Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Improvement of a Feedback Control System Using an Accelerometer-Enhanced Velocity Observer

The paper shows how an accelerometer- enhanced velocity estimator can be used to improve the tracking performance of a feedback control system. In contrast to conventional velocity estimators that use positional information only, the accelerometer-enhanced velocity estimator fuses the position sensor and the accelerometer together to produce an improved velocity estimation. Experimental results are presented to show the effectiveness of the accelerometer-enhanced velocity estimator on improving the tracking performance of a linear motion stage .
Document forgery detection using source printer identification: A comparative study of text‐dependent versus text‐independent analysis

Source printer identification represents an interesting modality for document forgery detection. Establishing the identity of the printer that was employed to print a questioned document allows concluding its authenticity. This paper investigates the effectiveness of deep visual features (learned using convolutional neural networks) in characterization of the source printer. Images of printed documents are divided into small patches as well as characters for extraction of features. An off‐the‐shelf recognition engine is also integrated, allowing experiments in text‐dependent as well as text‐independent modes. Experiments are carried out on a standard data set of documents from 20 different printers and identification rates of 95.52% and 98.06% are reported using patches and characters, respectively. Furthermore, the discriminating power of different characters, as well as their combinations, is also being studied. Unlike many existing techniques, which rely on pre‐segmented characters and report results by comparing same characters only, the proposed technique works on complete images of printed documents and reports high identification rates.
PhyloM: A Computer Program for Phylogenetic Inference from Measurement or Binary Data, with Bootstrapping

Quantitative and binary results are ubiquitous in biology. Inasmuch as an underlying genetic basis for the observed variation in these observations can be assumed, it is pertinent to infer the evolutionary relationships among the entities being measured. I present a computer program, PhyloM, that takes measurement data or binary data as input, using which, it directly generates a pairwise distance matrix that can then be subjected to the popular neighbor-joining (NJ) algorithm to produce a phylogenetic tree. PhyloM also has the option of nonparametric bootstrapping for testing the level of support for the inferred phylogeny. Finally, PhyloM also allows the user to root the tree on any desired branch. PhyloM was tested on Biolog Gen III growth data from isolates within the genus Chromobacterium and the closely related Aquitalea sp. This allowed a comparison with the genotypic tree inferred from whole-genome sequences for the same set of isolates. From this comparison, it was possible to infer parallel evolution. PhyloM is a stand-alone and easy-to-use computer program with a user-friendly graphical user interface that computes pairwise distances from measurement or binary data, which can then be used to infer phylogeny using NJ using a utility in the same program. Alternatively, the distance matrix can be downloaded for use in another program for phylogenetic inference or other purposes. It does not require any software to be installed or computer code written and is open source. The executable and computer code are available on GitHub.
S$^{2}$-Net:Semantic and Saliency Attention Network for Person Re-Identification

Person re-identification is still a challenging task when moving objects or another person occludes the probe person. Mainstream methods based on even partitioning apply an off-the-shelf human semantic parsing to highlight the non-collusion part. In this paper, we apply an attention branch to learn the human semantic partition to avoid misalignment introduced by even partitioning. In detail, we propose a semantic attention branch to learn 5 human semantic maps. We also note that some accessories or belongings, such as a hat, bag, may provide more informative clues to improve the person Re-ID. Human semantic parsing, however, usually treats non-human parts as distractions and discards them. To fetch the missing clues, we design a branch to capture the salient non-human parts. Finally, we merge the semantic and saliency attention to build an end-to-end network, named as S<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-Net. Specifically, to further improve Re-ID, we develop a trade-off weighting scheme between semantic and saliency attention and set the right weight with the actual scene. The extensive experiments show that S<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-Net gets the competitive performance. S<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-Net achieves 87.4% mAP on Market1501 and obtains 79.3%/56.1% rank-1/mAP on MSMT17 without semantic supervision. The source codes are available at <uri>https://github.com/upgirlnana/S2Net</uri>.
A 915 MHz IoT Transmitter Employing Frequency Tripler and Digitally Controlled Duty-Cycle/Phase Calibration

A 915 MHz binary frequency-shift keying (BFSK) transmitter (TX) for Internet-of-Things (IoT) applications is presented. The proposed TX adopts a passive frequency tripler, digital duty-cycle/phase calibration, and a low-cost on-chip power amplifier (PA) matching network (MN). The frequency tripler allows an ultralow-power (ULP) implementation of the frequency synthesizer by lowering the maximum operating frequency and relaxing the frequency tuning range requirement. The proposed frequency tripler provides 10.6 dB better 300 MHz spur reduction compared to that of the conventional frequency tripler by adding a series high-pass filter. The two-phase 20% duty-cycle and single-ended 50% duty-cycle calibration circuits drive the frequency tripler and Class-D PA, respectively, suppressing unwanted spurs significantly. The digital duty-cycle/phase calibration circuits, designed based on the quantitative analysis of the stability, offer advantage of technology scaling. The reduced spur relaxes the harmonic filtering requirement at the PA output, leading to an on-chip PA MN with low- ${Q}$ . Implemented in 55 nm CMOS, the proposed TX shows the peak output power of 5.2 dBm and efficiency of 30.6% over 902–928 MHz while dissipating 210 $\mu \text{W}$ in the phase-locked loop (PLL) for 0.9 V supply. All spur component levels stay below −41 dBm with the worst harmonic distortion of −48.5 dBc. The proposed TX achieves 100 kbps of data rate for the BFSK modulated signals with the corresponding FSK error of 3.2%.
Prevention of HBV Reactivation in Hemato-Oncologic Setting during COVID-19

Onco-hematologic patients are highly susceptible to SARS-CoV-2 infection and, once infected, frequently develop COVID-19 due to the immunosuppression caused by tumor growth, chemotherapy and immunosuppressive therapy. In addition, COVID-19 has also been recognized as a further cause of HBV reactivation, since its treatment includes the administration of corticosteroids and some immunosuppressive drugs. Consequently, onco-hematologic patients should undergo SARS-CoV-2 vaccination and comply with the rules imposed by lockdowns or other forms of social distancing. Furthermore, onco-hematologic facilities should be adapted to new needs and provided with numerically adequate health personnel vaccinated against SARS-CoV-2 infection. Onco-hematologic patients, both HBsAg-positive and HBsAg-negative/HBcAb-positive, may develop HBV reactivation, made possible by the support of the covalently closed circular DNA (cccDNA) persisting in the hepatocytic nuclei of patients with an ongoing or past HBV infection. This occurrence must be prevented by administering high genetic barrier HBV nucleo(t)side analogues before and throughout the antineoplastic treatment, and then during a long-term post-treatment follow up. The prevention of HBV reactivation during the SARS-CoV-2 pandemic is the topic of this narrative review.
Recent Trends in Magnetic Sensors and Flux-Based Condition Monitoring of Electromagnetic Devices

As one of the most promising techniques, magnetic flux-based condition monitoring of electrical machines, has gained significant traction in the past decade. One of the key enabling factor is the advancements in sensor technologies to measure both the airgap magnetic flux and stray magnetic flux around the motors with high accuracy. This study presents a comprehensive review of advances in flux sensors and the recent trends in magnetic flux-based diagnostic techniques for various faults and electrical machines. In this context, various magnetic sensor technologies suitable for measuring both the airgap flux and the stray magnetic flux are introduced. Each of the sensor technologies are discussed exhaustively, including their working principles and applications in electrical machine fault diagnostics. In the following sections, various flux-based diagnostic techniques are discussed for different types of common faults in various ac machines. Finally, the future trends are discussed briefly under the light of several applications and their impact.
A Novel Egg-In-Cube System Enables Long-Term Culture and Dynamic Imaging of Early Embryonic Development

The avian egg is a closed system that protects the growing embryo from external factors but prevents direct observation of embryo development. Various culture systems exist in the literature to study the development of the embryo for short periods of incubation (from 12 h up to a maximum of 60 h of egg incubation). A common flaw to these culture techniques is the inability to culture the unincubated avian blastoderm with intact tissue tensions on its native yolk. The goal of this work is to create a unique novel egg-in-cube system that can be used for long-term quail embryo culture initiated from its unincubated blastoderm stage. The egg-in-cube acts as an artificial transparent eggshell system that holds the growing embryo, making it amenable to microscopy. With the egg-in-cube system, quail embryos can be grown up to 9 days from the unincubated blastoderm (incubated in air, 20.9% O2), which improves to 15 days on switching to a hyperoxic environment of 60% O2. Using transgenic fluorescent quail embryos in the egg-in-cube system, cell movements in the unincubated blastoderm are imaged dynamically using inverted confocal microscopy, which has been challenging to achieve with other culture systems. Apart from these observations, several other imaging applications of the system are described in this work using transgenic fluorescent quail embryos with upright confocal or epifluorescence microscopy. To demonstrate the usefulness of the egg-in-cube system in perturbation experiments, the quail neural tube is electroporated with fluorescent mRNA “in cubo”, followed by the incubation of the electroporated embryo and microscopy of the electroporated region with the embryo in the cube. The egg-in-cube culture system in combination with the “in cubo” electroporation and dynamic imaging capabilities described here will enable researchers to investigate several fundamental questions in early embryogenesis with the avian (quail) embryo on its native yolk.
Overall Optimization for Offshore Wind Farm Electrical System

Based on particle swarm optimization (PSO), an optimization platform for offshore wind farm electrical system (OWFES) is proposed in this paper, where the main components of an offshore wind farm and key technical constraints are considered as input parameters. The offshore wind farm electrical system is optimized in accordance with initial investment by considering three aspects: the number and siting of offshore substations (OS), the cable connection layout of both collection system (CS) and transmission system (TS) as well as the selection of electrical components in terms of voltage level and capacity. Because hundreds of optimization variables, continuous or discrete, are involved in the problem, a mix integer PSO (MIPSO) is required to obtain the solution. The fuzzy C-means clustering (FCM) algorithm is used to partition the wind farm into several sub regions. The collection system layout in each sub region as well as the connection scheme between offshore substations are optimized by an adaptive PSO-minimum spanning tree algorithm (APSO-MST) which has been proposed in a previous work. The simulation results show that the proposed optimization platform can find an optimized layout that save 3.01% total cost compared with the industrial layout, and can be a useful tool for OWFES design and evaluation. Copyright © 2016 John Wiley & Sons, Ltd.
The Impact of Profit Warnings on Stock Prices in Kuwaiti Firms

This research explores the link concerning informativeness regarding stock price and warnings pertaining to profit in organisations operating in Kuwait as an emerging country. An extensive set of panel data were utilised by the researchers, where such data linked to profit warnings percentages and stock price synchronicity on the Stock Exchange of Kuwait for the years 2010 through to 2020. Multi-regression was chosen to be applied as a parametric test, which provided the ability to garner more robust findings that are seen to be aligned with our belief that, as opposed to common market data. The findings present a wealth of insight concerning the effects of earnings declarations when it comes to stock prices and data content. The approach and assessment of profit warnings presented in this work might also provide further support for researchers seeking to carry out other stock price and profit warnings researches in emerging economics, particularly when considering that support in this regard is seen to be lacking in emerging regions. It is the view of the researchers that the present work is one of very few centres on developing regions, especially those in the GCC (Gulf Cooperation Council). Furthermore, adding to regression corporate governance factors as moderating variables has also been recognised as being far more valuable when compared with other regressions.
Integration of Energy Storage for Voltage Support in Distribution Systems with PV Generation

The exploitation of PV generation offers substantial environmental benefits. Yet, the integration of these might be problematic due to its variable and intermittent nature. Energy storage can compensate for the deficiencies featured by variable PV systems. This paper provides a mathematical programming based approach for the strategic deployment of energy storage in the feeder circuits to achieve ubiquitous grid benefits. A genetic algorithm (GA) is used to solve the problem of appropriate size and location of the energy storage. Quasi-static time sequence analysis is carried out on diurnal basis, to correlate the coincidence of the PV resource output and demand. This will ensure accurate appraisal of storage impacts on the system hour by hour. The proposed approach is implemented on the IEEE 123 node test feeder under various penetration levels and operating scenarios. Results have consistently affirmed that the proposed approach is efficient in maintaining system’s voltage profile at various conditions as well as reducing the overall thermal losses.
Rabphilin-3A Drives Structural Modifications of Dendritic Spines Induced by Long-Term Potentiation

The interaction of Rabphilin-3A (Rph3A) with the NMDA receptor (NMDAR) in hippocampal neurons plays a pivotal role in the synaptic retention of this receptor. The formation of a Rph3A/NMDAR complex is needed for the induction of long-term potentiation and NMDAR-dependent hippocampal behaviors, such as spatial learning. Moreover, Rph3A can also interact with AMPA receptors (AMPARs) through the formation of a complex with myosin Va. Here, we used a confocal imaging approach to show that Rph3A overexpression in primary hippocampal neuronal cultures is sufficient to promote increased dendritic spine density. This morphological event is correlated with an increase in GluN2A-containing NMDARs at synaptic membranes and a decrease in the surface levels of GluA1-containing AMPARs. These molecular and morphological modifications of dendritic spines are sufficient to occlude the spine formation induced by long-term potentiation, but do not prevent the spine loss induced by long-term depression. Overall, our results demonstrate a key role for Rph3A in the modulation of structural synaptic plasticity at hippocampal synapses that correlates with its interactions with both NMDARs and AMPARs.
Trust in Automation as a Matter of Media Influence and Experi-ence of Automated Vehicles

Highly automated vehicles (HAV), which could help to enhance road safety and efficiency, are very likely to enter the market within the next decades. To have an impact, these systems need to be purchased, which is a matter of trust and acceptance. These factors are dependent on the level of information that one has about such systems. One important source of information is various media, such as newspapers, magazines and videos, in which highly automated driving (HAD) is currently a frequent topic of discussion. To evaluate the influence of media on the perception of HAD, 31 participants were presented with three different types of media addressing HAD in a neutral manner. Afterwards, the participants experienced HAD in the driving simulator. In between these steps, the participants completed questionnaires assessing comfort, trust in automation, increase in safety, intention to use and other factors in order to analyze the effect of the media and the driving simulation experience. Results indicate that the perception of some aspects of HAD were affected by the media presented, while experiencing HAD in the driving simulator generally did not have an effect on the attitude of the participants. Other aspects, such as trust, were not affected by either media or experience. In addition, gender-related differences in the perception of HAD were found.
Design of simulation teaching platform for traffic signal control optimization based on AI algorithm

With the rapid development of artificial intelligence, reinforcement learning techniques are emerging, and in the field of transportation, the control of traffic signals can be optimized using reinforcement learning algorithm to improve traffic efficiency. In teaching, the reinforcement learning algorithm is more difficult than the traditional timing algorithm to understand, and there is a lack of teaching platform - which can not only give students an in-depth understanding of the algorithm and advantages of reinforcement learning, but also facilitate teachers to evaluate students' mastery. In view of the above problems, this project designs and develops a traffic signal control optimization simulation teaching platform using AI algorithm, which realizes a number of reinforcement learning algorithms, and combines the basic characteristics of teaching, adopts a distributed system structure, which can meet the needs of online teaching and evaluation. The platform is divided into teacher side and student side, the main functions of the student side are: SUMO road network simulation model selection, action mechanism selection (and input related parameters), reinforcement learning algorithm selection (Q-learning and Sarsa), model input parameters and evaluation parameters (such as fuel consumption, queue number) selection, algorithm operation, statistical output and so on. During the operation, the platform displays the statistical results in real time in the form of dynamic graphs, and after the operation, outputs static charts of evaluation data of different algorithms. The platform has the function of evaluating and scoring according to the weighted average of the optimization of output results. According to the requirements set by teachers, it can score the aspects of pass efficiency and environmental impact, and generate experimental reports as the basis for teaching evaluation. The teacher can view the completion of the experiment and the evaluation results of the experiment, so as to check the students' learning of the reinforcement learning algorithm. The work done in this subject, on the one hand, helps students to study and experiment with advanced AI algorithms, on the other hand, it can also help teachers to master teaching situation, improve teaching quality, and it is of great significance to the application of AI algorithms in the field of traffic signal control optimization and personnel training.
De novo programming: establishment of epigenome in mammalian oocytes

Abstract Innovations in ultrasensitive and single-cell measurements enable us to study layers of genome regulation in view of cellular and regulatory heterogeneity. Genome-scale mapping allows to evaluate epigenetic features and dynamics in different genomic contexts, including genebodies, CpG islands, imprinting control regions, promoters, partially methylated domains, and repetitive elements. The epigenome of early embryos, fetal germ cells, and sperms has been extensively studied for the past decade, whereas oocytes remain less clear. Emerging evidence now supports the notion that transcription and chromatin accessibility precede de novo DNA methylation in both human and mouse oocytes. Recent studies have also started to chart correlations among different histone modifications and DNA methylation. We discuss the potential mechanistic hierarchy that shapes the oocyte DNA methylome, also providing insights into the convergent and divergent features between humans and mice. Summary Sentence Both human and mouse oocytes represent unique epigenomic landscapes. De novo DNA methylation occurs in growing oocytes, which correlates with transcription and chromatin accessibility. Graphical Abstract
Securing Real-Time Video Surveillance Data in Vehicular Cloud Computing: A Survey

Vehicular ad hoc networks (VANETs) have received a great amount of interest, especially in wireless communications technology. In VANETs, vehicles are equipped with various intelligent sensors that can collect real-time data from inside and from surrounding vehicles. These real-time data require powerful computation, processing, and storage. However, VANETs cannot manage these real-time data because of the limited storage capacity in on board unit (OBU). To address this limitation, a new concept is proposed in which a VANET is integrated with cloud computing to form vehicular cloud computing (VCC) technology. VCC can manage real-time services, such as real-time video surveillance data that are used for monitoring critical events on the road. These real-time video surveillance data include highly sensitive data that should be protected against intruders in the networks because any manipulation, alteration, or sniffing of data will affect a driver’s life by causing improper decision-making. The security and privacy of real-time video surveillance data are major challenges in VCC. Therefore, this study reviewed the importance of the security and privacy of real-time video data in VCC. First, we provide an overview of VANETs and their limitations. Second, we provide a state-of-the-art taxonomy for real-time video data in VCC. Then, the importance of real-time video surveillance data in both fifth generation (5G), and sixth generation (6G) networks is presented. Finally, the challenges and open issues of real-time video data in VCC are discussed.