Miracle-dz/UniFAR-FTUs huggingface dataset:
Net ozone production and its relationship to NOx and VOCs in the marine boundary layer around the Arabian Peninsula

Abstract. Strongly enhanced tropospheric ozone mixing ratios have been reported in the Arabian Basin, a region with intense solar radiation and high concentrations of ozone precursors such as nitrogen oxides and volatile organic compounds. To analyze photochemical ozone production in the marine boundary layer (MBL) around the Arabian Peninsula, we use ship-borne observations of NO, NO2, O3, OH, HO2, HCHO, actinic flux, water vapor, pressure and temperature obtained during the summer 2017 Air Quality and Climate in the Arabian Basin (AQABA) campaign, compare them to simulation results of the ECHAM-MESSy atmospheric chemistry (EMAC) general circulation model. Net ozone production rates (NOPR) were greatest over the Gulf of Oman, the Northern Red Sea and the Arabian Gulf with median values of 14 ppbv day−1, 16 ppbv day−1 and 28 ppbv day−1, respectively. NOPR over the Mediterranean, the Southern Red Sea and the Arabian Sea did not significantly deviate from zero; however, results for the Arabian Sea indicate weak net ozone production of 5 ppbv day−1, and net ozone destruction over the Mediterranean and the Southern Red Sea with −2 ppbv day−1 and −4 ppbv day−1, respectively. Constrained by measured HCHO/NO2-ratios, our photochemistry calculations show that net ozone production in the MBL around the Arabian Peninsula occurs mostly in a transition regime between NOx and VOC-limitation with a tendency towards NOx-limitation except over the Northern Red Sea and the Oman Gulf.
MRI findings of lesions in the cerebellopontine angle

Objective To investigate the MRI features of lesions in the cerebellopontine angle.Methods 83patients with lesions in the CPA were confirmed by pathology,the MR imaging features of them were analysed.Results In 83cases,50cases were acoustic neuroma,13cases were meningioma,13cases were epidermoid cyst,4cases were trigeminal neuroma,1case was arachnoid cyst,1case was strocytoma,and 1case was ependymoma.38lesions were located in the left CPA,45lesions were located in the right CPA.Compared with pathological results,the accuracy rates of location and qualitative diagnosis were 98.8% and 95.2%. Conclusion MRI can be used as the first choice for detecting the CPA tumors,and it has a high value in the diagonosis and differential diagnosis of CPA tumors.
POTENTIAL HABITAT MODELING OF WATER HYACINTH IN LAKES OF NEPAL USING MAXENT ALGORITHM

Abstract. Invasive Alien Plant Species are spreading outside of their natural geographic range. Water hyacinth (Eichhornia crassipes) is one of the most widely and rapidly spreading invasive species throughout the tropical and subtropical regions of Nepal. In the last decade, water hyacinth has become a chronic problem in many major lakes of Nepal which have affected the habitat aquatic plants and animals. Our study focuses on potential habitat modeling of Water hyacinth over the major lakes of Nepal using Maxent algorithm. Primary data used for modeling were 19 bioclimatic variables and Shuttle Radar Topography Model (SRTM) Digital Elevation Model (DEM). After preparation of the species distribution model, major lakes of Nepal were overlaid over the model to prepare potential invasive map. The performance and accuracy of potential habitat distribution model was evaluated using parameter Area under the Receiver Operating Characteristic Curve (AUC) which was within the range of 0.9–1. Validation of the model was done for the year 2015 with precision and recall, overall accuracy and F-measure and its values are 93% and 85%, 87% and 89% respectively. The model prepared for 2030 and 2050 shows the most suitable habitat for water hyacinth is in province 2 of Nepal and the moderately suitable habitat for this species is plain area of Province 4, 7 and 5. Similarly, the area of potential habitat has been increasing from current scenario to 2030 and 2050. From the potential invasion map, it can be observed that lakes in the Terai and Churey regions have the high risk of invasion of water hyacinth.
A catalogue of reptiles of MonfragÃ¼e National Park (Spain), with molecular characterization of populations of Blanus Wagler, 1830 in this protected area

MonfragÃ¼e National Park (CÃ¡ceres, Extremadura, Spain) is a protected area in central-western Iberia,including some of the best preserved primary Mediterranean vegetation. Legal protection dates back to 1979 (first as a Natural Park and then as a National Park), but knowledge about its reptile communities is so far limited to sparse records. In this paper we present an updated species list based on 521 records covering 163 1x1 km UTM grids in the study area, compiled in the period 2000-2019. We detected 20 native species, representing 71.4% of the reptile fauna in Extremadura and 35% of the ibero-balearic reptile fauna. Additionally, based on molecular analyses populations of the amphisbaenid genus Blanus in the study area are assigned to the oriental Iberian taxon, B. cinereus. The new records extend the known distribution of the different reptile species in the study area in 56 10x10 km UTM grids. Species presenting more restricted distributions in MonfragÃ¼e are Lacerta schreiberi (one 1x1 grid), Emys orbicularis, and Acanthodactylus erythrurus (four 10x10 grids each).
Experimenting with the transition rule in dynamic games

In dynamic environments where the strategic setting evolves across time, the specific rule governing the transitions can substantially alter the incentives agents face. This is particularly true when history-dependent strategies are used. In a laboratory study, we examine whether subjects respond to the transition rule and internalize its effects on continuation values. Our main comparison is between an endogenous transition where future states directly depend on current choices, and exogenous transitions where the future environment is random and independent of actions. Our evidence shows that subjects readily internalize the effect of the dynamic game transition rule on their incentives, in line with historydependent theoretical predictions.
Generating Conceptually Aligned Texts

A conceptually aligned text is one in which spans are systematically linked to a logical encoding of their meanings. We describe the model of alignment used in W YSIWYM systems, which support knowledge editing through an automatically generated feedback text, and show that this model has a series of practical advantages through which it can support applications requiring semantic interactivity and inference, as envisaged by the Semantic Web community. Some results from an efficient recent implementation are presented, showing the benefits of having all levels of linguistic description available for interactive services.
Salaxy: Enabling USB Debugging Mode Automatically to Control Android Devices

Android system attackers have proposed various attack schemes to invade users’ privacy. One way is to use ADB (Android Debug Bridge) with advanced permissions but low protection. In order to set up an ADB connection successfully, the USB debugging option of the target device must be turned on. However, the existing ADB-based attack schemes have not proposed how to enable the USB debugging, so it couldn’t be considered that their attack chain is completable. This paper presents an approach for attacking Android devices by exploiting JavaScript to enable USB debugging automatically in the device’s system settings, which fills in the gaps of existing solutions. This method can bypass the security mechanism of USB debugging mode and obtain an ADB connection without the user’s authorization. It can also bypass the alerts that ADB Action Monitor displays when sensitive behaviors are detected. Based on AccessibilityService, Auto.js and Scrcpy, an application called Salaxy is designed and implemented to demonstrate the effectiveness of this method. Besides, Salaxy can monitor and manipulate Android devices remotely.
Bandwidth Enhancement in a Dual Band Hexagonal Loop Antenna Loaded with NBSRR Metamaterial Unit Cell

A printed hexagonal loop antenna (HLA) is proposed which uses nonbianisotropic split ring resonator (NBSRR) metamaterial (MTM) loading to achieve bandwidth enhancement. This dual band antenna resonates at $4.02\mathrm {G}\mathrm {H}\mathrm {z}$ and $7.38\mathrm {G}\mathrm {H}\mathrm {z}$, making it useful in wireless applications and in X-band downlink satellite communication systems. This antenna achieves fractional -10dB impedance bandwidths of 34.26% and 22.7% in the two resonances and is of low-profile $0.58\lambda _{0}\times 0.63\lambda _{0}\times 0.04\lambda _{0}$, where $\lambda _{0}$ is the free space wavelength.
Content Based Facial Emotion Recognition Model using Machine Learning Algorithm

Emotion recognition or sentiment analysis is identified as an important research topic in computer vision community. The challenges include identification of face, recognition of accurate emotion, appropriate database and so on. We have proposed and implemented a general Convolutional Neural network (CNN) building framework for emotion recognition. The model is formalized by developing a coincident system which fulfills the tasks of face detection and emotion classification using our proposed CNN architecture. The model is validated using the FER-2013 dataset. In the proposed work, we discuss the applicability of the proposed CNN model. This model lays a valuable analysis of the effect of adjusting the network size, pooling, and dropout. For a given model, the final accuracy on the validation data is around 63%.
Fiber Nonlinearity Equalizer using MLP-ANN for Coherent Optical OFDM

We propose a novel artificial neural network nonlinearity equalizer based on multiple layers perceptron(MLP-ANN NLE) for16-quadrature amplitude modulation (16-QAM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. The scheme is effective for compensating for the four-wave mixing effect among subcarriers. Compared with the situation of non-compensated nonlinearity, this proposed scheme can increase the Q factor by 1.4 dB and 0.6 dB at 600 km and 2000 km respectively and the transmission distance can be increased by 400 km. It also shows a strong robustness in nonlinearity compensation.
Tire State Stiffness Prediction for Improving Path Tracking Control During Emergency Collision Avoidance

In this study, a model predictive path tracking control method based on the prediction of tire state stiffness is proposed to improve the path tracking performance at the limit of vehicle dynamics. Considering the influence of the nonlinear properties of tire force on vehicle dynamics, a nonlinear UniTire model is established, based on which a state stiffness 3D look-up table is designed to linearize the nonlinear tire model. The tire state stiffness in the prediction horizon is predicted by the vehicle motion model using the reference path information. A new linear time-varying path tracking control model in the prediction horizon is designed based on the predicted tire state stiffness. A nonlinear model predictive controller and a traditional linear time-varying model predictive controller are also designed and compared with the proposed method to verify the effectiveness and advantage of the latter. Results clearly show an improved control performance of the proposed method compared with the traditional method under the limit condition. Moreover, the calculation speed of the proposed method is faster than that of the nonlinear model predictive control method.
Multi-Task Learning for Authorship Attribution via Topic Approximation and Competitive Attention

Separating content from style is a fundamental problem in authorship attribution to represent topic independent personal style of authors. Previous work often ignores this problem by imposing strong but unrealistic assumptions or artificially determines a set of predefined stylistic structures. This paper proposes to separate topic and style based on neural multi-task learning. Our target is to learn separate representations for topic and style respectively. In addition to authorship attribution as the main task, we introduce a novel auxiliary task topic approximation to guide the learning of topic representations with the topic distributions inferred by topic models, which are trained from external corpus. Moreover, we propose a competitive attention mechanism and a separation-reconstruction constraint to assign different and competitive attentions to two tasks in order to separate topic and style as much as possible. Evaluation results demonstrate that the proposed multi-task learning based method is promising, especially on cross-topic settings. We found that topic approximation can help capture topical content and the competitive attentions benefit topic-style separation. It is encouraging since our model separates topic and style in a probabilistic way and doesn’t require human intervention.
Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

Among deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types due to different failure rates and when multiple faults are easily confused with single faults. To solve these problems, this paper developed a stochastic discrete-time series deep convolutional neural network (SDCNN) method based on random oversampling along with a progressive method with multiple SDCNNs to improve the diagnosis performance. To assess the developed method, datasets from three avionics 24-pulse auto-transformer rectifier units (ATRUs), which are secondary electric power supplies in aircraft, were analyzed and compared with other CNN methods.
Simulation of Tibialis Anterior Muscle Transfer for Congenital Clubfoot Based on OpenSim

Congenital clubfoot is a common deformity in children with cerebral palsy. It results in child's foot inversion with horseshoe shape, which seriously affects the basic function of normal walking. For the evaluation of the effect of treatment, in addition to clinical testing, the previous study used the three-dimensional gait analysis system to verify the effect of the treatment. These evaluations can only evaluate the results after the treatment but not predict the effect of the operation. Therefore, this paper adopts OpenSim modeling software to establish a personalized human body model by manually scaling and importing the joint angle. Changes in tendon moment and moment arm before and after transfer are examined in this experiment. The maximum dorsal flexion moment of the tibialis anterior muscle is changed from 15 N · m (before transfer) to 18 N · m (after transfer), and the average moment arm is changed from 24 mm to 29 mm. The maximum inversion moment of the tibialis anterior muscle is changed from 18 N · m (before transfer) to 1 N · m (after transfer), and the average moment arm is changed from 19 mm to -3 mm, which becomes eversion moment. It can be seen the tibialis anterior muscle transfer surgery increases the dorsal flexion moment, reduces the inversion moment, and also increases the eversion moment, which has significant effects in the treatment of clubfoot. By taking these effect into consideration, we can help the doctor to determine a better surgical plan.
Performance Estimation of Amorphouss-IGZO Based Thin Film Transistor

Among the high field mobility materials, amorphous-InGaZnO (α-IGZO) is very prominent for their application as a channel material in Thin Film Transistors (TFTs). Electrical parameters for α-IGZO TFT are retrieved from the simulation results and the extraction of the switching parameters as threshold voltage, field effect mobility, subthreshold slope, and current ratio (ION/IOFF) have been reported in this paper. The results display large field effect mobility (μFE) 12.27 to 13.3 cm2/Vs ranging threshold voltage (Vth) 1.03 to 1.27 V, Subthreshold Swing (SS) 23.82 to 21.78 mV/decade and significant ON-OFF current ratio (ION/IOFF) 3.8 × 104 to 1.7×105. The reported characteristics from the simulation results demonstrated superior electrical parameters due to α-IGZO channel and shows that it can provide rapid switching, better resolution in flat-panel, and OLED displays.
Suppression of the Attitude Error With a Modified Iterative Algorithm for Apparent Resistivity Imaging in GAFDEM Survey

The ground–airborne frequency-domain electromagnetic (GAFDEM) survey is a fast method to detect the deep underground structure in areas with difficult access. However, the suspended coil sensor in the survey system experiences attitude changes, which inevitably yield attitude error on the measured magnetic field and result in false apparent resistivity imaging. Due to the separated system structure, the attitude error in GAFDEM survey is significantly different from that in traditional airborne helicopter frequency-domain electromagnetic survey. In order to improve the detection accuracy, a modified iterative algorithm is proposed to suppress the attitude error for apparent resistivity imaging. Both simulation and practical experiment are conducted to verify the usefulness of the correction method. The simulation results indicate that the correction procedure is important and effective to reduce the attitude error in layered earth model. The field experiment results show that the corrected apparent resistivity imaging section is more consistent with the high-density resistivity topography and more reasonable than the uncorrected apparent resistivity imaging result obtained by conventional method. Consequently, the proposed suppression approach can reduce the attitude error to obtain more accurate apparent resistivity imaging results in GAFDEM survey, which may effectively promote the developments and applications of GAFDEM method and systems.
High Fidelity Visualization of Large Scale Digitally Reconstructed Brain Circuitry with Signed Distance Functions

We explore a first proof-of-concept application for visualizing large scale digitally reconstructed brain circuitry using signed distance functions. The significance of our method is demonstrated in comparison with using implicit geometry that is limited to provide the natural look of neurons or explicit geometry that requires huge amounts of memory and has limited scalability with larger circuits.
End-To-End Melody Note Transcription Based on a Beat-Synchronous Attention Mechanism

This paper describes an end-to-end audio-to-symbolic singing transcription method for mixtures of vocal and accompaniment parts. Given audio signals with non-aligned melody scores, we aim to train a recurrent neural network that takes as input a magnitude spectrogram and outputs a sequence of melody notes represented by pairs of pitches and note values (durations). A promising approach to such sequence-to-sequence learning (joint input-to-output alignment and mapping) is to use an encoder-decoder model with an attention mechanism. This approach, however, cannot be used straightforwardly for singing transcription because a note-level decoder fails to estimate note values from latent representations obtained by a frame-level encoder that is good at extracting instantaneous features, but poor at extracting temporal features. To solve this problem, we focus on tatums instead of notes as output units and propose a tatum-level decoder that sequentially outputs tatum-level score segments represented by note pitches, note onset frags, and beat and downbeat flags. We then propose a beat-synchronous attention mechanism constrained in order to monotonically align tatum-level scores with input audio signals with a steady increment. The experimental results showed that the proposed method can be trained successfully from non-aligned data thanks to the beat-synchronous attention mechanism.
The Magnetism of a Ni Monolayer on Vanadium: Structure and Exchange Correlation Effects

The influence of structure and exchange correlation on the electronic and magnetic properties of a Nickel monolayer on Vanadium is investigated using the density functional theory with norm-conserving pseudopotentials. The Ni and Ni/V surfaces in the (001) direction have been simulated by using a seven layer slab to calculate the structural, electronic and magnetic properties. The bcc structure of the substrate V induces an artificial bcc structure for the Ni monolayer. This results in a quenching of the Ni surface magnetic moment due to expansion and d band hybridization. The results predict a ferromagnetic p1 × 1 Ni surface with negligible values of moments within the local density approximation (LDA) and the generalized gradient approximation (GGA) exchange correlations. In contrast, there is no agreement for the induced magnetism in the interfacial V layer for the two exchange correlations; the magnetic moment values obtained are ∼zero and 1.32 μB for LDA and GGA respectively.
An Application of Convolutional Neural Network-Long Short-Term Memory Model for Service Demand Forecasting

The medical services are very important requirement for being healthy human. In order to ensure the availability of resources for the medicine needed, the most hospital makes an service demand estimation by forecasting a number of patients to provide the sufficient medical services. Therefore, the accurately forecast a number of patients would be valuable knowledge for managing. This work proposed the CNN-LSTM model, which was a combination of CNN and LSTM, to forecast the number of patients who used hospital services. The CNN model was used to interpret, and extract the features from the input data. Then, it was provided this information to the LSTM model for interpreting and making a forecast. The CNN-LSTM models were applied to forecast on the two datasets. The results indicated that CNN-LSTM model made reliable forecasting. This work measured the model performnace by calculating RMSE and MAE value. The result showed RMSE and MAE of the models were very low in all experiments. Forecasting the number of patients can help the hospital to estimate the service demand, make a better policy for managing the medical resources on demand, and improve the efficiency of medical services for the future.
Photoluminescence-Based Method for Imaging Buffer Layer Thickness in CIGS Solar Cells

Recent improvements in Cu(In,Ga)Se2 (CIGS) solar cells have made use of thinner CdS layers. The homogeneity of this layer is critical to maintain a high-quality CdS/CIGS heterojunction. We present an imaging method based on photoluminescence (PL) intensity ratios using UV and red excitation. UV light is partially absorbed by the CdS, while it is transparent to red photons. Hence, inhomogeneity in the CdS layer thickness produces different UV excitation of the underneath CIGS layer, while the red excitation is uniform, leading to contrast in the PL image ratio. The method is highly sensitive to detect defects or pinholes in the CdS layer and can be used as a rapid method for CdS deposition quality control.