Miracle-dz/UniFAR-FTUs huggingface dataset:
Emulation of IEEE STD 421.5/Industrial Excitation Systems Using a Micro-Alternator's Exciter

Micro-alternators, having similar parameters as turbo-alternators, play a significant role in the experimental evaluation of power system dynamics. Early thyristor excitation systems of micro-alternators have limited re-configurable options. Three possible control structures, two closed-loop control structures proposed in the literature and one open-loop control structure proposed in this paper, including a time constant regulator to mimic large machine time constants, are evaluated to emulate any IEEE 421.5 standard excitation system model or interface an industrial excitation system controller hardware in the loop (CHIL). It is shown that the communication delay introduced in CHIL does not impact the closed-loop stability within the bandwidth requirements of the excitation systems. One of the closed-loop structures, which is more generalized, and the open-loop structure performances are compared for emulating IEEE AC4C, DC1C, and ST1C excitation systems on a laboratory micro-machine based single machine infinite bus test system with buck exciter and H-bridge chopper (HBC) exciter using EMTP simulations under small and large disturbances. HBC exciter is used in the experimental validation with both the closed-loop and the open-loop control structures. The proposed open-loop structure is easy to implement and performs better than the closed-loop structure.
Effects of Resveratrol on Vascular Function in Retinal Ischemia-Reperfusion Injury

Ischemia-reperfusion (I/R) events are involved in the development of various ocular pathologies, e.g., retinal artery or vein occlusion. We tested the hypothesis that resveratrol is protective against I/R injury in the murine retina. Intraocular pressure (IOP) was elevated in anaesthetized mice to 110 mm Hg for 45 min via a micropipette placed in the anterior chamber to induce ocular ischemia. In the fellow eye, which served as control, IOP was kept at a physiological level. One group received resveratrol (30 mg/kg/day p.o. once daily) starting one day before the I/R event, whereas the other group of mice received vehicle solution only. On day eight after the I/R event, mice were sacrificed and retinal wholemounts were prepared and immuno-stained using a Brn3a antibody to quantify retinal ganglion cells. Reactivity of retinal arterioles was measured in retinal vascular preparations using video microscopy. Reactive oxygen species (ROS) and nitrogen species (RNS) were quantified in ocular cryosections by dihydroethidium and anti-3-nitrotyrosine staining, respectively. Moreover, hypoxic, redox and nitric oxide synthase gene expression was quantified in retinal explants by PCR. I/R significantly diminished retinal ganglion cell number in vehicle-treated mice. Conversely, only a negligible reduction in retinal ganglion cell number was observed in resveratrol-treated mice following I/R. Endothelial function and autoregulation were markedly reduced, which was accompanied by increased ROS and RNS in retinal blood vessels of vehicle-exposed mice following I/R, whereas resveratrol preserved vascular endothelial function and autoregulation and blunted ROS and RNS formation. Moreover, resveratrol reduced I/R-induced mRNA expression for the prooxidant enzyme, nicotinamide adenine dinucleotide phosphate oxidase 2 (NOX2). Our data provide evidence that resveratrol protects from I/R-induced retinal ganglion cell loss and endothelial dysfunction in the murine retina by reducing nitro-oxidative stress possibly via suppression of NOX2 upregulation.
Honeypot-Assisted Masquerade Detection with Character-Level Machine Learning

Intrusions into the shell of Linux operating systems through ssh, telnet, etc. are critical. It is important to detect the access of newly-emerging attackers, distinguishing them from the legitimate users. We propose the use of honeypots for collecting the trend of malicious commands, and to train character-level machine learning models for masquerade detection. In this paper, we provide a profiling of 1,314,834 commands collected in 173 days with our honeypot in 2021. We also provide our evaluation with Logistic Regression and several configurations of 1D-CNN and 2D-CNN, using the honeypot commands and legitimate commands collected from 32 users on 27 servers. The evaluation results indicate that 1D-CNN(shallow) and 2D-CNN(large) models provide a good performance regarding detection rate and false positive rate. Even when the trends of honeypot commands changed, the detection rate were almost 100% and the false positive rate were 0.0% regarding the two models.
A Smart Framework for IoT Based Garbage Monitoring System

The improper management of waste in urban areas with higher densities of people encourages the growth of microorganisms that can make you sick. The frequency with which these litter containers are used varies by location. Periodic verification with timing gaps is ineffective. Sometimes, even before it's expected they may overflow. This study suggests a smart Internet of Things (IoT) based garbage monitoring mechanism to determine alert when the trash can be full to prevent the overflow and improve cleanup. This design approach collects and sends this data using a wireless network. Advanced technology can be used to prevent human error and carelessness during the waste disposal process. When the trash can fill to the top, an alarm buzzer alerts the concerned area's municipal corporation to empty it. The IoT waste monitoring system implemented on RPi consists of a power supply circuit, ultrasonic level sensors, a Raspberry Pi (RPi) digital controller, a Liquid Crystal Display (LCD) display, and a remote server.
A training program to reduce reader search errors for liver metastasis detection in CT

Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, eight non-abdominal staff, and eight abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test. In the pre-test, each radiologist interpreted 40 liver CT exams containing 91 metastases, circumscribed suspected hepatic metastases while under eye tracker observation, and rated confidence. In search training, radiologists interpreted a separate set of 30 liver CT exams while receiving eye tracker feedback and after coaching to increase use of coronal reformations, interpretation time, and use of liver windows. In classification training, radiologists interpreted up to 100 liver CT image patches, most with benign or malignant lesions, and compared their annotations to ground truth. Post-test was identical to pre-test. Between pre- and post-test, sensitivity increased by 2.8% (p = 0.01) but AUC did not change significantly. Missed metastases were classified as search errors (<2 seconds gaze time) or classification errors (>2 seconds gaze time) using the eye tracker. Out of 2775 possible detections, search errors decreased (10.8% to 8.1%; p < 0.01) but classification errors were unchanged (5.7% vs 5.7%). When stratified by difficulty, easier metastases showed larger reductions in search errors: for metastases with average sensitivity of 0-50%, 50-90%, and 90-100%, reductions in search errors were 16%, 35%, and 58%, respectively. The training program studied here may be able to improve radiologist performance by reducing errors but not classification errors.
A New Median Filter Algorithm in Image Tracking Systems

A new method of median filter algorithm based on the last frame is proposed to meet the demands of real time image preprocessing. It makes full use of the relative information of adjacent windows to finish the median filter processing by arranging only the added column (or row) of pixels with the rest of the arranged pixels remaining unchanged when one column (or row) of pixels is deleted and another column (or row) added. It has been proved that the new algorithm can well reduce the complexity of traditional median filter algorithm from O(D 2) to O(D) . Its results of application turns out to be satisfactory.
Anatomy and homology of the caudal auricular muscles in greater short-nosed fruit bat (Cynopterus sphinx)

Bats can be phylogenetically classified into three major groups: pteropodids, rhinolophoids, and yangochiropterans. While rhinolophoids and yangochiropterans are capable of laryngeal echolocation, pteropodids lack this ability. Delicate ear movements are essential for echolocation behavior in bats with laryngeal echolocation. Caudal auricular muscles, especially the cervicoauricularis group, play a critical role in such ear movements. Previously, caudal auricular muscles were studied in three species of bats with laryngeal echolocation, but to our knowledge, there have been no studies on non-laryngeal echolocators, the pteropodids. Here, we describe the gross anatomy of the cervicoauricularis muscles and their innervation in Cynopterus sphinx by using diffusible iodine-based contrast-enhanced computed tomography and 3D reconstructions of immunohistochemically stained serial sections. A previous study on bats with laryngeal echolocation reported that rhinolophoids have four cervicoauricularis muscles and yangochiropterans have three. We observed three cervicoauricularis muscles in the pteropodid C. sphinx. The number of cervicoauricularis muscles and their innervation pattern were comparable to those of non-bat boreoeutherian mammals and yangochiropterans, suggesting that pteropodids, and yangochiropterans maintain the general condition of boreoeutherian mammals and that rhinolophoids have a derived condition. The unique nomenclature had been previously applied to the cervicoauricularis muscles of bats with laryngeal echolocation, but given the commonality between non-bat laurasiatherians and bats, with the exception of rhinolophoids, maintaining the conventional nomenclature (i.e., M. cervicoauricularis superficialis, M. cervicoauricularis medius, and M. cervicoauricularis profundus) is proposed for bats.
Microcell‐Net: A deep neural network for multi‐class classification of microscopic blood cell images

Blood‐related diseases are one of the major concerns in the biomedical domain and most of the disease symptoms are reflected through the analysis of blood cells. The diagnosis by experts in laboratories is very costly and time‐consuming, thus artificial intelligence‐based systems can help in the automatic diagnosis and monitoring of an individual's health. In this study, a CNN‐based architecture Microcell‐Net is proposed which is trained on a microscopic image dataset of peripheral blood cells in eight different classes. The images have several inter‐class and intra‐class diversity with different magnification levels and the noise present in the images makes the classification task significantly challenging. Experimental results indicated that the proposed model can efficiently classify various types of microscopic blood cells with good accuracy. The experimental findings accomplished 98.76% validation accuracy and 97.65% test accuracy in complex background conditions. The performance of the model is compared with other state‐of‐the‐art models and the proposed deep neural network performs significantly better than others. The proposed model can be utilized in a real‐time diagnosis system because it is fast, automatic and efficient, which can assist in taking clinical decisions and early diagnosis of haematological disorders.
Geodesic distances in the maximum likelihood estimator of intrinsic dimensionality

