Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of GaN Technology-Based DC/DC Converter for Hybrid UAV

Wide band-gap (WBG) semiconductors technology represents a potential candidate to displace conventional silicon (Si) technology used in power electronics. Between Silicon Carbide (SiC) and Gallium Nitride (GaN) power semiconductors, the latter is the least mature of both technologies, with many open research problems, especially in the aerospace industry. In this paper, we address the design and implementation of a DC/DC converter for a hybrid small unmanned aerial vehicle (UAV) based on GaN technology. Both theoretical and simulation comparisons of Si, SiC and GaN transistors for the converter are presented. The conclusion is that GaN devices are the most appropriate to fulfill converter requirements for the size and weight limitations of the selected UAV. The paper presents a buck converter which handles an input voltage range of 32 V to 40 V and provides a 12 V regulated output and output power up to 60 W. The experimental results carried out on the prototype converter show how promising the GaN technology is for aerospace systems, not only regarding its volume and size, but also its efficiency. Besides, practical implementation details are reported to contribute to the design of small, light and reliable GaN power converters for aeronautics.
A Tensor Foreground-Background Separation Algorithm Based on Dynamic Dictionary Update and Active Contour Detection

Foreground-background separation of surveillance video, that models static background and extracts moving foreground simultaneously, attracts increasing attentions in building a smart city. Conventional techniques towards this always consider the background as primary target and tend to adopt low-rank constraint as its estimator, which provides finite (equal to the value of rank) alternatives when constructing the background. However, in practical missions, although general sketch of background is stable, some details change constantly. Aimed at this, we propose to represent the general background by a linear combination of some atoms and record the detailed background by spatiotemporal clustered patches. Then, the moving foreground is considered as a mixture of active contours and continuous contents. Eventually, joint optimization is conducted under a unified framework, i.e., alternating direction multipliers method (ADMM), and produces our tensor model for hierarchical background and hierarchical foreground separation (THHS). The employed tensor space, which agrees with the instinct structure of video data, benefits all the spatiotemporal designs in both background modular and foreground part. Experimental results show that THHS is more adaptive to the dynamic background and produces more accurate foreground when compared against current state-of-the-art techniques.
Occupancy Estimation Using Sensor Data Analytics

IoT devices generate data at a remarkable speed which requires near real-time processing. Such need has inspired a new computing paradigm that advocates moving computation to the edge, closer to where data is generated for ensuring low- latency and responsive data analytics. This paper aims at monitoring indoor environment parameters inside the classroom in real time and estimate the occupancy of that room based on the collected data This is done by measuring different environmental parameters like temperature, CO2 etc. and then by performing regression on the collected data. For the purpose of real time monitoring an android application is used which uses MQTT for communication with the edge node. The estimation is then verified against the actual attendance in the classroom at that duration and it gives accuracy between 90%-95%.
SAR Images Co-registration Based on Phase Congruency Algorithm

As the first step in image processing operations, corner detection is a vital procedure where it can be applied to many applications such as matching features, image registration, image mosaicking, change detection…. Registration of images can be described as the process of getting the pixel location misaligned between two or more images. A modified corner detector is proposed in this paper based on a combination of both phase congruence, later named PC, and Harris corner detector where PC image can provide fundamental and meaningful features despite complex changes in intensity. The performance was similar to detectors for the Shi-Tomasi, FAST, and Harris corner. Experiments are carried out using simulated images. As metric metrics, MSE (mean square error) and PSNR (peak signal-to-noise ratio) are used. The experimental results verify the effectiveness where the advantages of image constitutional representation are utilized, allowing the extraction of the powerful key points since it preserves the robustness of the coregistration process using image frequency properties that are not variant to illumination. It also has the ability to produce reasonable results as opposed to state-of-the-art such as Shi-Tomasi, FAST, and Harris algorithms.
Mapping RDF Databases to Property Graph Databases

