Abstract
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals' activities.

Abstract
Due to the salient features of direct current (DC) microgrids (MGs) in integrating renewable energy sources, this paper offers a robust finite-time nonlinear observer (FTNO) for DC MGs comprising linear resistive and nonlinear constant power loads (CPLs) and a buck converter. It is assumed that the capacitor voltage is only accessible and the power system is subject to unknown time-varying uncertainties. A novel nonlinear observer is designed to estimate the inductance curren2t to prevent the ripples produced by current sensors and to eliminate the price of utilizing expensive sensors. The global finite-time stability analysis of the observer error dynamic is investigated via a Lyapunov function and an explicit finite convergence time (FCT) is derived. The convergence rate of the estimated current is tunable by adjusting the parameters in FCT. Eventually, simulations are carried out to confirm the superiority of the proposed observer performance in estimating unknown inductance current in a particular finite time.

Abstract
The intensification of agriculture has greatly enhanced crop productivity, but also its potential environmental impact. Nutrient recycling and an increase in resource use efficiency are the key points to keep production at high levels with minimum impact. The present work’s goal was to provide new insight on the spatial variability of soil chemical properties in a vineyard. For this, three different zones were identified in a 6.77 ha parcel, according to the remote sensing of apparent soil electrical conductivity (ECap) and the normalized difference vegetation index (NDVI). Soil samples from specific locations were then collected and chemically described, and the resulting data were statistically analyzed. ECap and NDVI appeared to be efficient tools to define different zones within the vineyard, with most of the soil chemical properties varying at the highest significance level (p < 0.001) according to the F test, except for extractable phosphorus (Égner-Rhiem) and organic carbon (TOC method). Overall, our results revealed potential for the implementation of site-specific soil fertilization and soil quality management.

Abstract

Abstract
Electricity markets are complex and dynamic environments with very particular characteristics. Ambitious goals, including those set by the European Union, foster the increased use of distributed generation, essentially based on renewable energy sources. This requires major changes in electricity markets and energy systems, namely through the adoption of the smart grid paradigm. The use of simulation tools and the study of different market mechanisms and the relationships between their stakeholders are essential. One of the main challenges in this area is developing decision support tools to address the problem as a whole. This work contributes to increasing interoperability between heterogeneous systems, namely agent-based, directed to the study of electricity markets, the operation of smart grid, and energy management. To this end, this work proposes the use of ontologies to ease the interaction between entities of different natures and the use of semantic web technologies to develop more intelligent and flexible tools. A multiagent systems society, composed of several heterogeneous multiagent systems, which interact using the proposed ontologies, is presented as a proof-of-concept. © 2021, Springer Nature Switzerland AG.

Abstract
In contrast to conventional data envelopment analysis (DEA), where a system is considered as a "black-box", network DEA acknowledges its internal structure to generate more enlightening results. It goes without saying that putting network DEA in practice is natural-and progressively rarer as the complexity of a system's structure increases. In particular, its employment in healthcare is no exception. Thus, we designed a slacks based model to measure the efficiency of the Portuguese secondary healthcare providers bearing in mind their internal services. However, the absence of data regarding the connections between those services called for the additional use of a simulation method - the well-known Monte Carlo method -, modelled with the judgements of a decision-maker. This unprecedented application of static systems with a matrix-type structure found that two-thirds of those providers were inefficient and allowed the identification of target areas for future policy reforms in the Portuguese National Health Service.