Miracle-dz/UniFAR-FTUs huggingface dataset:
Propeller-type Wall-Climbing Robots: A Review

Many efforts have been exerted in the robots field which have ability to climb vertical plane. ` The result was various types of climbing robots. These robots have a mixture of different principles of adhesion and locomotion in many applications. Propeller thrust is one of the approaches that has been developed to generate the required adhesion force to enable the robot to climb walls. Due to the promising merits of this principle, the current paper deals with propellers-type wall-climbing robots (PRWCRs) and introduces features, applications and challenges for these robots. PRWCRs are examined depending on a set of given requirements. This mechanism has connected between two wide fields: wall climbing robots (WCRs) and unmanned aerial vehicles (UAVs). For many reasons, each adhesion mechanism has more preference than others for a specific job; surface requirements are the main of them. Propeller-type WCRs are less affected by the nature of the surface, where it can climb many types of vertical planes: smooth, rough, and ferromagnetic or not. On the other hand, most of the introduced designs still have some problems and issues like transition operation, mission time and payload limitation.
Iterative parameter estimation of nonlinear systems

This paper discusses the identification methods for nonlinear systems. Using the negative gradient search, the gradient based iterative algorithm is derived to determine the parameters of the nonlinear system. In addition, in order to improve the computational efficiency, the gradient-based iterative algorithm based on the model transformation is derived. The basic principle is to transform a complex nonlinear system into a linear or simple nonlinear system. Finally, a numerical example is given to verify the effectiveness of the proposed methods.
The application of 3D printing systems in the bachelor’s engineering educational process

In this article the abilities of 3D-printing technologies in the bachelor’s educational process are considered in the specialities of electric power industry and electrical engineering. An example of the 3D printing systems implementing in the educational process and the experimental modeling is presented in research works of students. The analysis of problems arising in the implementation of 3D printing systems was made and the assessment of the student’s interest degree and motivation in operating with this technology were carried out. As a result, it was concluded that 3D printing can help create an improved learning environment for students, and takes into account different methodological styles of the educational process.
Smart bus transportation for tracking system: A study case in Indonesia

The current public transportation system must improve the quality of its services so that they can go hand in hand with technological advancements. Transportation, as a supporter of economic progress and development of the country, is expected to be able to answer the high mobility of the community of transportation needs by utilising the expansion of existing technology. Tracking and monitoring bus locations as public transportation is now an important issue that needs attention, but there is no bus tracking system based on mobile that can provide real-time locations. The research aimed to design a wireless sensor network that automatically identified and provided accurate information about travel routes and the movement of public transport buses in real-time. By using Global Positioning Systems (GPS) as vehicle tracking systems and sensor that support, this application provided bus position information and the nearest bus route recommendations to users in real-time. Thus contributing to the time management of public transport users in their activities.
The Population of H ii Regions in NGC 7020

The galaxy NGC 7020 displays an exotic hexagonal ringlike central structure with conspicuous ansae located at two opposite vertices and a tenuous external ring populated by H ii regions. Inside and around the hexagonal structure, Hα emission is also present at the inner disk. To characterize the population of the H ii regions, as well as their ionizing clusters, we imaged NGC 7020 with narrowband Hα and nearby continuum filters attached to GMOS-S installed on the Gemini South telescope. We found 202 H ii regions or complexes of H ii regions evenly distributed between the outer ring and the central disk The nucleus and ansae also present Hα emission. The equivalent width of the Hα line (W Hα ) is systematically greater at the regions of the outer ring relative to those of the inner disk. We discuss the influence of the metallicity gradient of the disk and the upper limit of the masses of the initial mass function on W Hα , and we conclude that the data are still consistent with the occurrence of a younger burst of H ii region formation in the outer ring. The central regions present more massive clusters, M ≥ 106 M ⊙, than those of the outer ring (M ≤ 106 M ⊙). Three clusters within 5″ of the nucleus present masses higher than 108 M ⊙. The presence of diffuse Hα emission in the inner 5″ suggests gas flows in the nuclear region.
A Review: Isolated Arabic Words Recognition Using Artificial Intelligent Techniques

In recent few years, deep learning has fast growing in many fields as natural language processing, image recognition, handwriting recognition, computer vision, and speech recognition. Automatic speech recognition (ASR) is a technique that refers to translating spoken words from an acoustic waveform into a text equivalent to what the speaker says. More recently, the advances in deep learning can support ASR in improving the performance of systems accuracies. Arabic is a Semitic language, one of the oldest used and most communicated languages in the world. But, it least concentrated in the case of Arabic speech recognition and under-resourced languages. This paper presents a survey that focuses on an automatic speech recognition system based on isolating words technique for Arabic speech. It also highlights the facilities and tools for developing speech recognition systems. This work is intended to be a useful starting point for those who are interested in ASR.
Reply to Comment on ‘Changes of inundation area and water turbidity of Tonle Sap Lake: responses to climate changes or upstream dam construction?’