RDF triplestores and property graph databases are two approaches for data management which are based on modeling, storing and querying graph-like data. In spite of such common principle, they present special features that complicate the task of database interoperability. While there exist some methods to transform RDF graphs into property graphs, and vice versa, they lack compatibility and a solid formal foundation. This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema. We show that two of the proposed mappings satisfy the properties of semantics preservation and information preservation. The existence of both mappings allows us to conclude that the property graph data model subsumes the information capacity of the RDF data model.
Range-Doppler Registration

This paper presents a novel local self-localization approach which formulates the registration problem as a range-Doppler response problem. The proposed registration algorithm utilizes sensor observations in range-Doppler domain in combination with a pre-built high precision map and motion sensors for velocity and yaw rate measurement. In contrast to state-of-the-art approaches which perform the registration based on a point cloud, the introduced algorithm in this work shifts the registration from detection domain down to the sensor range-Doppler response. This procedure offers several positive characteristics such as independence of targets angle estimation, registration without any necessity of further processing of range-Doppler response for extracting detections and generating a point cloud. Furthermore, the transformation of the extracted detection from range-Doppler to Cartesian domain is not required.The registration problem is constructed as an optimization-based approach. The map information is converted to the range-Doppler domain and the optimization problem searches for the best overlap between sensor observation and map information. The registration result is subsequently used for correcting the ego-pose which is initially estimated based on motion sensors. The extensive simulation results demonstrate the feasibility and precise performance of the proposed algorithm.
Comparative PCR analysis of toxic Nodularia spumigena and non-toxic Nodularia harveyana (Nostocales, Cyanobacteria) with respect to the nodularin synthetase gene cluster

A comparative PCR analysis of the nodularin synthetase gene cluster (nda) of two closely related Nodularia strains from the Baltic Sea was performed. Unlike the nodularin-producing Nodularia spumigena Huebel 1988/306, all 11 tested nda sequences were absent in the non-toxic Nodularia harveyana Huebel 1983/300. Our results suggest that the benthic N. harveyana strain lacks the entire 48-kb nodularin synthetase gene cluster. We describe new primers and the requisite experimental conditions for screening different nda sequences.
Sexual ornamentation relates to immune function in male red grouse Lagopus lagopus scoticus

Sexual ornaments may enable females to discriminate amongst potential mates and choose those having either fewer parasites, or those which are more immunocompetent, or better able to cope with parasites. Tetraonid birds exhibit supra-orbital combs that function in both intra-sexual competition and mate choice. Through a correlative and experimental approach we investigated whether comb size of male red grouse Lagopus lagopus scoticus was related to infection intensity by their main parasite, the gastro intestinal nematode Trichostrongylus tenuis, and to immune function. We first looked at the relationships between immune function, parasite infection and condition. We found that spleen mass, an indirect measure of immune response, correlated positively with T. tenuis load, and negatively with condition. Cell-mediated immunity, a measure of immune defence, correlated positively with condition, and although not significantly related to T. tenuis load, increased when nematode parasites were experimentally reduced. Secondly, we investigated whether comb size was related to condition, T. tenuis load or immune function. Comb size was not significantly related to T. tenuis infection and did not change significantly after nematode parasite removal. However, males with bigger combs were in better condition, had a lighter spleen, a lighter spleen than expected from their T. tenuis load and had greater cell-mediated immunity. The findings suggest that comb size relates to immune function rather than T. tenuis parasite infection intensity. Males with bigger combs are likely to be of higher phenotypic quality because they are more immunocompetent and might be better able to cope with the detrimental effects of parasites.
UIO-based Cyber Attack Detection and Mitagation Scheme for Load Frequency Control System

