Miracle-dz/UniFAR-FTUs huggingface dataset:
SELF-CARE PRACTICES REGARDING PREVENTION OF URINARY TRACT INFECTION AMONG SECONDARY NURSING STUDENTS

BackGround: Adolescents self-care practices and hygienic care are a critical issues that determines the health status of them. This study aimed to assess the preventive self-care practices againsturinary tract infection (UTI) among secondary nursing students. A cross sectional descriptive design was adopted. All secondary nursing schools in Port-Said Governorate were the setting. A total of 247of female nursing students were recruited suing simple random sampling technique. An interviewing Questionnaire sheet was used to assess students' self- care practices regarding prevention of UTI.The results revealed that 72.1% of girls had poor level of knowledge regarding anatomy and physiology of the urinary system and urinary tract infection. While, less than two-thirds of them had inadequate total practices. The results showed that, strong positive correlation was detected between students' total knowledge score andtotal practice score. Conclusion: Majority of nursing students scored poor level of knowledge regarding UTIs and its preventive hygienic practices. Recommendations: more attention should be given to establish educational strategies towards improving female students' health awareness in this topic.
Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Recently, several machine-learning-based DNA N-4-methycytosine (4mC) predictors have been developed to provide deeper insight into the biological functions and mechanisms of 4mC. However, the performance of the existing classifiers for identification of Escherichia coli DNA 4mC sites is inadequate. Here, we present a new support vector machine 4mC predictor, named iEC4mC-SVM, for Escherichia coli (E.coli) DNA 4mC site identification, optimized using light gradient boosting machine feature selection technology. The iEC4mC-SVM predictor had a 10-fold cross-validation accuracy of 85.4% and Jackknife cross-validation accuracy of 84.9%. The 83.2% independent testing accuracy of iEC4mC-SVM was 1.0–6.5% higher than those of state-of-the-art E. coli DNA 4mC site predictors. A t-distributed stochastic neighbor embedding analysis confirmed that the prediction performance enhancement of iEC4mC-SVM was due to the light gradient boosting machine feature selection.
The Serum Level of Fibulin- 5 in Patients with Acute Cerebral Infarction and Its Relationship with Severity of Illness

Objective To investigate the serum level of serum fibulin-5 in patients with acute cerebral infarction and its relationship with severity of illness. Methods 156 patients with acute cerebral infarction( time of onset 72 h) who admitted to our department from April to November 2012,were selected as the observation group,and 50 healthy people were selected as control group,the gender and age of control group were similar to those of observation group. The serum levels of fibulin- 5 of all cases were detected by ELISA,neurologic impairment scores in observation group were evaluated using NIHSS,the relationships between the serum levels of fibulin- 5 and OCSP subtype,infarct size,infarct location and NIHSS score. Results The serum level of fibulin- 5 in observation group 〔( 118. 7 ± 51. 6) μg / L〕was significantly higher than that in control group 〔( 40. 1± 12. 2) μg / L,t =- 11. 56,P 0. 01〕. The serum level of fibulin- 5 in observation group changed with OCSP subtype and infarct size,the differences were statistically significant( F values were 23. 48 and 208. 32,separately; P 0. 01); The serum levels of fibulin- 5 in patients with total anterior circulation infarction,posterior circulation infarction,partial anterior circulation infarction and lacunar infarction decreased progressively 〔( 182. 1 ± 53. 1) μg / L,( 135. 4 ± 48. 7) μg / L,( 104. 9 ± 21. 6)μg/L,( 69. 3 ±7. 9) μg/L; all P 0. 01〕; There was no significant difference in the serum levels of fibulin-5 among patients with different infarct locations( P 0. 05). The serum level of fibulin- 5 was positively correlated with NIHSS score among patients with acute cerebral infarction( r = 0. 792,P 0. 01). Conclusion The level of fibulin- 5 in patients with acute cerebral infarction was significantly higher than that in healthy people,the level of fibulin- 5 in patients with acute cerebral infarction increased with the increased infarct size. However,there was no relationship between fibulin- 5 levels and infract locations. The serum level detection of fibulin- 5 can offer some valuable guidances to disease assessment for patients with acute cerebral infarction.
Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach

In this research we focus on developing a reinforcement learning system for a challenging task: autonomous control of a real-sized boat, with difficulties arising from large uncertainties in the challenging ocean environment and the extremely high cost of exploring and sampling with a real boat. To this end, we explore a novel Gaussian processes (GP) based reinforcement learning approach that combines sample-efficient model-based reinforcement learning and model predictive control (MPC). Our approach, sample-efficient probabilistic model predictive control (SPMPC), iteratively learns a Gaussian process dynamics model and uses it to efficiently update control signals within the MPC closed control loop. A system using SPMPC is built to efficiently learn an autopilot task. After investigating its performance in a simulation modeled upon real boat driving data, the proposed system successfully learns to drive a real-sized boat equipped with a single engine and sensors measuring GPS, speed, direction, and wind in an autopilot task without human demonstration.
BP Neural Network Based On-board Training for Real-time Locomotion Mode Recognition in Robotic Transtibial Prostheses

Locomotion mode recognition based on the off-line trained model brings difficulties in integration and application to wearable robots. In this paper, we put forward an on-board training based on back propagation (BP) neural network and developed the real-time locomotion mode recognition research in robotic transtibial prosthesis. Three transtibial amputees participated in the study to finish the designed six experimental tasks (standing, level ground walking, stair ascending and descending, ramp ascending and descending) with robotic transtibial prostheses. Data of six locomotion modes were collected under normal speed condition as training data set to train model on board. Based on the on-board trained models, real-time recognition experiments were developed under three different speeds conditions. The total recognition accuracies were 91.54%, 96.72% and 95.35% corresponding to slow, normal and fast speeds, respectively. The results showed some adaptation of recognition for the six locomotion modes at different speeds. The on-board training strategy was feasible and effective with satisfactory performance.
Pose-Aware Placement of Objects with Semantic Labels - Brandname-based Affordance Prediction and Cooperative Dual-Arm Active Manipulation

The Amazon Picking Challenge and the Amazon Robotics Challenge have shown significant progress in object picking from a cluttered scene, yet object placement remains challenging. It is useful to have pose-aware placement based on human and machine readable pieces on an object. For example, the brandname of an object placed on a shelf should be facing the human customers. The robotic vision challenges in the object placement task: a) the semantics and geometry of the object to be placed need to be analysed jointly; b) and the occlusions among objects in a cluttered scene could make it hard for proper understanding and manipulation. To overcome these challenges, we develop a pose-aware placement approach by spotting the semantic labels (e.g., brandnames) of objects in a cluttered tote and then carrying out a sequence of actions to place the objects on a shelf or on a conveyor with desired poses. Our major contributions include 1) providing an open benchmark dataset of objects and brandnames with multi-view segmentation for training and evaluations; 2) carrying out comprehensive evaluations for our brandname-based fully convolutional network (FCN) that can predict the affordance and grasp to achieve pose-aware placement, whose success rates decrease along with clutters; 3) showing that active manipulation with two cooperative manipulators and grippers can effectively handle the occlusion of brandnames. We analyzed the success rates and discussed the failure cases to provide insights for future applications. All data and benchmarks are available at https://text-pick-n-place.github. io/TextPNP/
Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Currently, various attention-based neural networks have achieved successes in sentiment classification tasks, as attention mechanism is capable of focusing on those words contributing more to the sentiment polarity prediction than others. However, the major drawback of these approaches is that they only pay attention to the words, the sentimental information contained in the part-of-speech(POS) is ignored. To address this problem, in this paper, we propose Part-of-Speech based Transformer Attention Network(pos-TAN). This model not only uses the Self-Attention mechanism to learn the feature expression of the text but also incorporates the POS-Attention, which uses to capture sentimental information contained in part-of-speech. In addition, our innovative introduction of the Focal Loss effectively alleviates the impact of sample imbalance on model performance. We conduct substantial experiments on various datasets, and the encouraging results indicate the efficacy of our proposed approach.
Novel Method for Synchronization of Multiple Biosensors

Synchronization of signals in the post-trial analysis is a laborious process that is often a bottleneck during the signal analysis. As the concept of the Internet-of-Things (IoT) emerges and more sensors are implemented in a research trial, reliable synchronization schemes are becoming increasingly important. This article presents a synchronization algorithm that could align signals recorded by different platforms with different sampling frequencies to millisecond-level precision. The algorithm could also realign a recording that has been restarted after a device failure with the same alignment precision as the rest of the signals. The algorithm generates a secondary signal by permutation of six different step voltages in each cycle to produce a unique pattern before returning to a baseline. The algorithm has been deployed in an actual clinical trial involving 26 heart failure patients and five different bio-signal modalities. It has successfully aligned all trials, including one trial that had a device failure during the recording. Two aligned heart signals had an average beat-to-beat interval difference of 0.81 ± 0.79 ms or 0.90 ± 0.87% with no sign of a negative effect of the synchronization algorithm.
Bridging the Gap between Process Mining and DES Modeling in the Healthcare Domain

