Miracle-dz/UniFAR-FTUs huggingface dataset:
Unscrambling for Subgap Density-of-States in Multilayered MoS2 Field Effect Transistors under DC Bias Stress via Optical Charge-Pumping Capacitance-Voltage Spectroscopy

Herein, we quantitatively analyze the evolution of the subgap density of states (DOSs) for multilayered molybdenum disulfide (m-MoS<sub>2</sub>) field effect transistors (FETs) with bilayered SiN<sub>x</sub>/SiO<sub>x</sub> gate dielectrics under positive bias stress (PBS) and negative bias stress (NBS) by using optical charge-pumping capacitance-voltage spectroscopy. To decouple external gas ambient effects on device instability, hydrophobic fluoropolymers (cyclized transparent optical polymer; CYTOP) are employed for m-MoS<sub>2</sub> FETs followed by the evaluation of subgap-DOSs in the devices for the respective PBS (or NBS). Through extraction of subgap-DOSs and their deconvolution with an analytical model of acceptor (or donor)-like states, it is shown that the device instability is closely correlated with state transitions of DOSs, corresponding to shallow (or midgap) levels for monosulfur vacancy (V<sub>S</sub>) (or disulfur vacancy (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm {S2}}$ </tex-math></inline-formula>)). Moreover, after PBS, the initial states of V<sub>S</sub>(0) (or <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {S2}}$ </tex-math></inline-formula>(0)) transit toward V<sub>S</sub> (−1) (or <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {S2}}(-1$ </tex-math></inline-formula>)) via electron trapping, whereas the transition toward V<sub>S</sub> (0) and <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {S2}}$ </tex-math></inline-formula> (+1) during NBS is assessed from the initial <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm {S}}(-1$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {S2}}$ </tex-math></inline-formula> (0) states. Furthermore, technology computer-aided design (TCAD) simulation based on the extracted DOSs properly replicates the measured I-V characteristics of m-MoS<sub>2</sub> FETs with (and without) CYTOP encapsulation. In this study, subgap-DOS characterization via optical charge pumping and validation of the quantitative evolution of subgap-DOSs suggest that this platform can be potentially beneficial for an in-depth understanding of the origins of device instability for transition metal dichalcogenide (MX<sub>2</sub>)-based FETs, where M is a transition metal and X is a chalcogenide.
A KCF-Based Incremental Target Tracking Method With Constant Update Speed

With a good balance between tracking accuracy and speed, kernelized correlation filter (KCF)-based trackers have now become dominant approaches in the field of online object tracking. However, a KCF-based tracker updates templates by linearly combining historical objects, and its performance inevitably degrades when the accumulated error causes the model to look more similar to the background than the foreground. To address this model drifting problem, we propose an incremental kernel principal component analysis (IKPCA)-based KCF (IKPCA-KCF) tracking scheme. In IKPCA-KCF, IKPCA technology is used to incrementally learn the target features to alleviate the model drifting problem. Moreover, reduced-set (RS) expansions are adopted to compress the historical target samples to maintain a constant template update rate. Additionally, KCF-based trackers use the response map generated by the correlation filter to determine the target location. However, when the target leaves the field of view, using the maximum response value to reflect the target’s position information will certainly induce tracking errors. To further solve the tracking stability problem, a simple and effective nearest neighbor classifier is used to confirm the tracking target during the tracking process. Extensive experimental results demonstrate that the proposed IKPCA-KCF tracker is competitive with state-of-the-art trackers on eight challenging video datasets.
Bi-Polarization in Cascade Size Distributions

Predicting the final sizes of information cascades on online social networks (OSNs) has been a difficult problem. This is because OSNs may have complex topological structures and because user decisions may be influenced by several factors such as social reinforcement. Therefore, a considerable amount of research is being conducted with the objective of investigating in detail the effects of topology and user behavior on information diffusion. This study presents the cases where the final cascade size can be approximated without using further detailed information. Many cascade-size distributions obtained from Twitter-type information diffusion simulations reveal a new finding that as the retweet rates increase, cascades split more clearly into two groups: tiny and large cascades. This bi-polarization phenomenon is universal in that it always appears under various topological and user behavioral conditions. Moreover, the coefficient of variation of cascade sizes of the large cascade group decreases as the retweet rates increase. These findings suggest that the probability of the emergence of a large-scale cascade and its approximate final size are predictable if the global properties of topology and user behavior are time-invariant and given. A simple recurrence relation that generates bi-polarization is derived to verify the correctness of the simulation results.
Design and analysis for the flutter behaviour of different selected wing plan forms computationally

