Abstract

Abstract
GIS are an essential element in archaeology. Their use has become widespread for their potential to store, reference, analyse and visualise spatial information. Nonetheless, to the best of our knowledge, a systematic review of academic peer-reviewed publications related to the use of GIS, as a framework, in archaeology has never been presented before. Our goal in this work is to identify what has been published so far in relation to using GIS in archaeology within a small selected sample. We used the PRISMA guideline to perform a systematic review of 671 publications that we identified using the SCOPUS database and the keywords ‘GIS’ and ‘archaeology’. The collected publications were screened, analysed, and categorized into different relevant categories. Our analysis shows that GIS, in our selected sample, are mostly used for visualization and information management tasks. Moreover, spatial analysis studies were more common than other studies, and theoretical publications are scarce. The lack of a theoretical background in GIS may be the cause of some of the problems related to GIS applications in archaeology.

Abstract
<p>Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams.</p> <p>The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available.</p> <p>Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification – this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support.</p> <p>The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.</p>

Abstract
Due to the lack of unskilled labour force that has been verified in the last years, several processes have been automated, both at industrial and services level. In terms of logistics tasks and transport of materials, it is increasingly common to use mobile robots, given the advantages that this equipment presents. This is also the case in airports, where the adoption of these vehicles to perform several tasks is becoming visible. Considering the possibility of using mobile robots to transport luggage at the Francisco Sa, Carneiro Airport, this paper presents the development of a simulation model and the analysis of several scenarios, with different number of vehicles, in order to understand the time that passengers would have to wait for their luggage, in case this task is automated. The final objective is to determine the number of vehicles required and the changes that need to be made to the airport's operation in order to ensure a level of service identical to (or better than) that currently achieved, with these operations being carried out by human operators.

Abstract

Abstract
Sectorization problems, also known as districting or territory design, deal with grouping a set of previously defined basic units, such as points or small geographical areas, into a fixed number of sectors or responsibility areas. Usually, there are multiple criteria to be satisfied regarding the geographic characteristics of the territory or the planning purposes. This work addresses a case study of parcel delivery services in the region of Porto, Portugal. Using knowledge about the daily demand in each basic unit (7-digit postal code), the authors analysed data and used it to simulate dynamically new daily demands according to the relative frequency of service in each basic unit and the statistical distribution of the number of parcels to be delivered in each basic unit. The sectorization of the postal codes is solved independently considering two objectives (equilibrium and compactness) using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) implemented in Python.