Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Zero-Field Activation Energy for Traps in Fe- and C-Doped GaN-Based HEMTs

Carrier emission rate accelerates from a trap with the increase in drain voltage due to the self-heating (SH) and Poole–Frenkel (PF) emission effects; thus, the conventional Arrhenius analysis underestimates the trap activation energy. For this purpose, this work estimates the zero-field (actual) activation energy of a trap in InAlN/GaN and AlN/GaN high-electron mobility transistors (HEMTs) by including SH and PF effects in the emission rate equation. The HEMT thermal resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\text {TH}}$ </tex-math></inline-formula>) is computed from the technology computer aided design (TCAD) simulation results. Subsequently, the SH-induced increase in the channel temperature (<inline-formula> <tex-math notation="LaTeX">${\Delta }{T}$ </tex-math></inline-formula>) is incorporated in the Arrhenius plot, constructed from drain current transient (DCT) and output-admittance (<inline-formula> <tex-math notation="LaTeX">${Y}_{{22}}$ </tex-math></inline-formula>) parameters. The apparent trap activation energy (<inline-formula> <tex-math notation="LaTeX">${E}_{\text {ap}}$ </tex-math></inline-formula>) is calculated from the measured DCT and <inline-formula> <tex-math notation="LaTeX">${Y}_{{22}}$ </tex-math></inline-formula> at different drain voltages, and the respective electric field (<inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>) at each bias condition is extracted from the simulation. Finally, zero-field activation energy (<inline-formula> <tex-math notation="LaTeX">${E}_{{0}}$ </tex-math></inline-formula>) is estimated from the <inline-formula> <tex-math notation="LaTeX">${E}_{\text {ap}}$ </tex-math></inline-formula> versus (<inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>)<inline-formula> <tex-math notation="LaTeX">$^{{0.5}}$ </tex-math></inline-formula> plot intercept. <inline-formula> <tex-math notation="LaTeX">${E}_{{0}}$ </tex-math></inline-formula> for the well-known Fe-related buffer trap is identified at <inline-formula> <tex-math notation="LaTeX">${E}_{C}- {0.7}$ </tex-math></inline-formula> eV in the InAlN/GaN HEMT. Similarly, <inline-formula> <tex-math notation="LaTeX">${E}_{{0}}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${E}_{C}- {0.51}$ </tex-math></inline-formula> eV is found for the trap in the C-doped AlN/GaN HEMT.
Surface roughness measurement of large areas with high spatial resolution by spectral speckle correlation

Roughness is one of the most important surface quality parameters in metal sheet processing. Thus contact-free optical methods are highly interesting. Using the innovative approach of spectral speckle correlation (SSC) has the potential to measure the spatial roughness distribution of large surfaces quickly and thus inline. In SSC, speckle patterns are recorded at different wavelengths and correlated with each other to determine the roughness of the sample. We show that the relationship between roughness and correlation coefficient as a function of wavelength difference is valid over a large range. The roughness of larger surfaces can be determined by separate evaluation of sub-images. We present the importance of the sub-image size to get reliable, reproducible measurements relating to existing standards. In this work we show how to measure the roughness parameter Sa for values ranging from 0.59 μm to 7.75 μm with a spatial resolution below 1 mm by SSC. We demonstrate that the theory is valid over a large range of wavelength differences. This is shown using wavelength differences between 0.2 nm and 93 nm in the visible spectrum.
Large Vehicle Scheduling Based on Uncertainty Weighting Harmonic Twin-Critic Network

Large-scale online car-hailing platforms have greatly improved travel efficiency by dispatching orders quickly. However, effectively scheduling vehicles for a large-scale fleet is a challenging task for car-hailing platforms. Currently, the most popular methods for solving vehicle scheduling are reinforcement learning-based approaches. However, existing vehicle scheduling algorithms often overestimate the Q-value of an action due to the presence of out-of-distribution (OOD) data in the training datasets. Therefore, reasonable estimation of the action value is an important research problem in reinforcement learning. To address this problem, this article proposes a multiagent reinforcement learning algorithm to optimize the estimation of the Q-value, called the uncertainty weighting harmonic twin-critic (UWTC) algorithm. The UWTC algorithm is able to improve the scheduling efficiency of vehicles by reducing the influence of OOD data through the use of an uncertainty weighting module and a harmonic network. Extensive experiments were conducted on a dataset of DiDi in Chengdu, and the experimental results show that the UWTC algorithm achieves the best results in terms of both the average response rate of orders and the revenue of the platforms.
Time warp from cluster to grid

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Face Detection and Verification Using Lensless Cameras

