Abstract
Extreme weather events, such as windstorms, hurricanes, and heat waves, exert a significant impact on global natural catastrophes and pose substantial challenges for weather forecasting systems. To enhance the accuracy and preparedness for extreme weather events, this study explores the potential of using expert crowdsourcing in storm forecasting research through the application of stigmergic collaboration. We present the development and implementation of an expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena (eCSAAP) system, designed to leverage the collective knowledge and experience of meteorological experts. Through a participatory co-creation process, we iteratively developed a web-based annotation tool capable of capturing multi-faceted insights from weather data and generating visualizations for expert crowdsourcing campaigns. In this context, this article investigates the intrinsic coordination among experts engaged in crowdsourcing tasks focused on the semantic annotation of extreme weather events. The study brings insights about the behavior of expert crowds by considering the cognitive biases and highlighting the impact of existing annotations on the quality of data gathered from the crowd and the collective knowledge generated. The insights regarding the crowdsourcing dynamics, particularly stigmergy, offer a promising starting point for utilizing stigmergic collaboration as an effective coordination mechanism for weather experts in crowdsourcing platforms but also in other domains requiring expertise-driven collective intelligence.

Abstract
To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

Abstract
The sensitivity of one-dimensional Bloch surface wave (BSW) sensors to external refractive index variations using Kretschmann's configuration is calculated analytically by employing first-order perturbation theory for both TE and TM modes. This approach is then validated by com- parison with both transfer matrix method simulations and experimental results for a chosen photonic crystal structure. Experimental sensitivities of (8.4 +/- 0.2)x102 and (8.4 +/- 0.4)x102 nm/RIU were obtained for the TE and TM BSW modes, corresponding to errors of 0.02% and 4%, respectively, when comparing with the perturbation the- ory approach. These results provide interesting insights into photonic crystal design for Bloch surface wave sensing by casting light into the important parameters related with sen- sor performance.(c) 2023 Optica Publishing Group

Abstract
Managing multiple knowledge transfers between headquarters and subsidiaries, among subsidiaries, and also within each of these units is crucial for multinational corporations' (MNCs) survival. Therefore, this article aims to uncover the main factors influencing internal knowledge transfers in MNCs-including intra-unit knowledge transfers and transfers between units, namely, conventional, horizontal, and reverse knowledge transfers. To achieve this goal, a systematic literature review (SLR) was conducted to synthesize the content of 85 articles. From a set of 1439 papers, only 85 related to knowledge transfer and knowledge sharing were considered. Based on an inductive thematic approach, eight different research categories and 97 topics were identified. Four different internal knowledge transfers (intra knowledge transfer (IKT), horizontal knowledge transfer (HKT), conventional knowledge transfer (CKT), and reverse knowledge transfer (RKT)) are compared across eight thematic categories and 97 topics. According to the results obtained, the depth of the topics analyzed varies, as does the variety of categories, with RKT being more deeply analyzed than IKT. There is a clear dominance of vertical knowledge transfer (CKT + RHT) over HKT. The exercise of power (e.g., size, knowledge base) still dominates CKT and RKT in most of the studies analyzed, which are traditionally affected by the characteristics of MNCs, HQs and subsidiaries. The debate on HKT is affected by the classical perspectives of power-based relations (e.g., expatriates, size, knowledge base) among subsidiaries. Although important, intra-unit knowledge transfer is greatly influenced by characteristics.

Abstract
Machine Learning is a rising concept in today's society. In the past decade, ML-based systems have become part of people's daily routines, and their usage has been disseminated through diverse sectors. This evolution is supported by the exponential increase in data created worldwide. Feature Engineering is a critical process focused on transforming data into suitable inputs for Machine Learning algorithms. This work explores the Feature Engineering process by developing a baseline for its implementation. Hence, a pipeline of Feature Engineering techniques and their taxonomy is proposed, along with a set of R scripts to implement. The validity of the code is then demonstrated through its application to a real-world dataset.;MachineLearning é um conceito em crescente evolução na sociedade atual. Na última década, os sistemas baseados em ML tornaram-se parte do quotidiano da população e a sua aplicação tem vindo a disseminar-se por diversos setores. Este crescimento é suportado pelo aumento exponencial da quantidade de dados gerados a nível mundial. FeatureEngineering surge, assim, como um processo chave que permite transformar dados em inputs adequados para os algoritmos de MachineLearning. O presente trabalho pretende explorar o processo de FeatureEngineering, com vista a desenvolver uma base de suporte à sua implementação. Por conseguinte, é proposta uma pipeline de técnicas de FeatureEngineering em paralelo com a sua taxonomia, juntamente com um conjunto de scripts R, para as implementar. A validade do código é, posteriormente, demonstrada através da sua aplicação a um conjunto de dados reais.

Abstract
Motivation and engagement play a crucial role in student success in a course. Students may lose interest or underestimate courses that tackle non-core learning outcomes to their specific curriculum or program. Gamification, using game elements (e.g., rewards, challenges) in non-game contexts, is one way to motivate and engage students. Some educational courses use project-based learning, where students tackle problems, overcome obstacles, and gain knowledge. Quest-based games are designed as systems of challenges that players must complete to advance and win the game. They were linked with education by applying specific game mechanics to a computing course unit. This paper case studies the application of a quest-based gamification approach in a mandatory software engineering course to boost engagement among higher education students. Results were collected through observational methods and surveying the students, indicating a tendency for higher grades in course years implementing gamification while maintaining satisfactory levels of motivation and engagement. © 2023 International Conference on Higher Education Advances. All rights reserved.