Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight Recorder Localization Following at-Sea Plane Crashes

Recent aircraft disasters over oceans and seas unveiled critical deficiencies in the existing methods of submergedaircraft localization and recovery. The current localization procedures rely on an acoustic transmitter, denominated Underwater Locator Beacon (ULB), which is attached to the aircraft’s ”black box” and emitting sinusoid-shaped acoustic signals with the purpose of being detected and eventually located by a set of conveniently positioned hydrophones. This paper aims to quantify the precision gains in aircraft localization when some specifications of the currently used beacons are modified, namely its operating frequency, transmitted signal waveform and acoustic power. Moreover, we intend to compare two different source localization algorithms – TOA (Time Of Arrival) and TDOA (Time-Difference Of Arrival) – when applied to this kind of situations. This study is carried out in a simulation environment.
Synthesis of MgO Nanoparticles and Their Antibacterial Properties on Three Food Poisoning Causing Bacteria

Background: Application of nanoparticles in the removal of pathogenic bacteria is very important. The use of these materials can be appropriate for controlling pathogens and food-borne diseases. The purpose of this study was to synthesize magnesium oxide nanoparticles and investigate its antibacterial effect on several bacteria causing food poisoning. Materials & Methods: Oxide magnesium nanoparticles are synthesized by chemical deposition method. In order to control the quality and morphology of samples, XRD and SEM methods were used. The effect of different concentrations of nanoparticles on Staphylococcus aureus, Salmonella enterica and Bacillus cereus was evaluated by Agar well diffusion technique and the antibiotic resistance patterns of the bacteria used were also examined. Results: MgO nanoparticles had an extensive antibiotic resistance but were effective on all bacteria and the minimum inhibitory concentration of growth on Staphylococcus aureus, Salmonella enterica and Bacillus cereus was 0.75, 1.25, and 5 mg/mL and the minimum bactericidal concentration of them were determined to be 0.15, 2.5 and 10 mg/mL, respectively. Conclusion: MgO nanoparticles exhibited remarkable antibacterial activity against food poisoning causing bacteria and can be used as an antibacterial agent more effectively.
Biochip Architecture for Cardiac Pathologies Detection

Off-the-person electrocardiography is gaining momentum as superior quality single-lead ECG signals can now be acquired from a person’s hands or arms (Lead 1). Although several heart disease algorithms have been described in recent years, all of them make use of more than one ECG lead and, by being tested only against standard databases, are sensitive to the acquisition conditions. The purpose of this work was to design a robust Lead 1-only real-time heart disease detection system and implement it on an FPGA-based development board. The proposed system is based on a signal processing pipeline composed of: ECG signal denoising; heartbeat detection and segmentation; extraction of dynamic morphological features; and heartbeat classification (normal or abnormal and corresponding condition). Resorting to the only database from MIT’s Physiobank with Lead 1 annotated recordings, InCarTDb, software implementation of this pipeline resulted in a 4-class model with a classification of accuracy up to 96.5%. A biochip architecture for cardiac pathologies identification was then designed and simulated in VHDL, and mapped to a virtual FPGA evaluation board. The resulting architecture verified accuracies from 84 to 100% for different patients of the database, less than 30% of the evaluation board resources, a working clock frequency of 35 MHz and a total power consumption of 192 mW. Keywords—Single-Lead Electrocardiogram, Heart Disease Identification, Signal Denoising, Feature Extraction, Signal Processing Architecture, FPGA
The role of scratching in the control of ectoparasites on birds

ABSTRACT Grooming by birds is thought to serve essential anti-parasite functions. While preening has been well studied, little is known about the function of scratching in birds. We conducted a series of experiments to determine the effectiveness of scratching for controlling feather lice (Columbicola columbae) on Rock Pigeons (Columba livia). First, we used a hobbling technique to impair scratching. After 6 mo, hobbled birds had significantly more lice than controls that could scratch. In addition, lice on hobbled birds were concentrated on the birds' heads and necks (i.e. the regions that birds scratch). Secondly, we tested the role the claw plays in scratching by declawing nestlings. Once mature, declawed pigeons had significantly more lice than control birds with claws. Moreover, lice on declawed birds were concentrated on the head and neck. Next, we tested whether the flange found on the middle claw of many bird species enhances scratching. We experimentally manipulated the flange; however, the number and location of lice on birds without flanges was not significantly different than that on control birds with intact flanges. Finally, we tested whether scratching removes parasites directly or indirectly by “flushing” them onto body regions where they can be preened. When we impaired scratching (with hobbles) and preening (with “bits”) we found that scratching no longer reduced the number of lice on birds. Our results indicated that scratching and preening work synergistically; scratching reduces parasite load by flushing lice onto regions of the body where they can be eliminated by preening.
Computational study of contact patterns in the ankle joint after ligamentous injury