Camera-based face detection and verification have advanced to the point where they are ready to be integrated into myriad applications, from household appliances to Internet of Things devices to drones. Many of these applications impose stringent constraints on the form-factor, weight, and cost of the camera package that cannot be met by current-generation lens-based imagers. Lensless imaging systems provide an increasingly promising alternative that radically changes the form-factor and reduces the weight and cost of a camera system. However, lensless imagers currently cannot offer the same image resolution and clarity of their lens-based counterparts. This paper details a first-of-its-kind evaluation of the potential and efficacy of lensless imaging systems for face detection and verification. We propose the usage of existing deep learning techniques for face detection and verification that account for the resolution, noise, and artifacts inherent in today's lensless cameras. We demonstrate that both face detection and verification can be performed with high accuracy from the images acquired from lensless cameras, which paves the way to their integration into new applications. A key component of our study is a dataset of 24 112 lensless camera images captured using FlatCam of 88 subjects in a range of different operating conditions.
Unraveling the In Vitro Toxicity Profile of Psychedelic 2C Phenethylamines and Their N-Benzylphenethylamine (NBOMe) Analogues

Mescaline derivative (2C phenethylamines) drugs have been modified by the introduction of a N-2-methoxybenzyl group to originate a new series of compounds with recognized and potent psychedelic effects, the NBOMe-drugs. Although they are prevalent in unregulated drug markets, their toxicity profile is still poorly understood, despite several reports highlighting cases of acute intoxication, with brain and liver toxicity. Thus, in this study, mescaline, 2C-N (insertion of a nitro in the para position of the 2C phenethylamines aromatic ring) and 2C-B (insertion of a bromide in the para position of the 2C phenethylamines aromatic ring) and their corresponding NBOMe counterparts, mescaline-NBOMe, 25N-NBOMe and 25B-NBOMe, were synthetized and the in vitro neuro- and hepatocytotoxicity evaluated in differentiated SH-SY5Y and HepG2 cell lines, respectively. Cytotoxicity, oxidative stress, metabolic and energetic studies were performed to evaluate the main pathways involved in their toxicity. Our results demonstrated that the presence of the N-2-methoxybenzyl group significantly increased the in vitro cytotoxicity of 2C phenethylamines drugs in both cell lines, with the NBOMe drugs presenting lower EC50 values when compared to their counterparts. Consistently, our data showed a correlation between the drug’s lipophilicity and the EC50 values, except for 2C-B. The 2C-B presented higher cytotoxic effects in both cell lines than mescaline-NBOMe, a result that can be explained by its higher passive permeability. All the NBOMe derivatives were able to cross the blood–brain barrier. Considering metabolic studies, the cytotoxicity of these drugs was shown to be influenced by inhibition of cytochrome P450 (CYP), which suggests a potential role of this enzyme complex, especially CYP3A4 and CYP2D6 isoenzymes in SH-SY5Y cells, in their detoxification or bioactivation. Furthermore, in differentiated SH-SY5Y cells, the drugs were able to induce mitochondrial membrane depolarization, and to disrupt GSH and ATP intracellular levels, these effects being concentration dependent and more pronounced for the NBOMe derivatives. No ROS overproduction was detected for any of the drugs in the tested experimental conditions. A correlation between a drug’s lipophilicity and the EC50 values in both cell lines, except for 2C-B, was also obtained. In summary, the introduction of a NBOMe moiety to the parent drugs significantly increases their lipophilicity, brain permeability and cytotoxic effects, with GSH and ATP homeostasis disruption. The inhibition of CYP3A4 and CYP2D6 emphasized that CYP-mediated metabolism impacts the toxicity of these drugs.
Ontological Knowledge Management System Of Islamic Concepts

This study is focused on the conceptual relationship between different ayah of Al-Quran. Let’s take an example what Allah says about Muttaqee in the Al-Quran, the Quran say Muttaqee are those “They are on (true) guidance from their Lord” or “Those who got Falah” but when we go in the context of “Falah” we don’t find such word related Muttaqee means the relationship is there like if someone ask, who are Muttaqee the answer will be “Those who got Falah”; similarly if it is asked that who got Falah the answer is Muttaqee. There are several examples available in the Quran where we are not aware what will be the next related ayah to it. Another example in surah Al- Baqara Allah says: “The month of Ramadan in which was revealed the Qur'an” but next related ayah which we found in surah Al-Qadr in which Allah says: “Verily! We have sent it (this Qur'an) down in the night of Al-Qadr (Decree)” means the Allah revealed Al-Quran in Ramadan also in next surah it is further explained that it was revealed in lailat-ul-qadr.
Assessing students' metacognitive strategies in e-learning and their role in academic performance

