Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalization Strategies Based on Felder-Silverman Learning Styles and Its Impact on Learning: A Literature Review

Addressing different learner’s characteristics and needs is a critical issue in the process of learning. The advent of adaptive e-Learning technology has made it possible to tailor learning materials for different learner characteristics including learning styles. Delivering matching learning objects and strategies through an adaptive e-Learning system is believed to have a profound impact on learner’s learning performance. A body of research has offered various personalization strategies including learning object mapping according to learning styles. This paper focused on exploring how the learning objects are mapped to Felder-Silverman learning styles and the effect of implementing such approach. In this paper, 15 relevant publications were reviewed in order to gain some insights into the implementation of learning styles-based personalization. Based on the insights found this study proposed a conceptual learning object mapping and personalization strategies. The findings and recommendations of this study can be utilized as the basis to build an adaptive e-Learning system based on Felder-Silverman Learning Style Model (FSLSM).
Research on Modeling and Principle Verification of Full-duplex Technology Based on Phased Array

For full-duplex applications of phased array systems, the self-interference/mutual interference coupling link has complex characteristics and high self-interference power. The transmission and reception characteristics of a full-duplex channel were studied by using a phased array. In the integrated spatial beamforming interference suppression, RF interference cancellation and digital adaptive filtering processing, a multi-level comprehensive architecture, method and model of RF interference signals based on phased array system is proposed. Through principle test and simulation verification, the signal-to-interference and noise ratio is improved by 13dB for the reception matching processing of the desired signal. Therefore, in full-duplex applications of phased array systems, this method and the corresponding optimization model can effectively reduce the power of RF interference signals to support the simultaneous application of multiple functions.
A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

Urban arterial traffic coordination control has attracted much attention in smart city construction process. To achieve optimal signal timing, many studies have attempted to adjust green splits of a cycle time according to the distance between road intersections. However, existing green wave traffic control systems usually require a sophisticated calculation that depend upon the stability of vehicle speed and traffic flow, which can lead to weak robustness. Therefore, this article proposes two novel approaches to control arterial traffic coordination with the help of artificial intelligence: DDPG-BAND and ES-BAND. DDPG-BAND has two stages: a coarse-tuning stage reduces the blocking coefficient, and a fine-tuning stage optimizes the traffic evaluation index. ES-BAND introduces the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), a scalable alternative to reinforcement learning, into signal timing. Different traffic variables are adopted as parameters to search for the optimal value by the CMA-ES. To evaluate the feasibility and effectiveness of our approaches, we import real traffic flow data from Zhongshan Road, Ningbo, Zhejiang Province, China, into a traffic environment simulator for training and then conduct a series of experiments. The results show that ES-BAND outperforms the traditional methods in terms of better convergence, lower journey time, fewer stops, and more throughput.
Growth inhibition and suppression of the mTOR and Wnt/β‐catenin pathways in T‐acute lymphoblastic leukemia by rapamycin and MYCN depletion

T‐cell acute lymphoblastic leukemia (T‐ALL) is an aggressive malignancy. Understanding of the molecular pathogenesis may lead to novel therapeutic targets. Rapamycin, the mammalian target of rapamycin (mTOR) inhibitor, showed inhibitory effects on T‐ALL cells. In this study, we showed that rapamycin significantly reduced MYCN mRNA and protein in a concentration‐dependent manner in T‐ALL cells. Selective knockdown of MYCN by small interfering RNA had similar effects to rapamycin to inhibit T‐ALL proliferation and colony formation and to induce G1‐phase cell‐cycle arrest and apoptosis. The inhibitory effects of rapamycin and MYCN depletion were also found in a Molt‐4 xenograft model. Rapamycin and MYCN inhibition suppressed both Wnt/β‐catenin and mTOR signaling pathways. The results suggest the effects of rapamycin on adult T‐ALL is likely mediated by downregulation of MYCN. The findings suggest MYCN a potential target for the treatment of adult T‐ALL. Additionally, dual targeting of mTOR and Wnt/β‐catenin pathways may represent a novel strategy in the treatment of adult T‐ALL.
COVID Detection from Chest X-rays with DeepLearning: CheXNet

