Abstract
The role of digital technologies for fostering sustainability and efficiency in forest-based supply chains is well acknowledged and motivated several studies in the scope of precision forestry. Sensor technologies can collect relevant data in forest-based supply chains, comprising all activities from within forests and the production of the woody raw material to its transformation into marketable forest-based products. Advanced planning systems can help to support decisions of the various entities in the supply chain, e.g., forest owners, harvest companies, haulage companies, and forest product processing industry. Such tools can help to deal with the complex interdependencies between different entities, often with opposing objectives and actions-which may increase efficiency of forest-based supply chains. This paper analyzes contemporary literature dealing with digital technologies in forest-based supply chains and summarizes the state-of-the-art digital technologies for real-time data collection on forests, product flows, and forest operations, as well as planning systems and other decision support systems in use by supply chain actors. Higher sustainability and efficiency of forest-based supply chains require a seamless information flow to foster integrated planning of the activities over the supply chainthereby facilitating seamless data exchange between the supply chain entities and foster new forms of collaboration. Therefore, this paper deals with data exchange and multi-entity collaboration aspects in combination with interoperability challenges related with the integration among multiple process data collection tools and advanced planning systems. Finally, this interdisciplinary review leads to the discussion of relevant guidelines that can guide future research and integration projects in this domain.

Abstract
This work presents the state-of-the-art of visual sensing systems for monitoring and control purposes in both agriculture and forest areas. Regarding agricultural activities, four main topics are explored: robotics and autonomous vehicles, plant protection, feature extraction and yield prediction. Although vast literature can be found on image processing and computer vision applied to agriculture, its applications in forest-based systems are less frequent. Throughout this article, several research areas such as diseases control, post-processing, parameters estimation, UAVs and satellites will be addressed.

Abstract
The constant search for methods that allow the production processes improvement is a driving force for the development and integration of current technological solutions in systems which are, currently, still purely human based. It is in this context that the company "Factoryplay" comes forward with the challenge to upgrade its current sewing stations by adding a set of mechanization and automation solutions. This article documents the steps carried out to provide the current solution with the required technical attributes. In this paper, the instrumentation and actuation devised solutions, as well as the method employed to design an embedded PI controller, will be presented. The PI controller allows the closed-loop control of the station movement speed as a function of the sewing machine speed. The practical results obtained, regarding the dynamic response of the sewing station, are in line with the simulated ones.

Abstract
Current students and technologies demand using new learning/teaching techniques. The potentialities of using mobile devices such as smartphones for teaching/learning purposes are huge. However, in some teaching areas its use is still residual. The use of mobile applications in the context of teaching PLC programming techniques is addressed in this work. The MIT App-Inventor II is deployed to develop mobile applications for learning purposes. An android based application entitled Time-Counts is proposed here, developed to support the teaching/learning process of both Timers and Counters. Results regarding its use by students are presented.

Abstract
Carcass data were collected from 24 kids (average live weight of 12.5 +/- 5.5 kg; range 4.5 to 22.4 kg) of Jarmelista Portuguese native breed, to evaluate bioelectrical impedance analysis (BIA) as a technique for prediction of light kid carcass and muscle chemical composition. Resistance (Rs, Omega) and reactance (Xc, Omega), were measured in the cold carcasses with a single frequency bioelectrical impedance analyzer and, together with impedance (Z, Omega), two electrical volume measurements (Vol(A) and Vol(B), cm(2)/Omega), carcass cold weight (CCW), carcass compactness and several carcass linear measurements were fitted as independent variables to predict carcass composition by stepwise regression analysis. The amount of variation explained by Vol(A) and Vol(B) only reached a significant level (P < 0.01 and P < 0.05, respectively) for muscle weight, moisture, protein and fat-free soft tissue content, even so with low accuracy, with VolA providing the best results (0.326 <= R-2 <= 0.366). Quite differently, individual BIA parameters (Rs, Xc and Z) explained a very large amount of variation in dissectible carcass fat weight (0.814 <= R-2 <= 0.862; P < 0.01). These individual BIA parameters also explained a large amount of variation in subcutaneous and intermuscular fat weights (respectively 0.749 <= R-2 <= 0.793 and 0.718 <= R-2 <= 0.760; P < 0.01), and in muscle chemical fat weight (0.663 <= R-2 <= 0.684; P < 0.01). Still significant but much lower was the variation in muscle, moisture, protein and fat-free soft tissue weights (0.344 <= R-2 <= 0.393; P < 0.01) explained by BIA parameters. Still, the best models for estimation of muscle, moisture, protein and fat-free soft tissue weights included Rs in addition to CCW, and accounted for 97.1% to 99.8% (P < 0.01) of the variation observed, with CCW by itself accounting for 97.0% to 99.6% (P < 0.01) of that variation. Resistance was the only independent variable selected for the best model predicting subcutaneous fat weight. It was also selected for the best models predicting carcass fat weight (combined with carcass length, CL; R-2 = 0.943; P < 0.01) and intermuscular fat weight (combined with CCW; R-2 = 0.945; P < 0.01). The best model predicting muscle chemical fat weight combined CCW and Z, explaining 85.6% (P < 0.01) of the variation observed. These results indicate BIA as a useful tool for prediction of light kids' carcass composition.

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.