The presence of e-learning as a new way of learning in the education system has attracted the interest of researchers worldwide. Nowadays, higher education still uses the e-learning system as a part of the learning method. The important issue in E-learning is how to promote academic performance. Metacognition theory argued that academic performance is determined by students’ metacognitive skills. Through metacognitive skills, students set their own goals, learning, monitoring, and evaluating in e-learning. Less is, however, known about how students involve metacognitive strategies in e-learning in the Indonesian context. Also, there is a scarcity of empirical evidence about the role of metacognitive strategies on academic performance in the Indonesian context. The purpose of this study is to assess students' metacognitive strategies and their impact on academic performance (i.e., engagement and achievement) in the e-learning context. One hundred and fifty students participated in the present study. Descriptive statistics and structural equation modeling were performed for data analysis. The result of this study revealed that students have high skills in metacognitive strategies in e-learning. Our study suggested that metacognitive strategies for self-regulated learning were found to be significantly associated with achievement and engagement in e-learning. In comparison, metacognitive for time and environment was only significantly associated with students' engagement but not with achievements. The contribution of this study to academic practice was explored.
Measuring Point Localization and Sensor Pose Control for Gas Insulated Switchgear Partial Discharge Detection Robot

In recent years, substation intelligent inspection robots have been widely used. However, gas insulated switchgear (GIS) partial discharge detection still relies on manual. The key to using the robot instead of manual GIS partial discharge detection is to locate the measuring point accurately and make the partial discharge detection sensor move quickly and precisely to the measuring point. In this article, we propose a measuring point localization method based on virtual visual servoing (VVS) and a sensor pose control method based on the pose-based visual servoing (PBVS) for GIS partial discharge detection robot. First, AprilTag, a vision fiducial system, was used to locate the measuring point, and the VVS was used to refine the estimated location. Second, the PBVS control law was designed to control the manipulator to steer the partial discharge detection sensor to the target pose. Then, the visual servoing control law was optimized using adaptive proportional gain to speed up the convergence of the control system; the control law was improved according to the first-order nonhomogeneous differential equation of the visual feature error to make the motion trajectory of the manipulator smoother. Finally, the proposed method was verified on the experimental platform. As a result, the proposed method can steer the partial discharge sensor to the target position quickly and precisely, which provides the theoretical basis and technical support for the robot to complete the GIS partial discharge detection.
Latent Topic-Aware Multioutput Learning

Multioutput learning targeting predicting multiple outputs for each input has attracted increasing attention due to its ability to handle diverse data types in outputs. Most feature extraction-based methods for tackling multioutput problems neglect the local correlation between different outputs, while most sample extraction-based methods overlook the gap between inputs and outputs. To overcome these two major limitations, in this article, we propose a topic-aware method where we assume inputs and outputs can be jointly embedded in a topic space. Both feature and sample extraction are performed in a latent topic space that encodes output correlation at a topic level and aligns inputs and outputs in an explainable manner. Different from current topic models, we extract independent and interdependent topics based on informative topics. These two components are verified as useful in performance improvement in an ablation study. The proposed method can also be easily extended and applied in multiview learning. The experimental results of the proposed topic-aware model on multiple benchmarks for different multioutput tasks in single-/multi-views with $p$ -values less than 0.05 indicate that the improvement achieved by the proposed method over compared baselines is statistically significant.
Hybrid-Triggered-Based Control Against Denial-of-Service Attacks for Fuzzy Switched Systems With Persistent Dwell-Time

This article aims to develop a methodology for the hybrid-triggered control for nonlinear switched systems under denial-of-service attacks. By means of the T–S fuzzy method, the complex nonlinear system is transformed into a series of local linear models. Essentially different from the common switching signal design for DoS attacks, it challenges the situation that attack signals may appear with high frequency in a certain interval. The phenomenon aroused by attacks and the characteristics of the switched system constitute a double-layer switched structure, both of the switchings described by the PDT switching rule. A hybrid control strategy based on event-triggered mechanism is utilized to reduce the frequency of data transmission, where signal updates of the controller exhibit aperiodic. By constructing a Lyapunov function depending on dual switching signals, sufficient conditions to ensure the global exponential stability of the considered system are deduced. Furthermore, the minimum interval between two successful events under the event-triggered mechanism is calculated, which proves that the Zeno behavior can be avoided. Finally, a simulation example and an example of the single-link robot arm are studied, illustrating the proposed method's feasibility and effectiveness.
Another Twist to the Scalability in Cell-Free Massive MIMO Networks

