Miracle-dz/UniFAR-FTUs huggingface dataset:
Borehole Image Classification Using NSCT, Hu moment invariants and SVM based APBCS System

The role played by Axial View Borehole Camera System (AVBCS) in acquiring borehole images and in analyzing geographical structures opens new possibilities in geographical exploration. Considering the current situations, where the borehole images are interpreted by humans, a borehole image classification method based on NSCT and Hu moment invariants is proposed to classify them into three main classes of border, fracture and intact rock mass images. Firstly, the borehole images are decomposed into one low frequency subband and multiple band pass directional subband coefficients by NSCT, to obtain the mean and variance of these subband coefficients. In the subsequent phase, 18 features are acquired with NSCT which are combined with 7 Hu moment invariant features of source images to form the image feature vector. Finally, after the feature optimization based on variance ratio test, the optimal feature vector is furnished as input to the SVM to obtain the image classification results. Experiments show that the proposed classification method can effectively classify three major borehole images with the accuracy of 94.44% in the training samples and 93.33% in the testing samples.
Memristor Based Cryptographic Information Processing for Secured Communication Systems

Communication today plays a vital role as a means of transmitting energy from one place to another. This energy usually is a signal containing valuable information and needs to be transmitted securely to the intended user. In this paper, digital modulation techniques are used to modulate the binary data with the output of chaotic oscillator to bring the necessary encryption and decryption of signals in the communication system. Assessment of the system over the hardware and NI Multisim simulation confirms the validity of this approach.
Factors Affecting the Return and Volatility of Major Cryptocurrencies

The goal of this study is to analyze the predictive power of selected financial variables over principal cryptocurrencies (Bitcoin, Ethereum, and Ripple) by applying the PGARCH, EGARCH, TGARCH, and GARCH models. The studied variables include daily Google trend values of the selected cryptocurrencies' names considered as search terms; the daily log-returns of the exchange rate of major currencies per SDRs; and the daily log-returns of the S&P500 index, gold, and oil prices. Our results provide evidence that the log-returns of each studied cryptocurrency have significant explanatory over each other. We also find significant results for the daily Google trend values of the search terms “Bitcoin” and “Ripple,” but not for “Ethereum. ” Likewise, we find significant results for the log-returns of the exchange rate of the Chinese Yuan per SDRs for the three cryptocurrencies. Similarly, the log-returns of oil prices have a significant relationship with Bitcoin and Ethereum, but not with Ripple. Correspondingly, we find that the daily log-returns of the S&P500 index has explanatory power for all selected cryptocurrencies; however, the daily log-returns of gold have no significant relationship with any of them. Finally, we find that the log-returns of oil have a significant relationship with Bitcoin only. Regarding the volatility of the studied cryptocurrencies, our results provide mixed evidence about the presence of leverage effects.
Use of Games for Teaching Programming: Experience Report

For many years, teachers and experts connected to Education have studied and proposed new teaching-learning methodologies e new teaching tools, making changes to the pedagogical projects of the courses, creating projects that promote multidisciplinary, among other initiatives, with the aim of improving the process of transforming skills and abilities into knowledge, in addition to motivating students. One of these initiatives in the Computing area has been the use of games in course subjects. This article brings an experience report using the game development approach to teaching programming in an introductory course. The steps developed in the methodology, as well as the results are discussed in this work.
Simulation Study of Cascade H-bridge Multilevel Inverter 7-Level Inverter by SHE Technique

In this Paper, fuzzy logic approach is implemented as a switching technique. The traditional logic gate design is eliminated by using the proposed technique. There is a pulse generator which is designed by fuzzy logic acts as a pulse generator and a look-up table also. An input is based on a modulation index, well controlled membership functions used and the related fuzzy logic controller’s rules can be used to produce pulses directly without using any extra block. The mentioned technique is being implemented by using the cascaded multilevel H-Bridge inverter with symmetric operation using pulse width modulation with selective harmonic elimination technique (SHE-PWM). MFs are designed as per the calculated firing angles for various modulation index.
45nm CMOS 4-Bit Flash Analog to Digital Converter

Signal processing and communication systems are widely dependent on the analog to digital converters [ADC]. Low power consumption remains as a considerable benefit from the layout design. This study presents a four bit flash ADC using CMOS 45nm technology. Operational amplifier design, which remains as the integral part of ADC is also discussed. To enable an improved performance of the ADC, a potent operational amplifier is designed with a frequency range ± 5MHz along with an operating voltage of 2.5 V for serving at the heart of Flash ADC. The thermometer encoder circuit is a logic-based encoder built upon XOR and OR gates. Cadence Virtuoso circuit and layout editor along with verification tools (LVS and DRC) are used to design different layouts and schematics. The 4-Bit Flash ADC uses 9 mW of power with a delay of $1.11 \mu s$ in conversion.
Embedded Based Automatic Chain Lubrication System