The present paper aims at describing an original approach to link Process Mining techniques and Discrete Event Simulation modeling, via the implementation of ProM6 framework, in a hospital–based scenario. A specific methodology was elaborated, each step of which was validated in order to investigate the conformance between the original Event Log, and the Simulation tool data.
Robust Control Design of 6-DOF Robot for Nuclear Power Plant Dismantling

Controller design for robotic manipulator in a nuclear environment has been a challenging task for more than a decade. In this paper three different control techniques were proposed which include PID control, sliding mode control (SMC) and sliding mode control with sliding perturbation observer (SMCSPO). Dynamic modeling of the system was done using the Lagrange approach. The trajectory was planned for manipulator’s end-effector, then trajectory for each joint was computed. Then control algorithms were designed and implemented in MATLAB/Simulink. The simulation results illustrated that PID is a linear control, therefore, when controlling the system with uncertainties the performance of PID is not good. Sliding mode control is a robust non-linear control and have good performance characteristics but information of upper bound of perturbation is necessary. To overcome this problem SMCSPO a robust control with disturbance rejection logic has been implemented and result shows that SMCSPO performance is better than SMC and PID in the presence of uncertainties and external disturbance due to disturbance compensation.
Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks

In the past three years, deep convolutional neural networks (DCNNs) have achieved promising results in detecting skin cancer. However, improving the accuracy and efficiency of the automatic detection of melanoma is still urgent due to the visual similarity of benign and malignant dermoscopic images. There is also a need for fast and computationally effective systems for mobile applications targeting caregivers and homes. This paper presents the You Only Look Once (Yolo) algorithms, which are based on DCNNs applied to the detection of melanoma. The Yolo algorithms comprise YoloV1, YoloV2, and YoloV3, whose methodology first resets the input image size and then divides the image into several cells. According to the position of the detected object in the cell, the network will try to predict the bounding box of the object and the class confidence score. Our test results indicate that the mean average precision (mAP) of Yolo can exceed 0.82 with a training set of only 200 images, proving that this method has great advantages for detecting melanoma in lightweight system applications.
A Tablet-based Application to Study the Speed-Accuracy Tradeoff in Handwriting throughout Lifespan

Handwriting is a complex gesture whose monitoring can disclose age-related modifications or the presence of frailty conditions throughout lifespan. An important biomarker that has the potential to detect subtle yet relevant changes in motor tasks is the Speed-Accuracy tradeoff (SAT). Due to the complexity of handwriting, SAT still lacks a quantitative modeling in this domain. Our aim is to leverage a consumer tablet technology to develop an application to assess SAT in handwriting, through the Steering law. The developed app was tested on four age groups: preschoolers, grade-schoolers, adults, and elderlies. We found that SAT effectively differentiated between age groups, with a significant developing trend from early childhood to adulthood, and a significant regression to primary school levels at an elder age. We conclude that our application is useful to characterize SAT in handwriting throughout lifespan. Because of its sensitivity, future work should investigate the ability of the devised app to anticipate the insurgence of potential frailty conditions, due to ageing or pathology.
A Dynamic Optimization Approach for Sloshing Free Transport of Liquid Filled Containers using an Industrial Robot

The handling of liquid filled containers is a challenging task in industrial manufacturing processes. Without an appropriate trajectory sloshing of the liquid occurs during point-to-point motion. This paper presents a time optimal trajectory planning approach that is used for feedforward motion control and prevents the liquid filled container from sloshing. For this, a model of the liquid inside the container is included into the formulation of a constrained dynamic optimization problem to achieve rest-to-rest motion while simultaneously avoiding obstacles. The presented approach is validated by simulation and on an experimental setup using an industrial robot handling the container.
New Approach to Active Antennas Design

The use of regenerative amplifier as the part of active antenna is considered in the paper. Main parameters of regenerative amplifier are considered. The integrating of regenerative amplifier into microstrip antenna design is described. The microstrip antenna itself is considered as the opened resonant system of regenerative amplifier. The results of modeling of active antenna operation in receiving and transmitting modes in AWR design environment are shown. No switching between these modes has to be implemented. Antenna can operate in these modes simultaneously. According the simulation the active antenna possesses the additional gain in 17 dB in both modes. Small degradation in antenna gain takes place in transmitting mode due to signal compression in an active element. The results of experimental investigations of active antenna operation in both modes are presented. Experimental results affirmed the simulation on active antenna operation. Small deviation of experimental results from modeling is caused by not rigid representation of FET pins of not-zero length. Described antenna is especially useful for design of active transponder of the systems of RFID and localization. Described approach to an active antenna design will be irreplaceable at array construction. In such array antenna module can contain the microstrip patch antenna with FET and phase shifter, and no more. Additionally the voltage applied to second gate of the FET controls the gain of active antenna. Separate amplifiers, switches, complex systems of the feeders are no needed at array design.
CLAS: A Database for Cognitive Load, Affect and Stress Recognition