Cell-free massive multiple-input multiple-output (MIMO) networks, where a massive number of geographically distributed access points cooperate to jointly serve the users, have been proved to solve many of the interference related issues associated to cellular networks. One of the main challenges of cell-free massive MIMO networks, however, is to guarantee that the potential benefits this architecture entails can be achieved with signal processing computational complexities and fronthaul resource requirements that are scalable as the number of users goes to infinity. User-centric architectures have been proposed in the literature that potentially solve the scalability issues related to, first, the signal processing associated to channel estimation and massive MIMO combining/precoding, second, the power allocation algorithms and, third, the fronthaul signaling for data and channel state information sharing. One of the best centralized combiners/precoders that have been proposed in practice, however, which is termed as partial minimum mean square error (P-MMSE), does not allow obtaining expressions of its spectral efficiency in a scalable way except when using lower bounds based on the well-known use-and-then-forget (UatF) approach. These lower bounds, however, in addition to being rather inaccurate, are not suited for the design and analysis of non-linear combining/precoding schemes based on successive interference cancellation (SIC) when users are equipped with multiple antennas. In this paper, we propose novel linear and non-linear combining/precoding schemes, which we term improved P-MMSE (IP-MMSE), whose achievable spectral efficiency can be accurately analyzed in a scalable manner. The proposed IP-MMSE combiner/precoder achieves spectral efficiencies close to that provided by MMSE-based unscalable solutions, and outperforms to a great extent the P-MMSE-based counterparts.
Understanding Foreign Language Anxiety and Achievement in Chinese Secondary EFL Learners: The Role of Behavioural Engagement

: Drawing up the control-value theory, the present study attempted to understand the relationships among Chinese secondary school students’ English learning anxiety, behavioural engagement, and English performance. A total of 230 (113 males, 117 females) English as a foreign language (EFL) learners from one Chinese secondary school completed the academic anxiety scale and behavioural engagement scale. Participants final examination scores measured their English achievement. Structural equation model (SEM) analyses demonstrated that EFL-related anxiety could directly affect EFL achievement and indirectly via the mediator of behavioural engagement. EFL-related anxiety was able to explain 44.9% of the variance in behavioural engagement and 22.6% of the variance in English achievement. Implications for English education and limitations and directions for future studies are discussed.
A Wrapper Feature Selection Technique for Improving Diagnosis of Breast Cancer

Nowadays, cancer is considered as a fairly common disease. Regarding the number of newly detected cases, breast cancer is ranked as one of the most leading cancer types to death in women. It can be cured, if it is identified and treated in its early stages. Therefore, this study explores a proposed integrated wrapper feature selection method called wrapper naïve-greedy search (WNGS) to improve the accuracy of the breast cancer diagnosis. WNGS is based on a wrapper method, which is blended with a greedy forward search to select optimal feature subset. WNGS method integrates a wrapper method based on Naïve Bayes (NB) classifier as a learning scheme with a forward greedy search method. Then, the selected feature subset is fed to a classifier to determine breast cancer. In addition, K-nearest neighbor-greedy search (KNN-GS) is used for comparison. In KNN-GS method, k-nearest neighbor (KNN) classifier is used as a learning scheme while a forward greedy search method is used to search through features. NB is used as the classifier for classification process for both methods. By applying these two methods, data features are reduced, and the classification rate is improved. Both methods are tested on two different benchmark breast cancer datasets. Accuracy results showed that WNGS method outperformed KNN-GS method. Also, WNGS method overcame KNN-GS regarding precision, recall, F-measure, and sensitivity.
Gpr18 agonist dampens inflammation, enhances myogenesis, and restores muscle function in models of Duchenne muscular dystrophy

Introduction: Muscle wasting in Duchenne Muscular Dystrophy is caused by myofiber fragility and poor regeneration that lead to chronic inflammation and muscle replacement by fibrofatty tissue. Our recent findings demonstrated that Resolvin-D2, a bioactive lipid derived from omega-3 fatty acids, has the capacity to dampen inflammation and stimulate muscle regeneration to alleviate disease progression. This therapeutic avenue has many advantages compared to glucocorticoids, the current gold-standard treatment for Duchenne Muscular Dystrophy. However, the use of bioactive lipids as therapeutic drugs also faces many technical challenges such as their instability and poor oral bioavailability. Methods: Here, we explored the potential of PSB-KD107, a synthetic agonist of the resolvin-D2 receptor Gpr18, as a therapeutic alternative for Duchenne Muscular Dystrophy. Results and discussion: We showed that PSB-KD107 can stimulate the myogenic capacity of patient iPSC-derived myoblasts in vitro. RNAseq analysis revealed an enrichment in biological processes related to fatty acid metabolism, lipid biosynthesis, small molecule biosynthesis, and steroid-related processes in PSB-KD107-treated mdx myoblasts, as well as signaling pathways such as Peroxisome proliferator-activated receptors, AMP-activated protein kinase, mammalian target of rapamycin, and sphingolipid signaling pathways. In vivo, the treatment of dystrophic mdx mice with PSB-KD107 resulted in reduced inflammation, enhanced myogenesis, and improved muscle function. The positive impact of PSB-KD107 on muscle function is similar to the one of Resolvin-D2. Overall, our findings provide a proof-of concept that synthetic analogs of bioactive lipid receptors hold therapeutic potential for the treatment of Duchenne Muscular Dystrophy.