Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Trajectory and Power Control for Cognitive UAV Secrecy Communication

This paper investigates the physical layer security issue in an unmanned aerial vehicle (UAV) aided cognitive radio network. Specially, a UAV operates as an aerial secondary transmitter to serve a ground secondary receiver (SR) by sharing the licensed wireless spectrum assigned to primary terrestrial communication networks, and in the meantime multiple eavesdroppers (Eves) try to wiretap the legitimate UAV-to-SR link. Under the assumption that the location formation of the Eves is imperfect, we jointly optimize the robust trajectory and transmit power of the UAV over a finite flight period to maximize the SR’s average worst-case secrecy rate, while controlling the co-channel interference imposed on the primary receivers (PRs) below a tolerable level. The design is formulated as a non-convex semi-infinite optimization problem that is challenging to be optimally solved. To deal with it, we first prove that the considered problem can be simplified as a more tractable one, which resolves the location uncertainties of the Eves without the aid of $\mathcal S$ -Procedure adopted in conventional methods. After that, an efficient iterative algorithm based on successive convex approximation (SCA) is developed to obtain a locally optimal solution. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and offer important system design insights.
An Examination of Mental and Physical Health in College

The transitional years of college that a traditional student faces are full of change and adaptation; these years of emerging adulthood require learning how to balance new responsibilities. Although this new independence can be liberating, this transitional time has been found to be a stressful period of life (Kaddison & DiGeronimo, 2004). The purpose of this study is to examine longitudinal changes in percent body fat and experience of depressive symptomology throughout emerging adulthood. Research indicates that mental health can directly affect physical health (Faith, Butryn, Wadden, Fabricatore, Nguyen, & Heymsfield, 2011). The participants for this study consisted of 535(n=190 males, and n=345 females). The data indicated that although emerging adults in this sample showed symptoms of depression throughout college, their level of depression symptomology was more in line with sub-threshold depression rather than being diagnosed with depression. In regards to body fat, an average of 3.39 percent body fat was gained over the four years of college. When depression symptomology was used to predict changes in body fat significant results were not found. However, females’ depression symptomology was a significant predictor of initial (freshman year) percent body fat. Therefore, with mental and physical health being so important in emerging adulthood research needs to continue to focus on these variables.
Short-Term Load Forecasting using Minimalistic Adaptive Neuro Fuzzy Inference Systems

Accurate short-term load forecasting is of major importance for optimal control of modern power networks, especially for systems experiencing generation capacity constraints and in view of the increasing penetration of variable renewable energy sources. This paper investigates the performance of a minimalistic adaptive neuro fuzzy inference systems model for hourly short-term load forecasting. Separate models are derived for each hour of the day, and the input parameters are limited to the hourly load at the same time for the preceding day, the day of week and the forecast month. Results are presented for the South African national load profile, using a dataset consisting of 5 years of hourly load data. The effects of incorporating public holiday periods as an extra day of week type input is investigated. The results show that hourly mean absolute percentage errors of the order of 1.5 % to 2 % can be achieved.
Container-based MQTT Broker Cluster for Edge Computing

This paper presents the implementation of an edge-based MQTT broker cluster, by using a lightweight container orchestration framework and a cluster of single board computers. The main goal is to deliver a low-cost, scalable and lightweight messaging solution to support communication between IoT devices in remote areas, where computational resources and network connectivity are limited. An intermediate layer of cluster server is developed to complement and support the collective processing of distributed MQTT brokers. This paper also presents the evaluations of the MQTT broker cluster in terms of message throughput, end-to-end latency and runtime performance of the system. The broker cluster is able to maintain an average latency of less than 10 milliseconds, and a worst-case latency bound of 52 milliseconds, showing that the microservice-based implementation is feasible for many latency demanding applications in the IoT edge-cloud environment.
Reliability-Aware and Graph-Based Approach for Rank Aggregation of Biological Data

