Abstract
The rapid proliferation of human population and the growth of consumption leaded to an increase of hazardous waste. This becomes a worldwide ecological and environmental challenge. According to last available statistics the estimated global municipal solid waste reached 2.02 billion tons. Unfortunately there is not only solid waste: vegetable oil, one kind of liquid waste, used in food manufacturing, is responsible for serious contamination of water resources. Its collection is profitable by recycling into biodiesel and is already regulated in some countries. However it faces several logistic issues. Major issues are related to collection delays, which lead to oil giveaway, and reduced collection [1,2]. This paper proposes a solution to some of these logistic problems introducing a system to manage cooking oil collection in order to increase collection profits. The developed system follows a defined oil collection plan in order to optimize collection routes, introducing a sensor network that triggers collection events to a central system overcoming the current collection delays and wastes. The system is being tested in a real scenario with a Portuguese cooking oil collection company.

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Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker's performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.

Abstract
Assistive environments is a well established research area, particularly, the Ambient Assisted Living (AAL) paradigm, which relies in data and artificial intelligence to infer the user's actions and status, thus enabling assistive actions. In a very distinct environment - the manufacturing industry, we propose a twin concept - the Ambient Assisted Working (AAW), to provide assistance to the workers on the factory floor, using solutions with design principles similar to AAL systems. The Industry 4.0 (I4.0) Technical Assistance design principle, introduces the assistance concept in I4.0 solutions and the AAW builds on top of that design principle.

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.