The objective behind this work is to analyse different aerofoils and study their flutter behaviour and the trying to understand the influence the aerofoil parameters has on the dynamic aero-elastic behaviour of the wing that is constructed by that aerofoil. In an attempt to understand this, wings of same span and chord dimensions are constructed using different aerofoil sections which have different camber locations. One way FSI analysis was performed and the mode frequencies were extracted to observe the flutter of the wing. An attempt to understand the impact the aerofoil shape has on the wing flutter is made by selecting different aerofoil sections that has a varying camber location from the leading edge.
Sine Cosine Algorithm Approaches for Directly Estimation of Power System Harmonics & Interharmonics Parameters

In order to maintain the power quality, parameters of harmonic and sub-inter-harmonic components in the signal must be estimated accurately with less computational complexity. In power systems, the value of fundamental frequency changes between 49.5Hz and 50.5Hz and this situation causes the frequency of other harmonic components to change. In order to make efficient amplitude and phase estimation, initially, frequencies must be estimated properly. In this study, the Sine Cosine Algorithm is applied for the estimation of harmonics and sub-inter-harmonics parameters in the power systems. Unlike other optimization-based methods in the literature, it is assumed that the frequencies of the harmonic and sub-inter-harmonic components in the signal are not known. The proposed algorithm has been tested on synthetic signals at different noisy environment. Given the results obtained, SCA can predict the frequencies, amplitudes, and phases of harmonics and sub-inter-harmonics with less than 5% error, which is the maximum error limit specified by the standard, and proposed algorithm estimates the actual parameters with a low error rate even in noisy environments. In addition, SCA has a better convergence performance index than optimization algorithms used in studies in the literature assuming that frequency is known. Performance of the proposed algorithm is also tested on the data obtained from electrical transmission system. SCA estimates the frequencies of harmonics with almost no error, and the amplitude and phase estimation values of each harmonic are in corroboration with the results obtained by the DFT method.
A Systematic Optimization Design of New Constrained Robust Model Predictive Fault-Tolerant Control for Industrial Processes

A comprehensive optimization design method of new constrained robust model predictive fault-tolerant control is proposed for industrial processes with partial actuator failures and unknown disturbances. A new state space model which is composed of differential state variables, output tracking errors, and new state variables related to output tracking errors is first constructed according to the multiple single-input single-output (SISO) models. Considering the uncertainties existing in the system, the reproduced model is represented by a discrete switched model with polytopic uncertainties. Subsequently, the model predictive controller with min-max optimization is designed to ensure the performance improvement and solve the drawback that the controller gain is not adjustable. To further enhance the system performance, the designed controller is optimized in two steps based on the switching strategy. In addition, the corresponding switching strategy is designed to realize the mutual switching between different models of the system. Finally, taking continuous stirred tank and injection molding processes as examples, the effectiveness of the proposed method is verified by comparing with the traditional control method.
Studies on the GAN-Based Anomaly Detection Methods for the Time Series Data

Anomaly detection (AD) for times series data using the generative adversarial network (GAN) has been proposed in recent years. According to the previous study, the GAN-based AD outperformed the cumulative sum (CUSUM) chart. However, no framework for comparison is provided in their works. So, we conduct new studies crucial for the GAN-based AD methods (the MAD-GAN and the TAnoGAN). First, we propose a new framework for fair and systematic comparisons for the prediction performance of the GAN-based AD methods as well as the cumulative sum (CUSUM) chart. So, we evaluate the three methods with four simulation data and secure water treatment system data. Under the proposed comparison framework, the CUSUM chart generally shows prediction performances better than the GAN-based AD methods. Our results imply that more follow-up studies are required before deploying the GAN-based AD methods. Second, we find that adjusting the number of backpropagation steps of the inverse mapping technique can improve the prediction performance of the GAN-based AD methods. Furthermore, we find that monitoring the residuals of the fitted model significantly improves the prediction performance of the GAN-based AD methods as well as the CUSUM chart.
Survey on Clustering in VANET Networks

