Analytical Model for the Performance Curves of a Family of Propellers Based on Wind Tunnel Tests

Propeller aircraft performance is greatly influenced by the performance of the propeller it uses. Thus, proper selection of a propeller for a given aircraft design at the early stages of the design process is fundamental. During the design of a new aircraft, simple yet accurate performance models are required to properly optimize the design. Significant experimental performance data of low speed, small propellers is available. The main objective of this work is to create and validate an analytical model for     the performance curves of a family of propellers tested at low Reynolds numbers, which can be used in selecting a propeller for a given existing aircraft design or during its design optimization process. This kind of propellers is more commonly used in Unmanned Aerial Vehicles (UAVs). The model is designed in MATLAB® using a variety of regression techniques, such as the Least Squares Method (LSQ), applied to experimental data acquired at University of Illinois at Urbana-Champaign (UIUC), for seventeen APC Thin Electric propellers, and at the Department of Aerospace Sciences (DCA) of University of Beira Interior (UBI), for ten more APC Thin Electric propellers. The analytical model predicts propeller power coefficient and propulsive efficiency accurately for the family of propellers tested and can also be used for the propellers with dimensions close to those used for its development. The propeller performance data obtained during the experimental tests are made available the community to further increase the documentation on propellers tested at low Reynolds numbers. Keywords: Propeller, Low Reynolds propeller performance, Propeller tests, Wind tunne

Aerial Forest Fire Detection and Monitoring Using a Small UAV

In recent years, large patches of forest have been destroyed by fires, bringing tragic consequences for the environment and small settlements established around these regions. In this context, it is essential that fire fighting teams possess an increased situational awareness about the fire propagation, in order to promptly act in the extinguishing process. Recent advances in UAV technology allied with remote sensing and computer vision techniques show very promising UAVs applicability in forest fires detection and monitoring. Besides presenting lower operational costs, these vehicles are able to reach regions that are inaccessible or considered too dangerous for fire fighting crews operations. This paper describes the application of a real-time forest fire detection algorithm using aerial images captured by a video camera onboard    an Unmanned Aerial Vehicle (UAV). The forest fire detection algorithm consists of a rule-based colour model that uses both RGB and YCbCr colour spaces to identify fire pixels. An intuitive targeting system was also developed, allowing the detection of multiple fires at the same time. Additionally, a fire geolocation algorithm was developed in order to estimate the fire location in terms of latitude (φ),  longitude     (λ) and altitude (h). The geolocation algorithm consists of applying two coordinates systems transformations between the body-fixed frame, North-East-Down frame (NED) and Earth-Centered, Earth Fixed (ECEF) frame. Flight tests were performed during  a controlled burn in order to assess the fire detection algorithm performance. The algorithm was able to detect the fire with few false positive detections. Keywords: Aerial fire detection algorithm, Aerial fire monitoring, Forest fire, UAV, Remote sensin

A deep learning based object identification system for forest fire detection

POCI-01-0247-FEDER-038342Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column.publishersversionpublishe

Human toxocariasis: contribution by Brazilian researchers

In the present paper the main aspects of the natural history of human infection by Toxocara larvae that occasionally result in the occurrence of visceral and/or ocular larva migrans syndrome were reviewed. The contribution by Brazilian researchers was emphasized, especially the staff of the Tropical Medicine Institute of São Paulo (IMT)

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Effect of Control Parameters on Hybrid Electric Propulsion UAV Performance for Various Flight Conditions: Parametric Study

Nowadays, great efforts of ongoing research are devoted to hybrid-electric propulsion technology that offers various benefits, such as reduced noise and pollution emissions and enhanced aircraft performance and fuel efficiency. The ability to estimate the performance of an aircraft in any flight situation in which it may operate is essential for aircraft development. In the current study, a simulation model was developed that allows estimating the flight performance and analyzing the mission of a fixed-wing multi-rotor Unmanned Aerial Vehicle (UAV) with a hybrid electric propulsion system (HEPS), with both conventional and Vertical Takeoff and Landing (VTOL) capabilities. The control is based on the continuous specification of pitch angle, propulsion thrust, and lift thrust to achieve the required conditions of a given flight segment. Six different missions were considered to analyze the effect of control parameters exhibiting the most influence on the UAV mission performance. An appropriate set of control parameters was selected through a multidimensional parametric study. The results show that the control parameters, if not well tuned, affect the mission performance: for example, in the deceleration transition, a longer time to reduce the cruise speed to stand still may be the result because the controller struggles to adjust the pitch angle. In addition, the implemented methodology captures the effects of transient maneuvers, unlike typical quasi-static analysis without the complexity of full simulation models

A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks

POCI-01-0247-FEDER-033479The ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user's environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user's side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.publishersversionpublishe

Human activity recognition for indoor localization using smartphone inertial sensors

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users' performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users' current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.info:eu-repo/semantics/publishedVersio

Real-Time Integration of Segmentation Techniques for Reduction of False Positive Rates in Fire Plume Detection Systems during Forest Fires

POCI-01-0247-FEDER-038342Governmental offices are still highly concerned with controlling the escalation of forest fires due to their social, environmental and economic consequences. This paper presents new developments to a previously implemented system for the classification of smoke columns with object detection and a deep learning-based approach. The study focuses on identifying and correcting several False Positive cases while only obtaining a small reduction of the True Positives. Our approach was based on using an instance segmentation algorithm to obtain the shape, color and spectral features of the object. An ensemble of Machine Learning (ML) algorithms was then used to further identify smoke objects, obtaining a removal of around 95% of the False Positives, with a reduction to 88.7% (from 93.0%) of the detection rate on 29 newly acquired daily sequences. This model was also compared with 32 smoke sequences of the public HPWREN dataset and a dataset of 75 sequences attaining 9.6 and 6.5 min, respectively, for the average time elapsed from the fire ignition and the first smoke detection.publishersversionpublishe