Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinal Cord Diffuse Midline Glioma With Histone H3 K27M Mutation in a Pediatric Patient

Background: Diffuse midline glioma (DMG) with histone H3 K27M mutation is a recently identified entity documented in the 2016 World Health Organization (WHO) Classification of Tumors of the Central Nervous System. Spinal cord DMGs with H3 K27M-mutant are commonly reported in adults. Herein, we reported a pediatric patient with spinal cord H3 K27M-mutant DMG. Case Report: A 7-year-old girl with 1-month history of neck pain and 3-week history of progressive weakness in the right hand was presented. Spinal magnetic resonance imaging showed an intramedullary lesion with slight enhancement at the C2-7 levels. With intraoperative neuroelectrophysiological monitoring, the lesion was subtotally resected. Histopathological examination revealed a DMG with histone H3 K27M mutation corresponding to WHO grade IV. Postoperatively, the neck pain was relieved, and the upper-extremity weakness remained unchanged. Oral temozolomide was administrated for 7 months, and radiotherapy was performed for 22 courses. After an 18-month follow-up, no tumor recurrence was noted. Conclusion: Spinal cord H3 K27M-mutant DMGs are extremely rare in pediatric patients. Preoperative differential diagnosis is challenging, and surgical resection with postoperative chemoradiotherapy may be an effective treatment.
PD-1 Inhibitors Could Improve the Efficacy of Chemotherapy as First-Line Treatment in Biliary Tract Cancers: A Propensity Score Matching Based Analysis

Background There are limited treatment options for advanced biliary tract cancers (BTCs), including intrahepatic cholangiocarcinoma, extrahepatic bile duct cancer, gallbladder cancer. We compared the efficacy and safety of PD-1 inhibitors plus chemotherapy and chemotherapy alone as first-line treatment in patients with advanced BTC. Methods We retrospectively reviewed patients with BTC treated at the oncology department of the Chinese PLA general hospital receiving PD-1 inhibitor with chemotherapy (anti-PD-1+C group) or chemotherapy alone (C group). Propensity Score Matching (PSM) (1:1) was performed to balance potential baseline confounding factors. Progression-free survival (PFS) was analyzed using Kaplan–Meier survival curves with log-rank tests. Objective response rate (ORR), disease control rate (DCR), and safety were also analyzed. Results This study included 75 patients who received PD-1 inhibitors (including Pembrolizumab, Nivolumab, Sintilimab, Toripalimab) plus chemotherapy and 59 patients who received chemotherapy alone. After matching, there were no significant differences between the two groups for baseline characteristics. Within the matched cohort, the median PFS was 5.8m in the anti-PD-1+C group, which was significantly longer than the C group, at 3.2m (HR: 0.47, 95% CI 0.29 to 0.76, P = 0.004). The ORR was 21.7% and DCR was 80.4% in the anti-PD-1+C group, while the ORR was 15.2% and DCR was 69.6% in the C group. No significant differences were found in the ORR and DCR between the two groups (P=0.423, P=0.231). Grade 3 or 4 treatment was related to adverse events (AEs) that occurred in the anti-PD-1+C group, namely hypothyroidism (n=3, 6.5%), rash (n=2, 4.2%), and hepatitis (n=1, 2.2%). There was no AE-related death. The grade 3-4 leukopenia rate was similar in the two groups (4.3% vs. 6.5%). Conclusions Anti-PD-1 therapy plus chemotherapy prolonged the PFS compared with chemotherapy alone in advanced BTC with controllable AEs. Further clinical trials are needed to confirm this result.
57Fe Mössbauer Analysis of Meteorites and Tektites

This review presents studies on iron meteorites (Campo del Cielo fall and an unregistered iron meteorite), an unregistered stony meteorite from Northwest Africa, and 13 tektites from the American, European, and Australasian strewn fields. The main experimental technique used in the studies was Mossbauer spectroscopy, both in transmission and backscattering geometries. For the latter, a MIMOS II spectrometer was used. Additionally, optical and scanning electron microscopies and X-ray diffraction were used. In the studied iron meteorites, kamacite is found as the main mineral. Campo del Cielo meteorite exhibits Widmanstatten patterns and schreibersite inclusions. The unregistered iron meteorite has Neumann lines and schreibersite inclusions. We have assigned Campo del Cielo as an octahedrite and the unregistered iron meteorite as a hexahedrite. The unregistered stony meteorite is composed mainly of iron-free silicates; at 4.2 K, the spectrum indicates maghemite and 1% troilite. The Cambodian tektite appeared individualized from other australasites, unlike the moldavite, which tends to cluster with them. Our analyses do not allow dismissing doubts on the provenance of tibetanites. The Fe3+/Fe2+ ratio was found to be higher for Muong Nong-type tektites than for splash-form tektites, as expected from their morphology and solidification from melt at lower temperature.
High-quality Frame Interpolation via Tridirectional Inference

