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Self-thinning dynamics in cork oak woodlands: providing a baseline for managing density
No full textAim of study: The study aims to evaluate the maximum potential stocking level in cork oak (Quercus suber L.) woodlands, using the ecologically-based size-density relationship of the self-thinning law. Area of study: The study area refers to cork oak forests in mainland Portugal, distributed along its 18 districts from north to south. Material and Methods: A dataset with a total of 2181 observations regarding pure cork oak stands was collected from the Portuguese Forest Inventory (NFI) databases and from research plots. The dataset was subjected to two filtering procedures, one more restrictive than the other, to select the stands presenting the higher stocking values. The two resulting subsets, with 116 and 36 observations, from 16 and 10 districts of mainland Portugal, respectively, were then used to assess and describe the allometric relationship between tree number and their mean diameter. Main results: The allometric relationship was analysed and modelled using the log transformed variables. A slightly curvilinear trend was identified. Thus, a straight line and a curve were both fitted for comparison purposes. Goodness-of-fit statistics point out for a good performance when the data is set to the uppermost observed stocking values. A self-thinning line for cork oak was projected from the estimated relationship. Research highlights: The self-thinning model can be used as an ecological approach to develop density guidelines for oak woodlands in a scenario of increasing cork demands. The results indicate that the recommendations being applied in Portugal are far below the maximal potential stocking values for the species. It is therefore of the utmost importance to review the traditional silvicultural guidelines and endorse new ones
Promoting entrepreneurship among informatics engineering students: insights from a case study
Get PDFUniversities seek to promote entrepreneurship through effective education approaches, which need to be in permanent evolution. Nevertheless, the literature in entrepreneurship education lacks empirical evidence. This article discusses relevant issues related to promoting entrepreneurship in the software field, based on the experience of a 15-European Credit Transfer and Accumulation System course. This course seeks to instil in the students the recognition of the need to reconcile technical and business visions, organisational and commercial aspects, most of which have never been addressed previously. A series of semi-structured interviews made it possible to obtain relevant insights about the teaching-learning process underlying this course and its evolution over a seven-year period. Materials related with this course have been analysed, namely guidelines produced by the teachers and deliverables produced by the students. This article discusses the dimensions that were identified as fundamental for promoting entrepreneurship skills in the field of software, namely teamwork, project engagement, and contact with the market
Self-Care on Hemodialysis: Behaviors With the Arteriovenous Fistula
Get PDFEnd stage renal disease (ESRD) patients should be educated to maintain and preserve the arteriovenous fistula (AVF) in the best condition. The purpose of this work was to evaluate self-care frequency and factors that influenced such frequency. A prospective study was performed in 101 hemodialysis patients. Self-care behaviors were measured with the Scale of Assessment of Self-Care Behaviours with Arteriovenous Fistula in Hemodialysis. A regression model was used to determine the relevant predictors of self-care frequency and their influence. The incidence of self-care behaviors was 71.0%. The regression model showed that self-care behaviors were positively influenced by gender (female), ESRD etiology (hypertension, polycystic kidneys and other kidney diseases), duration of AVF and negatively by the existence of previous AVF and health professional (doctor). The frequency of self-care behaviors was lower than expected and below an appropriate standard. Education programs designed to improve self-care behaviors with AVF should be further explored in a prospective randomized trial
Towards a Uniform Metrological Assessment of Grating-Based Optical Fiber Sensors: From Refractometers to Biosensors
Get PDFMobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Get PDFMicroscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
Get PDFTraditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions. © 2017 by the authors
Domestic appliances energy optimization with model predictive control
No full textA vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new contribution of the proposal is a modulation scheme of a two-level Model Predictive Control's control signal as an interface block between the Model Predictive Control output and the domestic appliance that functions as a two-state power switch, thus reducing the Model Predictive Control implementation costs in home appliances with thermal regulation requirements
privy: Privacy Preserving Collaboration Across Multiple Service Providers to Combat Telecoms Spam
Get PDFDownscaling Aggregate Urban Metabolism Accounts to Local Districts
No full textUrban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste