Miracle-dz/UniFAR-FTUs huggingface dataset:
Interlayer distance effect on electronic properties and electrical conductivity of AA-stacked bilayer antimonene sheets

The electronic properties and electrical conductivity of AA-stacked bilayer flat and buckled antimonene sheets are studied under the effect of change in the interlayer distance starting from an experimental value of 1.87 Å. This theoretical study is conducting by first-principles calculations that allow us to analyze the data and give a physical justification for the studied quantities. The energy band gap is estimating for different interlayer distances. For bilayer flat antimonene. It is close to zero as graphene. However, the bilayer buckled antimonene energy gap is nearly independent of interlayer distance. The electrical transport constants are determined using the BoltzTraP code based on the Boltzmann transport equation (BTE) consider that the time relaxation is constant. The studied electrical conductivity over the relaxation time in terms of temperature results show the highest electrical conductivity corresponding to the narrower band gap. This result has an agreement with graphene conductivities. On the other side, bilayer flat with h=7.73 Å, and buckled antimonene electrical conductivities are six and three times larger than the single-layer conductivities, respectively.
Advanced EUV Resist Characterization using Scatterometry and Machine Learning

Comprehensive EUV resist characterization for line and space patterns at pitches between 32 and 40 nm using scatterometry in conjunction with machine learning algorithms is presented and discussed. Controlled experimental variations of EUV single expose resist lines were introduced by exposure dose and illumination conditions. Scanning electron microscopy and atomic force microscopy were used to collect reference data. The developed machine learning solutions allow for determination of four characteristic resist metrics with a single scatterometry measurement: line width, line height, line edge roughness, and line top roughness. Therefore, in excellent agreement with reference metrology, the scatterometry measurement, can replace four individual operations for rapid and accurate in-line monitoring.
Learning-Based Fuzzing of IoT Message Brokers

The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.
Incorporating Transmission Delays Supported By Diffusion Mri In Meg Source Reconstruction

White matter fibers transfer the information between brain regions with delays that are measurable with magnetoencephalography and electroencephalography (M/EEG). In the context of regularizing the dynamics of M/EEG and recovering electrical activity of the brain from M/EEG measurements, this article proposes a graph representation-based framework to solve the M/EEG inverse problem, where prior information about transmission delays supported by diffusion MRI (dMRI) are included to enforce temporal smoothness. Results of the reconstruction of brain activity from simulated MEG measurements are compared to MNE, LORETA and CGS methods and we show that our approach improves MEG source localization when compared to these three state-of-the-art approaches. In addition, we show preliminary qualitative results of the proposed reconstruction method on real MEG data for a sensory-motor task.
Molecular Simulation of CO2- and CO3-Brine-Mineral Systems

Atomistic simulations—molecular dynamics (MD) and Monte Carlo (MC) simulations, ab initio and density functional theory (DFT) calculations—have proved useful in gaining insight into the molecular basis of fundamental processes in aquatic geochemistry, such as solvation, ion pair formation, adsorption, molecular diffusion, and the energetics of mineral phases (Rotenberg et al. 2007; Bickmore et al. 2009; Hamm et al. 2010; Kerisit and Liu 2010; Hofmann et al. 2012; Stack et al. 2012; Wallace et al. 2013). Key strengths of these simulations are their ability to examine the behavior of individual atoms (where spectroscopic and other experimental methods probe the average behavior of large numbers of molecules) and to allow constraints that would be difficult or impossible to impose in the laboratory. These features make atomistic simulations powerful tools for elucidating the manner in which collective phenomena arise from molecular scale properties in geochemical systems. The range of length and time scales probed by atomistic simulations (from angstroms to tens of nanometers and from femtoseconds to microseconds, continuously expanding with advances in the availability and sophistication of computational resources) makes them ideally suited to complement several spectroscopic techniques, including X-ray, neutron, and nuclear magnetic resonance approaches. A well known limitation of the methods described in the present chapter, particularly in the case of classical mechanical (MD and MC) simulations, is the approximate nature of the models that are used to describe interatomic forces. In the simplest of these simulations, bond lengths and angles are fixed; inter-atomic interactions are modeled as the sum of two-body interactions that depend only on the identity of the interacting atoms and the distance between them; and chemical bonds are not allowed to break or form during a simulation (Allen and Tildesley 1987; Frenkel and Smit 2001). The choice of force …
XPGAN: X-Ray Projected Generative Adversarial Network For Improving Covid-19 Image Classification

