Abstract

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.

Abstract
This work models a distributed community-based market with diverse assets (photovoltaic generators and energy storage systems), accounting for network constraints and adopting the relaxed branch flow model. The market is modeled in a single and fully distributed approach, employing the alternating direction method of multipliers (ADMM) to prevent voltage and line capacity problems in the community network and improve data privacy and reduce the communication burden. Different scenarios, based on the penalty term and the agents' number, are tested to study the efficiency of the algorithm and the convergence rate of the ADMM distributed model. The proposed method is tested on 10-bus, 22-bus, and 33-bus medium voltage radial distribution networks, where each node contains a large prosumer with one or several assets. One important conclusion is that the implemented residual balancing technique improves the efficiency of the ADMM distributed algorithm by increasing the convergence rate and reducing the computational time.

Abstract