YeWang, Lian Feng2,∗, Junguo Liu and Deliang Chen 1 School of Hydraulic Engineering, Dalian University of Technology, Dalian, People’s Republic of China 2 School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China 3 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden ∗ Author to whom any correspondence should be addressed.
Electrode Models for Ionic Liquid-Based Capacitors

We investigate the influence of different electrode models on properties of a BMIM PF6 ionic liquid-based capacitor system using molecular dynamics simulations. We compared atomic graphene electrodes against smooth walls of equal average interaction energy. Furthermore, two different methods for simulating a constant potential drop between the electrodes were employed and compared to constant charge electrode models. In the evaluation of the voltage dependence of the ion configuration in the interfacial electrode regions, the influence of the electrode structure was determined by analyzing the in-plane radial distribution of the adsorbed ions, the interlayer ion exchange process, and the differential capacitance. We see similar results for the constant charge and constant potential approach for capacitance and structure of the adsorbed ions. We demonstrate that, due to the underlying ionic models, anions and cations adapt to the structural pattern of the graphene electrodes in different ways. This affects.
Generation of runaway electrons in plasma after a breakdown of a gap with a sharply non-uniform electric field strength distribution

The paper is devoted to the study of the initiation and formation of a negative streamer in a sharply inhomogeneous electric field and the generation of runaway electrons (REs) in air and helium at atmospheric pressure and below, as well as in sulfur hexafluoride at low pressure. Nanosecond voltage pulses of negative polarity with an amplitude of 18 kV were applied across a point-to-plane gap 8.5 mm long. The studies were carried out using broadband measuring sensors and equipment with picosecond time resolution, as well as using a four-channel ICCD camera. Using a special method for measuring the dynamic displacement current caused by the redistribution of the electric field during streamer formation, the waveforms of voltage, discharge current, RE current, and dynamic displacement current were synchronized to each other, as well as to ICCD images. Data on the generation of REs with respect to the dynamics of streamer formation were obtained. It was found that REs are generated not only during the breakdown of the gap, but also after that. It has been found that the formation time of explosive emission centers affects the generation of REs after breakdown. Based on the measurement data of the voltage, discharge current, and dynamic displacement current, the electron concentration in the plasma channel after breakdown and the electric field strength near the surface of the grounded electrode were calculated.
Ring resonator modulators based on double-layer graphene and Chalcogenide glasses waveguide in mid-infrared light

A ring resonator modulator based on double-layer graphene and Chalcogenide glasses waveguide is presented and investigated. Wide bandwidth mid-infrared light operation can be achieved. A strong interaction between mid-infrared light and graphene is obtained, which change the effective mode index in the waveguide significantly while the change of applied voltage. Besides, the modulator we proposed owns a large extinction ratio of 33.57 dB.
Active layer thickness as a function of soil water content

Active layer thickness (ALT) is a critical metric for monitoring permafrost. How soil moisture influences ALT depends on two competing hypotheses: (a) increased soil moisture increases the latent heat of fusion for thaw, resulting in shallower active layers, and (b) increased soil moisture increases soil thermal conductivity, resulting in deeper active layers. To investigate their relative influence on thaw depth, we analyzed the Field Measurements of Soil Moisture and Active Layer Thickness (SMALT) in Alaska and Canada dataset, consisting of thousands of measurements of thaw depth and soil moisture collected at dozens of sites across Alaska and Canada as part of NASA’s Arctic Boreal Vulnerability Experiment (ABoVE). As bulk volumetric water content (VWC) integrated over the entire active layer increases, ALT decreases, supporting the latent heat hypothesis. However, as VWC in the top 12 cm of soil increases, ALT increases, supporting the thermal conductivity hypothesis. Regional temperature variations determine the baseline thaw depth while precipitation may influence the sensitivity of ALT to changes in VWC. Soil latent heat dominates over thermal conductivity in determining ALT, and the effect of bulk VWC on ALT appears consistent across sites.
Robot Path Planning Method Based on Genetic Algorithm

