Miracle-dz/UniFAR-FTUs huggingface dataset:
DSHANSEP: Alternative considerations of the SHANSEP approach

Since it was first proposed by Ladd and Foott in 1974, the SHANSEP approach has become a quite useful and convenient approach for the evaluation of the undrained shear strength of clay soils in-situ. SHANSEP is commonly practiced with determination of its parameters using various correlations based on consistency limits. While seemingly handy, such correlations hide some details of the behaviour of clayey soils that are useful for engineering judgement. In this article, an alternative way of looking at the SHANSEP method is presented by examining an integrated framework for strength behaviour of clays. This resulted in a new approach called DSHANSEP, where the D represents the so-called “dilatancy parameter” which measures changes in pore pressure due to changes in shear stresses during undrained loading. The resulting association with dilatancy is logical as this parameter reflects failure mechanisms of the clays which is integral aspect of the undrained shear strength of clays. The implication of this relation is explored by looking at its dependence on the overconsolidation ratio, the mobilized stress ratio and sample quality. Furthermore, a formalized system of classification for clays based on the dilatancy parameter and their overconsolidation ratio is proposed and applied for Norwegian clays along with suggested parameters. Use of DSHANSEP and the proposed classifications are in line with the safety philosophy adopted for stability of slopes involving soft clays in Norway.
Study on the Path Planning Algorithm Based on Dueling Deep Q Network

In traditional path planning methods based on Deep Q-Network (DQN) algorithm for mobile robots, the target Q value was usually obtained by a greedy algorithm, and the estimation of Q value was usually high, which would result in slow training speed of the algorithm. To solve this problem, a Dueling DQN algorithm based on Dueling network was proposed in this paper, which was mainly realized by the structure optimization of neural network. The initial algorithm structure was divided into two states and a higher return is obtained through competitive comparison. Then, the long training time and slow speed convergence problem could be effectively solved than the traditional DQN. The simulation results show that the DQN algorithm based on Dueling DQN has high efficiency in Q value obtaining. Compared with the traditional DQN method, it has the better anti-interference ability and decision-making ability, less training time, and stable convergence.
Lead confinement and fluorimetric detection using zeolites: towards a rapid and cost-effective detection of lead in water

Metal clusters stabilized in zeolites have emerged as promising candidates for optoelectronic applications due to their remarkable luminescent properties. These optical properties have been exploited to develop fast and highly sensitive methods for optical sensing in environmental monitoring. However, to date, these materials have not been proposed as a detection method based on their luminescent response for sensing toxic metal ions. In this report, we synthesized luminescent lead (Pb) clusters into the cavities of synthetic F9-NaX zeolites, which were used as scaffolds to confine and detect Pb2+ ions in water through a fluorimetric mode. These Pb-F9 samples display an intense cyan emission in dehydrated form. Also, a correlation between the luminescence intensity of the materials and the lead loadings was observed, obtaining a low limit of detection of 1.248 ppb and a limit of quantification of 3.782 ppb. The results clearly demonstrate the potential of luminescent lead-exchanged F9 zeolites as one-step method for lead monitoring in water using a rapid and low-cost strategy.
The change of node importance and influencing factors in China’s high-speed rail network

High-speed railway can change the geographical location of cities and enhance the importance of cities. This paper based on the complex network theory, combined with data of 2015-2019 year China’s high-speed railway lines, constructs city as the node of the high-speed railway (HSR) network model, and probed the cities of PageRank in network, examined the high-speed rail network in the change of city node importance, analysed city node importance with time evolution and the regression model was used to determine the high-speed rail network in the city the affecting factor on the changes of node importance. The results show that: (1) Many cities in western China, such as Chengdu, Chongqing, have become significantly more important, while that of eastern coastal cities remains stable or even declines. (2)The level of economic development and population have the most obvious and positive influence on the importance of urban nodes. However, geographical location (latitude) and coastal area have negative effects on node importance.
Predictions of COVID-19 Spread by Using Supervised Data Mining Techniques

In the wake of the outbreak of the new coronavirus, the countries in the world have fought to combat the spread of infection and imposed preventive measures to compel the population to social distancing, which led to a global crisis. Important strategies must be studied and identified to prevent and control the spread of coronavirus COVID-19 disease 2019. In this paper, the effect of preventive strategies on COVID-19 spread was studied, a model based on supervised data mining algorithms was presented and the best algorithm was suggested on the basis of accuracy. In this model, three classifiers (Naive Bayes, Multilayer Perceptron and J48) depended on the questionnaires filled out by Basra City respondents. The questionnaires consisted of 25 questions that covered fields most related to and that affect the prevention of COVID-19 spread, including demographic, psychological, health management, cognitive, awareness and preventive factors. A total of 1017 respondents were collected. This model was developed using Weka 3.8 tool. Results showed that quarantine played an important role in controlling the spread of the disease. By comparing the accuracy of the algorithms used, the best algorithm was found to be J48.
Mobile application design of smart water supply chain based on IoT: A case study in Indonesia

Some areas in Indonesia often lack clean water even occur drought if the dry season strikes. However, several other regions of Indonesia have pure water abundance despite the dry season. Based on this problem, the authors aimed to design a mobile application of a smart water supply chain based on the Internet of Things (IoT) for admin and users. The app was Control Your Water (C_Water) application. The app developed aimed not only to detect water shortage quickly but also to accommodate online water purchasing. By installing sensors in the water reservoirs, the admin could find out the supply of clean water in the tanks. Information had obtained from the sensor will go to Microcontroller, then the Microcontroller will send it to the database via Wifi. The mobile application displayed information based on data in the database. The authors proposed a data backup process to prevent data loss. The authors designed a water reservoir consisting of positions of ultrasonic sensors and a division of water level. The result of this research finds that this design helps the government (admin) to overcome the problem of real water shortages that occur in Indonesia.
Intelligent coalbed methane production management and control technology based on reinforcement learning algorithm

The production control of coalbed methane wells has long been viewed as the most challenging step in its development process. For human engineers, they rely too much on previous experience. For artificial intelligence, there is no complete frame to use. Here we proposed a system with reinforcement learning algorithm to CBM production control optimization that used a proxy model to simulate the gas and water seepage in coal seam, and a ‘value networks’ to evaluate gas and water production capability and three control policy mode: bottomhole pressure (BHP) regression model, BHP reduction rate mode, BHP table to select moves. The system achieved a 20.99% and 38.14% increment in cumulative gas and water production, respectively.
Topological Data Analysis For Evaluating PDE-based Denoising Models

Image denoising is process of removing the noise (i.e. artifacts) in digital image. Noise reduction is an essential process of image processing in order to improve, analyze and interpret important information in an image. Edges are important to the visual appearance of images, to preserve important features such as edges and corners during the noise reduction process. A class of fourth- and second-order partial differential equations (PDEs) are used to optimize the trade-off between noise removal and edge preservation. Image quality assessment plays an important role in various image processing applications. It is still an active field of research. Several techniques have been suggested for measuring image quality but none of them are ideal for measuring the quality. This paper presents a new assessment of image quality based on topological data analysis (TDA) which is used for evaluating noise removal from colour images and also for assessing the performance of PDE-based denoising models. The experimental results show that the proposed assessment model gives high correlation. Furthermore, the proposed method provides very low computational load and similar extraction of characteristics to human perceptional assessment.
Changing the pulse duration of a PWM as a means of improving the quality of the ACS regulation using ANN

This paper investigates the influence of the pulse width modulator on the quality of the automatic control system. The approach to the study of ACS using real technical means of automation has been described. A comprehensive study using different structures of neuron connections of the neuroregulator is carried out. The dynamics of changes of quality indicators of the ACS regulation when the object parameters change towards increasing and decreasing inertia is considered together with it. The research results for each of the configurations are plotted. The values of the control quality parameters are tabulated for further comparison of the configurations’ efficiency.
Approaches to solving the resource supply problem of the railway company based on intellectual data analysis

The purchase of trade establishments is an important and complex process, on which companies spend a lot of resources in order to get the maximum benefit. There are many well-established methods for making a forecast of the required number of goods, but none of them can guarantee an accurate result, because in the matter of forecasting, there cannot be a method that produces a result with 100% accuracy. The object of our research is the process of creating a purchase forecast for trade enterprises. The subject is the analysis of the quality of the purchase forecast for retail outlets. To determine the quality of the purchase forecast of the retail outlets, it is necessary to understand how the purchasing process takes place, as well as highlight the factors that affect the forecast quality.
Electrochemical Corrosion Phenomenon and Prospect of Materials Selection in Curtailing the Challenges

Corrosion evolution over time has caused severe damage to life; affects the economic, environmental, and mechanical aspects of materials services. Corrosion is an electrochemical reaction; it is a reaction that involves the movement of electrons from one place to another. Electrochemical reactions include both oxidation and reduction reactions; oxidation reactions increase the valence number of material by removing particles from the material, making it positively charged. In other ways, technological advancement has provided several attempts to understand this catastrophe, providing consistent mitigating measures and control toward attaining less cost. This overview study the electrochemical corrosion phenomena and the prospect of materials selection in curtailing the ranging challenges.
The Research on Influencing Factors of Autonomous Vehicle Usage Behavior: Root Analysis Based on UTAUT2 Model

As a new sustainable means of transportation, how to successfully make autonomous vehicles accepted and used by the public is a key challenge for research. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, the research took the social media platform as the data source to conduct a grounded analysis, comprehensively considered the interaction of individual internal factors and external environmental factors, and combined the technology acceptance field, user behavior field, sociology, psychology, marketing and other disciplines to build an influencing factors of autonomous vehicle usage behavior model, which is suitable for Chinese market. The study found that performance expectation, effort expectation, social impact, facilitating conditions, price value, hedonic motivation, perceived risk and perceived trust influence individuals’ intention to use autonomous vehicles. Facilitating c zV Conditions, perceived risk and intention affect individual’s usage behavior. The level of individual knowledge plays a regulatory role in the use behavior. The results provide a decision basis for the government and enterprises to effectively promote the use of autonomous vehicles.
Retraction Retraction: Fatigue Monitoring Using Real-Time Facial Expression Based on Neural Technique

The face, as a crucial part of the body, communicates a great deal of information. When a driver is extremely tired, his or her facial expressions, as well as the frequency at which he or she wiggles, vary from those in a normal state. In this paper, we suggest a device that senses the driver's drowsiness and, as a result, triggers an alarm about the vehicle's ordinance and has a tendency to slow down the vehicle. When drowsiness is detected, alarms will sound, the seat will begin to vibrate, and the lights will turn on, all without the use of any sensors. We implement a brand new facetracking algorithm to improve tracking accuracy as a result of the limitations of previous algorithms. We have devised a facial region replacement detection system. Then we use these facial regions to assess the condition of the drivers. Our device will alert the driver by using a fatigue warning by integrating the features of the eyes and mouth. It's a computer vision system that can detect driver drowsiness in an approaching video stream and raise an alert if the driver seems tired.
Presence of moonrat (Echinosorex gymnura) at selected forest reserves in Central Forest Spine (CFS) landscape of Peninsular Malaysia

Surveys on moonrat were conducted at 13 sites under 11 fragmented forest reserves of five CFS ecological corridors located in the states of Negeri Sembilan, Johor, Terengganu, Pahang and Kelantan. The surveys were conducted between February 2018 to September 2019. Not much information on moonrat is documented at these forest reserves. Therefore, the study aims to document the presence of moonrat within CFS landscape in Peninsular Malaysia. A total of 100 collapsible cage traps were deployed at each surveyed site. Cumulatively, a total of eight individuals of moonrat were recorded from six forest reserves. Tanum FR and Ulu Jelai FR located in Pahang, harbour highest number of individual of moonrat, respectively with two individuals in each forest reserve. Forest conditions such as canopy closure, vegetation density and food resources may affect the presence of moonrat. Based on the information, further conservation measures can be strategized in order to conserve these fragmented forest reserves of CFS ecological corridor as a habitat for this mammal.
An R-SIFT Image Matching Intelligent Algorithm Applied to Hardware Trojan Detection

Malicious modification, deletion or addition of some modules in the integrated circuits (ICs) will cause the chip to be attacked, which is called Hardware Trojans (HTs). Reverse engineering (RE) is a classic destructive method and widely used in HTs detection. However, RE is very time-consuming and error prone. In this paper, based on RE, we propose the R-SIFT algorithm to match image and reformulate the Trojan detection problem as change detection problem. For the R-SIFT algorithm, the ratio of exponentially weighted averages (Roewa) operator is introduced into the scale-invariant feature transform (SIFT) algorithm. Experiments show that the proposed method has 7 to 56 times more matching points than the original SIFT can be effectively applied to HTs detection.
Induced Mutations Using Gamma Ray and Multiplication of Plantlet through Micro Cross Section Culture of Banana (Musa acuminata cv. Berangan)

The conventional breeding approach in banana was slow predominantly because of the limited genetic diversity, infertility and polyploid condition of the species. Induced mutation is one of the promising tools to create new varieties for the improvement of banana. While vitro propagation technique ensures a rapid and in an organized manner for production of banana. Thus, induced mutation using gamma ray in combination with in vitro culture micro cross section culture was conducted to evaluate the effects of gamma radiation on growth and development of plantlet of Musa acuminata cv. Berangan. Tissue cultured banana plantlet was used as the source of explant. The plantlet was cut into halved and radiated using gamma ray with the dose of 0 (control), 20, 25, 30, 35, 40, 45, 50, 55, 60 and 80 Gy. The highest survival rate (23.33%) was recorded in treatment 10 Gy while the lowest survival (0.33%) was in treatment 80 Gy. The lethal dose (LD50) which had caused 50% mortality to the irradiated material was found to be 37 Gy. The morphological study on the growth of in vitro irradiated plantlets showed that 25 Gy induced a stimulation effect on the number of shoot produced (5.56), root number per explant (8.5) and root length (37.36 cm) compared to untreated banana explants (0 Gy). Hence, this study introduces the in vitro multiplication of gamma irradiated culture system for future breeding of a new varieties of banana.
Microbes involved in arsenic removal in passive treatment systems

Biogeochemical cycling of arsenic and speculation on mechanisms of arsenic removal are interest in the environmental remediation of contaminated sites. In the present study, combination of metagenomic molecular biology techniques with mineralogical analyses were used to study a biochemical reactor (BCR) that was successfully removing arsenic, zinc, copper and cadmium. First the metal and mineralogical content of the BCR solids was investigated. X-ray diffraction (XRD) and automated quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) were used for mineralogical characterization. Analysis indicated that sulfates and sulfides were the predominant types of Zn and As minerals formed in the BCR. Arsenic minerals were detected as sulfides (arsenopyrite, tennantite), arsenates(wihelmkleinite), oxides (unknown zinc arsenic oxides) and zincarsenic sulfides, which showed evidence of metal adsorption on the surfaces of other solids such as silicates. Energy-dispersive X-ray spectroscopy verified that arsenic was associated with iron, zinc and sometimes cadmium as arsenopyrite-type minerals. Using a SSU rRNA survey of the site, the following taxa were correlated with high metal content: Bacteroidetes, Synergistaceae, Victivallales, methanogens (Methanocorpusculum, Methanospirillum, Methanosarcina) and new phyla such as VadinHA17, M2PB4-65, candidate division WS6, RF3 and TM6. Next, enrichment culturing and arsenic chemical speciation monitoring were performed to assess potential for arsenic species transformations in the BCR. Most predominant groups in the As(III) and As(V) media, were Simplicispira (β-proteobacterium) and Sedimentibacter (Firmicutes), respectively. Chemical arsenic speciation monitoring of the enrichments suggested that arsenite oxidation and arsenate reduction occurred. These genera were not previously reported for arsenic transformation. Finally, functional metagenomic workflow was applied to study arsenic resistance genes. Functional screening and end-sequencing of large insert fosmid libraries demonstrated that arsenic(V) resistance genes were taxonomically widespread and different class of arsenic resistance genes related to periplasmic arsenate reduction, arsenite efflux, bioaccumulation (phosphate, metal transporters) and arsenite oxidation were present. Fewer genes were associated with dissimilatory arsenate reduction and arsenic volatilization mechanisms. Methanomicrobia were predominant in the BCR and identification of methanogen-related arsenic resistance genes indicated that methanogens potentially played a role in arsenic removal inside the BCR.
Logo Based on Improved Generative Countermeasure Network Image Super Resolution Reconstruction Method

In order to solve the problem that the existing learning based super-resolution reconstruction methods rely too much on the degenerate model while the degradation model is unknown, which leads to noise and edge jagged in the reconstructed image, an image super-resolution reconstruction method based on improved generative countermeasure network is proposed. The generator of the network consists of up sampling module, denoising module and anti-aliasing module Firstly, the input image is sampled by the up sampling module to generate the initial super-resolution image; then, the denoising module and anti-aliasing module are used to reconstruct the clear super-resolution image; in order to reconstruct a better logo image, a joint training loss is introduced, including content loss and resistance loss, and the content loss includes perception loss and edge loss Lost. The experimental results show that, compared with the current super-resolution reconstruction method based on generative countermeasure network (SRGAN), the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved to 0.2675dB and the structural similarity is improved to 0.035, which can effectively improve the quality of logo image reconstruction.
Audio source separation using supervised deep neural network

The speech signals inserted in the computer may be mixed as a result of interference with signals from other sources. These signals may be speech signals or noise. One of the most famous examples of this problem when a group of people speaking in the same time is the “Cocktail Party”. This problem produces a mixture of different speech signals, called the mixed signal. To solve this problem, the audio signals that make up the mixed-signal must be restored to their sources. This task is called separating audio sources. In this paper, supervised Deep Recurrent Neural Networks with Bi-directional Long Short Term Memory (Supervised DRNN-BLSTM) were used. To achieve a monaural source separation, we build a model to separate audio signals from a monaural mixed signal. This mixed signal consists of two different audio signals (male-female). We predict two types of time-frequency masks (Ideal Ratio Mask (IRM), and Optimal Ratio Mask (ORM). They are used to achieve the separation of the target audio sources from the mixed signal. We test the model on a dataset with (500) mixed signals. Each mixed signal three seconds in length and consists of two speaker signals (Female-Male). They are recorded in a stereo format at 8192kHz, our approach achieves Signal-to-Distortion ratio (SDR) (0.183.db), Source-to-interference Ratio (SIR) (0.198.db), and Source-to-Artifacts Ratio (SAR) (0.13.db) gain using (ORM) mask compared to the existing model using (IRM) mask.