Abstract
Background: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.Objective: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. Methods: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. Results: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. Conclusions: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial Registration: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339(J Med Internet Res 2022;24(9):e39452) doi: 10.2196/39452

Abstract
New pocket-sized laboratories are proving to be an excellent tool as complementary equipment that students and lecturers can deploy to test control engineering design techniques. Here, the description and outcome results of an IFAC activity funded project entitled as Pocket-Sized Portable Labs: Control Engineering Practice Made Easy are presented. The project was executed in Portugal, from January 2021 to the end of June 2021, during the SARS-CoV2 pandemic. The global aim of this project was to motivate preuniversity students to enroll in control engineering courses by showing and demonstrating that simple practical experiments may be easily accomplished using portable pocket-size laboratories. Copyright (C) 2022 The Authors.

Abstract
Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R-2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees.

Abstract
Wireless Sensor Networks (WSNs) based on the Long-Range radio modulation (LoRa) can use the LoRaWAN protocol as the medium access layer. However, this protocol only supports a single-hop star topology. As a consequence, devices can not use retransmissions along the network to extend their coverage area or to circumvent signal attenuation with distance, obstacles or interference from other radio sources. This paper proposes a multi-hop LoRaWAN wide-scale WSN based on a scheduled cluster-tree topology. This methodology can expand the spatial coverage of the network, decrease collisions, and improve overall network performance. A multi-hop cluster-tree topology eliminates the need for adjustments of LoRa radio parameters as an attempt to expand the single-hop coverage limitation. Simulation results show that the scheduled cluster-tree topology can scale the network coverage and significantly improve communication and energy consumption performances.

Abstract
Over the years, methods and algorithms have been extensively studied to solve variations of the rectangular twodimensional strip packing problem (2D-SPP), in which small rectangles must be packed inside a larger object denominated as a strip, while minimizing the space necessary to pack all rectangles. In the rectangular 2D-SPP, constraints are used to restrict the packing process, satisfying physical and real-life practical conditions that can impact the material cutting. The objective of this paper is to present an extensive literature review covering scientific publications about the rectangular 2D-SPP constraints in order to provide a useful foundation to support new research works. A systematic literature review was conducted, and 223 articles were selected and analyzed. Real-life practical constraints concerning the rectangular 2D-SPP were classified into seven different groups. In addition, a bibliometric analysis of the rectangular 2D-SPP academic literature was developed. The most relevant authors, articles, and journals were discussed, and an analysis made concerning the basic constraints (orientation and guillotine cutting) and the main solving methods for the rectangular 2D-SPP. Overall, the present paper indicates opportunities to address real-life practical constraints.

Abstract
This study contributes to learning improvement in practical classes in Computer Network technology courses, using the Physical Technological Laboratory (PTL) as a tool. Multimodal narration content analysis was used, which aggregates and organises the data collected in the PTL environment. Based on the results, we infer that both the student and the teacher use the physical laboratory as a tool since the detected physical interactions prove its use and reuse. Evidence of causality between teacher epistemic movements and learning in terms of physical interactions, epistemic practices, and student autonomy was also noted. Contributions were: (1) In the context of work in networks PTL, the variety and quality of epistemic practices of students are enhanced if there is autonomous work concomitant with the physical interaction of students with the respective artifacts. (2) Teacher action can better promote epistemic practices, stretching beyond direct action if there is an 'orchestration' of teacher mediation patterns.