Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of STATCOM With Photovoltaic Systems

Static Synchronous Compensators known as (STATCOMs) are often used in power system transmission networks as voltage regulators and VAR compensators. This research proposes the integration of STATCOMs in distribution networks, particularly in PV grid-connected systems that use distributed energy resources in order to minimize active and reactive power demand from the grid by delivering variable reactive power from an alternate supply that adjusts to the load demand. This decreases the dependence on the utility power supply and promotes the integration of STATCOMs with renewables. Complete case studies comparing the applications of STATCOM, a voltage source converter with a decoupled active and reactive power control algorithm, and a fixed reactive power compensating condenser for the purpose of dynamic VAR compensation to loads connected at the point of common coupling in a grid-connected photovoltaic (PV) system are presented. The goal of this study is to investigate how dynamic loads, such as induction motors, behave under steady-state conditions when the supply's reactive power demand rises, as well as how this demand variance affects the network that supplies these loads. Complete system modeling and analysis for both scenarios, the fixed reactive power compensator and the STATCOM, supplying various load demands, have been developed. The simulation is based on a 100-kW rated PV grid-connected system to simulate behavior and performance of such study. Adaptive Neuro Fuzzy Inference System with Particle Swarm Optimization was used to extract the maximum power point of the PV array proceeded by a sliding mode controller. MATLAB/ Simulink was developed for modelling and analysis. Various load dynamics with varying solar irradiance and increasing reactive power demand of the associated load were simulated to evaluate various challenges and disturbances on the power supply.
Optimal equiripple solution to the Jaumann absorber problem

The general Chebyshev approximation method is used to find the optimal solution to the Jaumman absorber problem. The Chebyshev-like (CL) solution with all reflection zeros at real frequencies is used as a starting point in the iteration. The optimal solution, with the reflection zeros moved slightly off the imaginary S-axis, is in the immediate vicinity of the CL-solution, and convergence properties are good. <<ETX>>
Intelligent Control Design and Management of AC-Microgrid System

This paper presents the arithmetic optimization algorithm (AOA) to optimized the controller for an AC Microgrid (AC-MG) system. This paper employs the suggested technique to tuned the parameters of the PI controller for the renewable generation units. The suggested system consists of a Wind Energy System (WES), a DC-DC boost converter with maximum power point tracking to draw the most power from the WES, an Energy Storage System (ESS), a DC-DC buck boost converter, a DC-AC inverter, an LC filter, an induction motor (IM) with a single phase and an AC load varies over the time. The controller parameters for this system are tuned using the AOA, which is implemented in the MATLB Simulink software. If AC-MG is standalone or connected to the grid, the results are contrasted with those attained via designing the control system by using Cuckoo Search (CS), Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) approaches for various test scenarios. The results of the simulation show that the designed PI controller based AOA tuning for handling AC-MG control has a high level of efficiency and superiority because it consistently delivers reliable, stabilized performance in test studies.
Security Architecture for Secure Train Control and Monitoring System

A Train Control and Monitoring System (TCMS) is a vital part of monitoring sensors in a train. The data output of sensors is sent wirelessly to the data server for monitoring. However, as the wireless channel used to send the data is a shared public network, the transmitted data are prone to hackers and attacks. This paper proposes the Securebox architecture to manage secure data transfer from the onboard Vehicle Control Unit (VCU) to the data server in TCMS. The architecture is comprised of four main functions: network management, buffer management, data management, and security management. The architecture has been successfully developed in an HSM (Hardware Security Modul) and verified using alpha and beta software testing to form a secure TCMS. From the real-time testing phase in an electric-diesel train, the average performance of the AES-based HSM showed 55% faster time processing with unnoticed 0.1% added memory usage compared to the 3DES. The secure TCMS also withstands MITM attack and provides end-to-end data security compared to the (Mobile Station) MS to Base Station (BS) only in GSM-R.
Building Cybersecurity Ontology for Understanding and Reasoning Adversary Tactics and Techniques

Cyber threats have become more prevalent than ever. Cyber Threat Intelligence (CTI) reports and MITRE ATTCK® framework play an imperative role in helping experts and organizations assess current and potential attacks, such as Advanced Persistent Threats (APT). However, the task of extracting valuable information from unstructured texts remains an ongoing challenge. In this work, we present a framework for understanding and reasoning adversary tactics and techniques. We construct an ontology structure and propose an automatic information extraction method that is capable of integrating the parsed information from CTI reports into each instance. The ontology is represented in the Web Ontology Language (OWL) accessible with the SPARQL query language. Our evaluation shows that the proposed information extraction method outperforms other state-of-the-art neural network-based methods in terms of precision. Furthermore, our framework can effectively infer adversary information, which efficiently supports security analysts recognize tactics and techniques.
The DFT and molecular dynamics multiscale study of the corrugation of graphene on Ru(0001): the unexpected stability of the moire-buckled structure