The ankle is a complex joint, with the ligaments as its main stabilizer, which when injured, induce instability and a change in the cartilaginous contact pressures. The main goal of this work is to study the changes in those contact pressures as well as the changes in the ligamentous strain and in the ankle reaction moments, using the finite element method (FEM). This work involved the creation of the (FEM) of the ankle joint (AJ), namely of its mechanical properties and boundary conditions. Three internal rotation (IR) levels, experimentally measured, were applied in the ankle for three different ligamentous configurations. The results show a high strain rate at the anterior talofibular ligament (ATFL) and at the posterior tibiotalar ligament (PTTL), denoting their importance in the ankle’s (IR). There is also not only an increase of the maximum pressure in the ankle cartilage but also the occurrence of pressure spots at the anteromedial and posterolateral areas of the talar cartilage, possible precursors of cartilaginous injuries with degenerative implications.
Development of 5G Antenas for Millimeter Waves

The main aim of this paper is to develop new configurations of antennas compatible with 3D printing for 5G applications. Due to the 5G’s need for larger bandwidth allocations, the millimeter-wave band must be used. The antenna’s technology chosen are transmit-arrays, which are composed of several unit-cells, since they can reach high gain, are thin, lightweight and have a low cost of manufacture. This paper presents a new concept of unit-cells compatible with 3D printing for the V-band, that consists in printing only the dielectric part (PLA) of the unit-cells and then glue over it a thin substrate (Polyester or Rogers Duroid 5880) with the metallized elements. In this paper two types of transmit-arrays are designed, a collimated-beam one and a shaped-beam one to synthetize a radiation pattern complying with a cosecantsquared radiation pattern template. As a proof of concept, two collimated transmit-arrays’ prototypes are manufactured and tested. One composed of PLA and Polyester unit-cells with dimensions 42x42 mm2 and a focal distance of 30 mm, and another composed of PLA and Rogers Duroid 5880 cells with dimensions 63x63 mm2 and a focal distance of 45 mm. The first one presents a gain of 23.5 dB at 62.3 GHz and a radiation efficiency of 25% and the second one a gain of 24.5 dB at 60 GHz and a radiation efficiency of 14.1%. The experimental results comply with simulated ones, validating this new concept of unit-cells and the manufactured prototypes. Index Terms Transmit-arrays, millimeter-wave antennas, collimated-beam antennas, shaped-beam antennas, isoflux radiation pattern, 3D Printing
Point Localization in Surfaces

The aim of this thesis is to locate a point or several points in a surface, using a Microsoft Kinect camera. Our research is focused on articulated objects, such a human body. The motivation for this thesis is simple but very useful. It is tracking skin lesions (points) in human body (surface). We propose a novel method called “Trilateration” of geodesics. Representing the points which we want to track by its distance of “anchor” points (easy points to track), we trilaterate those in order to get the location of the points, using a technique similar to GPS. At the end, we have estimated the location of the points in the surface in diﬀerent time stamps. This method uses the concept of geodesic, which is invariant whatever pose of the object is. With this approach we solve the localization of point/points in a surface either for rigid or articulated objects. This is a powerful tool, since tracking in objects articulated objects is extremely diﬃcult.
Reinforcement Learning of Task Plans for Real Robot Systems

This paper is the extended abstract for the thesis entitled “Reinforcement Learning of Task Plans for Real Robot Systems”. The thesis focuses on the application of Reinforcement Learning (RL) methods to real robot systems. For that, we create a RL system using the ROS middleware and apply it to a Case Study in the context of domestic robotics. We start by reviewing the Markov Decision Making (MDM) ROS package, which provides programming tools for defining tasks as MDPs (as well as Partially Observable MDPs and DecentralizedMDPs). Afterwards, we explain how this package was extended with RL capabilities, allowing it to be used for learning. The application focus of this work is on the SocRob@Home research team, which aims to participate in domestic robotics competitions such as RoCKIn and RoboCup@Home. In this context, we explain how we connect the MDM package with other ROS out-of-the-box packages for task level definition, namely SMACH. We propose a Finite State Machine (FSM) task definition where states can be defined as MDPs using MDM. We also propose the usage of MDM’s RL for obtaining policies which can be later used for defining tasks as MDPs. A simulator was created to allow for testing and training for the SocRob@Home robot. Finally, we present the Case Study. We utilize one of the tasks to be used in RoCKIn and identify a subtask within it, which contains uncertainty and is also a possible subject for learning, given its environment complexity. The subtask is designed using SMACH with MDM, first by using the RL capabilities to learn the optimal policy and then by using the resulting policy to define the subtask as an MDP within a FSM.
Dynamics of sanctioning in structured populations

