Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Prediction of Ship Trajectory in Inland River Crossing Channels Based on GRU Neural Network

With the development of inland river transportation, the navigable conditions of inland waterways are becoming more and more complicated. Therefore, navigation with the aid of navigator alone is not enough. It is also necessary to make reasonable decisions on the navigation of the ship based on the future navigation behavior of the ship. Traditional trajectory prediction methods are difficult to accurately train models with the small amount of Automatic Identification System (AIS) data. In order to solve the problem of the data and the original models, this paper uses the Hermite interpolation method to expand the data. On this basis, we propose a model ship trajectory based on Gated Recurrent Unit (GRU) neural network. In the experiment, we select the AIS data of ships on the Huai'an section of the Beijing-Hangzhou Canal. Finally, Back Propagation Neural Network (BPNN) model and Support Vector Regression (SVR) model are conducted to a comparative experiments. The results show that the GRU neural network trajectory prediction model has high prediction accuracy, feasibility and effectiveness.
Transparent Wideband Circularly Polarized GNSS Antenna for Vehicular Applications

In this paper, a single-layer one-sided wideband coplanar waveguide (CPW)-fed transparent circularly polarized (CP) rectangular slot antenna with tilted beam is proposed. The antenna is designed on a glass substrate with a mesh grid in the conductive layer in order to increase the transparency. Since most of the vehicles’ windshields are inclined for about 30 degrees, the beam of the antenna is designed to be tilted for about 30 degrees in elevation. For measurement verification, an inclined stage is fabricated to resemble the inclined surfaces (such as the windshields). The 10-dB impedance bandwidth of the antenna is 40.8% (1.13 - 1.71 GHz) with a 3-dB axial ratio bandwidth of 47.4% (1.06 - 1.72 GHz) and a 3-dB gain bandwidth of about 54% (1.02 - 1.78 GHz) with a maximum right handed circular polarization (RHCP) gain of 5.3 dBic. Also, the transparency of the antenna is about 95%. Therefore, the antenna is a good candidate for vehicular applications such as location tracking, using global navigation satellite system (GNSS) and specifically global positioning systems (GPS). Furthermore, a small-size transparent reflector is designed to be used below the proposed antenna in order to increase the front to back ratio (FBR).
A Systemic Method of Traffic Flow Velocity Prediction in Narrow Waterways Using AIS Data

The vessel traffic flow refers to the overall movement of continuously operating vessels and other objects which exhibit similar characteristics in water transportation. There are five parameters that quantitatively describe the vessel traffic flow: traffic flow direction, traffic flow, traffic flow width, traffic flow density and traffic flow velocity. The current researches of vessel traffic flow mainly focus on flow forecasting. In order to solve the problem of traffic flow velocity prediction, a time series models which based on automatic identification system (AIS) data are used to predict the traffic flow velocity of narrow waterway in Beijing-Hangzhou Grand Canal. The Prophet Model, which is an open source time series analysis model developed by Facebook, is based on the Additive Model, which uses year, month, day, etc., plus holiday effects to fit non-linear trends. This model is most suitable for fitting data with strong periodicity and several cycles, and has good support for missing values, trend offsets and outliers. The results show that the Prophet model is superior to the Long Short-term Memory (LSTM) model in terms of ship traffic flow velocity prediction accuracy and training time. It has stronger applicability and higher accuracy. The Prophet time series prediction model can provide significant scientific basis for maritime administration authorities in their daily management and program of jurisdiction waters.
Design of High-Reliability Memory Cell to Mitigate Single Event Multiple Node Upsets

As technology scaling down, the sensitivity of SRAM cells to radiation-induced Single Event Upsets (SEUs) increases, and Single Event Multiple Node Upsets (SEMNUs) due to charge sharing has also become one of the major concerns in memory cell designs. In this paper, a high-reliability radiation hardened memory cell (RH-14T) is proposed to mitigate SEMNUs. SPICE simulations and 3D technology computer aided design mixed-mode simulations were performed to verify the high robustness of the RH-14T cell to SEUs. Compared with previous radiation hardened memory cells, the proposed RH-14T cell has similar read access time, smaller write access time, and the read access time and write access time are less sensitive to process variations. The Read Static Noise Margin (RSNM) and Write Margin (WM) of the RH-14T cell are larger than those of the unhardened conventional 6T SRAM cell. The improvement of reliability is often a trade-off with area, power consumption and performance. In order to achieve high reliability, the RH-14T cell employs more transistors, so it has 1.5 times the power consumption overhead of 6T and a larger area penalty.
Generation of Short Microwave Pulses by Compression of Chirped Signals Obtained by Raman Backscattering on Electron Beam With Variable Energy

