Abstract
While the objectives of forest management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective has often been the production of wood products. However, even in this case, forests play a key role in the conservation of living resources. Constraining the areas of clearcuts contributes to this conservation, but if it is too restrictive, a dispersion of small clearcuts across the forest might occur, and forest fragmentation might be a serious ecological problem. Forest fragmentation leads to habitat loss, not only because the forest area is reduced, but also because the core area of the habitats and the connectivity between them decreases. This study presents a Monte Carlo tree search method to solve a bi-objective harvest scheduling problem with constraints on the clearcut area, total habitat area and total core area inside habitats. The two objectives are the maximization of both the net present value and the probability of connectivity index. The method is presented as an approach to assist the decision maker in estimating efficient alternative solutions and the corresponding trade-offs. This approach was tested with instances for forests ranging from some dozens to over a thousand stands and temporal horizons from three to eight periods. In general, multi-objective Monte Carlo tree search was able to find several efficient alternative solutions in a reasonable time, even for medium and large instances.

Abstract
Software requirements engineers and testers generally define technical documents in natural languages, but this practice can lead to inconsistencies between the documentation and the consequent system implementation. Previous research has shown that writing requirements and tests in a structured way, with controlled natural languages like RSL, can help mitigate these problems. This study goes further, discussing new experiments carried out to validate that RSL (with its complementary tools, called “ITLingo Studio”) can be applied in different systems and technologies, namely the possibility of applying the approach to integrate test automation capabilities in security testing. The preliminary conclusion indicates that, by combining tools such as ITLingo Studio and the Robot Framework, it is possible to integrate requirements and test specifications with test automation, and that would bring benefits in the testing process’ productivity. © Springer Nature Switzerland AG 2020.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract
Polar ecosystems are generally limited in nitrogen (N) nutrients, and the patchy availability of N is partly determined by biological pathways, such as nitrification, which are carried out by distinctive prokaryotic functional groups. The activity and diversity of microorganisms are generally strongly influenced by environmental conditions. However, we know little of the attributes that control the distribution and activity of specific microbial functional groups, such as nitrifiers, in extreme cold environments and how they may respond to change. To ascertain relationships between soil geochemistry and the ecology of nitrifying microbial communities, we carried out a laboratory-based manipulative experiment to test the selective effect of key geochemical variables on the activity and abundance of ammonia-oxidizing communities in soils from the McMurdo Dry Valleys of Antarctica. We hypothesized that nitrifying communities, adapted to different environmental conditions within the Dry Valleys, will have distinct responses when submitted to similar geochemical disturbances. In order to test this hypothesis, soils from two geographically distant and geochemically divergent locations, Miers and Beacon Valleys, were incubated over 2 months under increased conductivity, ammonia concentration, copper concentration, and organic matter content. Amplicon sequencing of the 16S rRNA gene and transcripts allowed comparison of the response of ammonia-oxidizing Archaea (AOA) and ammonia-oxidizing Bacteria (AOB) to each treatment over time. This approach was combined with measurements of (NH4+)-N-15 oxidation rates using N-15 isotopic additions. Our results showed a higher potential for nitrification in Miers Valley, where environmental conditions are milder relative to Beacon Valley. AOA exhibited better adaptability to geochemical changes compared to AOB, particularly to the increase in copper and conductivity. AOA were also the only nitrifying group found in Beacon Valley soils. This laboratorial manipulative experiment provided new knowledge on how nitrifying groups respond to changes on key geochemical variables of Antarctic desert soils, and we believe these results offer new insights on the dynamics of N cycling in these ecosystems.

Abstract
Increasing the penetration of renewable resources has aggravated the operational flexibility at distribution level. In this study, a flexibility-oriented scheduling of microgrids (MGs) is suggested to reduce the power fluctuations in distribution feeders caused by the high penetration of wind turbines (WTs) in MGs. A flexibility constraint as viable and practical solution is used in MG scheduling to address this challenge. The presented scheduling model, implemented using mixed integer linear programming (MILP) and a stochastic framework, exercises risk constraints to capture the uncertainties associated with wind turbines, loads and market prices. The effectiveness of the model is investigated on a MG with high penetration of WTs in the presence of demand response (DR) and energy storage systems (ESSs). Numerical studies show the influence of risk parameters' changing on operation costs. In addition, the flexibility constraint mitigates the sharp variation of the net load at distribution level, which improves the flexibility of the distribution system.

Abstract
Hybrid programs combine digital control with differential equations, and naturally appear in a wide range of application domains, from biology and control theory to real-time software engineering. The entanglement of discrete and continuous behaviour inherent to such programs goes beyond the established computer science foundations, producing challenges related to e.g. infinite iteration and combination of hybrid behaviour with other effects. A systematic treatment of hybridness as a dedicated computational effect has emerged recently. In particular, a generic idealized functional language HYBCORE with a sound and adequate operational semantics has been proposed. The latter semantics however did not provide hints to implementing HYBCORE as a runnable language, suitable for hybrid system simulation (e.g. the semantics features rules with uncountably many premises). We introduce an imperative counterpart of HYBCORE, whose semantics is simpler and runnable, and yet intimately related with the semantics of HYBCORE at the level of hybrid monads. We then establish a corresponding soundness and adequacy theorem. To attest that the resulting semantics can serve as a firm basis for the implementation of typical tools of programming oriented to the hybrid domain, we present a web-based prototype implementation to evaluate and inspect hybrid programs, in the spirit of GHCI for HASKELL and UTOP for OCAML. The major asset of our implementation is that it formally follows the operational semantic rules.