Results from first principles density functional theory (DFT) calculations and classical molecular dynamics (CMD) simulations are presented on moire-corrugation of graphene (gr). We find that the moire-corrugated graphene could be surprisingly stable against the perfectly flat gr-sheet as pointed out by CMD simulations and DFT calculations. We also show that using the cost-effective CMD approach one can simulate graphene on e.g. Ru(0001) with a correct binding registry and reasonable corrugation and adhesion energy. A new force field has been parameterized for the interface using an angular-dependent Abell-Tersoff potential. The newly parameterized Abell-Tersoff interface potential provides correct moire superstructures in accordance with scanning tunnelling microscopy images and with DFT results. Based on ab initio DFT calculations, we also find that the CMD moire superstructure can be used as a preoptimized structure for DFT calculations and for further geometry optimization. The nearly flat gr (the corrugation $\xi \approx 0.2$ $\hbox{\AA}$) on Ru(0001) is slightly energetically unfavorable vs. the moire-corrugated gr-system ($\xi \approx 2.0$ $\hbox{\AA}$) as revealed by van der Waals DFT structural relaxation.
DeePaGait: Motor Assessment of Parkinson’s Disease Using a Multi-Layer 1D Convolutional Neural Network on Smartphone Gait Data

Nearly 1 million people in the USA and 10 million people worldwide are living with Parkinson’s disease (PD) [1]. The progression of the disease can be inferred from the changes in the patients’ gait to inform early intervention. Due to their near-ubiquitous ownership, smartphone sensing is a very effective, highly available way of assessing gait. Objectives: In this work, we explore the inference of PD gait, by analyzing the patient’s walk data collected from a smartphone in the patients’ homes, using Deep Learning (DL) methods. Specifically, we investigate DL models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Methodology: Using data from the mPower study [2], we selected 152 PD patients who recorded at least 3 walks before taking their medication. We extracted gait cycles from the accelerometer and gyroscope sensor records and fed them to DeePaGait; our DL multi-layer Conventional Neural Network (CNN). DeePaGait operates on 1Dimensional convolution filters to classify 30 seconds of walking data into one of five severity levels (Normal, Slight, Mild, Moderate, Severe). Results: Our DeePaGait DL network was able to Classify the PD Walking-Balance, Shaking/Tremor, and Freeze of Gait (FoG) symptoms, with an accuracy of: 99.1%,98.4%, and 98.2% respectively. Conclusion: Despite the challenges of working with a patient-labeled, crowdsourced dataset, we were able to demonstrate that gait classification based on smartphone sensor data is a viable approach to infer the severity of PD gait anomalies.
A finite point method for compressible flow

A weighted least squares finite point method for compressible flow is formulated. Starting from a global cloud of points, local clouds are constructed using a Delaunay technique with a series of tests for the quality of the resulting approximations. The approximation factors for the gradient and the Laplacian of the resulting local clouds are used to derive an edge‐based solver that works with approximate Riemann solvers. The results obtained show accuracy comparable to equivalent mesh‐based finite volume or finite element techniques, making the present finite point method competitive. Copyright © 2001 John Wiley & Sons, Ltd.
Empirical Likelihood for Conditional Density Under Left Truncation and α-Mixing Condition

In this article, we employ the method of empirical likelihood to construct confidence intervals of conditional density for a left-truncation model. It is proved that the empirical likelihood ratio admits a limiting chi-square distribution with one degree of freedom when the lifetime observations with multivariate covariates form a stationary α-mixing sequence.
Ocular imaging of Carassius auratus eyes using photoacoustic microscopy

The eyes function through the cooperation of different ocular components, and complications with any components would lead to ocular diseases that deteriorate vision. Hence early and precise detection and monitoring of ocular diseases, along with the improved understanding of pathological mechanisms, becomes essential for successful treatment. Photoacoustic Imaging (PAI) is a non-invasive and non-destructive imaging modality based on the photoacoustic effect, which gives high spatial resolution, sensitivity, contrast and penetration depth. Since PAI can provide anatomic and functional ocular characterizations, it can be a potential tool for medical screening/diagnosis of ocular diseases, staging, treatment, and continuous post-treatment monitoring. Here, we perform deep-tissue imaging of the Goldfish (Carassius auratus) eye using a home-built Photoacoustic microscope to identify various ocular components like iris, crystalline lens, retina, optic nerve and blood vessels. The study can be extended to observe changes in these structures under different ophthalmic disease conditions.
Conducting Space Physics Research: Wind the Frog, Work Hard, and Be Nice