This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-of-the-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. The proposed solution, X-ray Projected Generative Adversarial Network (XPGAN), addresses the most fundamental issue in training such a deep neural network on limited human-annotated datasets. By leveraging the generative adversarial network, we can synthesize a large amount of chest X-ray images with prior categories from more accurate 3D Computed Tomography data, including COVID-19, and jointly train a model with a few hundreds of positive samples. As a result, XPGAN outperforms the vanilla DenseNet121 models and other competing baselines trained on the same frontal chest X-ray images.
Non Invasive Stress Detection Method Based on Discrete Wavelet Transform and Machine Learning Algorithms

Stress can lead to many serious health problems as it can cause long-term chemical changes if our body is exposed to it for a lengthened period. Thus, it is important to develop a stress detection system to provide early warning for individuals. This is a study about the stress detection in students during their real time activities using the more convenient wearable smartwatch (Raqib) with the complete access to the raw ECG data. We have proposed a Discrete Wavelet Transform (DWT) method to preprocess the raw data along with the use of symlet4 filter to generate 4 levels of Daubechies coefficients. This study identifies 9 different time as well frequency-based features that show significant dependence on stress or simply directly related to stress. We have applied 7 different classifiers to our ECG data features obtained from 30 students and Naïve Bayes classifier produces the highest accuracy of 96.67% under 10-fold cross validation procedure.
An Optimal Envelope-Based Noise Reduction Method for Cochlear Implants: An Upper Bound Performance Investigation

Cochlear implants (CI) are surgically implanted auditory prostheses for partially compensating severe to profound sensorineural hearing losses. Despite providing significant intelligibility levels under silent conditions, their performance is highly affected by additive noise. Time-frequency masks, such as the binary mask, the Wiener filter (WF) and their variations, have been widely employed for noise reduction. However, they were not originally designed for CI applications, which have very particular characteristics. In this work, we propose a new method for noise reduction, especially designed for CI. It is based on the minimization of the mean square error between the squared envelopes of the estimated and target speech. The theoretical derivation of the proposed time-frequency mask is presented and a closed-form for the one-coefficient optimal solution is obtained. Numerical simulations with objective criteria indicate that the proposed method results in higher intelligibility scores as compared to the time-domain implementation of the WF time-frequency mask. Psychoacoustic experiments with normal hearing volunteers and vocoded signals, as well as CI users, corroborate simulation results, showing significant increases in intelligibility, especially for SNR < −5 dB. The proposed method may result in an intelligibility increase of up to 70%, for SNR = −25 dB, as compared to the WF. These findings were obtained considering an ideal signal-to-noise ratio estimator.
Impact of Hole Blocking Layer on the Performance of Solution-Processed Small Molecule Solar Cells

In this study, we report the use of poly [(9,9-bis (30- (N,N-dimethylamino) propyl) -2,7-fluorene) -alt-2,7- (9,9-dioctylfluorene) (PFN) as hole blocking layer (HBL) in inverted small-molecule solar cells (SM-iOSCs) using a bulk heterojunction of p-DTS(FBTTh2)2 as donor material and PC70BM as acceptor material. The behavior of these devices is compared to those SM-iOSCs where the HBL was zinc oxide (ZnO). Under 1 sun illumination, devices exhibited a power conversion efficiency (PCE) of 6.75%. It is demonstrated, through analysis of the external quantum efficiency (EQE), abortion UV-vis and atomic force microscopy of the active layer, that charge transport is the limiting factor in the performance of the cells, directly affecting the short circuit current (JSC).
A Two-Stage Multiple Criteria Decision Making for Site Selection of Solar Photovoltaic (PV) Power Plant: A Case Study in Taiwan

At the heart of Covid-19 responses, the transition from fossil sources to green energy is an urgent issue for nations to address the crisis and secure sustainable economies. As a country in a seismically active zone that relies heavily on imported fossil fuels, Taiwan is vigorously taking the next step in renewable energy development, which is pivotal to securing its position in global supply chains. Solar energy is today the most suitable renewable energy source for Taiwan. However, land prices and policies, and challenges of scale still hinder its development. In this context, identifying optimal sites for solar photovoltaic (PV) construction is a crucial task for major energy stakeholders. In this paper, a two-stage approach, combining the data envelopment analysis (DEA) models and the analytic hierarchy process (AHP), has been done for the first time to identify the most suitable locations among 20 potential cities and counties of Taiwan for constructing solar PV farms. DEA models were applied to filter out the areas with the most potential by measuring their efficiency indices with temperature, wind speed, humidity, precipitation, and air pressure, as inputs, and sunshine hours and insolation, as outputs. The locations with perfect efficiency scores were then ranked with the AHP method. Five selected evaluation criteria (site characteristics, technical, economic, social, and environmental) and sub-criteria of each were utilized to prioritize the locations with solar energy potential. AHP was used to determine the relative weights of the criteria and sub-criteria and the final weights of the areas. For criteria weighting results, “support mechanisms,” “electric power transmission cost,” and “electricity consumption demand” with weights of 0.332, 0.122, and 0.086, respectively, were found as the most significant sub-criteria. The final ranking suggests Tainan, Changhua, and Kaohsiung as the top three most suitable cities for constructing solar PV energy systems.
Coronary Artery Lumen Segmentation in CCTA Using 3D CNN with Partial Annotations

Accurate three-dimensional (3D) segmentation of the coronary artery lumen is an essential step in the quantitative analysis of coronary artery stenosis. However, due to the small size and complex structure of the coronary artery tree, it is difficult and laborious to perform voxel-by-voxel labeling of the lumen on 3D cardiac computed tomography angiography (CCTA) images. Since radiologists tend to focus only on the regions of interest, the annotations collected are often imperfect and contain some false-negative targets. To address the problem of partial annotations, a 3D convolutional neural network (CNN)-based approach for coronary artery lumen segmentation is proposed. Our CNN model adopts an U-Net like backbone and has multiple auxiliary branches on the expanding path. In the inference stage, an uncertain map of the network prediction is generated from the multiple abstract feature maps, which is used to refine the segmentation results. Experimental results on the MICCAI 2020 Automated Segmentation of Coronary Arteries (ASOCA) challenge dataset show that our method achieves even better segmentation accuracy with partial annotations than the backbone model trained with full annotations.
Throughput Analysis of In-Band Full-Duplex Transmission Networks With Wireless Energy Harvesting Enabled Sources

In this paper, we investigate a two-hop communication system in which the source utilizes radio frequency (RF) energy harvesting for the transmission of information. An intermediate relay works in a half-duplex (HD) mode in the energy harvesting (EH) phase to provide energy to the source. In the information transmission phase, the relay works in a full-duplex (FD) mode to receive information signals from the source and simultaneously transmit information signals to the destination in the same frequency band. The paper provides the analysis of outage probability and throughput of a FD relaying system and two HD relaying systems. The first HD system (<inline-formula> <tex-math notation="LaTeX">$HD_{1}$ </tex-math></inline-formula>) has the same EH duration as the FD system while the second HD one (<inline-formula> <tex-math notation="LaTeX">$HD_{2}$ </tex-math></inline-formula>) has the same transmitting power from the source as the FD one. The results show that the throughput with respect to the time split between the EH phase and the information transmission one is a concave function and the optimal time split can be calculated numerically. Besides, the polarization dissimilarity factor of the antennas used has an influence on the system throughput, which is maximized when polarization states are orthogonal. The FD system can double the system throughput while having the outage probability as low as that of the <inline-formula> <tex-math notation="LaTeX">$HD_{1}$ </tex-math></inline-formula> system, at a cost of adopting the extra polarization-enabled digital cancellation (PDC) scheme. Meanwhile, the FD system can nearly double the system throughput while having the outage probability superior to that of the <inline-formula> <tex-math notation="LaTeX">$HD_{2}$ </tex-math></inline-formula> system. Simulations are run to confirm the above analyses.
[The present status of accidental hypothermia in Japan (1)].

本報告は, 日本における偶発性低体温症の現況についての調査研究の第一歩として行ったものである. 北海道, 東京都, 神奈川県, 山梨県の4地区を選び“凍死および低体温症に関するアンケート”を送付した. 発送総数2,366通, 回答数597通, 症例数30例であった. I. 偶発性低体温症について次の結果が得られた. 1. 偶発性低体温症は, 遭難 (10例) のほか酩酊 (13例) や各種疾患 (7例) でみられた. 男性に多い. 2. 発症は冬期に多く夏期に少ない. 夜間とくに早朝に多い. 3. 発見時体内温20℃以上の場合は治療により全快しうる可能性があり, 30℃未満では, 合併症がなくても低体温により死に至る可能性がある. 4. 処置として, 再加温, 人工呼吸, 輸液, 強心剤などの薬剤投与が行われており, 有効であった. II. 老人性低体温症 (老人における偶発性低体温症) について次の結果が得られた. 症例数は, 偶発性低体温症30例中8例であった. 1. 発生時の状況は遭難2例, 酩酊2例, 疾患4例であった. 健常成人に危険のない環境条件下で, 健常老人が低体温症に罹患した例は報告されていない. 2. 老人性低体温症患者の社会環境については5例の回答があった. 家族との同居が多く, 経済状態は普通で, 家屋は一戸建である. 介護を要するもの5例中3例, 失禁・痴呆は5例中1例であった. 欧米でみられる一人暮しで, 経済状態の悪い老人に低体温症が多いとの傾向はあらわれていない.
ROS Based Six-DOF Robotic Arm Control Through CAN Bus Interface

In this research work we propose a mechanism where six actuator nodes which represent the six degrees of freedom (DOF) of a robotic arm are connected via the Controller Area Network (CAN) bus to forms its communication network and receives commands from a common controller (Raspberry Pi). A simulation environment is created in the Gazebo simulator of host simulator and real time analysis of the robotic arm is done via Rviz. Through the simulation, the robotic arm receives instructions from a joystick integrated to the same host PC. The position of each joint with respect to the command from the joystick is sent over WiFi to the common controller and an indication that the message has been received is shown on hardware using the CAN network setup. The arrangement creates an effective framework for learning the working of complex robotic systems with several DOFs.
Weddellite from renal stones: Structure refinement and dependence of crystal chemical features on H2O content

Abstract The refinement of the structures of 17 weddellite crystals [Ca(C2O4)·(2+x)H2O, I4/m, a = 12.329-12.378 Å, c = 7.345-7.366 Å, V = 1117.8-1128.6 Å3], which were taken from the oxalic renal stones of the St. Petersburg (Russian Federation) citizens of both sexes aged from 24 to 65 years, has been carried out by the means of single-crystal X‑ray diffraction (R1 = 0.024-0.057). According to the results of the study, the amount of “zeolitic” water molecules (x) in the structure of weddellite varies from 0.13 to 0.37 pfu. A significant positive correlation between the amount of “zeolitic” water in the structure of weddellite and the closest interatomic distance between coordination water molecules in the large channels (OW1-OW1) was found as well as positive correlation between the value of the a parameter and the average distance of in Ca polyhedron. Obtained linear regression equation: x = 5.43a - 66.80, can be used for determination of the “zeolitic” water amount using the known unit-cell a parameter with mean-root-square error ±0.03 pfu. It was found that the x value for the crystals selected from the “mono-weddellite” stones (x = 0.13-0.24) are at the bottom of the range, thus we can assume that weddellite crystals with fewer “zeolitic” water amounts would be relatively stable. This work expands the knowledge of pathogenic crystal growth processes in living organisms and the development of the theory of oxalate stone formation in humans and animals, and may provide a building block for biomolecular technologies that approach the prevention and treatment of diseases associated with lithiasis.
Micro-Ct Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference

Existing medical image super-resolution methods rely on pairs of low- and high-resolution images to learn a mapping in a fully supervised manner. However, such image pairs are often not available in clinical practice. In this paper, we address super resolution problem in a real-world scenario using unpaired data and synthesize linearly eight times higher resolved Micro-CT images of temporal bone structure embedded in the inner ear. We explore cycle-consistency generative adversarial networks for super-resolution and equip the model with Bayesian inference. We further introduce Hu Moments distance as the evaluation metric to quantify the shape of the temporal bone. We evaluate our method on a public inner ear CT dataset and have seen both visual and quantitative improvement over state-of-the-art supervised deep-learning based methods. Further, we conduct a multi-rater visual evaluation experiment and find that three inner-ear researchers consistently rate our method highest quality scores among three methods. Furthermore, we are able to quantify uncertainty in the unpaired translation task and the uncertainty map can provide structural information of the temporal bone.
Polpação soda com aditivos e cinética de deslignificação Kraft

VIII INTRODUCAO GERAL 1 CAPITULO 1 3 ESTUDO DO EFEITO DE ANTRAQUINONA E DE SURFACTANTE NA POLPACAO SODA . 3 RESUMO 3 ABSTRACT 4 4 1 INTRODUCAO 5 2 OBJETIVOS 6 3 MATERIAIS E METODOS 6 3.1. COZIMENTO LO-SOLIDS 6 3.2. LICORES DE COZIMENTO E ADITIVOS 7 3.3. ANALISES DE QUALIDADE DA POLPA 8 4 RESULTADOS 8 4.1. POLPA REFERENCIA 8 4.2. COZIMENTO SODA COM ADITIVOS 9 4.2. 1. COZIMENTO SODA COM ANTRAQUINONA (SODA AQ) 10 4.2. 2. COZIMENTO SODA COM SURFACTANTE (SURFAC.) 11 4.2. 3. COZIMENTO SODA COM MISTURA ANTRAQUINONA E SURFACTANTE (AQS) 12 5 CONCLUSOES 13 AGRADECIMENTOS 14 REFERENCIAS BIBLIOGRAFICAS 14 CAPITULO 2 16 OTIMIZACAO E ANALISE QUIMICA DA POLPACAO SODA COM ADITIVOS 16 RESUMO 16 ABSTRACT 17 17 1 INTRODUCAO 18 2 OBJETIVOS 19 3 MATERIAIS E METODOS 20 3.1. COZIMENTO LO-SOLIDS 20 3.2. LICORES DE COZIMENTO E ADITIVOS 20 3.3. ANALISES DE QUALIDADE DA POLPA 21 4 RESULTADOS 22 4.1. POLPA REFERENCIA 22 4.2. COZIMENTO COM ADITIVOS 23
Diffusion and Flux of Explosive-Related Compounds in Plastic Mine Surrogates

Abstract : Canine detection of buried land mines is thought to be an olfactory process and efforts are now underway to develop electronic sensing of explosives vapors. The quantity of these vapors is critical to any chemical-sensing-based procedure for detecting land mines. Plastic-cased mines release vapors of explosive-related compounds (ERC) by permeation through the casing as well as through holes and cracks. Measurement of permeation contributions to the release flux was reported previously for a few intact mines. Here we determine permeation-driven fluxes and diffusivities by analysis of ERC release into water as a function of time. The solubility of TNT and diffusivities of TNT, 2,4-DNT, and 1,3-DNB in five polymeric materials potentially used in mine casings are reported for the first time. This information can be used to estimate ERC fluxes from TNT-filled mines encased in any of these materials. Direct measurement of the release fluxes from intact mines are feasible and were described previously. Calculated diffusivities of the nitroaromatics in these materials were similar to those reported in the literature for other aromatic vapors. Diffusion was generally non-Fickian (concentration-dependent). TNT fluxes into Tedlar bags were also measured at 3, 13, and 22 degrees C for the five materials. The temperature dependence is well described by simple exponential relations of the van't Hoff/Arrhenius type, and an activation energy was calculated for the process. The temperature dependence of the fluxes was similar for all five materials. Fluxes of individual ERC components were also determined at 22 degrees C and reflected their abundance in the head space of the source explosive. Plastic/air partition coefficients of the contaminants ranged from 1 x 10(exp 3) to 3 X 10 (exp 3) and were about an order of magnitude higher for TNT than for DNB and were intermediate for DNT.