Intelligent mobile robots are robots that can automatically complete driving tasks without human intervention. Route planning is the main problem in robotics research. The goal is to find the lowest cost, conflict-free route from the known starting point of the obstacle route to the known end point. Genetic algorithms are based on the natural selection of populations and the random, repetitive and evolutionary processes of genetics. This is a very effective algorithm in the field of route planning research. In this article, I will first explain the design principles of mobile robots. After comparing the advantages and disadvantages of various routing methods, a genetic algorithm was chosen to solve the routing problem of mobile robots. Then, through detailed research on genetic algorithm, a solution to robot path programming based on genetic algorithm in static and dynamic environments is proposed. In the design of genetic operons, standardization, insertion and deletion of operons have been added to make up for the lack of basic operons. At the same time, the genetic algorithm is optimized through adaptive mutation and crossover rate. You can improve the algorithm by adding new operators and adaptive methods to solve the problem that the target point cannot be reached due to the local minima in the evolution process. Finally, in three static environments with different levels of complexity, simulation experiments are carried out and the simulation results are analyzed. The influence of different fitness parameters on the path planning results is discussed, and the dynamic path planning simulation is carried out. Through comparison with other methods, it can be found that in the same environment, the path planning method based on genetic algorithm saves at least 5% time than Dijkstra algorithm in search time.
Performance of CFD and ANN modeling of heat transfer enhancement in a circular tube with artificial roughness

This paper presents a comparison of three different approaches for modeling enhanced heat transfer characteristics of turbulent airflow in a circular tube with artificial roughness of transverse ribs. A number of CFD simulations are carried out forming the first dataset as well as the second dataset extracted from a number of classical works. A deep feed-forward neural network is developed to predict Nusselt number and friction factor for a variety of rib roughness and flow parameters. The ANN is trained by the first dataset (the CFD and ANN approach) and the second dataset (the experiment and ANN approach) independently and by a combination of datasets (the hybrid approach) showing good quality predictions in all the cases. All results are compared with experimental data and CFD modelled values showing the best results of the experiment and ANN approach.
Preliminary study of graphene like material fabrication from coconut shell using conventional furnace

Graphene has risen into a very interesting material because of its extraordinary properties. A new breakthrough that challenges the researcher is how to synthesis graphene from biomass as natural waste and plentiful resources. Coconut shell attracts the most attention because it contains the highest carbon atom among others. In this work, we present the experiment using conventional furnace to overcome the expensive one. Unfortunately, the result is not as expected that Fe2O3 is formed instead of amorphous carbon. We are trying to describe what caused this happen and recommendation to overcome this problem.
Performance of Stone Mastic Asphalt incorporating Kenaf fiber

Stone mastic asphalt (SMA) is a type of gap-graded asphalt mixture that contain large gap between big size and small size of aggregate. These criteria tend to reduce the tensile strength of mixture. To solve this, SMA usually mixed with synthetic fiber to increase tensile strength. However, synthetic fiber is costly and rarely obtained from local supplies. Thus, the aim of this study is to utilize different percentages of natural fiber which is kenaf fiber in SMA to overcome issues related to SMA. Among the test involved were LA Abrasion, Marshall Stability, Resilient Modulus, and Dynamic Creep. From the results, it shows that the addition of 0.2% fiber contributes to lowest value of abrasion. In addition, 0.2% fiber also produces the highest density of SMA20 (Stone Mastic Asphalt Grade 20). It could be seen that 0.6% of kenaf fiber producing highest value of resilient modulus. Dynamic creep also shows a significant value for 0.2% amount of kenaf fiber. Thus, it can be concluded that the existence of fiber is capable in enhancing the performance of SMA which is evident for instance, the density. The outcome of this study also contributes to the improvement of current guideline in designing SMA mixture especially for Public Work Department of Malaysia.
Research on the application of Fama-French 5-factor model in the steel industry during COVID-19

Based on the Fama-French 5-factor model (FF5), this paper analyzes the market changes of the American steel industry before and after the epidemic. In this paper, the Data from Kennethr French-Data Library were used to make multiple regression analysis of the U.S. steel industry before and after the epidemic. The Fama-French 5-factor model was also used for analysis. According to the results of multiple linear regression, due to the influence of COVID-19, the coefficient of market risk (Betam) decreased; Robust minus Week (RMW) changed from significant to insignificant; Small minus Big (SMB) and High minus Low (HML) were significant and the change of epidemic situation was not significant, while Conservative minus Aggressive (CMA) was not significant. The impact of the outbreak on the U.S. steel industry has been dramatic and has led to a huge decline in the entire industry.
The Multi-Task Deep Model-Based Pneumonia Detection

As a common lung disease, pneumonia affects millions of people worldwide each year and is often detected during physical examination using chest X-ray images (CXRs), which are usually diagnosed by radiologists. This time-consuming task often leads to fatigue-based diagnostic error and cannot be performed in countries or areas lack of radiologists. In this work, we proposed a multi-task learning model: Mt-pnet. We learn from the experience of imaging doctors in the diagnosis of pneumonia, let the network pay attention to the patient’s gender and age information in the diagnosis of pneumonia. The combined information is helpful for the network to learn robust image features for pneumonia diagnosis. At the same time, we also found that using multi-task model is helpful to improve the recall of patients with pneumonia, which has important clinical significance.
Algorithm for testing digital substation protection devices in conditions of network distortions in the process bus

The article deals with the digital substation equipment maintenance. Equipping the electrical substations secondary circuits with intelligent electronic devices (IEDs) is an important direction in the electric power industry digital transformation. The purpose of the work is to develop an algorithm for testing digital substation protection devices for the influence of network distortions in the process bus. The tasks are to identify the main distortion types, determine the maintenance content and test it. The need to expand the existing service is determined, the maintenance algorithm is developed and a pilot test of the network distortions effect on the IED protection functions is performed.
Laboratory evaluation of electrokinetic dewatering of dredged marine sediment as an option for climate change adaption

The climate change affects the coastal infrastructure including ports. This effect is through changes in the tides, waves, wind and coastal erosion. As a result, sedimentation in harbours and coastal area increases and therefore there is a need for more regular dredging as well as adaption to climate change to reduce the vulnerability. More frequent dredging means higher amount of dredging sediments need to be disposed or treated. One of the methods to be proposed to reduce the impact of high amount of dredging and reducing the environmental wastes as a by-product of dredging is to reuse or reproduce the dredged sediments. Electrokinetic stabilization is one of the environmentally friendly methods to dewater and strengthen the engineering properties of the soils and dredged sediments. This study investigates the effect of electrokinetic stabilization to improve the engineering properties of the dredged mud as an alternative option to reduce the environmental impact and use of a sustainable method for climate change adaption. Two laboratory designs are tested to determine the most efficient electrokinetic dewatering configuration and to examine the potential use of this method for dewatering and improving dredged mud. Electrokinetic stabilization is a promising method to dewater and expedite the settlement of the dredged marine sediments. However, the placement of electrodes can affect the power consumption and the efficiency of the technique and the resistivity of the soil. Some studies in the literature determine the best electrode configuration to optimize the electrokinetic stabilization. However, a few studies examined the electrode placement for electrokinetic dewatering and sedimentation. This study investigates the effect of electrode placement based on the efficiency of the method depending on power consumption versus dewatering, soil electrical resistivity, the settlement of the sediments, and treatment time. To reduce the energy expenditure first a constant voltage of 20 V is applied and the variation of electric current during the electrokinetic stabilization is monitored. Once the electric current approached zero, the voltage is increased to 30 V. Using constant voltage for both cases of electrode placement (anode on top, cathode at the bottom; anode at the bottom, cathode on top), it was observed that higher efficiency based on dewatering and power consumption is obtained when the cathode is placed on top.
Map style transfer using pixel-to-pixel model

The image style transfer is a well-known task and it is applied commonly in the field of artificial intelligence, which converts an image to an image with a specific style without modifying the image content and can be applied to many fields such as changing styled automatically for photos in the cellphone and map wrapping. Recent works focus on a general image style transfer. In this paper, we aim to solve the problem in a navigation application, which is to transfer the satellite maps into map images automatically by deep learning techniques. The methodology we choose for this project is a special GAN named pix2pix model. We decide to use the satellite images as our inputs for the model and let the corresponding Google map images be the output sources. Similar to a traditional GAN, we train the discriminator and generator simultaneously and we attempt to run the training process for 50 epochs in total. PNSR and SSIM are two features we are going to use to test the performance of our results. Finally, the accuracy needs to be improved.
Design Challenges in Wikipedia-style Spotlight Annotation

Wikipedia has set up a manual of style to provide the Wikipedian with guidelines to create links as spotlights in articles to help readers’ understanding. Wikification is coined following Wikipedia’s style guide as a task of automatically detecting mentions and linking to entities in plain-text articles. However, existing methods rely heavily on the mention-entity dictionary and focus on in-map entity precision. Some popular methods are limited to Named Entities. The comparison on the outcome of spotlight annotation is insufficient. We investigate the design challenges for constructing automatic annotating in Wikipedia-style systems. A dataset is built simulating real-world spotlight annotation problems in both the preparing and application phases. Popular methods are compared along with a straightforward framework we propose called WikiER. Experiments show that the performance of currently available systems varies a lot. It is important to train on the task-specific data. The best performance is achieved by WikiER.