This system was designed to automate the chain lubrication system for two wheeled automobiles particularly chain based vehicles. A chain is a movement of traveling day by day heading with an approach to attract the teeth of a sprocket and transmit force and development. To prevent the chain to cut off or rust, need to feed an oil to the chain much of the time. Oil of chains is certainly not a direct task daily there is a need to intensely watch the condition of the chain from time to time for oiling up it when the chain gets dried. To anticipate the chain to cut off or rust there constantly a need to grease ups the chain frequently. Nowadays Grease of chains is definitely not a straightforward assignment, there is a need to acutely watch the state of the chain every so often grease up it when the chain gets dried. An embedded based automatic chain lubrication system is a commonsense strategy to confine wear and the over the top issues it causes. In fact, authentic oil can twofold or even triple the life of two wheeler chains. This system passes on a full extent of chain oil structures, in sprinkle arrangements, to widen chain life, cut individual times, save time and addition benefits.
Comprehensive Study of Diabetes Miletus Prediction Using Different Classification Algorithms

Diabetes mellitus (DM) is a chronic disease. It has been rising more rapidly in middle- and low-income countries. World Health Organization (WHO) [1] estimates that diabetes was the seventh leading cause of death in 2016. In this paper a database concerning this disease where discussed and implemented by data mining techniques. Data mining techniques used to help the prediction of DM. It makes the prediction process faster, cheaper and more accurate for the benefit of both physicians and patients. In this paper, wellknown data mining algorithms explored to achieve DM prediction. The performance of these algorithms was evaluated and discussed using Orange Data Mining tool. The performance evaluation executed using two metrics: the recall and the precision; applied to each discussed classification algorithm. The studied classification algorithms are Naïve Bayes, K-Nearest Neighbours, Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression.
Prediction of Energy Demand in Smart Grid using Hybrid Approach

Predicting the energy consumption to provide valuable decision information for service providers and customers is a challenging task on the electricity market. The combination of mathematical and ANN methods results in a precise method of energy prediction than a single mathematical or ANN approach for predicting time-series data starting with different applications. The combination of (linear) seasonal auto-regressive integrated moving average (SARIMA) and (nonlinear) long-short term memory (LSTM) models are adopted to construct an advanced hybrid SARIMA-LSTM model for forecasting the time series data. Compared to existing models such as SARIMA and LSTM, a combination of the SARIMA-LSTM technique produces more accurate predictions. The results of the simulation reveal that better prediction is achieved by the proposed hybrid model. Further, the prediction model explores detailed energy consumption patterns with seasonal properties which makes both the service provider and customer to reduce their energy generation and consumption cost, respectively.
Communication and Trust as Facilitators for Collaborative Innovation Development in Virtual Teams

In this study, we investigate how relationship communication affects the innovation / creativity process in a virtual environment. We propose a multiple mediator model in which cognitive and affective trust act as mediating mechanisms that transmit the positive effects of communication to innovation/creativity development within teams. The main results of this study revealed significant positive correlations between relationship communication, trust and innovation / creativity. In addition, this study identified interpersonal trust as playing an important role in mediating the relation between relationship communication and innovation/creativity. Both the emotional aspect of trust and the rational aspect have proven to be mediators of innovation / creativity. These findings contribute to solve some of the emerging organizational challenges through a better understanding of the innovation development mechanism.
Planning of a Northern Isolated Microgrid

There have been strong drivers toward lessening diesel dependency in remote communities. These off-grid communities can be difficult to access, and often face challenges with diesel supply. More importantly, governments are striving to improve the environmental outlook in these areas. Tying this need with associated high operational costs and fuel handling challenges, alternative sources of energy and energy storage have emerged as a viable option to reduce diesel consumption. This paper presents the experience of a Canadian electric utility in planning the application of utility scale photovoltaic generation and Battery Energy Storage Systems to supplement existing diesel generators in a remote isolated community in Alberta. Economic drivers and opportunities to partner with first nations have sparked stakeholder attention and positioned the project as a national interest. Technical and economic studies were conducted to address all aspects of the project. These include forecasting load, sizing and siting each alternative generation option, customizing dispatch strategies, and determining required spinning reserve from diesel generators through time-domain studies. This paper covers the size and location optimization of the PV and BESS assets and dispatch strategies.
MCDM Analysis of Renewable and Conventional Energy Power Plants in Pakistan

Continued economic growth of any country requires a sustainable energy supply. Pakistan’s energy sector is suffering from a serious energy crisis from the past two decades and mainly depends on conventional energy sources, which are costly and unsustainable. Moreover, electricity production across the country is largely dependent on oil and gas products while the utility of renewable energy resources is dismal. To evaluate a country’s energy system is a complex analysis that can be defined as a multi-dimensional problem of different alternatives and its criteria. Analytical Hierarchy Process (AHP) of the Multi-Criteria Decision Making Analysis tool provides a reliable methodology to rank different energy alternatives on the basis of selected criteria. In this paper, renewable and conventional energy power plants are analyzed on the basis of actual data of technical, economic and environmental criteria. The AHP results of this study show that the most favorable and sustainable energy plants for electricity production in terms of the above criteria and sub-criteria are renewable power plants followed by conventional power plants.
Use of Artificial Neural Network in the Estimation of Detonation Velocity for Tetranitromethane-Nitrobenzene Mixture

Detonation velocity or rate of energy release is an important property to consider when rating an explosive. It is a critical parameter used for estimating explosive performance as it can indicate the intensity of detonation. The purpose of this research study is to propose an artificial neural network model that would aid in the estimation of detonation velocities of a high explosive specifically, tetranitromethane-nitrobenzene (TNM/NB) mixture, with varying parameters
Neural Q- Learning Based on Residual Gradient for Nonlinear Control Systems

To solve the control problem of nonlinear system under continuous state space, this paper puts forward a neural Q-learning algorithm based on residual gradient method. Firstly, the multi-layer feedforward neural network is utilized to approximate the Q-value function, overcoming the “dimensional disaster” in the classical reinforcement learning. Then based on the residual gradient method, a mini-batch gradient descent is implemented by the experience replay to update the neural network parameters, which can effectively reduce the iterations number and increase the learning speed. Moreover, the momentum optimization method is introduced to ensure the stability of the training process further and improve the convergence. In order to balance exploration and utilization better, ε-decreasing strategy replaces ε-greedy for action selection. The simulation results of CartPole control task show the correctness and effectiveness of the proposed algorithm.
Millimeter-Wave CMOS Phased-Array Transceiver Supporting Dual-Polarized MIMO for 5G NR

To realize the future smart society, wireless communication technology is one of the most significant technologies. In this presentation, a 28-GHz phased-array transceiver designed for the incoming 5G NR will be introduced. A neutralized bidirectional technique with minimized chip area is employed for a low-cost system. The data rate is improved significantly with a 2×2 dual-polarization MIMO configuration. The talk concludes with a discussion on future directions of millimeter-wave wireless communication, based on Shannon and Friis equations.
Design and Development of IoT based Robotic Arm by using Arduino

In the current scenario machines and robots are playing an important role in the automation industry. This paper is presenting the process through which robotic arm is made with the help of Arduino and Potentiometer for controlling and coordinating the industrial processes. Here, we realize that the robotic arm has the ability to move in four directions with the help of servo motors. The movement of servo motor, UNO is responsible which converts the analog signal into the digital signal which is further received by servo motor. This project discuss about the technical imputation, the issue related with the implications and application of robotic arm in the field of automation of industries. It can be used as a artificial arm for a human who have lost his hand in any accident.
Research of Internet User’s Use Intention on UGC Aggregation Platform Based on TAM

With the development of the Web2.0 era, user-generated content (UGC) has become the main mode of dissemination of network information. UGC aggregation interactive platforms have emerged as an important way for users to obtain information. The purpose of this paper is to study the factors that affect users' choices of using UGC aggregation interactive platforms. On the basis of technology accepted model (TAM), we introduce trust propensity and subjective norm as users’ individual characteristics, and integrate platform quality and information quality as platform features, and add perceived risk factor and demographic factor into the theory model to analyze the factors that affect users' willingness when they choose UGC aggregation interactive platforms to get information. Finally, through empirical research, we provide corresponding suggestions for product design, customer group targeting, market segmentation and sales promotion strategies.
Design, Fabrication, and Locomotion Analysis of an Untethered Miniature Soft Quadruped, SQuad