The novel corona virus is a rapidly spreading viral infection that has become a pandemic posing severe threats around the world. It is necessary to identify the cases priorly so that we can prevent the spread of this epidemic. But the availability of test kits is low which is main drawback. To overcome this AI is assistive and even used in COVID detection and prediction. A model for COVID prediction from chest X-rays using CheXNet is presented in this paper. This proposed model classifies the binary classes (COVID and normal) with 99.9% accuracy. CheXNet is a CNN model that used ChestXray14 dataset and was trained to detect abnormalities in chest X-rays. Generally, this model was extended to detect all the 14 pathologies in chestXray14 dataset. We used it’s pre trained model Densenet121 in our model to detect COVID19 from binary classes.
Identification of SRSF10 as a regulator of SMN2 ISS‐N1

Understanding the splicing code can be challenging as several splicing factors bind to many splicing‐regulatory elements. The SMN1 and SMN2 silencer element ISS‐N1 is the target of the antisense oligonucleotide drug, Spinraza, which is the treatment against spinal muscular atrophy. However, limited knowledge about the nature of the splicing factors that bind to ISS‐N1 and inhibit splicing exists. It is likely that the effect of Spinraza comes from blocking binding of these factors, but so far, an unbiased characterization has not been performed and only members of the hnRNP A1/A2 family have been identified by Western blot analysis and nuclear magnetic resonance to bind to this silencer. Employing an MS/MS‐based approach and surface plasmon resonance imaging, we show for the first time that splicing factor SRSF10 binds to ISS‐N1. Furthermore, using splice‐switching oligonucleotides we modulated the splicing of the SRSF10 isoforms generating either the long or the short protein isoform of SRSF10 to regulate endogenous SMN2 exon 7 inclusion. We demonstrate that the isoforms of SRSF10 regulate SMN1 and SMN2 splicing with different strength correlating with the length of their RS domain. Our results suggest that the ratio between the SRSF10 isoforms is important for splicing regulation.
Functional Architecture for Autonomous Driving and its Implementation

It is predicted that due to such technology-driven trends as electrification, connectivity, autonomous driving and diverse mobility, the automotive market will reach a size of 6.7 trillion USD in 2030. Nevertheless, the composition and architectures of the future autonomous vehicles are still unsettled due to the continuous advancement of sensors and computing hardware, availability and interpretation of new data and safety requirements. Given the existing architectures of the autonomous systems, there is a lack of start-to-end depiction of the self-driving car design and components. This scientific article aspires to serve as a starting guide and boilerplate for the evolution of future architectures of such autonomous systems. The article summarizes the previous research in the field, describes the overall hardware and software composition of the EDI Drive-by-Wire car and investigates the components of the autonomous system: data acquisition, perception algorithms, chosen hardware, software component management, vehicle interfacing, approach to failsafe and fail-operational functionalities.
Psychosocial intervention models and outcomes after a terror disaster

Abstract On July 22, 2011, a car bomb blast in the government quarter in Oslo killed 8, injured 209 of the 350 employees who were at work, and destroyed 1700 of the 3500 work places in the ministries. Shortly afterward, the terrorist killed 69 adolescents and young adults and injured another 110 of the 495 survivors at a summer camp on an island outside Oslo, organized by the Youth League of the ruling Labor Party. The paper describes the two disaster models that were applied in providing the preventive and therapeutic psychosocial interventions: the company/organization model for the governmental employees and a combined community and organization model for the victims of the massacre and their families. Some of the findings from the longitudinal research and outreach programs that were conducted are reported.
Backstepping Sliding Mode Maneuvering Control for a Class of Surface Ships

This paper investigates the maneuvering problem for a class of surface ships in the presence of wave disturbance. The maneuvering problem involves the geometric task and the speed assignment along the path. In order to solve the maneuvering problem, a backstepping sliding mode controller are designed under the wave influence. By establishing the conversing law between the thrust and propeller rotational speed, the rotational speed and rudder angle are taken as the controller output signal. Simulation results verify the controller is valid.
Bone marrow mesenchymal stem cells‐derived exosomes reduce Aβ deposition and improve cognitive function recovery in mice with Alzheimer's disease by activating sphingosine kinase/sphingosine‐1‐phosphate signaling pathway