A Vehicular Ad Hoc Network is a dynamic network due to uncertain vehicles presence on the road and data transmission among vehicles and Road Side Unit (RSU). Efficient communication among the vehicles and RSUs is useful for the Intelligent Transportation System (ITS). There are two types of communication Vehicle 2 Vehicle (V2V), and Vehicle 2 Infrastructure (V2I) performs in VANET by mobile ad hoc technology. The communication performance of the VANET depends on the routing data. The routing protocols play an important role in efficient data routing in VANET. VANET routing protocols are reviewed, and clustering in VANET is introduced in this study. An extensive survey is carried out on various benchmark clustering algorithms designed by different researchers for Cluster Head (CH) selection. Various parameters for evaluating the performance in a network are compared and analyzed. Also, we introduce our proposed algorithm which aims to increase the reliability of VANET routing.
Power Scaling of Graded-Channel GaN HEMTs With Mini-Field-Plate T-gate and 156 GHz fT

We report 60-nm gate-length graded-channel AlGaN/GaN HEMTs fabricated with a mini-field-plate T-gate. The devices exhibit an extrinsic f<sub>T</sub> and <inline-formula> <tex-math notation="LaTeX">$\text{f}_{\text {MAX}}$ </tex-math></inline-formula> of 156 GHz and 308 GHz, respectively. At 60-nm gate length, the f<sub>T</sub>*Lg of 9.4 GHz<inline-formula> <tex-math notation="LaTeX">$\ast \mu \text{m}$ </tex-math></inline-formula> is comparable to that of conventional scaled short gate-length AlGaN/GaN HEMTs. Due to minimal current collapse, the graded-channel GaN HEMTs demonstrated linear RF power scaling up to 5.5 W/mm at 30 GHz. This results in a record <inline-formula> <tex-math notation="LaTeX">$\text{f}_{\text {T}}~\ast $ </tex-math></inline-formula>power density product of 858 GHz<inline-formula> <tex-math notation="LaTeX">$\ast \text{W}$ </tex-math></inline-formula>/mm for Ga-polar GaN devices, indicating the promise of this device architecture for mmW amplifiers.
Deep Learning Approach for COVID-19 Detection Based on X-Ray Images

COVID-19 is an infectious disease that has invaded the world since 2019 starting from China. It is caused by a new member of the corona viruses' family that attacks the respiratory system of living body leading to fever, cough, general tiredness and to death in worst case scenarios. The disease is commonly detected by RT-PCR tests, which is time consuming and relatively expensive. The need for faster, cheaper, and more precise diagnostic tool raised the demand for the utilization of technology and artificial intelligence in this purpose. This study aims to build a robust deep learning algorithm using convolutional neural networks (CNNs) that is capable to classify chest X-ray images into COVID-19, viral pneumonia, and normal cases. X-ray is a safe and inexpensive imaging system that is available at all hospitals and healthcare centers. A novel CNN model built from scratch (COV-X) has been introduced and shown 94% accuracy in the classification purpose. VGG16, VGG19, RESNET50, XCEPTION, and MOBILENET are pre-trained models that have shown an accuracy of 95%, 95%, 33%, 65%, and 77%, respectively. Findings prove that deep learning is an effective technique for early detection of COVID-19, it provides automatic detection with high reliability to help the healthcare professions and avoid the pandemic from spreading more.
A General Compact Pinned Photodiode Model Capable of Miniature PPD Modeling

