Abstract
This paper presents the integration of multimodal sensor systems for an autonomous forestry machine. The utilized technology is housed in a single enclosure which consolidates a set of components responsible for executing machine control actions and comprehending its behavior in various scenarios. This sensor box, named Sentry, will subsequently be connected to a forestry machine from MDB, model LV600 PRO. The article outlines previous work in this field and then details the integration and operation of the equipment, integrated into the forest machine, providing descriptions of the adopted architecture at both the hardware and software levels. The gathered data enables the assessment of the forestry machine's orientation and position based on the information collected by the sensors. Finally, practical experiments are presented to demonstrate the system's behavior and to analyze the methods to be employed for autonomous navigation, thereby assessing the performance of the established architecture. The novel aspects of this work include the physical and digital integration of a multimodal sensor system on a forestry machine, its use in a real case scenario, namely, forest vegetation removal, and the strategies adopted to improve the machine localization and navigation performance on unstructured environments.

Abstract
Small and medium-sized enterprises (SMEs) play an important role in economic and development growth, particularly in developing countries. Their success depends on the expansion of their markets across borders, based on the strategies adopted, in which differentiation strategies and positional advantages play an important role. As an emerging country, Mozambican SMEs face a lack of resources and business environmental challenges in deploying their unique advantages when competing abroad. As such, the objective of this paper is to study the impact of differentiation strategies on the export performance of Mozambican SMEs, and the mediating effect of positional advantage on the relationship between the differentiation strategy and export performance of SMEs. To achieve this objective, an empirical study was conducted, based on a sample of 250 Mozambican firms, to test a theoretical model that applied Structural Equation Modeling using the Partial Least Squares (PLS-SEM) algorithm, based on SmartPLS software version 3.3.6 (SmartPLS GmbH, Oststeinbek, Germany). The results show that differentiation strategies positively impact the export performance of SMEs and that positional advantage mildly mediates the relationship between the differentiation strategy and export performance. This suggests that Mozambican SMEs may not be properly taking advantage of the positional advantage of the differentiation strategies, as the added value generated by the positional advantage is relatively modest. As such, Mozambican SMEs still need to support their positional advantages to overcome fierce international competition. This study contributes to the knowledge about the consequences of adopting differentiation strategies and positional advantages on the export performance of SMEs, particularly in the context of emerging countries.

Abstract
The manipulation of deformable objects poses a significant challenge for the automotive industry. In particular, the assembly of flexible cables and wire-harnesses in vehicles is still performed manually as there is yet to be a reliable and general solution for this problem. This paper presents a simple yet efficient motion planning algorithm to mount a flexible wire in an assembly jig, where the wire must traverse a set of forks in order. The algorithm uses a heuristic based on a set of control points to guide the wire's movement. Various controlled assembly scenarios are built in simulation using MuJoCo, a physics engine that can emulate the dynamics of Deformable Linear Objects (DLO). Experimental results in simulation demonstrated that the amount and orientation of the forks has a large impact in the solution's performance and highlighted several key ideas and challenges moving forward. Thus, this work serves as a stepping stone towards the development of more complete solutions, capable of assembling flexible items in vehicles.

Abstract
This paper investigates the impact of different immersive Virtual Reality (iVR) technological approaches in teaching and learning English as a Foreign Language (EFL). Specifically, this paper explores the passive iVR and interactive iVR in a real authentic learning context as didactic possibilities compared to the conventional method of listening, consisting of audio-only listening exercises. The study was conducted using university students of B1 level EFL classes. The dependent variables considered in the study were Knowledge Retention, Presence, User Satisfaction, Cybersickness, and Preferred Technology. Results indicated that users showed significant satisfaction and preference for using this technology for learning, revealing enjoyment and motivation which are vital factors when learning a foreign language. However, no significant differences were found between learning via traditional listening exercises or the virtual system. Correlation tests were conducted between the questionnaire subscales to understand better which elements can influence learning. The study concludes that using iVR-based learning tools to learn a foreign language as an alternative to audio listening can only produce a broader positive impact and greater motivation. The results also suggest that iVR can be a valuable tool in the education field for knowledge transfer and motivation when learning foreign languages.

Abstract
Emerging evidence of the relationship between the microbiome composition and the development of numerous diseases, including cancer, has led to an increasing interest in the study of the human microbiome. Technological breakthroughs regarding DNA sequencing methods propelled microbiome studies with a large number of samples, which called for the necessity of more sophisticated data-analytical tools to analyze this complex relationship. The aim of this work was to develop a machine learning-based approach to distinguish the type of cancer based on the analysis of the tissue-specific microbial information, assessing the human microbiome as valuable predictive information for cancer identification. For this purpose, Random Forest algorithms were trained for the classification of five types of cancer-head and neck, esophageal, stomach, colon, and rectum cancers-with samples provided by The Cancer Microbiome Atlas database. One versus all and multi-class classification studies were conducted to evaluate the discriminative capability of the microbial data across increasing levels of cancer site specificity, with results showing a progressive rise in difficulty for accurate sample classification. Random Forest models achieved promising performances when predicting head and neck, stomach, and colon cancer cases, with the latter returning accuracy scores above 90% across the different studies conducted. However, there was also an increased difficulty when discriminating esophageal and rectum cancers, failing to differentiate with adequate results rectum from colon cancer cases, and esophageal from head and neck and stomach cancers. These results point to the fact that anatomically adjacent cancers can be more complex to identify due to microbial similarities. Despite the limitations, microbiome data analysis using machine learning may advance novel strategies to improve cancer detection and prevention, and decrease disease burden.

Abstract
The Software-Defined Storage (SDS) paradigm has emerged as a way to ease the orchestration and management complexities of storage systems. This work aims to mitigate the storage performance issues that large-scale HPC infrastructures are currently facing by developing a scalable and dependable control plane that can be integrated into an SDS design to take full advantage of the tools this paradigm offers. The proposed solution will enable system administrators to define storage policies (e.g., I/O prioritization, rate limiting) and, based on them, the control plane will orchestrate the storage system to provide better QoS for data-centric applications.