Exosomes are associated with the development and progression of Alzheimer's disease (AD), although the impact of these extracellular vesicles in brain pathological condition remains incompletely understood. Therefore, this study aimed to investigate the role and mechanism of exosomes signaling in AD. Double transgenic APP/PS1 mice were injected with bone marrow mesenchymal stem cells (BM‐MSCs)‐derived exosomes or combined with SKI‐Ⅱ (sphingosine kinase [SphK] inhibitor) or VPC23019 (sphingosine‐1‐phosphate [S1P] 1 receptor blocker). We observed the spatial learning and memory ability of mice, and assessed the levels of amyloid and proteins. We found that exosomes improved spatial learning and memory ability of APP/PS1 mice, and enhanced the expression of SphK1 and S1P1. Moreover, exosomes inhibited the levels of amyloid and enhanced the expression of NeuN in cortex and hippocampus of APP/PS1 mice. Exosomes repressed the levels of Aβ1‐40, Aβ1‐42, BACE1, and PS1, and promoted the expression of neprilysin in APP/PS1 mice. The influence conferred by exosomes was abolished by SKI‐Ⅱ or VPC23019. In conclusion, our article confirms that BM‐MSCs‐derived exosomes reduce Aβ deposition and promote cognitive function recovery in AD mice by activating SphK/S1P signaling pathway. Thus, our data suggest that S1P/SphK‐containing exosomes should be explored as potential AD cure.
EEG Identification Based on Brain Functional Network and Autoregressive Model

Due to the advantages of high concealment, non-stealing and liveness detection, electroencephalography (EEG) is a promising biometric technology for security requirements and personal identification. Many EEG identification method focuses on specific task. The generalization of these methods to other tasks or datasets is limited. In this work, we propose a new EEG identification method, which is based on the combination of brain functional network and autoregressive model, under different tasks. Specifically, we employ Phase Locking Value (PLV) between paired EEG channels to construct the functional connectivity matrix. Using a data-driven threshold, we get the corresponding binarized brain network. Then the degree of each node is obtained by graph theory. Additionally, the autoregressive (AR) coefficients are employed using Burg algorithm. The feature vector is formed combined these AR coefficients and node degrees. The support vector machine (SVM) is used to accomplish the EEG personal identification task. Experimental results on three public datasets with different tasks show that the proposed method has superior performance in terms of classification accuracy and stability.
The Influence of the Arc Resistance on the Functioning of Backup Protection

The study of the arc resistance influence on the sensitivity of the remote backup protection of overhead lines with branches was carried out and the limiting dependences of the parameters of the arc resistance and the power of the protected transformer on the branch, the parameters of the short-circuit loop consisting of the overhead line, the transformer and the arc resistance were obtained.
NFT-I technique using IoT to improve hydroponic cultivation of lettuce

The significant decrease in agricultural land and the rapid development of hydroponic system technology have brought a huge challenge to farmers. This paper describes the NFT-I (Nutrient Film Technique based on IoT) hydroponic system, and it is a variant of traditional NFT and Floating Root (RF) systems. The system measures several parameters, such as temperature, water level, and acidity (pH). The system collects the information using sensors connected to Arduino microcontroller and Raspberry PI to store the collected data. The results show that this system can reduce the electricity consumption by 91.6%; on the other hand, it helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT-I Hydroponic Farm. Finally, in these times of confinement due to coronavirus disease (COVID-19), in which the economy has decreased, and the needs are multiple, this NFT-I system could help people to create their vegetable growing system quickly and cheaply.
Prediction of Motor Temperature using Linear Regression