We present the overall design and the implementation of the CLAS dataset, a multimodal resource which was purposely developed in support of research and technology development (RTD) activities oriented towards the automated recognition of some specific states of mind. Although the particular focus of our research is on the states of mind associated with negative emotions, mental strain and high cognitive effort, the CLAS dataset could offer an adequate support to research of a wider scope, such as general studies on attention assessment, cognitive load assessment, emotion recognition, as well as stress detection. The dataset consists of synchronized recordings of physiological signals, such as Electrocardiography (ECG), Plethysmography (PPG), ElectroDermal Activity (EDA), as well as accelerometer data, and metadata of 62 healthy volunteers, which were recorded while involved in three interactive tasks and two perceptive tasks. The interactive tasks aim to elicit different types of cognitive effort and included solving sequences of Math problems, Logic problems and the Stroop test. The perceptive tasks make use of images and audio-video stimuli, purposely selected to evoke emotions in the four quadrants of the arousal-valence space. The joint analysis of success rates in the interactive tasks and the information acquired through the questionnaire and the physiological recordings enables for a multifaceted evaluation of specific states of mind. These results are important for the advancement of research on efficient human-robot collaborations and general research on intelligent human-machine interaction interfaces.
Multi-Layer Environmental Affordance Map for Robust Indoor Localization, Event Detection and Social Friendly Navigation

In this paper, we propose a novel system architecture called multi-layer environmental affordance map for social and service companion robots. Based on this architecture, robots can organize the perception and inference information efficiently and generate social friendly navigation strategies. In other words, robots are able to strengthen their perception and inference abilities to interact with domestic environment and users under our efficient framework. The main feature of this architecture is that the relations between layers can be viewed as affordances to improve the accuracy and the robustness of the detection and inference. The results show that our architecture achieves robust indoor localization, scene localization, human event detection and socially friendly navigation in real time under limited computational resource.
Understanding Multi-Robot Systems: on the Concept of Legibility

Legibility can be defined as the ability of a robot to communicate its intent to the user. Legibility is relatively little investigated in multi-robot systems, but, in the literature, studies exist where the trajectory of manipulators is analyzed as a factor to improve the collaboration between robots and users. In this paper, we focus on the legibility of a group of mobile robots. To this end, we consider a set of motion-variables: trajectory, dispersion and stiffness. They are typical parameters that determine the motion of a group of robots. To analyze the effect of the motion-variables over legibility, Fisher’s exact test and ANOVA (analysis of variance) were carried out. The data for the statistical analysis cover a full factorial plan and they were collected in a virtual reality set-up, where the users shared the environment with a group of robots. We investigate two aspects of legibility: the correctness and the rapidity of communication, namely if the communication happens correctly and how fast it happens. Trajectory was found to be relevant to correctly communicate the intention of the robots, while stiffness and dispersion were relevant for the rapidity of legibility.
Deep Learning Based Hotspot Prediction and Beam Management for Adaptive Virtual Small Cell in 5G Networks

To meet the extremely stringent but diverse requirements of 5G, cost-effective network deployment and traffic-aware adaptive utilization of network resources are becoming essential. In this paper, a hotspot prediction based virtual small cell (VSC) operation scheme is adopted to improve both the cost efficiency and operational efficiency of 5G networks. This paper focuses on how to predict the hotspots by using deep learning, and then demonstrates how the predictions can be leveraged to support adaptive beamforming and VSC operation. We first leverage the feature extraction capabilities of deep learning and exploit use of a long short-term memory (LSTM) neural network to achieve hotspot prediction for the potential formation of the VSCs. To support the operation of VSCs, large-scale antenna array enabled hybrid beamforming is adaptively adjusted for highly directional transmission to cover these hotspot-based VSCs. Within each VSC, an appropriate user equipment is selected as a cell head to collect the intra-cell traffic in the unlicensed band and relays the aggregated traffic to the macro-cell base station by using the licensed band. Our simulation results illustrate that the proposed LSTM-based method can extract spatial and temporal traffic features of hotspot with higher accuracy, compared with some existing deep and non-deep learning approaches. Numerical results also show that VSCs with hotspot prediction and hybrid beamforming can improve the energy efficiency dramatically with flexible deployment and low latency, compared with the scenario of the convolutional fixed small cells.