Abstract
This paper presents machine digitalization solutions with particular application in forest machines, such as harvesters and wood processing machines. In line with all the requirements of Industry 4.0, this type of machines also needs digitization to align with the concept defined as Forestry 4.0, where we think of a smarter forest in which all stakeholders, humans, forest producers, machines and factories communicate. For machine manufacturers is a step that must be taken to modernize machines, enabling remote access services for maintenance, productivity monitoring, and management of forest operations. It consists of developing cyber-physical systems around the machines with digital twins that allow the simulation and identification of faults that may occur. A solution is presented to enable CAN Bus communication between the controller, operator joysticks, and sensors/actuators, as well as a Digital Twin solution to emulate machine operations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.

Abstract
Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

Abstract

Abstract
A growing body of empirical literature has considered very long-time horizons when studying the sources of a country's economic growth. Nevertheless, the growth experiences of emerging economies (EEs) have been overlooked. This study examines to what extent human capital, structural change, and institutional quality contribute to the economic growth of one of the largest EEs in the world, Brazil, between 1822 and 2019. Resorting to the ARDL cointegration technique, the results suggest that years of schooling (human capital) have a positive and long-lasting impact on Brazil's economic growth. Moreover, there is solid evidence that sectoral changes toward more advanced and sophisticated manufacturing basis is growth-enhancing in the country. Finally, institutional quality does not constitute over the very long-run, a significant booster of Brazilian economic growth.

Abstract
Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.