Videos have recently become an omnipresent form of media, gathering much attention from industry as well as academia. In the video enhancement field, video frame interpolation is a long-studied topic that has dramatically improved due to the advancement of deep convolutional neural networks (CNN). However, conventional approaches utilizing two successive frames often exhibit ghosting or tearing artifacts for moving objects. We argue that this phenomenon comes from the lack of reliable information provided only by two frames. With this motivation, we propose a frame interpolation method by utilizing tridirectional information obtained from three input frames. Information extracted from triplet frames allows our model to learn rich and reliable inter-frame motion representations, including subtle nonlinear movement, which can be easily trained via any video frames in a self-supervised manner. We demonstrate that our method generalizes well to high-resolution content by evaluating on FHD resolution, and illustrates our approach’s effectiveness via comparison to state-of-the-art methods on challenging video content.
The Determination of Teachers' Motivation Based on Herzberg’s Motivation Theory

Motivation initiates, maintains and directs spiritual and physical activity of individuals. It is important to determine the motivation of teachers who play important role in education and technology usage. The purpose of this study is to use the motivational scale to measure the motivation of teachers working in Zonguldak city of Turkey. The the cross-sectional survey design is used in the study. The motivation scale adopted from the components of the Herzberg’s motivation theory was used in the study for data collection. 463 teachers working in Zonguldak city of Turkey completed the scale and became the sample of the study. The findings of the study showed that the motivation of teachers in Zonguldak city of Turkey is high. It is also found that the motivation level of female teachers is higher than that of male teachers. Moreover the motivation of teachers does not change based on the school type, the type of school that teachers graduated from and the school level teachers work for. In future studies, the motivation of teachers working in other cities of Turkey should be investigated. The data of the study was obtained from the responses of the participants to the scales. In future studies, motivation of teachers should be examined using the qualitative data obtained through interviews and observations.
A Cognitive Model of the Tactile Vibration Sense and Experiments on a Touch Simulation System

The tactile sensing and interaction technologies mimicking human skin and sensor mechanisms are a highly demanded research in both theories and applications. This paper presents the design of an experimental system for sensing the tactile vibration sensations by mimicking human skin. An experimental device for vibration and tactile perception is developed based on LabVIEW technologies, which can control the vibration intensity, time interval and sequence of five vibration sources. By specifying a set of psychophysical parameters on the device, a series of vibration and tactile experiments are carried out on fingers, arms, abdomen and other parts of the body. The tactile perceptions on vibration mechanisms have been tested by different vibration intensity, sequence, and interval. The experimental results have enabled applications in tactile perception and interaction interfaces towards a portable blind guide system.
Experimental and Numerical Demonstration of Superior RBSOAs in 1.2 kV SiC Trench and SBD-integrated Trench MOSFETs

In this study, we investigated the reverse bias safe operating areas (RBSOAs) of three types of state-of-the-art 1.2 kV SiC trench MOSFETs, through experiments and numerical simulations. The experimental results revealed that both SiC trench MOSFETs had significantly larger RBSOAs than a commercial 1.2 kV silicon trench field stop (FS) IGBT. And interestingly, unlike the short-circuit safe operating area (SCSOA), the RBSOA of the Schottky barrier diode-wall integrated trench MOSFET (SWITCH-MOS) was sufficiently large and roughly that of the trench MOSFETs without SBD integration (IE-UMOSFETs). Further, we found that SiC trench MOSFETs with extremely low on-resistance had a very wide RBSOA.
Quantification of Performance Variation and Crack Evolution of Bond-Wire Interconnects Under Harsh Temperature Environments by S-Parameter Analysis

Electrical interconnects play important roles in both the performance and reliability of integrated circuits. As the operating frequency has increased and products have been exposed to harsh environments such as temperature extremes and thermal cycling, it has become essential to quantify the thermal effects on the reliability of such circuits. In particular, the effects on the signal transmission performance of the interconnects and the evolution of cracks caused by thermo-mechanical stresses need to be investigated. This study proposes that both research goals can be accomplished by S-parameter analysis alone. First, the performance variation was experimentally quantified by in situ measurements of S21 (an indicator of signal transmission performance) on bond-wire test vehicles at static temperatures between −60 °C and 120 °C. An impedance model of the interconnect was developed focusing on thermal expansion effects and it predicted the performance variation of the interconnect successfully at the given temperatures. Second, the results of ex situ measurements of S11 (an indicator of return signal) obtained using a test vehicle under thermal cycling were compared with scanning electron microscopy images of crack propagation in the interconnect. In the crack initiation phase, a resonance peak occurred on the S-parameter pattern and the peak moved toward lower frequencies as the crack propagated until failure. SPICE simulations using a suggested impedance model of the cracked interconnect confirmed the experimental results. In addition, this study demonstrates that monitoring the resonant frequency in the S-parameter pattern as a prognostic factor can predict the initiation and evolution of cracks earlier and is more sensitive than the conventional dc resistance measurement method.
Evaluation on a Drone Classification Method Using UWB Radar Image Recognition with Deep Learning

This paper presents a method for recognizing various drones from images of ultra-wideband (UWB) radar range profile by using a convolutional neural network (CNN) model. CNN is a Deep learning algorithm that provides high accuracy for image recognition tasks. We investigated the recognition performance for five types of drones with different shapes, sizes, and the number of rotor blades (Matrice 600, 3DR Solo, Phantom 3, Mavic pro, and Bebop drone) and a radio-controlled flapping bird (Bionic bird). As a result, we have confirmed that our presented method can recognize each target with high accuracy of 90 % or more.
Feasibility of Standalone TDoA-based Localization Using LoRaWAN

The growing need for localization has created an array of alternative approaches to GNSS, based on Wi-Fi, Bluetooth, Ultra-Wideband, etc. Long Range Wide Area Network (LoRaWAN) is one such technology that has garnered tremendous attention due to its low power and long-range capabilities. Many attempts to achieve localization using LoRaWAN have been made till now, based on a variety of techniques such as Angle of Arrival (AoA), Time Difference of Arrival (TDoA), Received Signal Strength Index (RSSI). In this paper, we present a novel, standalone localization approach by developing a collaborative, TDoA-based methodology using LoRaWAN. The server determines the target node location by means of TDoA measurements from the target node to the gateways. The introduction of an additional stationary node allows the synchronization of the gateways without utilizing GNSS, either inbuilt or external. The cooperation between target node, synchronization node and server, is the innovative feature which makes this approach attractive for GNSS-free localization. Further, we explore the effects of timing resolution, time-on-air, and duty cycle constraints on the localization error. Finally, the distribution of error in a triangle of gateways situated approximately 8.6 kilometers apart is simulated in ideal Line of Sight (LoS) conditions, showing the maximum error to be around 23 meters.
LEO Satellite Network Routing Algorithm Based on Reinforcement Learning

Satellite networking is a network composed of satellite connections, through which global coverage can be achieved to meet the communication requirements of users in remote areas. Low-orbit (LEO) satellites have the advantages of small transmission delay and global coverage, and have always been a concern hot spot. Due to the fast running speed of LEO satellites and its frequently changing satellite topology, it is difficult to directly apply routing protocols applied on the ground to LEO satellite networks. Therefore, it is necessary to design a suitable routing algorithm to make the LEO satellite network run smoothly. This paper first models the operating environment of a LEO satellite network. Then it proposes a satellite routing algorithm based on reinforcement learning according to the operating characteristics of LEO satellites and improves the traditional reinforcement learning algorithm. Simulation results show that the improved algorithm has better convergence time and results than the previous algorithm.
Current Control System of Hybrid Converter for Suppressing Current Harmonics

The driving electric machine of high-power flywheel energy storage system (FESS) has the characteristics of high fundamental frequency and low inductance, which lead to high current harmonic content and increases the loss of FESS. To suppress the current harmonics, a compound control scheme is proposed for an innovative hybrid converter in this paper. The mathematical model of the hybrid converter is analyzed and divided into two parts: main voltage source converter (MVSC) and filtering voltage source converter (FVSC). According to the characteristics of MVSC and FVSC in output power and switching frequency, two different regular sampling strategies are described and applied. On this basis, the exact MVSC model in discrete time-domain is established, and the current controller with complex vector decoupling is designed. Similarly, a current controller with feedback decoupling is designed for FVSC in the continuous time-domain. Both the current controllers have excellent robustness and dynamic response ability. Finally, the proposed control strategy in this paper is verified by simulation results.
Internet of Medical Things for Health Care Applications: Future Trends and Challenges

Healthcare is rapidly moving to a completely digitized environment (automation) and, as a result, many devices have been introduced which is mainly used for easy patient health monitoring to various health problems. The internet-based devices are mainly designed to lower the cost which helps people of all sectors and for better health of the people. The IoMT (Internet of Medical Things) is the connection of big data and the devices, that are operated automatically like robots, etc. The devices are mainly used for increasing accuracy, productivity, and reliability. This technology has been all over the world and there are still many challenges to overcome in this field and make it more reliable and accurate because it means the life of a human. This paper depicts such challenges and the application of IoMT in all the fields of healthcare and also the improvement in the above-mentioned challenges in recent years. It also identifies the problem and the minor challenges in the existing paper and also more concerns and ideas.
Predicting the Retweet Level of COVID-19 Tweets with Neural Network Classifier

A convolutional neural network (CNN) based classifier, to predict the retweet level of COVID-19 tweets, is proposed in this paper. The proposed CNN is able to predict whether a given COVID-19 tweet would be more retweeted, or less retweeted. The network is trained and validated with 100,000 and 5,000 English tweet samples, respectively, which were all posted within the last week of March 2020, and 81% accuracy has been achieved. The network is also evaluated by English tweet samples posted at the end of April. The result shows that the accuracy is about 80%. Therefore, the proposed approach is robust and capable to process tweets of chosen contents/topics.
Remez Exchange Algorithm for Approximating Powers of the Q-Function by Exponential Sums

In this paper, we present simple and tight approximations for the integer powers of the Gaussian Q-function, in the form of exponential sums. They are based on optimizing the corresponding coefficients in the minimax sense using the Remez exchange algorithm. In particular, the best exponential approximation is characterized by the alternation of its absolute error function, which results in extrema that alternate in sign and have the same magnitude of error. The extrema are described by a system of nonlinear equations that are solved using Newton– Raphson method in every iteration of the Remez algorithm, which eventually leads to a uniform error function. This approximation can be employed in the evaluation of average symbol error probability (ASEP) under additive white Gaussian noise and various fading models. Especially, we present several application examples on evaluating ASEP in closed forms with Nakagami-m, Fisher–Snedecor $\mathcal{F}$, η − µ, and κ − µ channels. The numerical results show that our approximations outperform the existing ones with the same form in terms of the global error. In addition, they achieve high accuracy for the whole range of the argument with and without fading, and it can even be improved further by increasing the number of exponential terms.
Performance of NLOS Base Station Exclusion in cmWave 5G Positioning

Positioning methods relying on cellular signals are subject to severe degradation errors due to the inherent harsh working conditions of urban scenarios. In contrast, emerging applications are gradually requesting a more accurate and reliable positioning solution, thus requiring the implementation of alternative measures to minimize such degradation. This paper describes a method for detecting faulty measurements from base station (BS) affected by non-line-of-sight (NLOS) propagation. The method monitors the residuals resulting from the least-squares positioning solution, inspired by the approach implemented by Receiver Autonomous Integrity Monitoring (RAIM) techniques in Global Navigation Satellite Systems (GNSS) receivers. The method has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of user’s position. Positioning Reference Signal (PRS) of 5G New Radio (NR) operating in the centimeter-wave (cmWave) band is used. Results confirm the utility of implementing NLOS monitoring method to achieve a better performance assessment under realistic assumptions while using 5G positioning signals.
An Improved Complex Vector Decoupling Control for high-speed PMSM

The complex vector decoupling control (CVDC) can achieve complete decoupling when the proportional coefficient ($k_{p}$) and the integral coefficient ($k_{i}$) of the current loops meet a certain proportion. But in most occasions, the $k_{p}$ and $k_{i}$ are out of that proportion, leading to incomplete decoupling. Thus, an improved complex vector decoupling control (ICVDC) is proposed. Based on the traditional CVDC, the current compensation is introduced to improve decoupling performance. The simulation results demonstrate the effectiveness of this method. Furthermore, the system shows good dynamic response performance and strong robustness.
VolMemLyzer: Volatile Memory Analyzer for Malware Classification using Feature Engineering

Memory forensics is a fundamental step that inspects malicious activities during live malware infection. Memory analysis not only captures malware footprints but also collects several essential features that may be used to extract hidden original code from obfuscated malware. There are significant efforts in analyzing volatile memory using several tools and approaches. These approaches fetch relevant information from the kernel and user space of the operating system to investigate running malware. However, the fetching process will accelerate if the most dominating features required for malware classification are readily available. This paper introduces VolMemLyzer, a python-based tool developed to excerpt the most critical characterization feature set from the memory dumps taken during live malware infection. It extracts thirty-six most essential features and ranks them to classify malware. The tool is tested with a dataset of 1900 benign and malware samples with high true positive rate for binary and multi-class malware classification.
Contactless Optical Packaging Concept for Laser to Fiber Coupling

A novel method for the laser to fiber coupling is presented in which coarse alignment is achieved through micromachined v-grooves and mirrors in a fused silica substrate. Furthermore, fine repositioning is done in a noncontact manner by using a femtosecond laser to induce localized nanoscopic volume changes in specific locations within the bulk of the substrate, which, once combined with monolithic flexures within the substrate, produces a highly controlled motion. Using this principle, we overcome inherent manufacturing/assembly tolerances to achieve near theoretical coupling efficiency in a permanent manner. This proof of concept demonstrates a novel packaging principle where a sub-nm positioning resolution is achieved over tens of micrometers range of motion, efficiently overcoming intrinsic manufacturing tolerances.