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Abstract The scientific literature has presented evidence that participants in dialogic scientific gatherings (DSGs) transform their scientific interest in science. DSGs are based on a dialogical perspective, where egalitarian dialogue and the co-creation of knowledge are the two corner pieces that allow the development of new meanings for participants, improving their scientific literacy. There is a clear gap in scientific studies regarding DSGs in prison. This is the first research to address the impact of DSG and dialogic scientific workshops in prisons. The study presents the positive impact of the DSGs and scientific activities in promoting scientific interest in incarcerated people. A communicative case study was conducted in a Catalonian (Spain) prison between February and April 2022. Nineteen males who were incarcerated aged between 29 and 44 participated in twenty-one DSGs and twelve scientific workshops following a communicative approach. Data were collected through fifteen communicative observations during the application of the DSG and eight interviews with incarcerated people and two interviews with prison workers involved in implementing the DSG. The activities conducted and analysed in this study demonstrate that bringing science closer to all people, including the traditionally excluded population, is possible. The results show that the persons who experience incarceration’s motivation to participate in these scientific activities, which fostered their awareness of and interest in science, gave meaning to their learning and transformed their interactions in and out of prison.

The adverse effects of agriculture and livestock production on the environment are well-known and require mitigation in order to achieve sustainability in the food production chain. This study focused on adverse effects related to biogeochemical flows of phosphorus and nitrogen cycles which natural balances have been greatly disturbed by current practices. To assess the potential benefits and detrimental effects of proposed mitigation measures, adequate impact indicators are required. The challenge lies in identifying and providing indicators that cover the important aspects of environmental sustainability and allow a direct comparison of policy alternatives. A review of potential indicators that are also consistent with those used to indicate the performance of agricultural and general sustainability (i.e. the European Green Deal) led to the selection of fifteen agri-environmental indicators covering the main environmental issues in agriculture. The indicators identified offered an effective representation of environmental behaviour and would be useful in communicating a comprehensive ‘dashboard’ for professional end users of solutions to nutrient recovery and nutrient efficiency improvement in arable and livestock systems. The selected dashboard indicators (DBI) covered the dimensions of ‘use of primary resources’, ‘emissions to the environment’ and ‘resilience to climate change’. Five case studies were investigated to test the DBI using an Excel questionnaire applying the qualitative approach of the Delphi method together with expert knowledge. As expected, the results indicated that there were potential benefits of the technologies in terms of improved ‘nutrient recovery’ and decreased ‘nitrate leaching’. Potential disadvantages included increased electricity and oil consumption and greater ammonia volatilisation due to the increased use of organic fertilisers. The indicator ‘water’ received more neutral responses; thus, the specific technology was not expected to consistently affect the indicator. In relation to ‘particulate matter’, the results were indicated to be ‘unknown’ for some solutions due to the difficulty of predicting this indicator. Furthermore, methodologies for estimating quantitative values for the dashboard indicators were proposed, and a quantitative assessment was performed for the solution ‘catch crops to recover nutrients’, confirming the responses in the qualitative assessment. The dashboard indicators selected covered the main aspects of the solutions, identified in more comprehensive studies of environmental impacts, as being suitable for the rapid assessment of technologies for nutrient recovery in agriculture. As such, they can be used as a pre-screening method for technologies designed to improve the environmental sustainability of arable and livestock systems.

The heat exchanger network (HEN) synthesis problem can be characterized as non-convex, non-linear and highly combinatory. Mainly for large-scale HEN, the complexity increases even more and mathematical formulation may require very sophisticated methods. In this work, the simultaneous synthesis problem is solved by a bi-level hybrid method: in the upper level a Genetic Algorithm (GA) is used to optimized discrete variables and in the lower level, the continuous variables are optimized by Differential Evolution (DE). Two examples from the literature were used to test the applicability of the proposed solution and results found in terms of the total annual cost (TAC) were lower than those published in the literature.

The energy supplied by photovoltaic (PV) panels connected to the grid creates more flexibility for energy management; however, oversizing the PV system may result in an energy surplus, an essential factor to be considered during energy efficiency optimization. The economic analysis of energy supply systems for buildings and industry should include a detailed feasibility analysis and a life cycle perspective. Simulations were performed to quantify the potential savings when the excess of PV energy (surplus) is supposed to be exported to the grid by considering the net metering and net billing approaches. Our objective was to evaluate the electrical demand of a logistics center with pre-design modeling and simulation, and determine the adequate system configurations by considering the life cycle costing (LCC). We established a baseline and three alternative economic scenarios for optimization. Combining the use of TRNSYS 180 Simulation Studio and its optimization library component, GenOp (Generic Optimization Program), we simulated different options of grid energy contracts considering the variable tariffs and the integration with PVs. Based on the LCC, a single-objective optimization (SOO) process was performed. This approach allowed us to envisage possible configurations, reducing up to a quarter of annual grid energy consumption that represents savings of around 21% for the LCC in a timeframe of 20 years, reaching up to 39% when the export of the PV surplus energy is considered. The payback period of investments is below six years for the optimal scenarios.

Lifelong learning now plays a key role labour market policies within the EU. Against a background of increasing rates of highly educated people and changes in graduate labour markets, universities have started to become involved in adult learning and active labour market policies. The article presents the results of 21 nonrepresentative case studies of university adult learning programmes from seven EUcountries with particular focus on people in mid-life, who are becoming more and more socially vulnerable. One of the main features of the case studies was the social effectiveness of university adult learning programmes in terms of access to jobs and quality of work/life. The results of the case studies together with a review of the results of other European lifelong learning projects made it possible to draw up a scheme of the core dimensions of socially effective university adult learning.

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.