We suggest method of short microwave pulse generation by means of compression of chirped pulses obtained in the process of Stimulate Raman Backscattering (SRB) on electron beam with variable energy. We assume that in frequency modulator the pump wave, co-propagating with the electrons is scattered into counter propagating signal wave with frequency downshift. The absolute instability, developing in this system, allows for the smooth tuning of the signal frequency by varying the energy of electrons. The SRB modulator can produce a frequency-tunable signal with a bandwidth up to 10% and conversion efficiency up to 70%, when using a 50 kW, 35 GHz gyrotron as a source of pump wave. Simulation of subsequent compression with multi-fold helically corrugated waveguide (HCW) as dispersive line showed that optimization of energy temporal dependence in modulator together with compressor parameters provides the possibility to produce pulses with the peak power up to 3 MW which exceeds the power of driving gyrotron by the factor of 60.
Comparative Study of Sugeno and Mamdani Fuzzy Inference Systems for Virtual Inertia Emulation

Renewable energy sources (RESs) have drawn significant attention due to its several advantages such as clean, cheap, and sustainable energy. The inverter-based RESs invoke undesirable influences such as lowering the entire system inertia and lacking system voltage/frequency stabilization, causing the weakness of system stability and resiliency. This work investigates a novel concept of islanded microgrid utilizing the virtual inertia control-based prominent fuzzy designs (i.e., Mamdani and Sugeno models) to emulate virtual inertia to the system during high RESs penetration, thus augmenting system reliability and resiliency. The efficiency of the Mamdani and Sugeno-based inertia controllers is performed under critical (off-line) operating conditions. In such conditions, the actual system parameters including inertia, governor, and turbine units may change based on time-varying, while the controller still retains the normal values of those units. The results show that, with Sugeno system, the overall microgrid performance demonstrates the robustness in the face of uncertainties during high penetration of RESs/loads and system parameter variations.
Performance Analysis of a Nine-Phase Asynchronous Motor with Open-Phase Faults

This paper deals with the performance analysis of a nine-phase squirrel cage asynchronous motor (SCAM) with open-phase faults. The 2D Finite Element Analysis is used for ac magnetic transient solution to study some electromagnetic parameters of interest for the nine-phase SCAM. The mathematical expressions that describe the dynamic model of the nine-phase SCAM are presented in arbitrary rotating reference frame. Although the simulation results show that the electromagnetic torque is more pulsating under open-winding phase faults, but the nine-phase SCAM has proven to be capable of developing a good starting and accelerating torque under faulty conditions. This performance behavior positions the nine-phase SCAM as a strong candidate in critical operations that require fault-tolerant capabilities, such as combat ships and sub-marine applications.
Performance Analysis of Resource Allocation in THz-Based Subcarrier Index Modulation Systems for Mobile Users

The recent demand for connections to a huge number of users with very high data rates have motivated research communities to investigate communications into the higher frequency bands, such as millimeter waves and TeraHertz (THz) bands. Moreover, Subcarrier Index Modulation (SIM) has recently received a significant research efforts analyzing its different performance aspects. In this paper, we propose a SIM system operating over the THz band and analyze its performance considering mobile users and different resource allocation schemes. Specifically, Random WayPoint (RWP) mobility model is considered for users’ mobility with different numbers of mobility dimensions, and three different spectrum resource allocation schemes, namely, fixed-order allocation, random allocation and distance-aware allocation are considered. Our analysis includes deriving mathematical expressions of both the average Bit Error Rate (BER) and the average Outage Probability (OP) for the three resource allocation schemes. Simulation results analyze different performance metrics like the BER and the OP and are shown to validate the accuracy of the derived expressions.
Detecting Sexual Predatory Chats by Perturbed Data and Balanced Ensembles

Securing the safety of the children on online platforms is critical to avoid the mishaps of them being abused for sexual favors, which usually happens through predatory conversations. A number of approaches have been proposed to analyze the content of the messages to identify predatory conversations. However, due to the non-availability of large-scale predatory data, the state-of-the-art works employ a standard dataset that has less than 10% predatory conversations. Dealing with such heavy class imbalance is a challenge to devise reliable predatory detection approaches. We present a new approach for dealing with class imbalance using a hybrid sampling and class re-distribution to obtain an augmented dataset. To further improve the diversity of classifiers and features in the ensembles, we also propose to perturb the data along with augmentation in an iterative manner. Through a set of experiments, we demonstrate an improvement of 3% over the best state-of-the-art approach and results in an F1-score of 0.99 and an Fβ of 0.94 from the proposed approach.
Development of Enhanced Stimulus Content to Improve the Treatment Efficacy of EEG–Based Frontal Alpha Asymmetry Neurofeedback for Stress Mitigation