While analyzing multidimensional data, we often have to reduce their dimensionality so that to preserve as much information on the analyzed data set as possible. To this end, it is reasonable to find out the intrinsic dimensionality of the data. In this paper, two techniques for the intrinsic dimensionality are analyzed and compared, i.e., the maximum likelihood estimator (MLE) and ISOMAP method. We also propose the way how to get good estimates of the intrinsic dimensionality by the MLE method.
On the internal quantum efficiency for InGaN/GaN light‐emitting diodes grown on insulating substrates

The internal quantum efficiency (IQE) for InGaN/GaN light‐emitting diodes (LEDs) grown on [0001] sapphire substrates is strongly affected by various factors including polarization effect in the InGaN/GaN multiple quantum wells (MQWs), insufficient electron and hole injections, low p‐type GaN doping efficiency, carrier loss due to the Auger recombination, and current crowding effect especially for the hole current in the p‐GaN region. In this work, the remedies taken by the scientific community to enhance the IQE are reviewed, compared and summarized. Meanwhile, this review also discusses alternative ways including polarization self‐screening effect, polarization cooling, hole accelerator, and hole modulator. The structural solutions we propose in this work can better improve the device performance without increasing the processing difficulty significantly, and their effectiveness in improving the IQE is further supported by the numerical and experimental studies. For example, on the contrary to common belief, the polarizations in the [0001] oriented InGaN/GaN LEDs can be advantageously used to improve the device performance based on our designs.
Polyphenols—Ensured Accessibility from Food to the Human Metabolism by Chemical and Biotechnological Treatments

Polyphenols are plant-based compounds famous for their positive impact on both human health and the quality of food products. The benefits of polyphenols are related to reducing cardiovascular diseases, cholesterol management, cancers, and neurological disorders in humans and increasing the shelf life, management of oxidation, and anti-microbial activity in food products. The bioavailability and bio-accessibility of polyphenols are of the highest importance to secure their impact on human and food health. This paper summarizes the current state-of-the-art approaches on how polyphenols can be made more accessible in food products to contribute to human health. For example, by using food processing methods including various technologies, such as chemical and biotechnological treatments. Food matrix design and simulation procedures, in combination with encapsulation of fractionated polyphenols utilizing enzymatic and fermentation methodology, may be the future technologies to tailor specific food products with the ability to ensure polyphenol release and availability in the most suitable parts of the human body (bowl, intestine, etc.). The development of such new procedures for utilizing polyphenols, combining novel methodologies with traditional food processing technologies, has the potential to contribute enormous benefits to the food industry and health sector, not only reducing food waste and food-borne illnesses but also to sustain human health.
Mitigating Malware Attacks using Machine Learning: A Review

In this digital era, lots of technology is available to reduce human effort through industrial automation. Industrial automation protects digital data from unauthorized access while reducing human effort. Securing digital data from unauthorized access, corruption, or theft is becoming increasingly difficult. Attackers easily find helpful information and use it for other malicious activities that could harm the legitimate user. Due to the wide range of options available to steal users’ essential data, most hackers use malware by writing malicious computer programs. To prevent these attacks, efficient machine learning (ML)-based algorithms are required to identify malicious code in the files used by hackers. The authors reviewed existing literature for preventing various malware attacks using a machine learning approach in this study.
Effects of Home Based Serious Game Training (Brain Talk™) in the Elderly With Mild Cognitive Impairment: Randomized, a Single-Blind, Controlled Trial

Mild cognitive impairment (MCI) increases with aging society. Serious games may be effective in improving cognitive function in patients with MCI; however, research on their effects remains insufficient. This study aimed to confirm the efficacy and safety of cognitive rehabilitation training using a serious game (Brain Talk™) for the elderly with MCI. Twenty-four elderly individuals with MCI were randomized into study and control groups. The study group received 12 training sessions (30 min/session, 3 times/week), whereas the control group did not receive training. Blinded evaluations were conducted before and after the training and four weeks after the training. The primary outcome measures were the Korean Mini-Mental State Examination (K-MMSE) and K-MoCA (Korean Montreal Cognitive Assessment). Secondary outcome measures were the Semantic Verbal Fluency Task (SVFT), Trail-Making Test-B, and 2-back test. In the study group, the K-MMSE, K-MoCA, and SVFT scores after finishing the training and 4 weeks after training showed a significant increase; however, there was no significant change in the control group. No significant differences were observed between the two groups. Cognitive function significantly improved in the study group after training. Home-based serious games are considered helpful in improving cognitive function.
Discriminant power analyses of non-linear dimension expansion methods

Most non-linear classification methods can be viewed as non-linear dimension expansion methods followed by a linear classifier. For example, the support vector machine (SVM) expands the dimensions of the original data using various kernels and classifies the data in the expanded data space using a linear SVM. In case of extreme learning machines or neural networks, the dimensions are expanded by hidden neurons and the final layer represents the linear classification. In this paper, we analyze the discriminant powers of various non-linear classifiers. Some analyses of the discriminating powers of non-linear dimension expansion methods are presented along with a suggestion of how to improve separability in non-linear classifiers.
Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data

Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of customer transactions available from open banking APIs. The first model is a deep learning model trained from scratch, while the second model uses transfer learning with a multilingual BERT model. We evaluate the predictive performance of these models using the area under the receiver operating characteristic curve (AUC) and Brier score. We find that a deep learning model trained from scratch outperforms a BERT transformer model finetuned on the same data. Furthermore, we find that SHAP can be used to explain such models both on a global level and for explaining rejections of actual applications.
Optimal target VOI size for accurate 4D coregistration of DCE-MRI

Dynamic contrast enhanced (DCE) MRI has emerged as a reliable and diagnostically useful functional imaging technique. DCE protocol typically lasts 3-15 minutes and results in a time series of N volumes. For automated analysis, it is important that volumes acquired at different times be spatially coregistered. We have recently introduced a novel 4D, or volume time series, coregistration tool based on a user-specified target volume of interest (VOI). However, the relationship between coregistration accuracy and target VOI size has not been investigated. In this study, coregistration accuracy was quantitatively measured using various sized target VOIs. Coregistration of 10 DCE-MRI mouse head image sets were performed with various sized VOIs targeting the mouse brain. Accuracy was quantified by measures based on the union and standard deviation of the coregistered volume time series. Coregistration accuracy was determined to improve rapidly as the size of the VOI increased and approached the approximate volume of the target (mouse brain). Further inflation of the VOI beyond the volume of the target (mouse brain) only marginally improved coregistration accuracy. The CPU time needed to accomplish coregistration is a linear function of N that varied gradually with VOI size. From the results of this study, we recommend the optimal size of the VOI to be slightly overinclusive, approximately by 5 voxels, of the target for computationally efficient and accurate coregistration.
Detection Method for Equivalent Ice Thickness of 500-kV Overhead Lines Based on Axial Tension Measurement and Its Application

Overhead line icing may threaten the operational stability of power grid. In this article, a detection method for equivalent ice thickness of 500-kV overhead lines based on axial tension measurement was proposed. First, according to the tension of insulator string when the transmission lines are not covered with ice, the horizontal distance from the lowest point of the transmission line to the tower on the side where the tension sensor is installed is calculated. Second, the state equation of the transmission line in the whole tension Section is established. Then, the state equation is solved by an iterative method. The initial ice thickness is taken as 0 mm. The ice thickness when the iteration value of tension $F_{i}$ is equal to the measured value of tension $F$ is taken as the equivalent ice thickness of transmission lines. The actual cases of single-span transmission line and multispan transmission line were used for verification and application effect analysis of the proposed method. The results show that the detected results of this method were consistent with the artificial ice observation values of typical 500-kV single-span and multispan transmission line cases. The accuracy of the proposed method can be further verified through the finite-element simulation analysis. The variation trends of the detected ice thickness are consistent with the icing conditions created by the meteorological factors during the same period and can be verified by the icing photos captured on site.
H_∞ Output Feedback Control Design of Fuzzy Dynamic Systems via LMI

This paper presents H∞ output feedback control design of complex nonlinear systems which can be represented by a fuzzy dynamic model. Based on a common Lyapunov function and a piecewise differentiable Lyapunov function respectively, two kinds of new H∞ output feedback fuzzy control design methods are developed. The H∞ output feedback controller can be obtained by solving a set of suitable linear matrix inequalities. An example is given to demonstrate the application of the proposed design methods.
High Fidelity Quantum Teleportation

Quantum teleportation is one of the most fundamental subjects in quantum information technology. This is because quantum teleportation can be used as not only a communication network but also a building block for universal quantum information processing. It is well known that there are two approaches (i. e. discrete and continuous variable approaches) in quantum information processing. In particular, continuous variable one has some advantages, that is, capability for deterministic and efficient quantum operations. In the case of continuous variable quantum teleportation, however, we cannot teleport quantum states perfectly due to finite level of quadrature squeezed states. Hence it is important to improve the fidelity of teleportation to realize more advanced quantum network and circuits. Thanks to the recent improvement of squeezing level, fidelity up to F=0.83 has been achieved. In this article, we review the experiment of high fidelity quantum teleportation for continuous variables, and its applications.