Cyber attack affects the frequency regulation of load frequency control (LFC) system, which could cause instability in power systems. In this paper, false data injection (FDI) attacks for tie- line power measurements are specifically studied, and a novel attack detection and mitigation scheme is proposed to reduce the harm caused by it. First, we design an unknown input observer (UIO) to estimate the true value of tie-line power. Then, a signal credibility function is designed to detect attacks. The signal credibility function is based on the numerical difference and the slope difference between the estimated signal and the measured signal. We detect the attack by comparing the evaluation function of the signal with the evaluating indicator. Finally, the purpose of attack mitigation is achieved by replacing the measured value with the estimated value of the tie line power. From the simulation results of a multi-area LFC control system, the effectiveness of the proposed scheme is demonstrated.
An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of word-level emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (https://www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively).
Origin of 3 Rabid Terrestrial Animals in Raccoon Rabies Virus–Free Zone, Long Island, New York, USA, 2016–2017

During 2016–2017, three rabid terrestrial animals were discovered in the raccoon rabies virus–free zone of Long Island, New York, USA. Whole-genome sequencing and phylogenetic analyses revealed the likely origins of the viruses, enabling the rabies outbreak response (often costly and time-consuming) to be done less expensively and more efficiently.
Automated Detection of Malarial Retinopathy Using Transfer Learning

Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.
Home range, survival, and dispersal of endangered Saimaa ringed seal pups: Implications for conservation

Lake Saimaa in eastern Finland is inhabited by a critically endangered ringed seal subspecies Pusa hispida saimensis. Since accidental mortality in gill nets, resulting in reduced pup survival, is considered to be the main factor contributing to the decline in its population, fishing restriction areas have been established. In this study, 10 pups were located daily using very high frequency (VHF) telemetry to estimate their home ranges, movements, and survival. The pups dispersed after weaning at the age of ca. 3 mo and moved up to 15 km a day between consecutive locations and up to 25 km away from their birth sites. The home ranges of the pups at the age of 3–4 mo were variable in size, from 3 to 162 km2. The pups preferred the same shallow water areas (<6 m) that were used for gill net fishing. The annual fishing restrictions covered an average of 83% of the pups’ home ranges. Four of the pups were nevertheless killed in fishing gear. The results have implications for Saimaa ringed seal management and conservation. For instance, large home ranges of pups and the long distances movements should be taken into account when zoning shore use and imposing fishing restrictions.
The Influence of Oxygen Concentration on the Swimming Performance of Juvenile Pacific Salmon at Various Temperatures

Abstract The sustained swimming speeds of juvenile coho and chinook salmon tested in a tubular chamber at temperatures ranging from 10 to 20° C. usually were dependent on the dissolved oxygen concentration at any tried concentration below the air-saturation level. Some underyearling chinook salmon tested at 19.5° C. were notably exceptional; their performance at high oxygen concentrations was not markedly better than that observed at lower concentrations between 5 and 7 mg/1. These fish may have been anemic. Reduction of oxygen concentration from air-saturation levels to 7, 6, 5, 4, and 3 mg/1 usually resulted in reduction of the maximum sustained swimming speed of coho salmon by about 5, 8, 13, 20, and 30 percent, respectively. These percentages are based on the observed performance of the first-failing and second-failing individuals of a group of five fish used in a performance test. The corresponding estimated percent reductions of the swimming speed of first-failing fish in most of the early explorato.
Effect of follicle cells on the maturation and developmental competence of ovine oocytes matured outside the follicle

The hypothesis that follicle cells play a central role in endowing the oocyte with developmental competence during maturation has been tested in coculture studies. Oocytes and follicle cells in various combinations were cultured for 24 h with gonadotrophins and estrogen in a nonstatic culture system. Developmental competence was assessed by transfer of oocytes to inseminated receipient ewes followed by embryonic examination 12 days later or at parturition. Denuded and corona-enclosed oocytes resume meiosis in culture but remain immature and developmentally incompetent. By contrast, oocytes supported by the cumulus and underlying granulosa (cumulus-oocyte complexes) undergo full maturation and normal subsequent embryonic development (42.6% to embryos). Addition of supplementary follicle cells during culture (5 × 106 cells/ml medium) is without beneficial effect on denuded oocytes. However, supplementary cells confer competence on corona-enclosed oocytes (37% to embryos). A dual cellular requirement for full maturation involving both cell numbers and some direct cell-oocyte contact is highlighted by our experiments. The results demonstrate further that the nonstatic system provides a simple but reliable method of producing large numbers of fully matured oocytes for both research and clinical purposes.
Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Driving in Challenging Terrain