This report briefly summarizes the key mentors in my scientific career and some lessons learned from those influential people. My primary advice to others: it is okay to do something wrong. By doing science we are doing something hard that, by definition, has not been done before. I believe that impostor syndrome is a real threat to researcher wellbeing and we should acknowledge its presence and support each other to get through it. Regarding an approach to science, I encourage you to get started and make something bad. Also, take time for yourself, it really does help your productivity. To lead others, I recommend to be enthusiastic, actively listen, and make connections across disciplines. I think it is important to foster creativity in those around you. I advocate that you actively make the future that you want to have.
LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management

Prolonged air leak is a complication arising from a collapsed lung which can lead to serious illness such as pneumonia and empyema, and patient suffering from indwelling chest tubes. Drainage of air and liquid from chest drains can be monitored and recorded using novel digital chest drainage devices. The collected data can be analyzed by predictive models, which can provide decision support in chest tube management. Despite the promising adoption of predictive models in this context, existing approaches are still in their infancy and are mostly based on autoregressive and conventional machine learning models. In this paper, we present a LSTM-based model architecture for air leak forecasting that is able to deal with non-linear dependencies among different features and contiguous time points. We devise a post-processing procedure that leverages predictions to suggest whether the patient could have their chest tube safely removed in the upcoming hours, and evaluate the results according to a medical protocol. Experimental results show that our model is able to outperform currently adopted models, in terms of both forecasting and classification performance, suggesting the feasibility of our approach for chest tube management.
Establishment of a Pseudovirus Platform for Neuraminidase Inhibiting Antibody Analysis

Neuraminidase (NA)-based immunity to influenza can be useful for protecting against novel antigenic variants. To develop safe and effective tools to assess NA-based immunity, we generated a baculovirus-based pseudotyped virus, N1-Bac, that expresses the full-length NA of the influenza A/California/07/2009 (H1N1)pdm09 strain. We evaluated the level of NA-inhibiting (NI) antibodies in the paired blood sera of influenza patients by means of an enzyme-linked lectin assay (ELLA) using the influenza virus or N1-Bac. Additionally, we evaluated the level of NA antibodies by means of the enzyme-linked immunosorbent assay (ELISA) with an N1-expressing Sf21 culture. We detected a strong correlation between our results from using the influenza virus and NA-Bac pseudoviruses to detect NI antibodies and a medium-strong correlation between NI antibodies and NA antibodies determined by an N1-cell ELISA, indicating that baculovirus-based platforms can be successfully used to evaluate NI or NA antibodies. Furthermore, animal experiments showed that immunization with N1-Bac protected against infection with a drift variant of the A/H1N1pdm09 influenza virus. Our results demonstrate that recombinant baculovirus can be an effective influenza pseudotype to evaluate influenza serologic immunity and protect against influenza virus infection.
In vivo detecting matrix metalloproteinase (MMP) activity by a genetically engineered fluorescent probe

Degradation of the extracellular matrix (ECM) by matrix metalloproteinases (MMPs) enhances tumor invasion and metastasis. To monitor MMP activity, we constructed plasmid that encoded a fluorescent sensor DC, in which an MMP substrate site (MSS) is sandwiched between DsRed2 and ECFP. MMPs are secretory proteins, only acting on the outside of cells; hence, an expressing vector was used that displayed the fluorescent sensor on the cellular surface. The DC was expressed in cells with high secretory MMP, so MSS was cleaved by MMP. Also, GM6001, an MMP inhibitor, causes DsRed2 signals to increase in living cells and on the chick embryo chorioallantoic membrane (CAM). Thus, this fluorescent sensor was able to sensitively monitor MMP activation in vivo. Potential applications for this sensor include high-throughput screening for MMP inhibitors for anti-cancer research, and detailed analysis of the effects of MMP inhibitors.
A Simple Goodness-of-Fit Test for Parametric Regression Models

Abstract A simple test is proposed for examining the adequacy of a family of parametric models against nonparametric alternatives. The usual diagnostic tools based on the residuals plots are useful but do not lead to formal statistical tests. We propose a formal statistical test that complements the graphical analysis. Technically, the possible bias of the residuals of the parametric fit is assumed to be a squared summable function of the predictive variable and is decomposed along a family of orthogonal polynomials. Our simulation studies indicate that this test has a similar power as that of the adaptative Neyman test. Moreover, it is simple to put on using usual statistical software and may be easily extended to generalized linear and partial linear models.
3D-STGPCN: 3D Spatio-Temporal Graph Point-wise Convolutional Network for Traffic Forecasting

