Miracle-dz/UniFAR-FTUs huggingface dataset:
Power system Extreme Event Risk assessment based on conditional value at risk

Risk assessment of power system is of great significance to ensure the safe and stable operation of power grid. Traditional risk assessment methods mainly focus on the system loss under common conditions, but they cannot reflect the possible load loss of the power grid under extreme conditions, which may lead to the misjudgment of operational designers. Based on the generalized extreme value theory and conditional value at risk measurement theory, this paper proposes a risk assessment method of power system extreme events based on conditional value at risk, focusing on the power network risk under the condition of low probability and high loss failure. Finally, combined with IEEE-RBTS6 node system, the difference between the proposed method and the traditional method in risk assessment results is compared, and the effectiveness of the proposed method in power system extreme event risk assessment is verified.
Organophosphate Pesticides Sensor using Copper Oxide Nanorods Modified on Screen-Printed Carbon Electrodes

Non-enzymatic electrochemical sensor is a promising platform for pesticide residue analysis due to its high performance, low cost, and fast analysis. This work synthesized copper oxide nanorods on a screen-printed carbon electrode through an anodization process. The modified electrodes were used to analyze organophosphate pesticides (i.e. chlorpyrifos and parathion) by cyclic voltammetry. The copper oxide modified electrode has achieved the average peak current of $\mathbf{60}. \mathbf{3} \boldsymbol{\mu} \mathbf{A}$ from cyclic voltammetry with 6.75% RSD. In addition, the calibration plot of chlorpyrifos and parathion were well fitted with log-linear $(\mathbf{R}^{\mathbf{2}}=\mathbf{0}.\mathbf{98})$ and power $(\mathbf{R}^{\mathbf{2}}=\mathbf{0}.\mathbf{94})$ regression model with a corresponding LOD of $\mathbf{3}. \mathbf{41}\ \boldsymbol{\boldsymbol{\mu}} \mathbf{M}$ and $\mathbf{6}. \mathbf{83}\ \boldsymbol{\mu}\mathbf{M}$, respectively.
Design & Implementation of Product Recommendation Solution using Sentiment Analysis

E-Commerce portals and online selling websites are becoming popular day by day. This paper presents a product recommender model using the natural language processing of the reviews and feedbacks of the customers to enhance the quality of recommendation module. The descriptive data mining can be used to find the most accurate recommendations based on their preferences and behaviors. Various studies on product recommendation for e-commerce portals are being conducted to improve the selection in the quickest time frame, and it has been found that the majority of recommender system only works on selection frequency and user rating. The user's past buying history, as well as the opinions of other users on a product, can aid the development of trust in an online shopping website. In this dimension, an approach is being used and implemented on a variety of relevant projects to investigate the gap area in the traditional system and potential solutions to close it. This research work considers unstructured dataset as data input and perform data cleaning followed by stop word removal and lemmatization. Afterwards Sentiwordnet and ambiguity word net library has been used to estimate two different sentiment score for same sentence to prepare a hybrid sentimental score based on natural meaning and probability of ambiguous word arrangement. This work also integrates FP Intersect clustering algorithm to improvise searching queries after product recommendation. Proposed solution has been implemented using java technology and hadoop ecosystem to provide a big data infrastructure and consider Amazon dataset for experimental analysis. The complete solution was estimated on basis of computation time and also performed for two different dataset to evaluate the consistency of proposed solution. A significant improvement has been observed in multi node cluster solution in compare to single node cluster setup irrespective of enhancement in data size.
Substrate Effect on Low-frequency Noise of synaptic RRAM devices

The analysis of noise signals produced by semiconductor devices provides significant information for the origin and the physical mechanisms causing them, allowing for the mitigation of the noise sources, and improving the device performance. Furthermore, noise signals generated by RRAM and memristive devices can be utilized in cryptography security for applications due to their stochastic nature, revealing the positive effects of noise signals. In this work, the noise signals originated from metal-nitride-semiconductor memristive devices are investigated in terms of the influence of the wafer substrate used to fabricate them. Specifically, metal-insulator-semiconductor memristive devices on bulk Si and Silicon-On-Insulator substrates are examined. Parameter extraction and statistical analyses are performed assuming the typical models used for MOSFET devices. Preliminary results suggest that the measured noise signals originated from electron traps sited in the silicon nitride resistance switching layer. However, devices on SOI substrate generate lower noise signals compared to bulk devices, probably due to the better isolation of the active layer.
Random Telegraph Noise of MIS and MIOS Silicon Nitride memristors at different resistance states

Resistive memories (RRAM) are promising candidates for replacing present nonvolatile memories and realizing storage class memories; hence resistance switching devices are of particular interest. These devices are typically memristive, with a large number of discrete resistance levels and in some configurations, they perform as analog memories. Noise studies have been used to enlighten further the resistance switching mechanism in these devices. Recently, a strong interest arose on the noise generated by RRAM/memristors because due to their inherent stochasticity can be used for cryptography and other security applications. In this work, two fully CMOS compatible memristive devices both with silicon nitride as switching material, were examined in terms of multi-level resistance operation. Using appropriate SET/RESET pulse sequences through a flexible analog tuning protocol, four stable resistance states were investigated through random telegraph noise measurements and analysis. A possible model enlightening the role of silicon nitride defects in the observed noise signals at different resistive states is presented.
INTERNAL-CYCLE VARIATION OF SOLAR DIFFERENTIAL ROTATION

The latitudinal distributions of the yearly mean rotation rates measured by Suzuki in 1998 and 2012 and Pulkkinen & Tuominen in 1998 are utilized to investigate internal-cycle variation of solar differential rotation. The rotation rate at the solar equator seems to have decreased since cycle 10 onward. The coefficient B of solar differential rotation, which represents the latitudinal gradient of rotation, is found to be smaller in the several years after the minimum of a solar cycle than in the several years after the maximum time of the cycle, and it peaks several years after the maximum time of the solar cycle. The internal-cycle variation of the solar rotation rates looks similar in profile to that of the coefficient B. A new explanation is proposed to address such a solar-cycle-related variation of the solar rotation rates. Weak magnetic fields may more effectively reflect differentiation at low latitudes with high rotation rates than at high latitudes with low rotation rates, and strong magnetic fields may more effectively repress differentiation at relatively low latitudes than at high latitudes. The internal-cycle variation is inferred as the result of both the latitudinal migration of the surface torsional pattern and the repression of strong magnetic activity in differentiation.
Analysis of Flight Delay Data Using Different Machine Learning Algorithms

Accurate prediction of flights arrival remains a challenge due to dynamic environments. On predominantly challenging days, unforeseen peaks in flight volumes can stretch operational capacity and adversely distress the service levels pro-vided. Anomaly detection is a growing field of study with a variety of approaches and applications. We performed exploratory data analysis to establish factors that determine flight delays using the UK aviation operation data on flight volumes from 2015 to 2020. Further, we applied different machine learning and data analysis approaches to detect anomalies in the data. We adopted both supervised and unsupervised algorithms applicable to the data set because of their strength in analyzing the aviation operational data which is usually unlabeled. We applied the K means clustering algorithm, K-Nearest Neighbors (KNN) algorithm, support vector machine (SVM) and XGBoost algorithms with the data to determine the most efficient model in predicting the delay in the flight. Our results suggest that average time delays have generally decreased over the years in the UK. The XGBoost algorithm and SVM algorithm performed well with the training data but yielded poor performance with the test data. On the other hand, the KNN algorithm gave an accuracy of 75.47 % and 76.31 % for training and test data respectively. Hence, these results suggest that KNN algorithm will perform better even for new unseen data and is more suitable algorithm than the SVM and XGBoost in predicting the flight delay.
INVESTIGATING TWO SUCCESSIVE FLUX ROPE ERUPTIONS IN A SOLAR ACTIVE REGION

We investigate two successive flux rope (FR1 and FR2) eruptions resulting in two coronal mass ejections (CMEs) on 2012 January 23. Both flux ropes (FRs) appeared as an EUV channel structure in the images of high temperature passbands of the Atmospheric Imaging Assembly prior to the CME eruption. Through fitting their height evolution with a function consisting of linear and exponential components, we determine the onset time of the FR impulsive acceleration with high temporal accuracy for the first time. Using this onset time, we divide the evolution of the FRs in the low corona into two phases: a slow rise phase and an impulsive acceleration phase. In the slow rise phase of FR1, the appearance of sporadic EUV and UV brightening and the strong shearing along the polarity inverse line indicates that the quasi-separatrix-layer reconnection likely initiates the slow rise. On the other hand, for FR2, we mainly contribute its slow rise to the FR1 eruption, which partially opened the overlying field and thus decreased the magnetic restriction. At the onset of the impulsive acceleration phase, FR1 (FR2) reaches the critical height of 84.4 ± 11.2 Mm (86.2 ± 13.0 Mm) where the decline of the overlying field with height is fast enough to trigger the torus instability. After a very short interval (∼2 minutes), the flare emission began to enhance. These results reveal the compound activity involving multiple magnetic FRs and further suggest that the ideal torus instability probably plays the essential role of initiating the impulsive acceleration of CMEs.
Sunspot waves and flare energy release

We address a possibility of the flare process initiation and further maintenance of its energy release due to a transformation of sunspot longitudinal waves into transverse magnetic loop oscillations with initiation of reconnection. This leads to heating maintaining after the energy release peak and formation of a flat stage on the X-ray profile. We applied the time-distance plots and pixel wavelet filtration (PWF) methods to obtain spatio-temporal distribution of wave power variations in SDO/AIA data. To find magnetic waveguides, we used magnetic field extrapolation of SDO/HMI magnetograms. The propagation velocity of wave fronts was measured from their spatial locations at specific times. In correlation curves of the 17 GHz (NoRH) radio emission we found a monotonous energy amplification of 3-min waves in the sunspot umbra before the 2012 June 7 flare. This dynamics agrees with an increase in the wave-train length in coronal loops (SDO/AIA, 171 {\AA}) reaching the maximum 30 minutes prior to the flare onset. A peculiarity of this flare time profile in soft X-rays (RHESSI, 3-25 keV) is maintaining the constant level of the flare emission for 10 minutes after the short impulse phase, which indicates at the energy release continuation. Throughout this time, we found 30-sec period transverse oscillations of the flare loop in the radio-frequency range (NoRH, 17 GHz). This periodicity is apparently related to the transformation of propagating longitudinal 3-min waves from the sunspot into the loop transverse oscillations. The magnetic field extrapolation showed the existence of the magnetic waveguide (loop) connecting the sunspot with the energy release region. A flare loop heating can be caused by the interaction (reconnections) of this transversally oscillating waveguide with the underlying twisted loops.
Research on control system design and energy management strategy of hybrid electric vehicle

The research direction is hybrid electric vehicle control system design and energy management strategy. The main purpose of this study is to study the control strategy of hybrid electric vehicle, which will be used as a substitute for traditional vehicles in the future. Based on the application of fuzzy logic technology, the proposed control strategy can provide better performance than traditional technologies such as PID controller and neural network. A method is developed by using fuzzy logic technology, which can be used to automatically adjust the transmission ratio. The purpose of this study is to develop an efficient control system for hybrid electric vehicles. The study will also help reduce fuel consumption and emissions and improve the overall performance of the vehicle. The aim is to find out how much money can be saved by using hybrid cars instead of traditional cars.
Optimal switching time to minimize store energy in MTJ-based flip-flops under process and temperature variations

Introducing a magnetic tunneling junction (MTJ) into a flip-flop enables non-volatile power gating but large store energy to MTJ is a critical concern. A two-step store (TSS) to first perform a short store for all non-volatile flip-flops and then perform a long store only at the failed ones is an effective approach to reduce energy under switching time variation. However, properties of the optimal short-store time Tshort_opt to minimize the total store energy in TSS and physical/statistical parameters affecting Tshort_opt have not been studied yet. In this paper, we propose an analytical expression for Tshort_opt,assuming that the switching time variation follows a normal distribution. The derived expression revealed that Tshort_optis affected only by µ and σ of the distribution and the long store time. Results with fabricated chips in a 40nm CMOS/MTJ hybrid process demonstrated that the analytical Tshort_optshows a good agreement with the measured value (within 8% difference) under process and temperature variations. The TSS with Tshort_optreduced energy for the store operation to 0.32X of that of the conventional long-store-only technique.
Comparison of 3D Angle-Only Localization Algorithms

This work compares various algorithms for the 3D angle only emitter localization problem. We compare the Extended Kalman Filter, Particle Filter, a Weighted Least Squares solution and combinations of the Extended Kalman Filter with a Particle Filter or the Weighted Least Squares algorithm. The algorithms are compared based on their localization accuracy and speed and on their computational cost. The Particle Filter and the Extended Kalman filter initialized with a Particle Filter-derived estimate appear to be promising solutions for this problem. Especially the Extended Kalman filter initialized with a Particle Filter has significantly lower computational complexity than a standard Particle Filter.
Strain-induced band gaps in bilayer graphene

We present a tight-binding investigation of strained bilayer graphene within linear elasticity theory, focusing on the different environments experienced by the A and B carbon atoms of the different sublattices. We find that the inequivalence of the A and B atoms is enhanced by the application of perpendicular strain $\epsilon_{zz}$, which provides a physical mechanism for opening a band gap, most effectively obtained when pulling the two graphene layers apart. In addition, perpendicular strain introduces electron-hole asymmetry and can result in linear electronic dispersion near the K-point. When applying lateral strain to one layer and keeping the other layer fixed, we find the opening of an indirect band gap for small deformations. Our findings suggest experimental means for strain-engineered band gaps in bilayer graphene.
Young star clusters in external galaxies

I review the characteristics of cluster populations in other galaxies, with particular emphasis on young star clusters and a comparison with the (known) open cluster population of the Milky Way. Young globular cluster-like (compact, massive) objects can still form at the present epoch, even in relatively quiescent spiral discs, as well as starbursts. Comparison with other nearby spiral galaxies, like M83 and NGC 6946, suggests that the Milky Way should host about 20 clusters with masses above 10^5 Msun and ages younger than about 200 Myr. No such clusters have been found, however. I discuss the important roles of selection and evolutionary effects that may account for many of the apparent differences between cluster populations in different galaxies. One potentially important difference between ancient GCs and young star clusters is the presence of complex star formation / chemical enrichment histories in the GCs. Little is currently known about the presence or absence of such features in massive (>10^5 Msun) young star clusters, but some tantalizing hints of extended star formation histories are now emerging also in young clusters.
Measure of the impact of future dark energy experiments based on discriminating power among quintessence models

We evaluate the ability of future data sets to discriminate among different quintessence dark energy models. This approach gives an alternative (and complementary) measure for assessing the impact of future experiments, as compared with the large body of literature that compares experiments in abstract parameter spaces (such as the well-known w{sub 0}-w{sub a} parameters) and more recent work that evaluates the constraining power of experiments on individual parameter spaces of specific quintessence models. We use the Dark Energy Task Force (DETF) models of future data sets and compare the discriminative power of experiments designated by the DETF as stages 2, 3, and 4 (denoting increasing capabilities). Our work reveals a minimal increase in discriminating power when comparing stage 3 to stage 2, but a very striking increase in discriminating power when going to stage 4 (including the possibility of completely eliminating some quintessence models). We also see evidence that even modest improvements over DETF stage 4 (which many believe are realistic) could result in even more dramatic discriminating power among quintessence dark energy models. We develop and demonstrate the technique of using the independently measured modes of the equation of state (derived from principle component analysis) as a common parametermore » space in which to compare the different quintessence models, and we argue that this technique is a powerful one. We use the PNGB, Exponential, Albrecht-Skordis, and Inverse Tracker (or inverse power law) quintessence models for this work. One of our main results is that the goal of discriminating among these models sets a concrete measure on the capabilities of future dark energy experiments. Experiments have to be somewhat better than DETF stage 4 simulated experiments to fully meet this goal. « less
On-demand semiconductor source of 780-nm single photons with controlled temporal wave packets

We report on a fast, bandwidth-tunable single-photon source based on an epitaxial GaAs quantum dot. Exploiting spontaneous spin-flip Raman transitions, single photons at 780 nm are generated on demand with tailored temporal profiles of durations exceeding the intrinsic quantum dot lifetime by up to three orders of magnitude. Second-order correlation measurements show a low multiphoton emission probability [g2(0)∼0.10–0.15] at a generation rate up to 10 MHz. We observe Raman photons with linewidths as low as 200 MHz, which is narrow compared to the 1.1-GHz linewidth measured in resonance fluorescence. The generation of such narrow-band single photons with controlled temporal shapes at the rubidium wavelength is a crucial step towards the development of an optimized hybrid semiconductor-atom interface.
MTPA Based Direct Torque Control for Energy-Efficient Operation of Induction Motors in Electric Vehicles

This paper presents a demonstration of the maximum torque per ampere (MTPA) based direct torque control (DTC) scheme for improving the energy efficiency as well as dynamic response of induction motor drives used in electric vehicles. The flux reference for MTPA condition was first formulated in the synchronous frame of reference and then it was converted into stationary frame of reference for implementing the DTC scheme. The MTPA controller was modified to incorporate the core losses and the DTC scheme was tested for varying torque reference. The results show that the MTPA based DTC scheme draws lesser current from the inverter than the traditional DTC scheme especially at light loads. This can lead to energy savings in electric vehicles in which the requirement of torque varies widely. An electric vehicle (EV) was modelled in Matlab-Simulink software and simulated to compare the performance of the MTPA based DTC scheme and the traditional DTC schemes for FTP75 drive cycle. The simulation results show that the MTPA based DTC controlled induction motor drives provides significant energy savings in electric vehicles for the specified drive cycle. The MTPA based DTC scheme was also experimentally verified on a laboratory prototype.
THE PECULIAR PHOTOMETRIC PROPERTIES OF 2010 WG9: A SLOWLY ROTATING TRANS-NEPTUNIAN OBJECT FROM THE OORT CLOUD

We present long-term BVRI observations of 2010 WG9, an ∼100 km diameter trans-Neptunian object (TNO) with an extremely high inclination of 70° discovered by the La Silla–QUEST southern sky survey. Most of the observations were obtained with ANDICAM on the SMARTS 1.3 m at Cerro Tololo, Chile from 2010 December to 2012 November. Additional observations were made with EFOSC2 on the 3.5 m NTT telescope of the European Southern Observatory at La Silla, Chile in 2011 February. The observations reveal a sinusoidal light curve with amplitude 0.14 mag and period 5.4955 ± 0.0025 days, which is likely half the true rotation period. Such long rotation periods have previously been observed only for tidally evolved binary TNOs, suggesting that 2010 WG9 may be such a system. We predict a nominal separation of at least 790 km, resolvable with the Hubble Space Telescope and ground-based systems. We measure B − R = 1.318 ± 0.029 and V − R = 0.520 ± 0.018, consistent with the colors of modestly red Centaurs and Damocloids. At I-band wavelengths, we observe an unusually large variation of color with rotational phase, with R − I ranging from 0.394 ± 0.025 to 0.571 ± 0.044. We also measure an absolute R-band absolute magnitude of 7.93 ± 0.05 and solar phase coefficient of 0.049 ± 0.019 mag deg−1.