Abstract
An attempt has been made to address the difficulty of identifying and measuring the benefits derived from investment projects and capturing capital gains for an organization, focusing on developing and implementing a management model and realizing benefits for a leading company in its activity sector. Thus, the objective is to understand how it is possible to achieve the expected benefits of an investment project: A model characterized as generalist was developed (applied to all areas of the company), with the objective of optimizing the realization of benefits, measuring them and thus create value for the organization. Among the methods used, we highlight, in a first phase, the research of some existing Frameworks, which later enabled the development of a proposed framework, validated internally using the existing Business Intelligence platform. Subsequently, based on a satisfaction questionnaire about the framework proposed to users, data related to its development and implementation were collected, with the aim of understanding its acceptance among the users and employees of the company. With the data from this questionnaire, an artifact was developed: a PowerBI dashboard that reflects the benefits identified and captured. In summary, the artifact made it possible to identify, measure, and achieve the benefits generated by the project in question, but also to motivate its use in other existing investment projects, by adapting it to each of the other ones. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5, and NOx) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution.

Abstract

Abstract
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive syn-thesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increas-ingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and rec-ommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.

Abstract
Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal patternThe results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.

Abstract
Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.