The direct measurement of the temperature of a permanent magnet synchronous motor (PMSM) is difficult due to its complexity of the construction of the motor. It is difficult to embed thermal sensors in the motor. Thus, the temperature of various components of the motor must be modeled from other parameters. The classical methods of thermal modeling lack accuracy and require expertise on heat models as well as knowledge of the individual motor construction. In this paper, efficient and fast predictive linear models are evaluated. Linear regression with gradient descent and normal equations is evaluated to predict the dynamic temperatures inside PMSM. The features used to train the data are selected as per correlation analysis. Results are further optimized using regularization techniques such as L1 and L2 regularization. K-nearest neighbor regression is evaluated, and then different predictive models are compared.
Simulation and Validation of a New IEEE Reliability Test System (RTS-GMLC) using PyPSA

RTS-GLMC is a new IEEE Reliability Test System, to address the major challenges of the modern power system such as complexity due to increased penetration of renewable energy generation. Existing simulations of RTS-GLMC use costly software or open sources toolboxes, which typically focus on network flows in single time period. They are capable of showing intermittency effect of renewable energy and load fluctuations to different network conditions. To address these issues, the RTS-GLMC was simulated in an open sources toolbox called Python for Power System Analysis (PyPSA) to provides higher computation capability for power system analysis. The non-linear power flow analysis results based on the simulated RTS-GLMC in PyPSA was compared with the results from other toolbox to evaluate the accuracy of PyPSA. The simulation results show that PyPSA has a high computational capability to be used for assessment and planning modern power systems effectively.
DNase-mediated eDNA removal enhances D-LL-31 activity against biofilms of bacteria isolated from chronic rhinosinusitis patients

Abstract Chronic rhinosinusitis (CRS) is a chronic infection of the nasal cavity and paranasal sinuses associated with the presence of a microbial biofilm. Extracellular DNA (eDNA) is an important component of the biofilm matrix. Antimicrobial peptides (AMPs) are natural peptides with the ability to kill microorganisms. D-LL-31 is a synthetic variant of the AMP cathelicidin with increased resistance to proteolytic breakdown. In this study it is shown for 3 clinical CRS isolates that treatment of 24 h biofilms with DNase I enhanced the antimicrobial activity of D-LL-31. Conversely, co-incubation of D-LL-31 at the IC50 value with exogenous DNA resulted in reduced antimicrobial activity. DNase I alone did not show antimicrobial activity against the isolates tested but caused dispersal of an established biofilm. Hence, the presence of eDNA in the biofilm matrix reduced AMP-mediated killing. These results suggest that combination therapy with proteolysis resistant AMP D-LL-31 and DNase could be considered for effective treatment of CRS.
The Research of RBPF-SLAM Accuracy under the Influence of Depth Camera Noises

In order to study the influence of random noises of ToF depth camera on the pose estimation of mobile robot based on improved RBPF-SLAM algorithm, the basic principles and processes of improved RBPF-SLAM algorithm were described in this paper. Secondly, we analyzed and established the Gaussian model of random noises of ToF depth camera. Finally, through the computer simulation method, we used the ROS robot operating system to estimate the trajectory of mobile robot under depth camera noises. The RMS errors of mobile robot trajectory were calculated. The research method and simulation results in this paper can be referenced for the design of mobile robot SLAM system.
Near-Optimal Consensus of Multi-Dimensional Double-Integrator Multi-Agent Systems

In this paper, a distributed near-optimal method is presented for the consensus of multi-agent systems with multiple multi-dimensional double integrators. The method is developed based on the performance index regarding the consensus behavior with respect to the agent state differences and the control efforts of the agents. By using the Taylor expansion method, an analytical distributed near-optimal controller is presented for solving the optimal consensus problem. A simulative example is presented to demonstrate the performance of the method.
Deciphering the role of a membrane-targeting domain in assisting endosomal and autophagic membrane localization of a RavZ protein catalytic domain