The neurofeedback stimulus content has direct implications for the efficacy of the psychophysiological applications for neurofeedback modality. In particular, enhancements of neurofeedback stimulus content can facilitate improvements in the efficacy of neurofeedback applications in clinical practice. To further elaborate on this aspect, this study introduced systematic enhancements in neurofeedback stimulus content by developing enhanced neurofeedback stimulus content for stress mitigation. The enhancements included the automatic selection of colour of neurofeedback stimulus content environment and instruction messages, as well as, the adaptive selection of threshold of quantitative electroencephalogram (QEEG) features, such as frontal alpha power and frontal alpha asymmetry. The enhancements were based on the outcomes from previous research on the selection of neurofeedback stimulus content for stress mitigation. The improvement in the efficacy of neurofeedback stimulus content was measured statistically by comparing the QEEG and topographic maps. In this study, electroencephalogram data from 20 participants were acquired during multiple sessions of neurofeedback. Analysis of variance and a post hoc test were used to verify the improvement on the efficacy of the neurofeedback application for stress mitigation after the enhancements of the neurofeedback stimulus content; a t–test was used to verify the statistical significance of the stress mitigation by the neurofeedback. The results indicate that the enhancement of the developed neurofeedback stimulus content facilitated stress mitigation during the early sessions of neurofeedback. In conclusion, the efficacy of neurofeedback can be improved using the developed stimulus content with enhancements.
Real Time Energy Monitoring and Control Model for Peer-to-Peer Integrated Hybrid Supply System

Energy wastages arising from isolated households operating on standalone or hybridized energy systems constitute major source of economic losses. Application of formidable energy management, monitoring and control schemes is a remedy. This research designed a real time energy monitoring and control model for peer-to-peer integrated hybrid supply system. Five co-located households in semi-estate scenario, each connected to a central grid supply; renewable energy supply and individual standby generators with associated loads formed the network. A central controller was designed to monitor and control the network generation and consumption of energy. Excess power from individual household generators and or renewable energy sources during grid down time are accumulated by the central controller and shared in equal proportion to the remainder of the households with automatic overload control. The system was designed and simulated with Proteus simulation software and Arduino Integrated Development Environment (IDE). Results obtained from the simulation showed effective elimination of power wastages and improved efficiency of generators. Subsequent research will explore the transformation of the design to a physical model.
IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

Education 4.0 contemplates the involvement of emerging technologies like IoT (Internet of Things), Fog and cloud computing. Ubiquitous real-time monitoring in the challenging environment of an educational institution is the key requirement for proper implementation of Education 4.0 around the world. Conspicuously, this research presents a novel monitoring and irregularity detection framework for educational institutions. In this research, acquisition of data while deploying IoT is being suggested in the comprehensive environs of educational institutions. Pre-processing of acquired data for feature extraction is implemented using the Fog-Cloud nodes. Subsequently, the Temporal Chunk (TC), which is articulated with Temporal Data Mining, is further employed to detect irregularities based on SoTL (Set of threshold limits) and HAA (Historical Adversity Approximation). Successively, a Multi-Layered Bi-Directional Long Short Term Memory (M-Bi-LSTM) oriented irregularity prediction model is deployed. Furthermore, a fog-inspired alert generation and reporting module are employed for real-time reporting to notify the concerned stakeholder for in- time preventive action corresponding to a predicted irregularity. To authenticate the proposed framework and to avoid the experimental implementation cost, experimental simulations are performed. The experimental results verify that the proposed framework is capable of performing better in comparison to other contemporary decision-making methods for delay efficiency, data classification, irregularity prediction, and system stability. Moreover, the proposed framework can successfully estimate most of the irregularities in an educational environment and provide support for the proper implementation of Education 4.0 in a proficient and realistic manner.
Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

The concept of transactive energy (TE) in smart grid systems is gaining increased research attention for its potential to optimize distributed energy resources, improve system reliability, as well as provide a balanced ecosystem for fair economic transaction between prosumers. TE is defined by the GridWise Architecture Council as a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. With control mechanisms being a key part of TE systems, in this article, we discuss the state-of-the-art in TE control strategies, architectures, and relevant simulators for designing, evaluating, and analysing TE systems. Most importantly, existing TE control strategies are examined and discussed via a hierarchical structure comprising four different levels wherein TE control strategies/controllers can be deployed. Architecture-wise, we highlight the different types of TE architectures including the centralized, decentralized, distributed, and hierarchical architecture. In terms of existing and potential simulators for designing and evaluating TE models, we discuss and compare notable software across different characteristics of interest. We conclude this article by highlighting the basic components of a typical TE controller and other future research directions spanning across security concerns, privacy issues, communication challenges, simulation and validation demands. As a main contribution, different from existing survey articles, this article presents a synthesis of existing works regarding TE control strategies, architectures, and TE-based simulators for the benefit of the budding researcher whose interest may lie in the study of TE systems.
Predicting building energy consumption during holiday periods

