Abstract
To provide a safe and fair-value health service that ensures quality, hospitals must provide efficient processes, trained and committed personnel, appropriate technology and a strategic platform which integrates these aspects effectively. At present, a broad set of tools and methodologies are available, associated to the reconfiguration of processes for enhancing efficiency and enabling excellence and sustainability. Of these, the most noteworthy are Lean and Six-Sigma methodologies. A literature review was performed covering the implementation of these methodologies in health services over the last 5 years. The aim was to determine the current approach in this sector and propose guidelines aligned with the future challenges and the needs of healthcare managers. The influence of team management strategies in the final project outcomes has also been addressed representing a novelty.

Abstract
Background: Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Abstract
n-Alkanes and long-chain alcohols (LCOH) have been used as faecal markers to assess the feeding behaviour of both wild and domestic herbivore species. However, their chemical analysis is time-consuming and expensive, making it necessary to develop more expeditious methodologies to evaluate concentrations of these markers. This work aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) technology in the near infrared (NIR) and mid infrared (MIR) intervals, for the determination of n-alkane and LCOH concentrations of different plant species and faecal samples of domestic herbivores. Spectra of 33 feed samples, namely L. perenne, T. repens, U. gallii, short heathers (mixture of Erica spp. and Calluna vulgaris), improved pasture grasses (mixture of L. perenne and A. capillaris), heath grasses (mixture of P. longifolium and A. curtissii), improved pasture species (mixture of L. perenne, T. repens and A. capillaris) and herbaceous species (mixture of all herbaceous species found in the plot)) and 181 faecal samples (cattle and horses) were recorded. In order to develop calibrations for the prediction of n-alkanes and LCOH concentrations, partial least squares (PLS) regression was used. Regarding the models developed for plant species, the best results were observed for the calibrations using NIR. The best external validation coefficients of determination (R(2)v) obtained were 0.90 and 0.79 for LCOH and n-alkanes, respectively. For faecal samples, in the NIR interval, results indicate similar external validation predictions (R(2)v) for both animal species (0.64). On the contrary, in the MIR interval, differences between cattle (0.70) and horses (0.57) faecal samples in R(2)v were observed. Regarding the models created for both animal species faeces, LCOH (C-26-OH and C-30-OH concentrations ranging from 713.3 to 4451.9 mg/kg DM, respectively; R(2)v values ranging from 0.72 to 0.95) and n-alkanes (C31 and C33 concentrations ranging from 112.8 to 643.2 mg/kg DM, respectively; R(2)v values ranging from 0.19 to 0.90) present in higher concentrations tended to be those with better estimates. Results obtained suggest that the selection of the technique to be used may depend on the type of matrix, being the homogeneity of the matrices one of the most important factors for its success. In order to improve the accuracy and robustness of the models created for the estimation of the concentrations of these markers using these methodologies, the database (greater variability) used for the calibrations of these models must be increased.

Abstract
Kaolin-particle film has been considered a low-cost technology to mitigate the adverse effects of high light and temperature, and drought in several crops. However, the underlying excess energy absorption and dissipation mechanisms, and related components associated with kaolin photoprotective effects in grapevines are poorly explored. This study aims to understand the interactions between kaolin foliar treatment and photosynthetic pigments accumulation, carotenoids metabolism, xanthophyll cycle regulation, and its putative role on the non photochemical quenching (NPQ) processes in Touriga-Franca (TF) and Touriga-Nacional (TN) varieties. The experiments were conducted during the 2017 summer season in a commercial vineyard, and measurements were performed at pre-dawn and midday in each sampling date (EL35 - veraison; EL38 - full mature). Overall, TF variety showed higher accumulation of chlorophylls, xanthophylls, and de-epoxidation state (DPS) than TN. Kaolin treatment enhanced TN chlorophyll accumulation up to 114 % at EL35 (veraison) and 123 % at EL38 (full mature), highlighting its protective role on chlorophyll degradation, while no changes were found in TF, which might indicate a lower need for particle-film technology in this variety under the current environmental conditions. Individual carotenoids were mainly higher in the treated leaves of both varieties, as well as the xanthophyll cycle pigments zeaxanthin (Z(x)) and violaxanthin (V-x). Simultaneously, the DPS and NPQ values were lower in TN and TF treated leaves (1.92 - 2.36) compared to untreated vines (3.19 - 3.24), suggesting that there might be other components influencing NPQ levels beyond Z(x), with an indirect role in long-lasting NPQ processes. In addition, in the TF kaolin-treated leaves, violaxanthin de-epoxidase (VvVDE1) and zeaxanthin epoxidase (VvZEP1) gene expression were respectively 3-fold and 4-fold upregulated at stage EL35, while VvZEP1 gene expression decreased at stage EL38 in TN kaolin-treated leaves, indicating an optimised regulation of the xanthophyll cycle. These findings suggest that kaolin treatment promoted a fine-tuning of grapevine summer stress responses under sustained summer stress factors, by managing xanthophyll cycle dynamics, and pigments accumulation.

Abstract
BACKGROUND The application of kaolin particle film is considered a short-term strategy against several environmental stresses in areas with a Mediterranean-like climate. However, it is known that temperature fluctuations and water availability over the season can jeopardize kaolin efficiency in many Mediterranean crops. Hence, this study aims to evaluate the effects of kaolin foliar application on berry phytohormones, antioxidant defence, and oenological parameters at veraison and harvest stages of Touriga-Franca (TF) and Touriga-Nacional (TN) grapevines in two growing seasons (2017 and 2018). The 2017 growing season was considered the driest (-147.1 dryness index) and the warmest (2705 degrees C growing degree days) of the study. RESULTS In 2017, TF kaolin-treated berries showed lower salicylic acid (-26.6% compared with unsprayed vines) and abscisic acid (ABA) (-10.5%) accumulation at veraison, whereas salicylic acid increased up to 28.8% at harvest. In a less hot season, TN and TF kaolin-treated grapevines showed a twofold in ABA content and a threefold increase in the indole-3-acetic acid content at veraison and lower ABA levels (83.8%) compared with unsprayed vines at harvest. Treated berries showed a decreased sugar content, without compromising malic and tartaric acid levels, and reactive oxygen species accumulation throughout berry ripening. CONCLUSION The results suggest kaolin exerts a delaying effect in triggering ripening-related processes under severe summer stress conditions. Treated berries responded with improved antioxidant defence and phytohormone balance, showing significant interactions between kaolin treatment, variety, and developmental stage in both assessed years. (c) 2021 Society of Chemical Industry.

Abstract
Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information.