Understanding the evolutionary mechanisms that promote and maintain cooperative behaviour remains an open challenge for a wide range of scientific disciplines. There are several different situations where humans cooperate in the so-called public goods games, contrary to what one would expect from reasonings based on rationality, game theory, or fitness maximization. Costly punishment of free-riders is often emphasised as one of the possible routes to cooperation. However, those that refuse to support the costs of sanctioning reduce the effectiveness of punishment institutions, leading to a second-order cooperation problem. In this thesis, we study how an underlying network of contacts affects the prevalence of sanctioning institutions and their impact on cooperation in Public Goods Games. We show that homogeneous network structure enhances the chances of getting stable sanctioning institutions, yet within a small window of game parameters. Differently, when the network structure leads to diversity in the number and size of the collective endeavours each agent participates, both cooperation and sanctioning institutions prevail for a wide range of parameters. Our results rely on large-scale computer simulations and evolutionary game theoretical models and suggest that the network of contacts play an important role in solving both first and second-order free-riding problems.
The Virtual Tailor: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style

In this paper, we present TailorNet, a neural model which predicts clothing deformation in 3D as a function of three factors: pose, shape and style (garment geometry), while retaining wrinkle detail. This goes beyond prior models, which are either specific to one style and shape, or generalize to different shapes producing smooth results, despite being style specific. Our hypothesis is that (even non-linear) combinations of examples smooth out high frequency components such as fine-wrinkles, which makes learning the three factors jointly hard. At the heart of our technique is a decomposition of deformation into a high frequency and a low frequency component. While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models. The weights of the mixture are computed with a narrow bandwidth kernel to guarantee that only predictions with similar high-frequency patterns are combined. The style variation is obtained by computing, in a canonical pose, a subspace of deformation, which satisfies physical constraints such as inter-penetration, and draping on the body. TailorNet delivers 3D garments which retain the wrinkles ∗Equal contribution. from the physics based simulations (PBS) it is learned from, while running more than 1000 times faster. In contrast to classical PBS, TailorNet is easy to use and fully differentiable, which is crucial for computer vision and learning algorithms. Several experiments demonstrate TailorNet produces more realistic results than prior work, and even generates temporally coherent deformations on sequences of the AMASS [33] dataset, despite being trained on static poses from a different dataset. To stimulate further research in this direction, we will make a dataset consisting of 55800 frames, as well as our model publicly available at https://virtualhumans.mpi-inf.mpg. de/vtailor/.
Spring 2000, Vol. 5, No. 1, pp. 20-29

Student teaching evaluations attempt to provide information about an instructor’s teaching effectiveness. While the majority of evaluations consist of written questionnaires administered at the end of the academic term, mid-term evaluations can provide timely and useful feedback. Many instructors regard the use of evaluations for personnel decisions with distrust, especially since concerns have been raised about the validity and bias of these instruments. Although many articles have been written about teaching evaluations, this topic still needs to be researched and discussed in construction management education. This article discusses the most common approaches to student teaching evaluation, as well as the concerns that have been raised by educators and researchers about their use. Further research needs are discussed in its Conclusion.
Transfer Learning for Detecting Chronic Obstructive Pulmonary Disease via Computed Tomography Images

Chronic obstructive pulmonary disease (COPD) is characterized by irreversible obstruction of the airway tree and progressive damage to the lung parenchyma. A poor quality of patient life, calls for early detection of the disease. In this study, we propose a transfer learning approach for analyzing lung CT images to predict disease status in COPD. Instead of training a deep convolutional neural network from scratch, we use deep features from a pre-trained VGG-19 model. We compare our results to a current state-of-the-art method and demonstrate that a simpler, transfer learning approach can be used to detect COPD in lung CT images by achieving a mean area under the curve of 0 . 92 ± 0 . 03.
Mastering the Concurrency of Shared Path TCP Connections

—Concurrency has adverse effects on networks: having too many parallel Transmission Control Protocol (TCP) connections is proven to be too aggressive and cause unnecessary congestion, throttling throughput for all network users. This work addresses the problem at the sender, by grouping parallel connections that share the same path, at the sender, reducing per connection redundancies. We survey implementations for this and adapt them for our own protocol. Our solution enables TCP to: group connections from hosts in close proximity, have ﬁner network state estimates, react quickly to congestion, skip slow start and have an increased average throughput.
Mental Health Resources: Reflection on AffecTech Platform

This positon paper presents an overview of the mental health resources developed within the AffecTech, an interdisciplinary European Commission-funded project supporting the wok of 15 PhD students in the space of digital interventions for affective health. It describes different types of targeted resources highlighting both traditional and innovative ones, our approach to developing them, and the challenges and opportunities that our work has uncovered.
Otimização de concentradores solares luminescentes com recurso ao método de Monte Carlo