Predicting sudden changes in energy consumption within a short time period remains a challenging task for long-term building energy consumption Prediction. In order to better predict long-term energy consumption during holiday periods, this paper proposes a novel Prophet model to adequately capture the energy usage patterns of a classroom room building during Christmas periods under several data scenarios. The results showed that the incorporation of additional weather information as often advocated by several earlier studies failed to improve the prediction accuracy. Although the extension of the training data size can significantly improve the prediction outcomes under certain scenarios, it failed to capture the sudden drop in energy consumption when holiday effects were incorporated. The best performance was achieved when the model was fed with 2-year training data as well as integrating holiday effects.
School Health as the Mediator Variable: Determinants of the Principal Instructional Leadership Behavior

The primary purpose of this research was to determine the effect of principal instructional leadership behavior on several determinants: organizational commitment, efficacy, and teacher satisfaction through school health. Five instruments that had been used are the principal instructional management rating scale (PIMRS); organizational health inventory for elementary schools (OHI-E); online course questionnaire (OCQ); teacher satisfaction scale (TSS); teacher efficacy scale (TES). All of these instruments had high validity and reliability; with Cronbach’s alpha values of each was PIMRS (.83), OHI-E (.81), OCQ (.80), TSS (.86), and TES (.79). A total of 350 public primary school teachers in Surakarta had been selected as respondents in this study with satisfied random sampling. AMOS software version 6.0 is used to analyze CFA and SEM. The results of SEM analysis to the structural model built by the researcher was good, with Probability = . 001; RMSEA = . 043; GFI = . 941; AGFI = . 907; CMIN/DF = 1.522), so the relationship between the variables in the constructs was interpreted; (1) The Principal instructional leadership behavior had a significant direct influence on the school health, (2) The school health parameter had a significant direct influence on organizational commitment, efficacy, and teacher satisfaction, (3) The principal instructional leadership behavior did not have a significant direct effect on organizational commitment, efficacy, and teacher satisfaction through the variables and school health.
Editorial: Platelets as Players in Neuropathologies: Novel Diagnostic and Therapeutic Targets

1. Platelets are anucleate cells of 1.5-3 μm in diameter in humans (0.5-1.0 μm in mice), of high abundance (150-450 x10/μl in humans; 1,000-1,500 x10/μl in mice) and short lifespan (8-9 days in humans; 4-5 days in mice). However, their estimated 3,700 different proteins, relate not only to hemostasis, but to defence, cell-cell communication and the inflammatory response (6, 7). 2. Platelets carry rough endoplasmic reticulum, polyribosomes and stable megakaryocyte-derived mRNA transcripts, selected during thrombopoiesis. Also identified are 284 miRNA species regulating protein expression via miRNA-mRNA pairings (8, 9). 3. Platelets are high extracellular vesicle (EV) producers. Platelet-derived microvesicles account for up to 70–90 % of total EV in peripheral blood (10). 4. Platelets exhibit rapid changes of phenotype by acquiring unique mRNA and protein profiles, depending on pathological status (11).
Model of Post-traumatic Growth in Newly Traumatized vs. Retraumatized Adolescents

Background: In our analysis of adolescents affected by the 2016 Fort McMurray wildfire, we observed many negative mental health effects in individuals with a prior history of psychological trauma. Elevated rates of depression and markers of post-traumatic stress disorder (PTSD) were observed, consistent with the hypothesis that prior trauma may reduce sensitivity thresholds for later psychopathology (stress sensitization). Surprisingly, levels of anxiety did not differ based on prior trauma history, nor were retraumatized individuals at increased risk for recent (past month) suicidal ideation. These results are more suggestive of inoculation by prior trauma than stress sensitization. This led us to consider whether individuals with a prior trauma history showed evidence of Post-Traumatic Growth (PTG), a condition in which the experience of a previous trauma leads to areas of sparing or even improvement. Method: To investigate this issue, we generated a structural equation model (SEM) exploring the role of anxiety in previously traumatized (n = 295) and wildfire trauma alone (n = 740) groups. Specifically, models were estimated to explore the relationship between hopelessness, anxiety, PTSD symptoms, self-efficacy and potential protective factors such as friend and family support in both groups. The model was tested using a cross-sectional sample of affected youth, comparing effects between the two groups. Results: While both models produced relatively good fit, differences in the effects and chi-squared values led us to conclude that the groups are subject to different causal specifications in a number of areas, although details warrant caution pending additional investigation. Discussion: We found that adolescents with a prior trauma history appear to have a more realistic appraisal of potential difficulties associated with traumatic events, and seem less reactive to potentially unsettling PTSD symptoms. They also seemed less prone to overconfidence as they got older, an effect seen in the adolescents without a history of trauma. Our findings provide preliminary evidence that the construct of anxiety may work differently in newly traumatized and retraumatized individuals, particularly in the context of mass trauma events.