The bacterial effector protein RavZ from a pathogen can impair autophagy in the host by delipidating the mammalian autophagy-related gene 8 (mATG8)-phosphatidylethanolamine (PE) on autophagic membranes. In RavZ, the membrane-targeting (MT) domain is an essential function. However, the molecular mechanism of this domain in regulating the intracellular localization of RavZ in cells is unclear. In this study, we found that the fusion of the green fluorescent protein (GFP) to the MT domain of RavZ (GFP-MT) resulted in localization primarily to the cytosol and nucleus, whereas the GFP-fused duplicated-MT domain (GFP-2xMT) localized to Rab5- or Rab7-positive endosomes. Similarly, GFP fusion to the catalytic domain (CA) of RavZ (GFP-CA) resulted in localization primarily to the cytosol and nucleus, even in autophagy-induced cells. However, by adding the MT domain to GFP-CA (GFP-CA-MT), the cooperation of MT and CA led to localization on the Rab5-positive endosomal membranes in a wortmannin-sensitive manner under nutrient-rich conditions, and to autophagic membranes in autophagy-induced cells. In autophagic membranes, GFP-CA-MT delipidated overexpressed or endogenous mATG8-PE. Furthermore, GFP-CAΔα3-MT, an α3 helix deletion within the CA domain, failed to localize to the endosomal or autophagic membranes and could not delipidate overexpressed mATG8-PE. Thus, the CA or MT domain alone is insufficient for stable membrane localization in cells, but the cooperation of MT and CA leads to localization to the endosomal and autophagic membranes. In autophagic membranes, the CA domain can delipidate mATG8-PE without requiring substrate recognition mediated by LC3-interacting region (LIR) motifs.
Emotion Recognition Through Speech Signal Using Python

Emotion recognition helps in monitoring and understanding human emotionalstatewhich plays a key role in the current and future computational technologies. Speech emotion recognition is the demonstration to perceive human feeling and emotional states from speech. The emotions considered for the experiments include neutral, joy, and sadness. The emotions of an individual mainly depends on physical characteristics like muscular tension, skin elasticity, blood pressure, heart rate, breath, speech etc. Emotions of a person are unique in nature but their understanding, interpretation and reflections can be distinct. This analysis is done by using python libraries.
A Novel Trajectory Generator Based on a Constrained GAN and a Latent Variables Predictor

Forecasting pedestrian trajectory is critical for versatile applications, such as autonomous driving and social robot, when they work in human-centric environments. However, it is challenging to predict pedestrians’ future trajectories due to the inherent human properties and pedestrians’ social interactions. Recent works predict future trajectories by using a generative model, which captures social interactions with pooling- or graph-based strategies and generates multi-modal outputs with latent variables sampled from random Gaussian noise. Nevertheless, they introduce little human knowledge, which is beneficial for improved prediction performance. In this work, we propose to learn informative latent variables from pedestrians’ future trajectories. Moreover, we present a distance-direction pooling module, which captures social interactions in a more intuitive manner. Besides, we introduce an additional constraint on generative adversarial network optimization to generate more realistic results. Two benchmarking datasets, ETH (Pellegrini et al., 2010) and UCY (Leal-Taixé et al., 2014), are used to evaluate the proposed method. Comparisons between our method and several state-of-the-art methods demonstrate the superiority of the proposed method in generating more accurate future trajectories.
Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals

Atrial fibrillation (AF) is one of the most common chronic cardiac arrhythmias often associated with stroke, heart failure, coronary artery disease, etc. Based on the stage of progression and chances of restorability, AF can be categorized into paroxysmal, persistent and permanent. Among all, automatic detection of paroxysmal (early stage) AF (PAF), is challenging as it is clinically silent and occurs in short duration (episodic pattern) an has a high chances of being undiagnosed. In this paper, we present a multiscale deep convolution neural network (MS-DCNN) framework for automatic detection of paroxysmal AF episodes from single lead short electrocardiogram (ECG) signals. The MS-DCNN employs the architecture of multiple-stream CNNs for the multiscale decomposed single lead ECG signal. The proposed method is evaluated on the Physionet/CinC Challenge 2017 ECG dataset consists of 5048 normal sinus rhythm (NSR) and 756 AF rhythm subjects acquired using AliveCor hand-held device. The proposed method achieves an average accuracy, sensitivity, specificity and F1-score of 84.31%, 84.80%, 83.82% and 84.31% respectively on the validation dataset.