Despite photovoltaic (PV) advances in recent times, the technology still has not established itself as a key role player in the electricity supply market. Promising lower costs, Luminescent Solar Concentrators (LSCs) absorb short wavelength solar radiation and emits red-shifted light. The emission is then guided towards the sheet edges, where the concentrated light is absorbed by the solar cell to produce electricity. Moreover, their ability to concentrate sunlight onto small areas makes LSCs a useful complement to silicon-based PVs in a series of applications, such as urban integration and flexible fabrics towards mobile solar-energy. The present Thesis tries to provide an insight on these devices, allowing a deeper understanding of HOW they work and could be used to generate green energy. A theoretical view of the state-of-art is presented, not only regarding the greatest LSC’s achievements, but also on the components used (Organic dyes, Quantum Dots and Lanthanide ions), plus a reproduction of LSCs working mechanisms with the assistance of the developed Monte Carlo Ray Tracing Simulation program. A batch of tests were prepared in order to study matrix geometry influence on device performance. Using two sets of phosphores with different properties the results show that despite not all matrix dimensions have the same impact on efficiency, in general smaller plate areas allow better results. Increasing LSCs surface, showed that it is possible to maintain performance for larger devices, being that the distance from the PV cell from the centre of the matrix remains the same. It is important to point out that the algorithm can be used not only to study geometry’s influence, but also to predict the effectiveness of recently discovered dyes or multiple solutions combination, since the simulator accurately describes the main optical features of LSCs. Keywords— Luminescent Solar Concentrators (LSCs), Monte Carlo,Waveguide, Fluorescent Dyes, Quantum Dots, Lanthanide Ions.
An Exploratory Study of the Diurnal Variation and Reliability of Biomarkers Related to Physiological Damage Experienced in Work-Related Musculoskeletal Disorders

Abstract Objectives: Select biomarkers derived from such tissue damage have been proposed as a tool for predicting MSD risk. Factors beyond the exposure to biomechanical stress, although, may influence these biomarkers and thus should be quantified. The purpose of this exploratory study was to determine (1) whether diurnal variations exist for Cartilage Oligomeric Matrix Protein (COMP), Interleukin-6 (IL6), and Creatine Kinase (CK); and (2) if these diurnal variations are influenced by personal factors including age, obesity status, and gender. Methods: Twenty-seven participants were dichotomously classified by age (young = 24.7 [3.9], older = 55.2 [5.0]), body mass index (BMI) (not obese = 23.1 [2.2], obese = 34.1 [2.9]), and gender (males = 14, females = 13), and had blood samples drawn at six time points over a 24-hour period (22:00, 07:00, 10:00, 14:00, 18:00, and 22:00) during which any strenuous physical activity was restricted. Results: COMP levels were significantly higher for males, had significant diurnal variation (p < 0.01), and this diurnal variation differed between genders (p = 0.05). IL6 did not have any significant diurnal variation or differences related to age, obesity status, or gender. CK levels exhibited significant diurnal variation (p = 0.05), and levels were significantly influenced by age (p < 0.07) and an obesity status × gender interaction (p < 0.02). Reliability (between two samples at 22:00), measured by the intraclass correlation, was classified as “excellent” for all biomarker levels. Conclusions: Diurnal changes in COMP and CK may reflect differences in body composition between groups. The present results may aid in future study interpretation or design utilizing these biomarkers in the occupational environment.
Deep learning of dynamic functional connectivity states during sleep and epilepsy using simultaneous EEG-fMRI

Functional Connectivity (FC) estimated from functional Magnetic Resonance Imaging (fMRI) data has emerged as a powerful metric to investigate the brain’s intrinsic organization. Since it is modulated by the level of arousal and by disease, FC-based classiﬁers targeting sleep staging and diagnosis of brain disorders have been proposed. Emerging trends in this context include the estimation of dynamic FC (dFC) features and the transition from conventional machine learning methods to deep learning, both explored in the present work. The starting point was the application of Convolutional Neural Networks (CNNs) to classify sleep stages at individual level, provided the success of conventional classiﬁers reported by previous works. Subsequently, the method was extended to an exploratory scenario comprising the classiﬁcation of epileptic states at individual and group levels, as well as in a single-subject classiﬁcation scenario. In the latter, dataset augmentation and transfer learning strategies were also investigated. Regarding the sleep staging problem, the implementation of diﬀerent CNN architectures resulted in balanced accuracies over 80%; however, the best performance was obtained using a shallow neural network. Similar results were obtained when applying this model to the epilepsy scenario at both individual and group levels. A systematic comparison with Support Vector Machines (SVMs) revealed an equivalent performance but presented some drawbacks in terms of computational cost and interpretation. In the single-subject classiﬁcation scenario, however, the signiﬁcant inter-subject variability compromised the generalization ability of the tested classiﬁers. Further validation of the implemented deep learning methods is largely dependent on an increased data availability.