Massive biological datasets are available in public databases and can be queried using portals with keyword queries. Ranked lists of answers are obtained by users. However, properly querying such portals remains difficult since various formulations of the same query can be considered (e.g., using synonyms). Consequently, users have to manually combine several lists of hundreds of answers into one list. Rank aggregation techniques are particularly well-fitted to this context as they take in a set of ranked elements (rankings) and provide a consensus, that is, a single ranking which is the "closest" to the input rankings. However, the problem of rank aggregation is NP-hard in most cases. Using an exact algorithm is currently not possible for more than a few dozens of elements. A plethora of heuristics have thus been proposed which behaviour are, by essence, difficult to anticipate: given a set of input rankings, one cannot guarantee how far from an exact solution the consensus ranking provided by an heuristic will be. The two challenges we want to tackle in this paper are the following: (i) providing an approach based on a pre-process to decompose large data sets into smaller ones where high-quality algorithms can be run and (ii) providing information to users on the robustness of the positions of elements in the consensus ranking produced. Our approach not only lies in mathematical bases, offering guarantees on the result computed but it has also been implemented in a real system available to life science community and tested on various real use cases.
Galaxy Image Classification Based on Citizen Science Data: A Comparative Study

Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur- and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results.
A Partitioning-Stacking Prediction Fusion Network Based on an Improved Attention U-Net for Stroke Lesion Segmentation

Due to the narrow time window for the treatment of acute ischemic stroke, the stroke lesion area in the patient must be identified as quickly and accurately as possible to evaluate the risks and get the most timely and effective treatment. Therefore, there is great clinical significance in the study of automatic identification and segmentation methods for stroke lesions. In this paper, we propose a partitioning-stacking prediction fusion (PSPF) method based on an improved attention U-net to solve the problems of 3D-CNN-based networks, including their high computational cost and insufficient training data, and to achieve accurate segmentation of 3D stroke lesions. Our proposed PSPF method includes three steps in the first part. In Step 1, partitioning, we partition the slices obtained in a certain plane direction by slicing a Magnetic Resonance Imaging (MRI) into subsets according to the 2D graph similarity, then use each partitioned subset to perform training and prediction separately. In Step 2, stacking, we stack the 2D slice results of all subsets according to the position order in MRI before slicing and partitioning to form a 3D lesion result. In Step 3, fusion, we use soft voting to fuse the three orthogonal planes’ 3D results that were obtained voxel by voxel in Steps 1 and 2. In the second part, we propose an improved attention U-net, which uses the features from three different scales to generate the attention gating coefficients that further improve training efficiency and segmentation accuracy. We implement a 6-fold cross-validation on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset to validate our method and model using metrics such as Dice Coefficient (DC), F2 score, and Area under Precision-Recall curve (APR). The results show that compared to the existing methods, our proposed method can not only improve the segmentation precision on unbalanced data but also improve the detailed performance of lesion segmentation. Our proposed method and model are generalized and accurate, demonstrating the good potential for clinical routines. The source codes and models in our method have been made publicly available at [available.upon. acceptance].
Study on the PhotoThermoelectric Characteristic of Graphene with Double-Gate

When light passes through the interface of two materials with different Seebeck coefficients (S), a temperature gradient will be occurred at the interface, causing photo-thermoelectric effect, which in turn produces photocurrent. Since photocurrent is positively correlated with the difference of Seebeck coefficient of the material and negatively correlated with the resistance of the material, a method for calculating the thermoelectric parameters of graphene with double-gate structure is proposed in this paper. Taking the back gate voltage as a global variable and the top gate voltage as a local variable, the resistance of graphene sheets in different regions is regulated. By solving the resistance of graphene sheets in two regions with top gate and without top gate, their Seebeck coefficients can be obtained. The results show that under the control of the global back gate voltage, a small range of fine adjustment of the top gate voltage can accurately achieve precise control of the resistance and Seebeck coefficient of different regions of the graphene sheet. This paper provides a theoretical basis for the subsequent study on the photoelectric characteristics of graphene.
Influence of Supply Voltage Frequency on Cavity Partial Discharge Parameters: Simulation Results

