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@proceedings{Bianes2025,

title = {Essential Soft Skills for Construction Project Managers to Achieve Sustainable Development Goals: A Bibliometric and Comprehensive Review},

author = {Joseph Raniel Alvarez Bianes and Armando Victoria Jr and Manuel M. Muhi},

url = {https://catalog.lib.kyushu-u.ac.jp/opac_detail_md/?lang=0&amode=MD100000&bibid=7395700

https://catalog.lib.kyushu-u.ac.jp/opac_download_md/7395700/2025_p1450.pdf},

doi = {/10.5109/7395700},

year  = {2025},

date = {2025-10-30},

urldate = {2025-10-30},

journal = {Kyushu University Institutional Repository},

issue = {11},

pages = {450-145},

school = {Kyushu University},

abstract = {The construction industry is considered one of the significant industries behind the economic success of most developed and developing countries. Its detrimental effects on the environment and society raise concerns among global leaders and environmental groups, leading to the birth of the United Nations Sustainable Development Goals (SDG) in 2015. Project managers play a crucial role in the success of any construction project. They must possess technical and soft skills to handle evolving needs, financial limitations, and diverse teams with various cultural backgrounds. However, soft skills are often overlooked and have only recently gained interest in global research. Hence, the main objective of this study is to identify the essential soft skills of project managers and uncover their connections with attaining the SDGs using the published articles in the Scopus database. The bibliometric review was performed on a corpus of 403 articles through CorText Manager to identify the prominent journals, visualize their relationship with the top keywords, and analyze the co-occurrence and evolution among the keywords based on the string search keyword used. A comprehensive review of these articles found that the essential soft skills of project managers are Leadership, Communication, Stakeholder Management, and Conflict Resolution. Also, the reviewed papers were most aligned with attaining SDGs 9, 11, and 12. Likewise, most of these studies used the PLS-Structural Equation Modeling (PLS-SEM). Future research should continue to explore the interplay between soft skills and project outcomes, employing diverse methodologies to capture the nuances of interpersonal dynamics in construction management.},

note = {International Exchange and Innovation Conference on Engineering & Science (IEICES)},

keywords = {},

pubstate = {published},

tppubtype = {proceedings}

}

@proceedings{Denoyelle2025,

title = {Review of Autonomous Racing Competitions and Technologies},

author = {Lanzo Denoyelle and Rédha Hamouche and Rémy Kocik and Eva Dokladalova and Nicolas Hautière},

url = {https://hal.science/hal-05330945v1},

year  = {2025},

date = {2025-10-25},

abstract = {This work comprehensively analyzes autonomous racing competitions and their technologies, addressing a gap in current literature. We evaluate these platforms through five key metrics: cost, system modularity, speed performance, technical complexity, and academic impact. Using bibliometric analysis of scientific literature, we identify dominant technologies, including Model Predictive Control and reinforcement learning. Our comparative framework reveals clear trade-offs: Formula Student Driverless offers accessibility and academic involvement but features lower speeds. At the same time, full-scale competitions like Indy Autonomous Challenge provide high-velocity testing but at increased cost. Reduced-scale platforms serve as valuable precursors to full-scale projects, offering cost-effective insights into perception, control, and system integration. The study demonstrates how competitions complement each other in advancing autonomous driving technologies and identifies patterns in simulator usage for algorithm development and validation. This comparison provides researchers with guidance for selecting appropriate platforms based on research objectives and available resources. },

note = {Proceedings of 8th International Symposium on Future Active Safety Technology towards Zero-Traffic Accidents

hal-05330945},

keywords = {},

pubstate = {published},

tppubtype = {proceedings}

}

@proceedings{Gornet2024,

title = {Mapping AI ethics: a meso-scale analysis of its charters and manifestos},

author = {Mélanie Gornet and Simon Delarue and Maria Boritchev and Tiphaine Viard},

url = {https://dl.acm.org/doi/10.1145/3630106.3658545

https://dl.acm.org/doi/pdf/10.1145/3630106.3658545},

doi = {/10.1145/3630106.3658545},

year  = {2024},

date = {2024-06-05},

pages = {127-140},

organization = {2024 ACM Conference on Fairness, Accountability, and Transparency},

abstract = {The recent years have seen a surge of initiatives with the goal of defining what “ethical” artificial intelligence would or should entail, resulting in the publication of various charters and manifestos discussing AI ethics; these documents originate from academia, AI industry companies, non-profits, regulatory institutions, and the civil society. The contents of such documents vary wildly, from short, vague position statements to verbatims of democratic debates or impact assessment studies. As such, they are a marker of the social world of artificial intelligence, outlining the tenets of different actors, the consensus and dissensus on important goals, and so on.



Multiple meta-analyses have focused on qualitatively identifying recurring themes in these documents, highlighting the high polysemy of themes such as transparency or trust, among others. The broad term of “AI ethics” and its guiding principles hide multiple disparities, shaped by our collective imaginations, economic and regulatory incentives, and the pre-existing social and structural power asymmetries; through quantitative analyses, we validate and infirm previous qualitative results.



In this paper, we create and present a corpus of charters and manifestos discussing AI ethics through the process of collection and its quantitative analysis using text analysis to shed light on common and distinct vocabularies. Through frequency analysis, hierarchical topic clustering and semantic graph modelling, we show that the charters and manifestos discuss AI ethics along three broad axes: technical documents, regulatory ones, and innovation and business ones. We use our quantitative analysis to back up and nuance previous qualitative results, showing how some themes remain specific while others have fully permeated the space of AI ethics. We document and release our corpus, comprising of 436 documents, charters and manifestos discussing AI ethics. We release the corpus, its datasheet and our analysis, to open the way to further studies and discussions around vocabulary, principles and their evolution, as well as interactions among actors of AI ethics, in order to foster further studies on the topic.},

note = {FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency},

keywords = {},

pubstate = {published},

tppubtype = {proceedings}

}