about the potential Return on Investment (ROI) measures from Artificial intelligence (AI) investments that business can leverage. It discusses the concepts and describes the dimensions, features and tools of AI investments in Marketing business, to assist the readers to understand about the topic. The authors also describe the major drivers of ROI measures for business applications and discusses the concerns and limitations of tangible measures. So, this document contributes to the literature on ROI (in)tangibles measures that leverage AI investments and features issues in digital marketing, at large and potentially offers a theoretical grounding for many empirical and theoretical future studies. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
This chapter aims to contribute to the understanding of how artificial intelligence (AI) technologies can promote increased business revenues, cost reductions, and enhanced customer experience, as well as society's well-being in a sustainable way. However, these AI benefits also come with risks and challenges concerning organizations, the environment, customers, and society, which need further investigation. This chapter also examines and discusses how AI can either enable or inhibit the delivery of the goals recognized in the UN 2030 Agenda for Sustainable Business Models Development. In this chapter, the authors conduct a bibliometric review of the emerging literature on artificial intelligence (AI) technolo¬gies implications on sustainable business models (SBM), in the perspective of Sustainable Development Goals (SDGs) and investigate research spanning the areas of AI, and SDGs within the economic group. The authors examine an effective sample of 69 publications from 49 different journals, 225 different institutions, and 47 different countries. On the basis of the bibliometric analysis, this study selected the most significant published sources and examined the changes that have occurred in the conceptual framework of AI and SBM in light of SDGs research. This chapter makes some significant contributions to the literature by presenting a detailed bibliometric analysis of the research on the impacts of AI on SBM, enhancing the understanding of the knowledge structure of this research topic and helping to identify key knowledge gaps and future challenges. © 2024, IGI Global. All rights reserved.

Abstract
This study develops some sustainable tourism advertising effects and consumer environmental awareness-raising and examines them by advertising certification and advertising format in a field experiment. The tourism advertising effects are analyzed by five dependent variables: trust and credibility, environmentalism, ad relevance, realism, and flow. Several ANOVA and multiple comparison tests were performed to understand whether these variables varied between groups. Experimental research findings indicate that flow and video format affect tourism advertising and consumer environmental awareness-raising. This study demonstrates the importance of understanding the concept of sustainable tourism and awareness-raising. It also points to identifying the best communication strategies to promote a sustainable destination, as different communication methods may lead to different results. In addition, it provides valuable information for marketers to consider when implementing their communication strategies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract

Abstract
This study focuses on the assessment of ground vibrations due to pile driving activities. Given the likelihood of excessive vibration due to the driving process, it is imperative to predict vibration levels during the design phase. The primary goal of this work is to integrate machine learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANNs) for real-time vibration prediction. The training dataset was generated using a validated numerical model and the trained models were validated based on experimental results. This validation process highlights the efficiency and accuracy of Extreme Gradient Boosting in predicting the-free-field response of the ground.