Abstract
The pancreas is a highly important organ, since it produces insulin and prevents the occurrence of diabetes. Although rare, pancreatic cancer is highly lethal, with a small life expectancy after being diagnosed. The pancreas is one of the organs less studied in the field of biophotonics. With the objective of acquiring information that can be used in the development of future applications to diagnose and treat pancreas diseases, the spectral optical properties of the rabbit pancreas were evaluated in a broad-spectral range, between 200 and 1000 nm. The method used to obtain such optical properties is simple, based almost on direct calculations from spectral measurements. The optical properties obtained show similar wavelength dependencies to the ones obtained for other tissues, but a further analysis on the spectral absorption coefficient showed that the pancreas tissues contain pigments, namely melanin, and lipofuscin. Using a simple calculation, it was possible to retrieve similar contents of these pigments from the absorption spectrum of the pancreas, which indicates that they accumulate in the same proportion as a result of the aging process. Such pigment accumulation was camouflaging the real contents of DNA, hemoglobin, and water, which were precisely evaluated after subtracting the pigment absorption.

Abstract

Abstract
Conducting systematic studies during a postgraduate program, such as systematic review, systematic mapping, and tertiary review, can benefit the project’s success. They provide an overview of the literature considering currently available research findings, establish baselines for other research activities, and support decisions made throughout the research project. However, there is a shortage of research that presents systematic studies experiences in supporting academic projects. This paper’s main contribution is reporting our experience on how the evidence found in tertiary and secondary studies positively influenced a PhD project’s decisions. Initially, a tertiary study was conducted, followed by a systematic mapping. The evidence returned by the tertiary study led to the definition of the PhD research proposal in the Requirement Engineering field. Moreover, a systematic mapping contributed to the definition of the PhD research problem. From this experience in undertaking systematic studies to support a PhD project, the paper also presents lessons learned and recommendations to guide PhD students’ decisions.

Abstract
Natural gas will play a key role in the transition to a lower-carbon economy, constituting a natural alternative to coal and acting as a backup resource to the intermittent nature of renewable generation. These energy carriers can be structurally linked together by Power-to-X technologies because of their interaction to increase energy efficiency. For this purpose, this paper proposes an innovative model to optimally manage the electricity and natural gas grids in a cost-efficient manner. In this model, an energy hub has water, electricity, and gas oil as inputs, supplying electric and thermal loads. Besides, the energy hub uses the Power-to-gas (P2G) technology to produce natural gas, selling it to a gas network to reduce the congestion in gas pipelines and the energy hub owner's costs. A demand response program has been also applied in this model to shift the loads from on-peak times to off-peak ones. Various technologies such as energy storage and distributed generation have been used in the modeling to reach the goals targeted by operators. Furthermore, a scenario generation method has been applied to model the uncertainty of wind turbine output. The proposed problem has been finally formulated as mixed-integer linear programming that has been solved under GAMS software by using CPLEX solver to reach the global optimality. The results obtained from simulations demonstrate that the proposed model can significantly reduce the operation cost, while properly alleviating gas network congestion.

Abstract
Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants' phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches' accuracy. Two applications were developed to evaluate Vinelnspector's consistency while a viticulturist' assistant in everyday practices. One was intended to determine the size of the very first grapevines' shoots, one of the required parameters of the well known 3-10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard's phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases.

Abstract
In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.