Traffic forecasting has been an important research topic in the intelligent transportation system for smooth and safe transportation. Among research directions, spatiotemporal-graph-based traffic forecasting has been known as one of the popular directions. Unfortunately, many requirements, such as lightweight architecture, low resource consumption, and flexible transfer learning, have not had satisfactory answers. Hence, we propose a new method called 3D Spatial Temporal Graph Point-wise Convolutional Network (3D-STGPCN) to address these requirements. Differing from others, we proceed with spatial and temporal information simultaneously (i.e., wrap them into one structure). We evaluate and compare our model on three public traffic network datasets, METR-LA, PEMS-BAY, and STREETS, with different methods in the same domain. The experimental results show the advantage of our model over others. We publish our source code at https://github.com/dophanh-26/3D-STGPCN.
Feedback Information on Cumulative Payoff in a Bandit Experiment: Meaningful Learning in Weighted Voting

In a two-armed bandit experiment with the contextual information on weighted voting, we investigated whether subjects who had experienced a binary choice problem for many periods increased the number of choosing the answer which would give a higher expected payoff when they were faced with a similar but different binary choice problem in the subsequent periods (or meaningfully learned the correct answer). Receiving both cumulative payoff and current payoffs as the feedback information, subjects learned the correct answers of three binary choice problems we examined, but for any binary choice problem they did not meaningfully learn it from their experience in a similar but different one. Compared with the previous study where subjects received only current payoffs as the feedback information, the additional feedback information on cumulative payoff might induce subjects to learn the correct answers but would not promote their meaningful learning of the latent feature of the contextual information in this experiment.
RTrap: Trapping and Containing Ransomware With Machine Learning

With advances in social engineering tricks and other technical shortcomings, ransomware attacks have become a severe cybercrime affecting organizations of all shapes and sizes. Although the security teams are making plenty of ransomware detection tools, the ransomware incident report shows they are ineffective in detecting emerging ransomware attacks. This work presents “RTrap,” a systematic framework to detect and contain ransomware efficiently and effectively via machine learning-generated deceptive files. Using a data-driven decoy file selection and generation strategy, RTrap plants deceptive decoy files across the directory to lure the ransomware to access it. RTrap also introduced a lightweight decoy watcher to monitor generated decoy files in real time. As the timing of the ransomware attack is not known to the victim in advance, and the ransomware encryption process is speedy, the proposed decoy-watcher executes an automatic/automated response after the detection promptly. The experiment shows that RTrap can detect ransomware with an average 18 file loss per 10311 legitimate user files.
A review of different control topologies of PMSG-based wind energy conversion systems

The main direction these days is to use renewable energy to cover the power demand instead of fossil fuels. There are many reasons to make renewable energy resources be used than the fossil fuels, such as depletion of fuels and the need to use clean energy. The wind energy conversion system (WECS) is the most beneficial renewable energy resource. The significant advantage of WECS is that wind power is cost-effective, environment friendly, domestic installation, and sustainable. So challenging endeavor is exerted to enhance the WECS behavior. This paper reviews the different methods to improve the performance of the WECS based permanent magnet synchronous generator (PMSG). The performance enhancement of the WECS is represented in smoothing and maximizing the generated power and controlling the output voltage. The main control methods will be illustrated in this work, such as the various types of control topologies and the different optimization methods used. The advantages and drawbacks of each class will be discussed in this work.
A Novel Estimate Fitness Method by Measuring A large number of Sub-event Logs Conformance

Conformance checking compares a process model to its corresponding execution log, to detect inconsistencies and improve compliance with business processes. Nowadays, driven by trends such as big data and process automation, business processes are becoming more complex and integrated. Unfortunately, a major downside of the current standard-de-facto technique of alignment is that alignment suffers from poor performance (worst-case exponential time complexity) on an unstructured event log or a spaghetti-like process model. Therefore, the main purpose of this paper is to provide an approach to estimating the fitness. Since the fitness of different traces could be extremely close, we first propose that an event log contains a tiny number of traces with new information in the context of token-based replay. Then, we can estimate the fitness through a sub-event log composed of at most 6 % traces. Experiments on real-life event logs demonstrate that the fitness of the complete log can be estimated using only 1% traces.
European heatwaves: Link to large‐scale circulation patterns and intraseasonal drivers

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe.