Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanostructured Platinum Catalysts by Atomic‐Layer Deposition for Solid‐Oxide Fuel Cells

IO N A critical bottleneck in reducing the operating temperature of solid-oxide fuel cells (SOFCs) is the sluggish reaction kinetics at the electrode/electrolyte interface. To improve SOFC performance at low temperatures, we combined atomic-layer deposition (ALD) and sputtering of platinum to create catalyst structures with high triple-phase-boundary (TPB) densities and low currentcollecting resistance. We studied the nucleation behavior of ALD platinum using plan-view transmission electron microscopy (TEM), and identifi ed deposition conditions that produced high TPB densities. A porous platinum layer was sputtered on top of the nanostructured ALD platinum layer, thereby reducing the resistance for current collection. A 90% increase in the fuel cell’s peak power density was observed relative to catalyst structures that were deposited by sputtering only. SOFCs are known for their high energy-conversion effi ciency, as well as for the wide range of fuel usage in stationary applications. [ 1–6 ] However, the high operating temperatures of SOFCs have reduced their practicality for mobile applications. [ 7 , 8 ] Lowering the operating temperature adversely affects the rate of the oxygen-reduction reaction (ORR) and the hydrogen-oxidation reaction (HOR) at the electrode/electrolyte interfaces, thereby reducing the exchange current density and causing a drop in fuel-cell performance. To overcome this loss, we need, amongst other things, to search for catalyst structures that deliver fast reaction kinetics at comparatively low temperatures. One of the commonly used electrode-catalyst materials for low-temperature fuel cells is platinum. [ 9 , 10 ] Platinum can serve as an electrode for current collection and a catalyst for the ORR and the HOR. The superior catalytic capability of platinum provides high exchange-current densities in fuel cells operated at low temperatures. However, the exchange-current density is not only determined by the catalyst material, but also by the catalyst microstructure. [ 11 ] A key microstructural feature of the electrode catalyst is the TPB, where the electrode catalyst, gas, and electrolyte meet. Increasing the TPB density provides more reaction sites per unit area, resulting in a higher exchange-current density. Therefore, optimizing the platinum microstructure can signifi cantly improve the electrochemical performance of the platinum electrode catalyst.
Generalized correlations for the aqueous liquefaction of lignocellulosics

This paper shows that liquefaction (i.e. solubilization) of lignocellulosics in aqueous phase and in the absence of catalysts results in two general correlations. The extents of solubilization of both holocellulose and lignin are linearly related to overall conversion within the temperature range 150–270°C and up to conversions of 83%. Cet article presente l'evidence experimentale concernant deux correlations generales observees lors de la liquefaction (i.e. solubilisation) des matieres lignocellulosiques en phase aqueuse et en absence de catalyseurs. Les niveaux de solubilisation de l'holocellulose et de la lignine sont relies de facon lineaire a la conversion dans la plage de temperature comprise entre 150 et 270°C et pour des conversions allant jusqu'a 83%.
Further neighbor magnetic exchange interaction in a novel pseudolinear tetramer of copper(II)

The synthesis, crystal structure, and magnetic properties are reported for the novel compound CuoL 2 (CH 3 COO) 6 , where LH=1-(5-bromosalicylaldimino)-3-(2-methylpiperidino)propane. The compound, Cu 4 C 44 H 62 N 4 O 14 Br 2 , crystallizes in the monoclinic space group P2 1 /n. Cell dimensions are as follows: a=11.627(3) A, b=22.442(3) A, c=9.693(3) A, β=94.46(2) o , and Z=2. The structure was refined to an R(unweighted) value of 0.076. The tetranuclear unit of the complex is centrosymmetric and has an almost linear geometry
Planning for Success: Factors Influencing Change in Teacher Education

Abstract Teacher education is widely recognised as a key strategy that is yet to be effectively utilised to embed environmental education and/or education for sustainability in schools. This paper reports on a research study that examined a range of pre-service teacher education initiatives, both in Australia and internationally, that were seeking to reorient teacher education towards environmental sustainability. This paper reports on six factors utilised across the initiatives that were critical to their success. These were (1) the nature and length of funding arrangements; (2) the range and quality of partnerships and networks; (3) the curriculum focus and the teaching and learning processes used; (4) the nature of, and incentives for, participant engagement; (5) the level at which a change was being sought; and (6) the use of evaluation as a tool for learning and ongoing improvement. This paper discusses why and how each of these six factors proved critical and explores the implications for initiatives seeking to reorient teacher education towards environmental sustainability.
TiN Films Prepared by Flow Modulation Chemical Vapor Deposition using TiCl4 and NH3

We propose a new chemical vapor deposition (CVD) process, the flow modulation chemical vapor deposition (FMCVD) process, to obtain high quality titanium nitride (TiN) films at low deposition temperature in a single CVD chamber. FMCVD uses sequential deposition and reduction processes, such as the deposition of TiN films followed by chlorine reduction. This cycle was repeated to achieve sufficient film thickness. By decreasing the thickness in one cycle, the residual chlorine concentration and the resistivity of the films decreased. Using FMCVD process, we could achieve low resistivity (250 µΩcm), low residual chlorine concentration (2 at.%) with uniform step coverage at low deposition temperature (380°C).
Disturbance observer-based backstepping control for quadrotor UAV manipulator attitude system

In this work, we approach high-precision attitude control problem of UAV manipulators in presence of external disturbances. A disturbance observer is exploited to estimate the disturbance, meanwhile we used the estimated result as a feed-forward compensation. A backstepping controller is employed to stabilize the UAV attitude by feedback. The mathematical model of UAV manipulator is constructed by combining the UAV base and the equipped robotic arm together. Furthermore, to relieve the influence of sensor noise, we fused a tracking differentiator into the whole system. The constructed method is validated in realistic simulations, where we executed UAV manipulator hovering flight with varying external disturbances.
A Fault Detection Method based on Convolutional Gated Recurrent Unit Auto-encoder for Tennessee Eastman Process

Due to the powerful ability of feature learning, deep learning has been widely used in the field of manufacturing process monitoring. This paper proposes a new fault detection method, convolutional gated recurrent unit auto-encoder (CGRU-AE) for feature learning from process signals. CGRU-AE is used to learn the effective features of time series data for fault detection. Two statistics, the T2 and the squared prediction error (SPE), are generated in the feature space and the residual space of CGRU-AE, respectively. Finally, the feasibility and advantages of CGRU-AE are shown on the Tennessee-Eastman process. The results show that CGRU-AE is capable of learning effective features from complex process variables and outperforms other typical methods.
Designing banana-shaped liquid crystals without Schiff's base units: m-terphenyls, 2,6-diphenylpyridines and V-shaped tolane derivatives

This paper reports attempts to obtain (anti)ferroelectric switchable achiral banana-shaped molecules without Schiff's base units. For this purpose we have synthesized novel V-shaped molecules consisting of rigid angular central units [1,3-disubstituted benzene, 2,7-disubstituted naphthalene, 1,3-diphenylbenzene, 2,6-diphenylnitrobenzene, 2,6-diphenylpyridine, 1,3-bis(phenylethynyl)benzene, 1-phenyl-3-(phenylethynyl)benzene] connected via ester linkages to two rigid cores (1,4-disubstituted benzenes, biphenyls, 2-phenylpyrimidines, phenylbenzoates). Most compounds have rather high melting points. Only molecules with seven aromatic rings show liquid crystalline properties. Two-dimensional modulated smectic phases (rectangular columnar phases) were found for molecules with phenylbenzoate rigid units. Intercalated fluid smectic phases were detected for the corresponding 2-phenylpyrimidine derivatives. For the first time in the case of banana-shaped molecules a nematic phase was observed for a 2′-nitro-m-terphenyl-4,4″-diyl bisbenzoate. However, none of the synthesized compounds exhibit the typical texture of the (anti)ferroelectric switchable mesophases, known from the Schiff's base derivatives.
E-Service Quality Assesment of Mobile-based Smart Regency with M-S-QUAL Approach

Today, almost every country struggles to implement city management with the concept of smart cities. Several previous studies have produced several models to measure the quality of information system services. Previously there had been several studies that measured service quality and experience quality in the context of smart city application. However, less work still explores the quality of service calculation of the Smart District model, which is quite different from the common smart - city concept. This study aims to evaluate the quality of service from the application of mobile-based smart regency or districts. The model and approach used is the Mobile Service Quality (M-S-QUAL) model, which is a model of development and adaptation of the previous model, namely SERVQUAL, a viral model for measuring the quality of information system services. Data collection was carried out from one hundred and seventy respondents who were successfully collected using the online survey method - data processing using SmartPLS v.3. 2.8 software. Research findings indicate that the variables proposed in the M-S-QUAL model are positive and essential relationships for measuring the quality of smart district services. This research contributes to determining the effective use of intelligent regional mobile applications. This research confirms that lawmakers pay more attention to important issues that influence the performance of smart district mobile service quality.
RGB-D Based Semantic SLAM Framework for Rescue Robot

Semantic information has proven to be an enabling factor for robots to better understand their surroundings. In this paper, we propose an RGB-D based semantic simultaneous localization and mapping (SLAM) framework for rescue robots. By augmenting the RGB-D SLAM system with a convolutional neural network (CNN), our framework can generate not only dense geometric point-cloud maps but also corresponding point-wise semantic information (i.e. a semantic map). After obtaining the semantic map, the rescue robot can distinguish types of terrains, so as to avoid obstacles and find out paths with higher traversability. On the one hand, we utilize depth information to determine whether neighboring pixels in semantic images belong to the same object, so as to filter the segmentation results of each frame. On the other hand, we filter the semantic map by accumulating data of multiple frames and searching consistent semantic labels. To validate the efficiency of the proposed semantic SLAM framework, we generate an RGB-D dataset of the RoboCup Rescue-Robot-League (RRL) competition environment. The experiment proves that our semantic SLAM framework can generate dense and accurate semantic maps for the complex RRL competition environment.
An adaptive dead fish detection approach using SSD-MobileNet

Aiming at the difficulty in the recognition of dead fish in a large cage, a method of detecting dead fish on water surface based on SSD-MobileNet is proposed. In this paper, the models of SSD-MobileNet V1, V2, and V3 are compared, which are suitable for an embedded mobile terminal. Through theoretical and experimental analysis, it has been proved that SSD-MobileNet V3 is not only real-time object detection algorithm, but also a new architecture combining hardware network architecture search (NAS) and NetAdapt. SSD-MobileNet V3 realizes the cooperation between automatic search algorithm and network design, and the complementary method improves the overall technical level.
Highly-Rectifying Graphene/GaN Schottky Contact for Self-Powered UV Photodetector

In this study, we demonstrated self-powered fast-response ultraviolet (UV) detection based on a highly-rectifying graphene/GaN Schottky contact. The device was fabricated by mechanically transferring a monolayer graphene onto lightly-doped GaN and showed excellent Schottky behaviors, including a high rectification ratio (<inline-formula> <tex-math notation="LaTeX">$> 10^{8}$ </tex-math></inline-formula>), a close-to-unity ideality factor and a relatively high Schottky barrier height (1.01 eV). At zero bias, the graphene/GaN Schottky photodiodes exhibited a strong photovoltaic response to UV illumination with a competitively short rise/decay time of 221/<inline-formula> <tex-math notation="LaTeX">$546~\mu \text{s}$ </tex-math></inline-formula>. A trap-associated photoconductive mechanism started to dominate the device’s response when its bias went beyond −1 V, which could be identified from the suddenly increased responsivity and response time.
Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification

The main problem of health datasets is having a lot of data attributes and irrelevant features, so that the computation of the classification method takes more time to solve it. The purpose of this study is to implement the CFS feature selection method to improve the accuracy of the Correlated Naive Bayes method. Therefore, there are some stages used in this study such as: collecting dataset of Pima Indian Diabetes, preprocessing data especially for transformation data, selection of CFS feature, classification, and then performance evaluation based on accuracy. Based on the test results using the 10-fold cross validation method, the best accuracy is about 69.4% compared without feature selection, it is obtained by a combination of Correlated Naive Bayes and CFS methods. Thus, the CFS feature selection method may increase the accuracy of the Correlated Naive Bayes method by 2.25%.
Deep Networks Based Classification of COVID-19 Chest X-Ray Images

The worldwide spread of COVID-19 (Corona virus) has been declared a global health crisis by WHO. In order to perform early diagnosis of COVID-19 infection a computer-aided diagnosis (CAD) system is of great importance. Chest X-ray radiographic imaging is a preferred diagnostic mean used by radiologists. Machine and deep learning algorithms have been found useful in many CAD equipped health care systems. In this paper, we present an approach to classify chest X-ray radiographic images into normal, COVID-19, and viral-pneumonia categories. The proposed approach performs feature extraction using pre-trained and fine tuned deep networks using transfer learning. A support vector machine (SVM) is trained using the extracted features and used for classification. We collect a dataset comprised of publicly available chest X-ray images from normal, COVID-19 and viral-pneumonia patients. Experimental results demonstrate that the proposed approach achieves 99.29% classification accuracy in case of COVID-19 images and an overall classification accuracy of 97.36%, which is better than most of the existing approaches.
LVRT Improvement of DFIG driven Wind Generator with Rotor Control

The grid faults generally perturb the performance of wind turbine (WT) generator. Consequently, these issues offer the low voltage ride through (LVRT) as a solution for the grid associated wind generator under the fault conditions. This paper presents the enhancement of the LVRT capabilities of the DFIG based wind generator through the rotor side controller. The active and reactive power controls are used along with the proposed PID controller. Furthermore, the performance of the proposed PID controller is compared with the results of the PI controller. In addition, the drawbacks/failures of the PI controller are also highlighted. Finally, the simulation results with the PID controller exhibits the LVRT enhancement and stability of the DFIG system.
Quadruped Robot Control Base on Adaptive Neuro-Fuzzy Inference System With V-REP Simulator

This study aims to design a Quadruped robot control on a new medium-sized Quadruped robot design called Kancil, which has 4 arms with 2 freedoms (4 x 2-DOF) with an overall weight of 5kg. ANFIS here is used as a balance regulator for the movement of the robot legs and CPG-VDP as a periodic drive system. The MIMO ANFIS structure is designed with two inputs and four outputs which are used to control the shoulder movements of the Quadruped robot, to maintain body balance so as not to fall. Input data for the gyro sensor tilt is -45 degrees to 45 degrees, studied in the ANFIS machine. The ANFIS output is then simulated using the V-REP simulator software, by converting the output data into a leg path so that it can be simulated. From the test results, the robot can pass through obstacles while walking down 30 degrees and 45 degrees in a balanced state and not falling.
Fire Detection based on Convolutional Neural Networks with Channel Attention

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.
The Collaboration Routing of Heterogeneous Vehicles on Traveling Salesman Problem

It has been realized that the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) can bring great benefits in delivery recently. The collaborative system with a UGV and a UAV can also be applied to inspection, searching and so on, where the UAV works continuously before returning to the UGV. The scenario is abstracted as the collaboration of heterogeneous vehicles on traveling salesman problem, called HCVTSP in this paper. To minimize the mission completion time of HCVTSP, a mixed linear integer programming (MILP) model is built. In order to quickly solve the larger scale HCVTSP , a heuristic algorithm called heterogeneous collaboration vehicles iterative local search (HCVILS) is proposed. The experiments confirmed that the heuristic algorithm performs much better than MILP in terms of running time compromising the quality of the solution negligibly. Numerous instances are used for comparing the solutions of HCVTSP with an existing model while the UAV works discontinuously for delivery. The results show that the solution of HCVTSP is superior to the other in both mission completion time and length of truck’s path.
Research on Electric Heating Cut Tobacco Dryer Based on Fuzzy PID Control Algorithm

PID control has many advantages such as good robustness, high reliability, simple algorithm, flexible use, etc., and is widely used in the field of industrial control. In the process of cut tobacco drying, there are problems such as time delay, non-linearity, time-varying of the drying object, and it is difficult to establish an accurate mathematical model. As a result, the conventional PID controller has problems such as poor tuning, poor performance, and poor adaptability to the controlled process. In view of the above problems, this paper presents a self-tuning fuzzy PID control algorithm with the advantages of no need to set up accurate mathematical model and good robustness. The simulation results show that the response is faster, the temperature control of the dryer can achieve good results, meeting the requirements of high-quality production.
Mesoporous, 2D Hexagonal Carbon Nitride and Titanium Nitride/Carbon Composites

Carbon and metal nitrides are appealing classes of materials, astheir properties can complement carbon, on the one hand, andmetal oxides, on the other, in a variety of applications. A largernumber of reports in the literature concern the synthesis ofdifferent modiﬁcations of carbon nitrides, while at ambientconditions graphitic-C
Evaluation of New-type Medium-speed Maglev Train by Riding Comfort and Running Stability

Based on LMS Virtual. Lab, the dynamics simulation analysis of a medium-speed maglev train with mid-mounted linear motor was carried out to evaluate passenger riding comfort and running stability. Firstly the dynamics simulation model of medium-speed maglev train with mid-mounted linear motor was established, then two kinds of methods for assessing passenger riding comfort in relation to vibration in railway vehicles were introduced. The result shows that medium-speed maglev train meets the needs of passenger riding comfort and running stability.
Improved Adversarial Attack against Black-box Machine Learning Models

The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm——Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.
A Quaternion-based Sliding Mode Controller for UVMS

As an important tool for underwater operations, underwater vehicle manipulator system (UVMS) is becoming more flexible and miniaturized. Because of the flexibility, the posture of UVMS can be more diversified, which might lead representation singularities of orientation in the case of Euler angles. And the lightweight vehicle increases the torque coupling between the vehicle and the manipulator. In order to avoid singularity and obtain a good dynamic control performance, a sliding mode controller is designed by unit quaternion feedback for this nonlinear and strong coupling system. The stability of the controller is proved by a generalized Lyapunov theory, and the parameters of the controller are optimized by genetic algorithm (GA). The simulation results show that the proposed control law has good performance .