Abstract
This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to 0.17% of economic savings when compared to the clustering with ILP approach (an exact approach).

Abstract
Listening to internal body sounds, or auscultation, is one of the most popular diagnostic techniques in medicine. In addition to being simple, non-invasive, and low-cost, the information it offers, in real time, is essential for clinical decision-making. This process, usually done by a doctor in the presence of the patient, currently presents three challenges: procedure duration, participants' safety, and the patient's privacy. In this article we tackle these by proposing a new autonomous robotic auscultation system. With the patient prepared for the examination, a 3D computer vision sub-system is able to identify the auscultation points and translate them into spatial coordinates. The robotic arm is then responsible for taking the stethoscope surface into contact with the patient's skin surface at the various auscultation points. The proposed solution was evaluated to perform a simulated pulmonary auscultation in six patients (with distinct height, weight, and skin color). The obtained results showed that the vision subsystem was able to correctly identify 100% of the auscultation points, with uncontrolled lighting conditions, and the positioning subsystem was able to accurately position the gripper on the corresponding positions on the human body. Patients reported no discomfort during auscultation using the described automated procedure.

Abstract
Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.

Abstract
Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem.

Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Cross-docking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the CrossLog - Automatic Mixed-Palletizing for Crossdocking Logistics Centers Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the modelling and simulation of a crossdocking system, with an integrated AS/RS, to analyse possible alternatives including not only the fast movement of products, but also its storage in case needed. Different scenarios were modelled and simulated, on the FlexSim software, and the obtained results for each one were critically analysed to draw conclusions on the best storage policy.

Abstract
Byzantine Fault tolerant (BFT) protocols are implemented to guarantee the correct system/application behavior even in the presence of arbitrary faults (i.e., Byzantine faults). Byzantine Fault tolerant State Machine Replication (BFT-SMR) is a known software solution for masking arbitrary faults and malicious attacks (Liu et al., 2020). In this survey, we present and discuss relevant BFT-SMR protocols, focusing on deterministic and probabilistic approaches. The main purpose of this paper is to discuss the characteristics of proposed works for each approach, as well as identify the trade-offs for each different approach.& COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )