Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Evaluation of Two-Dimensional Ferroelectric Material CuInP2S6 for Ferroelectric Tunnel Junction Device

Ferroelectric tunnel junction (FTJ) exploiting the switchable polarization of ferroelectric material holds great potential for the low-power non-volatile memory. Recently, two-dimensional (2D) ferroelectric material CuInP2S6 (CIPS) which can provide ferroelectricity at the ultimate atomic-scale has been successfully introduced in FTJ to achieve significantly improved TER. Here, we present a theoretical study on the performance of FTJ based on CIPS through the quantum transport simulation using kp Hamiltonian obtained from density functional theory. Benchmarking with ferroelectric HfZrO2-based FTJ reveals that much higher TER can be achieved in CIPS-based FTJ due to a lower tunneling potential barrier and a larger tunneling effective mass.
Impact of Doppelgängers on Face Recognition: Database and Evaluation

Lookalikes, a.k. a. doppelgängers, increase the probability of false matches in a facial recognition system, in contrast to random face image pairs selected for non-mated comparison trials. In order to analyse and improve the robustness of automated face recognition, datasets of doppelganger face image pairs are needed. In this work, we present a new face database consisting of 400 pairs of doppelgänger images. Subsequently, two state-of-the-art face recognition systems are evaluated on said database and other public datasets, including the Disguised Faces in The Wild (DFW) database. It is found that the collected image pairs yield very high similarity scores resulting in a significant increase of false match rates. To facilitate reproducible research and future experiments in this field, the dataset is made available.
Adaptive Interval Type-2 Fuzzy Tracking Control of PV Grid-Connected Inverters

Fuzzy logic systems with approximation capabilities provide effective control for nonlinear and uncertain systems. Due to the characteristics of photovoltaic (PV) and the PWM method, a grid-connected PV system is a considerably nonlinear system with unpredictable parameters. In this study, a new adaptive interval type-2 fuzzy approximation-based controller (AIT2FAC) was developed to control a three-phase grid-connected PV system. The proposed controller can be implemented without any prior knowledge of the system mathematical model. In the presence of both parametric and modeling uncertainty, the developed controller can achieve the control objectives. The proposed controller utilizes the principle of input-output feedback linearization and the approximation capability of fuzzy systems to the control inverter current components to track prescribed reference values. The proposed AIT2FAC controller is capable of handling system uncertainties due to the interval type-2 fuzzy logic system capability to cope with a high level of uncertainty. Lyapunov analysis is used to determine the closed-loop system stability and the updating laws of the proposed controller parameters. The effectiveness of the designed controller to achieve the required tracking is validated for different operating cases, including system disturbances, modelling, and parameter uncertainties. For evaluation, the proposed type-2 fuzzy controller is compared to a type-1 fuzzy controller in terms of some performance measures. The comparison results demonstrate that the proposed type-2 fuzzy controller has better tracking performance than the type-1 fuzzy controller in terms of the settling time, the maximum overshoot, the integral absolute error (IAE) and the integral time of absolute error (ITAE).
Statistical Approximations of Seasonal Peak Loads for Commercial Areas in South Africa

Load forecasting is important process, which ensures that power consumption at the consumer end, is effectively delivered. The selection of an appropriate statistical distribution for a dataset is one of the approaches required to analyse load profiles. Typically, probability distributions associated with the daily peak load measurements is often of interest to Forecasters. In this paper, the daily peak load at a commercial location in Johannesburg, South Africa is examined on a seasonal cyclical basis. The measurement, undertaken over a period of about two years, between 2019 and 2020, is classified into four seasons: summer, autumn, winter, and spring. Two probabilistic models, lognormal and normal distributions, are applied to approximate the behaviour of daily peak loads. Results from this investigation suggest that normal and lognormal distributions are suitable models for understanding daily peak load profiles during summer and spring seasons. However, both autumn and winter seasons are found to exhibit a different trend, for which these two distributions are found as unstable fits. Further observation suggests that the annual distributions of the peak loads at this location is multimodal in behaviour.
Distribution System Network Resilience Enhancement Against Predicted Hurricane Events Using Statistical Probabilistic System Line Damage Prediction Model

To enhance the resilience of the distribution power system network against hurricane events, quality proactive operational planning is required. The first action is to leverage the oncoming predicted hurricane events data for accurate proactive prediction of system line outage under hurricane events. In this article, a hybrid system line outage prediction method based on a statistical probabilistic system components fragility curve (FC), Monte-Carlo simulation (MCS), and scenario reduction algorithm (SCENRED) is proposed. The proposed hybrid model is meant to consider the system network configuration and the hurricane dynamic as the two main causes of grid topology failure during contingencies. Hurricane historical data are used to develop the system line outage prediction model. This model is investigated on a standard IEEE 15-bus test system. The system line outage prediction results validate the effectiveness of the proposed system component's FC-MCS-SCENRED model. The system line outage prediction provides further insights into the proactive system network optimal reconfiguration against hurricane events hence enhances grid resilience against hurricane event via operational planning.
Machine Learning Assisted Visible Light Sensing of the Rotation of a Robotic Arm

With the rise of LED (light-emitting diode)-based luminaires, artificial lighting has become a technology platform, which, besides providing illumination, also provides communication and positioning functionalities. Apart from this, most recently Visible Light Sensing (VLS), in which lighting is used for sensing purposes, emerged as another embodiment of functionalities lighting could take over in the future. Here we show that machine learning assisted VLS has promising potentials to become a meaningful enabler for the Industrial Internet of Things. We show that the rotation of a robotic arm can be accurately monitored by VLS simply by equipping the robotic arm with sequences of colored retroreflective foils. Moreover, we show that the sensing task is compatible with a modulation of the light. This paths the way that sensing and communication tasks can be performed in parallel with one and the same low-complexity infrastructure, that apart from this also could take over the task of the obligatory room lighting. We demonstrate the capability of the approach even if the overall illumination conditions change. Therewith, VLS accentuates as an alternative option for industrial robot monitoring in combination with optical wireless communication.
Does Augmented-Reality Head-Up Display Help? A Preliminary Study on Driving Performance Through a VR-Simulated Eye Movement Analysis

Augmented reality heads-up display (AR-HUD) is becoming increasingly popular as a way to keep drivers focusing on roads. By overlaying visuals on the windshield, AR-HUDs improve the drivers’ view of the environment outside the car, creating a stronger sense of awareness of the surroundings. However, whether AR-HUD and to what extent different AR-HUD layouts could improve drivers’ driving performance are still questionable. Unfortunately, AR-HUD is still at a research stage, not yet fully commercialized. Hence, there are few actual products in the market available for testing. For this reason, this study developed a virtual reality driving simulator to tested drivers’ driving performance environment under three scenarios: without AR-HUD, dispersed layout (AR-HUD1), and dense layout (AR-HUD2). Twelve subjects were invited to join the experiment. Their driving performance was measured in various aspects. This study showed that AR-HUD with interfaces that conform to human-computer interaction principles and visual design rules could improve cognitive resource allocation and promote driving safety. Conversely, a poor designed AR-HUD could negatively impact driving safety.
Construction of Optimized Dynamic S-Boxes Based on a Cubic Modular Transform and the Sine Function

The protection of sensitive data from illegitimate users is one of the main challenges in today’s technological era. To handle prevailing security-related problems and challenges amicably, cryptographic techniques are applied for the fortification of data. State-of-the-art cryptographic ciphers generally use substitution-boxes (S-boxes) that help in accomplishing robust sanctuary of data. Provision of data security by a cipher is proportionate directly to the cryptographic strength of an S-box employed in the respective cipher. This research paper proposes to project a simple and innovative scheme for the generation of dynamic S-boxes by employing a novel cubic modular transformation along with the trigonometric sine function. A pioneering optimization phase, dynamic in nature, is also suggested that improvises the nonlinearity of the initial configuration of S-box. The overall proposed scheme possesses the potential to spawn a large count of strong S-boxes by smearing a minute variation in input parameters used in initial and optimization phases. Cipher key is used to employ values to the input parameters for the creation of dynamic S-boxes. A specimen S-box is presented, and its performance has been achieved through standard criteria of S-box evaluation along with the comparative analysis with some existing S-boxes. Recital and comparative investigations validate that the anticipated S-box possesses the real capability for its usage in cryptosystems for much needed data security.
Optimal Sizing of CPP-GMR Read Sensors for Magnetic Recording Densities of 1–4 Tb/in²

Several studies have confirmed that current-perpendicular-to-the-plane giant magnetoresistance (CPP-GMR) technology is appropriate for next-generation read sensors for ultrahigh areal densities (ADs) of data storage applications. Since the physical dimension of the read sensor is a crucial factor for developing the reader to overcome its limitations, this paper proposes an optimal sizing prediction of the CPP-GMR read heads for ADs of 1-4 Tb/in<sup>2</sup>. Micromagnetic modelling was performed in the simulations. The appropriate length of the stripe height (SH) and the read width (RW) of the readers was estimated based on a consideration of sensor outputs including the readback signal, asymmetry parameter, dibit response and power spectral density (PSD) profile. It was found that a variation of SH and RW lengths had an influential impact on the readback signal waveform. Those affectations were further characterized through the echoes of dibit response showing that shortening the SH length or increasing the RW length could improve the resolution and reduce the distortion occurring in the readback signal. Moreover, the PSD profile indicated that the reader operation became more stable at shorter SH lengths or longer RW lengths. The head response spectrum was also examined. In addition, the magnitude of the bias current was studied in relation to the head response. Lastly, the optimal physical dimension (SH <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> RW) of the CPP-GMR readers for ADs of 1-4 Tb/in<sup>2</sup> was predicted to be (<inline-formula> <tex-math notation="LaTeX">$40 \times 48$ </tex-math></inline-formula>) nm, (<inline-formula> <tex-math notation="LaTeX">$28 \times 29$ </tex-math></inline-formula>) nm, (<inline-formula> <tex-math notation="LaTeX">$25 \times 26$ </tex-math></inline-formula>) nm and (<inline-formula> <tex-math notation="LaTeX">$19 \times 20$ </tex-math></inline-formula>) nm, respectively. The results can be utilized to design the CPP-GMR sensors at ultrahigh magnetic recording capacities.
Surface Wave Attenuation in Multilayer Structures With Lossy Media and Impedance Surfaces

A methodology to analyze the attenuation constant of surface wave propagating in general multilayer structures is proposed. The multilayer structure may include any number of layers, that can be made up of lossy dielectric, magnetic-material, and impedance surfaces. The non-linear characteristic equation (transverse resonance equation - TRE) that representing multilayer structure is defined by utilizing transmission line (TL) theory. Consequently, the root(s) of the TRE is/are computed which indicates the complex propagation constant of that characteristic equation. The obtained results are validated over the completely shielded waveguide structures where the full-wave simulation is possible. The proposed approach is employed to analyze and discuss the propagation and attenuation constant of surface wave modes in several representative examples. The analysis is carried out in the frequency range from 1 to 12 GHz.
Model Predictive Current Control of Multilevel Smart Load Based on MPUC7 Converter

Considering the poor power quality performance of a two-level capacitor-based electric spring (ES1), this article presents a high-power density multilevel ES1 configuration based on a seven-level modified packed U-cell converter (MPUC7-ES1). A novel control strategy based on finite control-set model predictive current control (FCS-MPCC) is also proposed for MPUC7-ES1 application. This algorithm is designed to predict the system behavior for all the conceivable switching vectors based on the discrete models of MPUC7-ES1 that is developed for the first time. To guarantee a desirable operation, the main control objectives are the MPUC7-ES1 current, auxiliary capacitors’ voltages, and switching frequency reduction. Compared with the conventional ES1 control methods, the proposed strategy has key merits including considering the dynamic models of ES1 converter, not requiring a modulator, and lower switching frequency. The operation and robustness of MPUC7-ES1 and the introduced nonlinear FCS-MPCC technique are also illustrated through extensive simulation and experimental results.
Extreme Learning Machine (ELM) for Fast User Clustering in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

Non-orthogonal multiple access (NOMA) has been envisaged as a successor of orthogonal multiple access (OMA) in the fifth generation (5G) networks and beyond because NOMA has been theoretically and empirically proven to be more bandwidth-efficient than OMA. Nevertheless, user clustering (UC) in NOMA is another prevalent issue. To maximize the throughput and fulfill the successive interference cancellation (SIC) constraints, the UC has been formulated as a clustering optimization problem which has been extensively researched in literature. Recently, an artificial neural network-based UC (ANN-UC) scheme has emerged as a viable solution that can optimally cluster users after exhaustive training. However, the ANN model has an extremely slow learning speed, due to the gradient-based back-propagation (BP) algorithm used by the ANN. To address these issues, this paper proposes a novel fast-learning extreme learning machine-based UC (ELM-UC) scheme. Unlike the ANN-UC technique, the input weights and the bias for the hidden layer nodes of ELM are randomly generated and tuning of parameters is not required, thereby leading to a faster learning rate. In this work, the ELM architecture is adapted to operate in NOMA environments where the optimal cluster formation can be predicted rapidly based on the users’ channel gains and powers. Performance comparisons with the state-of-the-art UC schemes are investigated via extensive simulations. Remarkably, simulation results demonstrate that the proposed ELM-UC technique can achieve near-optimal performance compared to the brute-force search (B-FS) method and outperforms the existing clustering techniques including ANN-UC and dynamic user clustering (DUC).
Research on Overvoltage Suppression by Blocking Diode and DC Circuit Breaker in LCC-VSC Hybrid HVDC System

In this study, a LCC-VSC hybrid HVDC transmission system was taken as an example. The valve and neutral overvoltage of the hybrid HVDC system were simulated and then calculated at the VSC converter station by installing a blocking diode and a DC circuit breaker, as well as by grounding after neutral bus outage. The isolation effect of the blocking diode and the DC circuit breaker on the VSC converter station in the protection and the effect on the DC overvoltage were compared, and the difference was analyzed by complying with the mechanism of overvoltage. The effect of different locations of the blocking diode on the overvoltage suppression was compared, and it was concluded that different locations of blocking diode could raise different requirements for the insulation coordination of the converter valve. By analyzing energy sources of the neutral bus overvoltage at different stages, it was further concluded that the energy stored in the distributed inductance of the metal return line could be a vital source of neutral bus overvoltage energy. On that basis, an overvoltage suppression strategy for grounding neutral bus directly after the stop of the converter station was proposed.
Bipartite Average Tracking for Multi-Agent Systems With Disturbances: Finite-Time and Fixed-Time Convergence

This paper addresses the finite-time and fixed-time bipartite average tracking (BAT) problems in multi-agent systems (MASs) subject to bounded disturbances. A novel non-smooth protocol is first constructed on the basis of utilizing the finite-time control method, which can realize the finite-time BAT with structurally balanced graph. Then, the fixed-time BAT problem is dealt with nonlinear fixed-time algorithm, the setting time of which is obtained without depending on initial states. By employing Lyapunov stability theory, some sufficient criteria are obtained to achieve the finite-time and fixed-time BAT for MASs with disturbances. Numerical examples are finally developed for illustration.
Forest History—New Perspectives for an Old Discipline

The scientific field of forest history studies the development of woodlands and their interrelationship with past human societies. During the last decades, the subject has experienced a constant decrease of importance, reflected in the loss of representation in most universities. After 200 years of existence, an insufficient theoretical basis and the prevalence of bibliographical and institutional studies on post-medieval periods have isolated the field and hindered interdisciplinary exchange. Here we present possible new perspectives, proposing wider methodological, chronological, thematic, and geographical areas of focus. This paper summarizes the development of the field over time and recommends content enhancement, providing a specific example of application from Roman France. Furthermore, we introduce a topical definition of forest history. Following the lead of other fields of the humanities and environmental sciences focussing on the past, forest history has to adapt to using other available archives in addition to historical written sources. In particular, historical and archeological timber as well as pollen are essential sources for the study of past forests. Research into forest history can substantially add to our understanding of relevant issues like societal responses to climate change and resource scarcity in the past and contribute to future scenarios of sustainability.
Fungal Endophyte: An Interactive Endosymbiont With the Capability of Modulating Host Physiology in Myriad Ways

Endophytic fungi ubiquitously dwell inside the tissue-spaces of plants, mostly asymptomatically. They grow either intercellularly or intracellularly in a particular host plant to complete the whole or part of their life cycle. They have been found to be associated with almost all the plants occurring in a natural ecosystem. Due to their important role in the survival of plants (modulate photosynthesis, increase nutrient uptake, alleviate the effect of various stresses) they have been selected to co-evolve with their hosts through the course of evolution. Many years of intense research have discovered their tremendous roles in increasing the fitness of the plants in both normal and stressed conditions. There are numerous literature regarding the involvement of various endophytic fungi in enhancing plant growth, nutrient uptake, stress tolerance, etc. But, there are scant reports documenting the specific mechanisms employed by fungal endophytes to manipulate plant physiology and exert their effects. In this review, we aim to document the probable ways undertaken by endophytic fungi to alter different physiological parameters of their host plants. Our objective is to present an in-depth elucidation about the impact of fungal endophytes on plant physiology to make this evolutionarily conserved symbiotic interaction understandable from a broader perspective.
Comparison of Clinical Efficacy and Safety of Metformin Sustained-Release Tablet (II) (Dulening) and Metformin Tablet (Glucophage) in Treatment of Type 2 Diabetes Mellitus

Objectives This study investigated the clinical efficacy and safety of metformin hydrochloride sustained-release (SR) tablet (II) produced by Dulening and the original metformin hydrochloride tablet produced by Glucophage in the treatment of type 2 diabetes mellitus (T2DM). Methods This randomized, open and parallel controlled clinical trial consecutively recruited a total of 886 patients with T2DM in 40 clinical centers between May 2016 and December 2018. These patients were randomly assigned to the Dulening group (n=446), in which patients were treated with Dulening metformin SR tablets, and the Glucophage group (n=440), in which patients were treated with Glucophage metformin tablets, for 16 weeks. The changes in the levels of glycated hemoglobin (HbAc1) and fasting blood glucose (FBG) as well as weight loss were compared between these two groups. Also, the overall incidence of adverse drug reactions (ADRs) and the incidence of ADR of the gastrointestinal system observed in patients of these two groups were also compared. Results There were no significant differences in demographic and basal clinical characteristics between these two groups. The Dulening and Glucophage groups showed comparable levels of decrease in HbA1c levels, FBG and weight loss after 12-week treatment (all p>0.05). The Dulening group had a significantly lower overall incidence of ADRs as well as gastrointestinal ADR than the Glucophage group. Conclusions Metformin SR tablets (II) and the original metformin tablets exhibit similar therapeutic efficacy in the treatment of T2DM, but metformin SR tablets (II) has the significantly lower incidence of ADRs than the original metformin tablets.