This paper presents simulation results on the influence of supply voltage frequency on cavity partial discharges (PD) parameters. The capacitance equivalent model is implemented in MATLAB® Simulink® to achieve the results. Frequency dependent mechanisms responsible for this influence are the statistical time lag, surface and bulk conductivity, and surface charge decay and electron de-trapping. Only the first two of these mechanisms are implemented in the model to get their effect on partial discharge inception voltage (PDIV) as well as the phase-resolved partial discharge patterns. PDIV and PD magnitude increase as the supply voltage frequency is increased. Pulse repetition rate (PRR) has two trends whereby for frequencies between very low frequency to 50 Hz it increases whereas for frequencies above 50 Hz it decreases. More analysis is required to understand why these two trends behave the way they do. Modeling of the surface charge decay and electron de-trapping factor, and experimental results is recommended to validate and give more confidence and reliability to the simulation results.
Underreporting of gestational, congenital and acquired syphilis among indigenous peoples in Mato Grosso do Sul State, Brazil, 2011-2014.

OBJECTIVE to describe the distribution, incidence, and underreporting of syphilis among indigenous peoples from Mato Grosso do Sul, Brazil. METHODS descriptive study performed with secondary data of the Information System for Notifiable Diseases (Sinan) and of the Special Indigenous Sanitary District of Mato Grosso do Sul (DSEI-MS), from 2011 to 2014; the data from both sources were compared to identify underreporting. RESULTS the highest incidence rates of syphilis in pregnant women were observed in 2014 (41.1/1,000 live births) and of congenital syphilis, in 2013 (10.7/1,000 live births); the highest numbers of underreporting of cases were for syphilis in pregnant women on Sinan (45/79), of congenital syphilis at DSEI-MS (8/17) in 2014, and of acquired syphilis on Sinan in 2011 and 2013 (5/9 and 10/18, respectively). CONCLUSION syphilis has a high incidence; underreporting hides the extent of the disease in indigenous peoples from Mato Grosso do Sul.
Wideband Variable Gain Amplifier for D-band Backhaul Transceiver in 55nm BiCMOS Technology

This paper presents a Variable Gain Amplifier (VGA) for D-band (130–175) GHz backhaul transceiver using 55nm BiCMOS Technology. It includes a linear-in-dB control circuit to achieve a dB-linear gain that can be steered by a single analog voltage. A 6 dB gain with a control range of over 20 dB, and an input power at −1dB compression better than −10 dBm have been achieved over the full D-band and gain range. The input and output return loss are both greater than 7 dB. This circuit has been designed to be a building block in our future transmitter and receiver chips for a phased array antenna in D-band.
Absorptive Frequency Selective Surface with a High Selective Passband and Two Absorption Bands

In this paper, an absorptive frequency selective surface with a wide high selective passband and two absorption bands is proposed. The absorptive frequency selective surface, is formed by generating two absorption bands besides the broadband transmission bands. The resistive layer and FSS layer consist of a wideband absorber and frequency selective surface respectively. It is shown by full-wave simulation that the structure has a 3dB transmission window from 7.8 to 12 GHz with a 42.4% fractional pass-bandwidth. And from 4 to 16.5GHz, the reflection coefficient can be reduced to −10dB.
Control of a check ball in hydraulic L-shaped pipe by Magnus effect

This paper presents a control method of a check ball in hydraulic L-shaped pipe by Magnus effect. The spring-driven ball-type check valve is one of the most important components of hydraulic systems: it controls the position of the ball and prevents backward flow. To simplify the structure, the spring must be eliminated, and to accomplish this, the flow pattern and the behavior of the check ball in the L-shaped pipe must be determined. In this paper, by using CAE tool, we clarified the relationship between the position of the inlet pipe and the check flow rate. By moving the position of the inflow pipe from the center of the housing, a strong swirling flow is generated in the entire housing to give rotational motion to the check ball. Magnus force was exerted by this rotation and it was found that the check flow rate was small and the levitation time was advanced.
The Use of Embedded Diagnostic Systems for Ensuring Safe and Energy-efficient Driving of a Locomotive

The current stage of development of locomotive control systems can be described in terms of omnipresent automation of all involved processes. However, time-sensitive adaptation of the control system to the current state of the rail track and the locomotive itself is impossible without the use of an information-measuring system integrated into its composition. Each locomotive equipped with such a system, in this case, becomes not only an object of management and control, but also a source of up-to-date information on the state of the rail track. This article is devoted to the possibilities of using such built-in diagnostic systems to solve the problems of ensuring safe and energy-efficient driving of locomotives, by taking the state of the rolling stock and rail track into account. Criteria for choosing the speed mode, as well as determining the composition, location and basic principles of information processing in an embedded diagnostic system, are considered.
Early Protection Against Sudden Death by n-3 Polyunsaturated Fatty Acids After Myocardial Infarction Time-Course Analysis of the Results of the Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto Miocardico (GISSI)-Prevenzione

Background— Our purpose was to assess the time course of the benefit of n-3 polyunsaturated fatty acids (PUFAs) on mortality documented by the GISSI-Prevenzione trial in patients surviving a recent (<3 months) myocardial infarction. Methods and Results— In this study, 11 323 patients were randomly assigned to supplements of n-3 PUFAs, vitamin E (300 mg/d), both, or no treatment (control) on top of optimal pharmacological treatment and lifestyle advice. Intention-to-treat analysis adjusted for interaction between treatments was carried out. Early efficacy of n-3 PUFA treatment for total, cardiovascular, cardiac, coronary, and sudden death; nonfatal myocardial infarction; total coronary heart disease; and cerebrovascular events was assessed by right-censoring follow-up data 12 times from the first month after randomization up to 12 months. Survival curves for n-3 PUFA treatment diverged early after randomization, and total mortality was significantly lowered after 3 months of treatment (relative risk [RR] 0.
Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

Major depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers.
Robust heart activity measurement using webcam

Recent studies have shown that Photoplethysmographic (PPG) signal commonly measured through skin contacting sensors can be recorded in a contactless manner. The PPG signal results from the optical damping of the skin that varies with the changes in blood volumes. In this study, we propose a robust method to remotely measure heart activity using a low-cost webcam by low software complexity and reduced computational time. Experimental results demonstrate that the proposed approach is an effective method. The obtained results with video PPG (vPPG) show high correlation with those obtained by an approved contact sensor.
Transcriptomic analyses reveal anti-viral responses of epithelial cells and multiple immune cell types in HRV infected human lung tissue

Human rhinovirus (HRV) is a main cause of airway infections and a major risk factor of exacerbations in asthma and COPD. Exploration of HRV pathogenesis has been hampered by the lack of complex in vitro models that closely represent the human disease. Aim of the study was to characterize the immune response of human lung tissue to ex vivo HRV infection using Precision-Cut Lung Slices (PCLS). Human PCLS containing airways were inoculated with HRV1B, UV-inactivated HRV, medium or HRV in presence of Rupintrivir. At 1d and 3d post infection (p.i.) tissue vitality, viral load and cytokine levels were measured and transcriptomic analyses upon RNA isolation from PCLS were performed. HRV infection of human PCLS exerted no strong cytopathic effect as indicated by intact tissue viability. The transcriptomic analyses revealed that HRV infection of PCLS induced 5977 and 4322 gene expression changes at 1d or 3d p.i. , respectively. These gene signatures were indicative of interferon signaling, epithelial cell differentiation, lymphocyte regulation, antigen presentation and NK cell cytotoxicity. Rupintrivir downregulated about 1/3 of the HRV upregulated genes. These data were confirmed by increased protein levels of pro-inflammatory and anti-viral cytokines induced by HRV, e.g. TNF-α and IFNα2a, which were also impaired by Rupintrivir. In conclusion, ex vivo infection of human lung tissue with HRV induced a strong antiviral and pro-inflammatory immune response. The observed gene regulation profile revealed involvement of epithelial but also multiple immune cells. This enables us to study HRV induced immune responses in the human lung microenvironment.
Novel Lyapunov-Based Autonomous Controllers for Quadrotors

In this paper, we look into the dynamic motion planning and control of an unmanned aerial vehicle, namely, the quadrotor, governed by its dynamical equations. It is shown for the first time that the Direct or the Second Method of Lyapunov is an effective tool to derive a set of continuous nonlinear control laws that not only provide smooth trajectories from a designated initial position to a designated target, but also continuously minimise the roll and pitch of the quadrotor en route to its targets. The latter successfully addresses the challenging problem of a quadrotor autonomously transporting valuable and fragile payloads safely to the designated target. Computer simulations are used to illustrate the effectiveness of the proposed control laws.