This article presents a simple yet powerful general pinning process model for 3-D pinned photodiodes (PPDs), which can be used to model PPDs with small dimensions (miniature PPDs). For the development of a pinning model, capable of dealing with different PPD shapes and dimensions, the charge population in the space charge region (SCR) is calculated. Moreover, to improve the model’s accuracy, the PPD inner region depletion mechanism is investigated, and the inner SCR width is extracted analytically. The model’s application in analyzing PPDs is verified with six previously published experimental results.
Application of Data Science for Understanding Emotional Dimensional Behavior and Their Connection to Uncertainty and Risk Behavior

This paper explores multidisciplinary application of emotions and human psychology within market behavior to analyse how emotional dimensions, extracted from Twitter messages and related to cryptocurrency market, are connected to future uncertainty and risk exposure. Although Twitter messages are often used to derive sentiment scores that are then linked to market performance, specific values of emotional components have not been utilised in the previous academic literature to analyze risk behavior. We connect VAD (valence, arousal and dominance) dimensions with the future hourly absolute value of returns, future 24-hour return standard deviation, future 24-hour downside deviation and future 24-hour maximal drawdown (all measuring market uncertainty and risk) on the cryptocurrency market. Results show that all target variables have various predictability from VAD dimensions, where lagged dominance variable (perceived level of control) is the key driver of predictability. Additionally, VAD dimensions have been grouped into distinct clusters by using the k-Means approach. A comparison of selected clusters on in-sample and out-of-sample data showed consistent predictability of identified clusters to all target variables. Results show that emotional components of human emotions, derived from cumulative Twitter messages, actually predict future uncertainty and risk with consistent clustering profile from lagged dominance VAD dimension where lower dominance values predict higher future risk and vice versa.
An Improved Finite-Control-Set Model Predictive Flux Control for Asymmetrical Six-Phase PMSMs With a Novel Duty-Cycle Regulation Strategy

This work presents an improved finite-control-set model predictive flux control (FCS-MPFC) for asymmetrical six-phase permanent magnet synchronous machine (ASPMSM) with a novel duty-cycle regulation strategy. Firstly, virtual vector concept is adopted to suppress harmonic currents without additional cost function and the corresponding weighting factor. Then, 12 virtual vectors with higher voltage utilization are developed. Meanwhile, for the sake of strengthening steady-state performance, a novel duty-cycle regulation strategy incorporating the well-known Lagrange multiplier method is proposed. In this strategy, the reconfigurable set of voltage vectors (SVV), which consists of an active and a null virtual vector, can be determined without second modification, hence highly mitigating additional computation burden. Furthermore, in order to realize the purpose of unified switching frequency, the SVVs are implemented in terms of regular rather than irregular PWM signal pattern. Finally, experimental results prove the effectiveness of the proposed method.
Efficient Pipelined Execution of CNNs Based on In-Memory Computing and Graph Homomorphism Verification

In-memory computing is an emerging computing paradigm enabling deep-learning inference at significantly higher energy-efficiency and reduced latency. The essential idea is mapping the synaptic weights of each layer to one or more in-memory computing (IMC) cores. During inference, these cores perform the associated matrix-vector multiplications in place with O(1) time complexity, obviating the need to move the synaptic weights to additional processing units. Moreover, this architecture enables the execution of these networks in a highly pipelined fashion. However, a key challenge is designing an efficient communication fabric for the IMC cores. In this work, we present one such communication fabric based on a graph topology that is well-suited for the widely successful convolutional neural networks (CNNs). We show that this communication fabric facilitates the pipelined execution of all state-of-the-art CNNs by proving the existence of a homomorphism between the graph representations of these networks and that corresponding to the proposed communication fabric. We then present a quantitative comparison with established communication topologies and show that our proposed topology achieves the lowest bandwidth requirements per communication channel. Finally, we present one hardware implementation and show a concrete example of mapping ResNet-32 onto an IMC core array interconnected via the proposed communication fabric.
Farkl› Muhafaza S›cakl›¤› ve Süresinin Fermente Sucuklarda Biyojen Aminlerin Oluflumu Üzerine Etkisi*

