Abstract
Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-ahead Unit Commitment/ Economic Dispatch (UC/ED) for low-inertia power systems including dynamic security constraints for key frequency indicators computed by an Artificial Neural-Network (ANN)-supported Dynamic Security Assessment (DSA) tool. The ANN-supported DSA tool infers the system dynamic performance with respect to key frequency indicators following critical disturbances and computes the additional synchronous inertia that brings the system back to its dynamic security region, by dispatching Synchronous Condensers (SC) if required. The results demonstrate the effectiveness of the methodology proposed by enabling the system operation within safe frequency margins for a set of high relevance fault type contingencies while minimizing the additional costs associated with the SC operation.

Abstract
Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors' experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities.

Abstract
Budgets are important management tools recognized for their help in planning, communication, monitoring the expense performance, and even motivating collaborators. However, recently there has been criticism of the traditional Budgeting Process due to its cumbersomeness, long duration, and eventual diversion of the focus from the day-to-day activities. Thus, improving the Budgeting Process by incorporating Expense component uncertainties is of uttermost importance to accelerate its approval. This paper presents a methodology for companies to assess their budget risk based on their historical Expense data by applying Monte Carlo Simulation and Time Series Bootstrapping Techniques. Besides, some state-of-the-art sensitivity Importance Measures are also implemented to help evaluate the relative importance of the Expense components. The methodology proposed, based on a real case study with data from a major Portuguese retailer, has the advantage of being objective and supported by data, thus not being subject to bias from the management. © 2023, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease. © 2022 The Authors

Abstract
This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon. © 2023, The Author(s).

Abstract
Depth control is crucial for underwater vehicles, not only to perform certain tasks that require the vehicle to be still at a given depth but also because most propeller-driven vehicles waste a considerable amount of energy to counteract the passively tuned positive buoyancy. The use of a variable buoyancy system (VBS) can effectively address these items, increasing the energetic efficiency and thus mission length. Achieving accurate depth controllers is, however, a complex task, since experimental controller development in sea or even in test pools is unpractical and the use of simulation requires accurate vertical motion models whose parameters might be difficult to obtain or measure. The development of simple, yet comprehensive, dynamic models for devices incorporating VBS is therefore of upmost importance, as well as developing procedures that allow a simple determination of their parameters. This work contributes to this field by deriving a unified model for the vertical motion of a VBS actuated device, irrespective of the specific technological actuation solution employed, whether it be electromechanical or electrohydraulic. A concise analysis of the open-loop stability of the unified model is presented and a straightforward yet efficient procedure for identifying several of its parameters is introduced. This identification procedure is designed to be convenient and can be carried out in shallow waters, such as test pools, while its results are applicable to the deeper water model as well. To validate the procedure, experimental values obtained from an electromechanical VBS actuated device are used. Closed-loop control of the electromechanical VBS actuated device is conducted through simulation and experimental tests. The results confirm the effectiveness of the proposed unified model and the parameter identification methodology.