Wheeled-legged robots are an attractive solution for versatile locomotion in challenging terrain. They combine the speed and efficiency of wheels with the ability of legs to traverse challenging terrain. In this letter, we present a trajectory optimization formulation for wheeled-legged robots that optimizes over the base and wheels’ positions and forces and takes into account the terrain information while computing the plans. This enables us to find optimal driving motions over challenging terrain. The robot is modeled as a single rigid-body, which allows us to plan complex motions and still keep a low computational complexity to solve the optimization quickly. The terrain map, together with the use of a stability constraint, allows the optimizer to generate feasible motions that cannot be discovered without taking the terrain information into account. The optimization is formulated as a Nonlinear Programming (NLP) problem and the reference motions are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The trajectories have been experimentally verified on quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels. Our trajectory optimization framework enables wheeled quadrupedal robots to drive over challenging terrain, e.g., steps, slopes, stairs, while negotiating these obstacles with dynamic motions.
Fabrication and Characterization of a Low-Cost Microfluidic System for the Manufacture of Alginate–Lacasse Microcapsules

The development of microfluidics-based systems in the recent years has provided a rapid and controlled method for the generation of monodisperse microencapsulates for multiple applications. Here, we explore the design, manufacture and characterization of a low-cost microsystem for the encapsulation of the fungal laccase from Pycnoporus sanguineus CS43 in alginate microcapsules. Multiphysics simulations were used to overview the fluid behavior within the device and estimate the resulting capsule size. Polymethylmethacrylate (PMMA) sheets were used for final microsystem manufacture. Different flow rates of the continuous (Qc) and discrete (Qd) phases in the ranges of 83–293 mL/h and 1–5 mL/h, respectively, were evaluated for microcapsule fabrication. Universal Serial Bus (USB) microscope and image analysis was used to measure the final particle size. Laccase encapsulation was evaluated using spectrophotometry and with the aid of fluorescent dyes and confocal microscopy. Results showed microcapsule size was in the range of 203.13–716.00 μm and Qc was found as the dominant parameter to control capsule size. There was an effective enzyme encapsulation of 65.94% with respect to the initial laccase solution.
A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores

Network traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, network operators need to perform the accurate and real-time acquirement for network traffic. However, to quickly and accurately estimate network traffic is an open problem in current communication networks including power communication networks. This paper propose an accurate acquirement approach to obtain network traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end network traffic in power telecommunications. Secondly, based on the STFT method, network traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model network traffic. The corresponding model method is used to construct and capture network traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain network traffic. Simulation results show that our approach is promising.
A Social Recommendation Based on Metric Learning and Network Embedding

Traditional recommender systems usually face data sparseness and cold-start problems. To alleviate these problems, social recommendations use social information between users to improve the quality of recommendations. At present, most social recommendation is based on matrix factorization, but with the combination of recommendation and metric learning, the user’s preference is reflected by distance, which has proven to be an effective recommendation method. However, these social recommendation methods usually use simple similarity information as users’ social regularization, which cannot analyze users’ social relationship characteristics well. In order to overcome the shortcoming of social recommendation, we propose a new model for which combines network embedding and metric learning (SRMN) in this paper. SRMN believes that users and items have interactive behaviors in social space. The interaction can be regarded as the user’s preferences for the item in the social space, and then combines with the use of metrics to reflect the user’s preferences. Users’ preferences for items are determined by both ratings and social information. Finally, the effects of different network embedding methods on the model are compared. Experimental results on three real data sets show that our method is effective in improving the accuracy of score prediction.