The aim of this study was to determine the effect of maturating and storing time at different temperatures on biogenic amine formation in fermented sucuk produced in Turkey. For this purpose, fermented sucuks were analysed for biogenic amines (tryptamine, β-phenylethylamine, putrescine, cadaverine, histamine, tyramine, spermidine and spermine) by means of HPLC using a fluorescence detector, for physicochemical characteristics (humidity, pH, water activity) and for microbiological characteristics (aerobic plate count, Enterobacteriaceae, Pseudomonas, lactic acid bacteria) before and after maturation and on days 3, 7, 14. 21, 30, 45, 60, 90 and 180 of storage using different storage conditions (refrigerator and room temperature). Before maturation, biogenic amines were as follows: tryptamine 2.3 mg/kg, β-phenylethylamine 2.9 mg/kg, putrescine 54.8 mg/kg, cadaverine 4.3 mg/kg, histamine 42.4 mg/kg, tyramine 29.8 mg/kg, spermidine 9.2 mg/kg and spermine 12.9 mg/kg. After maturation, biogenic amine values were determined to be tryptamine 8.5 mg/kg, β-phenylethylamine 21.3 mg/kg, putrescine 110.6 mg/kg, cadaverine 10.1 mg/kg, histamine 121.7 mg/kg, tyramine 159.9 mg/kg, spermidine 12.0 mg/kg and spermine 26.1 mg/kg. During storage, increases were determined in the amount of biogenic amines, except spermine and spermidine. In the analysis performed on day 180 the amount of biogenic amines were as follows: tryptamine 23.4 and 50.3 mg/kg, β-phenylethylamine 33.1 and 60.6 mg/kg, putrescine 223.5 and 351.5 mg/kg, cadaverine 23.5 and 61.9 mg/kg, histamine 188.3 and 426.4 mg/kg, tyramine 245.1 and 351.0 mg/kg, spermidine 7.3 and 6.0 mg/kg, and spermine 14.2 and 12.8 mg/kg respectively, in refrigeration and at room temperature. Both before and after maturation and during storage, putrescine, histamine and tyramine levels were higher than those of other biogenic amines. In conclusion, biogenic amines were higher in sucuks stored at room temperature than in refrigeration. Some suggestions can be made in order to prevent the occurrence of biogenic amines in fermented sucuks. These include using the meat in hygienic conditions, shortening the maturaton time with amine-negative starter cultures and storing the sucuks at low temperature after production.
Dynamic stability improvement of a solar penetrated power system by Fractional optimal IPFC based controller

Fractional interline power flow controller (IPFC) is proposed in this work for enhancing dynamic stability of a random solar penetrated power system. IPFC being a series-series FACTS device is much versatile damping controller. Here the damping action is provided by IPFC based fractional lead-lag controller. The controller gains are optimized by a random walk grey wolf optimizer (RGWO). A random variation in solar output is more prone to system oscillations as compared to fix solar generation. So separate cases have been conducted in this work pertaining to step and random solar power variations. The effectiveness of proposed control action has been compared with lead-lag controller and proposed optimization technique with standard PSO and GWO algorithms. Detail analysis has been performed in time domain with eigen analysis and it is found that fractional RGWO tuned IPFC is much robust to damp system oscillations subject to variable solar penetration in comparison to PSO and GWO tuned lead-lag controller.
A Controller to Impose a RCM for Hands-on Robotic-Assisted Minimally Invasive Surgery

In Robotic-Assisted Minimally Invasive Surgery a long and thin instrument attached to the robotic arm enters the human body through a tiny incision. To ensure that no injury occurs when the surgeon is manipulating the instrument, the incision point must be a remote center of motion (RCM) for the instrument. For this purpose, a novel target admittance model is designed in the joint space for hands-on procedures that can be applied in all commercially available general-purpose manipulators with six or more degrees of freedom. It is proved that the joint reference trajectories generated by the proposed target admittance model under the exertion of a human force are stable and satisfy the RCM constraint. The measurements of the human force and the robot’s forward kinematic model are only required. Its use spans all hands-on surgical procedures. The proposed model can be easily extended to achieve additional objectives. Simulation results validate the theoretical findings and experimental results utilizing a KUKA LWR4+ demonstrate that trocar displacements are less than 1mm.