The conventional robotics, which involves utilization of robots made out of hard materials like metals and hard plastics, has helped humankind automate many different sorts of labor and such robots have been assisting the humans in various tasks. Nevertheless, some applications require very delicate interactions and adaptability of the robots to unstructured elements and obstacles; which can only be provided by softness. The miniature and untethered robot in this work is fully made out of soft structural materials and uses a flexible circuit board. Only the electronic components, actuators and several little connection parts are hard. Its soft legs, body, and circuit enables it to overcome obstacles that conventional hard miniature robots tend to be stopped by. For the soft robot presented, walking and obstacle climbing experiments were done and pitch angle, roll angle, robot's centroid position and stiffness analyses were conducted. Additionally, three other robots are fabricated in hard body - hard leg, hard body - soft leg, and soft body - hard leg configurations and the effects of body and leg compliance on the locomotion performance are investigated. The results show that a soft body - soft leg robot configuration can scale an obstacle 1.44 times its body height whereas the hard bodied and hard legged robot can only go over 0.88 times its body height. The results also indicate that the softness of the body effects the scalable obstacle height more than the softness of the legs at this length scale.
How do primary care doctors in England and Wales code and manage people with chronic kidney disease? Results from the National Chronic Kidney Disease Audit

Background. In the UK, primary care records are electronic and require doctors to ascribe disease codes to direct care plans and facilitate safe prescribing. We investigated factors associated with coding of chronic kidney disease (CKD) in patients with reduced kidney function and the impact this has on patient management. Methods. We identiﬁed patients meeting biochemical criteria for CKD (two estimated glomerular ﬁltration rates < 60 mL/ min/1.73 m 2 taken > 90 days apart) from 1039 general practitioner (GP) practices in a UK audit. Clustered logistic regression was used to identify factors associated with coding for CKD and improvement in coding as a result of the audit process. We investigated the relationship between coding and ﬁve interventions recommended for CKD: achieving blood pressure targets, proteinuria testing, statin prescription and ﬂu and pneumococcal vaccination. CKD, diabetes or hypertension or those prescribed statins were more likely to have a CKD code. Among those with continued biochemical CKD following audit, these same characteristics increased the odds of improved coding. Patients without any kidney diagnosis were less likely to receive optimal care than those coded for CKD [e.g. odds ratio for meeting blood pressure target 0.78 (95% conﬁdence interval 0.76–0.79)]. Conclusion. Older age, male sex, diabetes and hypertension are associated with coding for those with biochemical CKD. CKD coding is associated with receiving key primary care interventions recommended for CKD. Increased efforts to incentivize CKD coding may improve outcomes for CKD patients.
Functional Response of Eretmocerus delhiensis (Hymenoptera: Aphelinidae) on Trialeurodes vaporariorum (Hemiptera: Aleyrodidae) by Parasitism and Host Feeding

The parasitoid wasp, Eretmocerus delhiensis Mani is a thelytokous and syn-ovigenic parasitoid. To evaluate E. delhiensis as a biocontrol agent in greenhouse, the killing efﬁciency of this parasitoid by parasitism and host-feeding, were studied. Killing efﬁciency can be compared by estimation of functional response parameters. Laboratory experiments were performed in controllable conditions to evaluate the functional response of E. delhiensis at eight densities (2, 4, 8, 16, 32, 64, 100, and 120 third nymphal stage) of Trialeurodes vaporariorum Westwood on two hosts including; tomato and prickly lettuce. The maximum likelihood estimates from regression logistic analysis revealed type II functional response for two host plants and the type of functional response was not affected by host plant. Roger’s model was used to ﬁt the data. The attack rate ( a ) for E. delhiensis was 0.0286 and 0.0144 per hour on tomato and 0.0434 and 0.0170 per hour on prickly lettuce for parasitism and host feeding, respectively. Furthermore, estimated handling times ( T h ) were 0.4911 and 1.4453h on tomato and 0.5713 and 1.5001h on prickly lettuce for parasitism and host feeding, respectively. Based on 95% conﬁdence interval, functional response parameters were signiﬁcantly different between the host plants solely in parasitism. Results of this study opens new insight in the host parasitoid interactions, subsequently needs further investigation be-fore utilizing it for management and reduction of greenhouse whiteﬂy. (parasitoids or predators) to increasing host density. Different factors may influence the type of functional response or parameters values by change in searching pattern (Holling 1959). The objectives of the current study were to determine the type of functional response, its parameters and evaluation of host-feeding and parasitism of parasitoid wasp, E. delhiensis on T. vaporariorum on two plant hosts, tomato and prickly lettuce at different densities. The results from this study will help to our knowledge about, in-teraction between parasitoid and host density to improve it use in biological control program.
Regulation of reproduction and longevity by nutrient-sensing pathways

the connections between reproductive senescence and somatic aging and give an overview of the involvement of nutrient-sensing pathways in controlling both reproductive function and lifespan. Although the molecular mechanisms that affect these processes can be influenced by distinct tissue-, temporal-, and pathway-specific signaling events, the progression of reproductive aging and somatic aging is systemically coordinated by integrated nutri-ent-sensing signaling pathways regulating somatic tissue maintenance in conjunction with reproductive capacity.