Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept of Gamification in Adaptation of Snake Ladder Online Representation Education Covid-19

Education can be implemented through various concepts, one of which is the concept of gamification which is then adopted as a learning medium. Gamification to make a change in the use of instructional media, namely of the “conventional” to the more “innovative”. This study aims to adopt a concept of gamification into a game of snakes and ladders to support learning about the understanding of Covid- 19. The research method with the waterfall model, which is a simple classic model with a linear system flow, where the output of each stage is input for the next stage, this is done by manipulating program scripts and algorithms to provide novelty to the rules of the snakes and ladders game concept. will attract users and provide an impact of understanding created through the pleasure of playing in solving challenges. The learning of snakes and ladders itself is designed in such a way that the board interface changes randomly every time you play until the base board game becomes a digital game that can be applied n offline and online. The game is developed to the stages of the “Online Play” mode, which allows matches to be played from various devices via the internet. This game uses a box system that functions as a “lobby” in the game, so online players can choose to join or host the game and invite other players through the game code.
An Improved Interacting Multiple Model Algorithm for INS/DVL Integrated Navigation System

In Inertial Navigation System (INS)/ Doppler Velocity Log (DVL) integrated navigation system, due to the time-varying measurement noise, the inaccurate measurement model results in substantial estimation errors or even filter divergence. To address this problem, Interacting Multiple Model (IMM) algorithm which uses more than one model is always considered. The efficiency of IMM algorithm depends on the accuracy of model set. In order to cover the real model with smaller model set, an Improved IMM(IIMM) algorithm is proposed in this paper. A variable model set based on the model probability weighted average of the model parameter is generated to pursue the real model. Meanwhile, the time-varying state transition probability matrix based on model probability difference is introduced to accelerate the switch of model set to the true model. Simulation and experiment indicate that the model set can cover the true model rapidly and precisely with only three models, and the accuracy of the SINS/DVL integrated navigation system is improved by applying the proposed method.
Intelligent OAM of New Generation Access Network Based on SDN

With the rapid development of the optical access network and the trend of full service access bearer, the operation, administration, and maintenance (OAM) of the optical access network also face more and more challenges. This paper analyzes the limitations of the traditional access network OAM interface, introduces the key enable technologies in access network to implement agile, automated and intelligent OAM. It provides application scenario for intelligent OAM and supports passive OAM. The evolution from OAM to active OAM effectively improves the intelligent OAM capabilities of the optical access network, thereby helping network operators to cope with various challenges in OAM of new services, enhancing service operation capabilities, and improving the end user’s business experience.
Theoretical Calculation of Photoemission Yield Spectrum of Space Metal Materials

In the theoretical calculation of the photoemission yield spectrum of space metal materials, the mathematical model is found based upon three steps process of photoemission combined with the metal free-electron gas model. We take Cu as an example, the variation of photoemission yield spectrum caused by the electric field with different field strength and different incidence angle is calculated. This paper shows that the regions of quantum efficiency of Cu and Au when the materials are in the vacuum, under certain conditions, are respectively calculated between 8×10–8 and 2.6×10–3, 8.53×10–6 and 3.8 × 10–3, corresponding to the regions of incident ultraviolet wavelength are respectively between 138nm and 287nm,108nm and 249nm. Its quantum efficiency decreases with the increase of incident wavelength. The greater the intensity of the applied electric field, which is perpendicular to the copper surface, the greater the value of the calculated copper yield spectrum. The greater the angle formed by the incident ultraviolet light with the copper surface, the smaller the value of the calculated copper yield spectrum.
A Short Review on Graphene Nanoribbon Interconnect

On-chip VLSI interconnects is considered very promising area in the field of IC design in recent years. The delay of interconnect system becomes pre-dominant than the on-chip transistor gate delay in ultra large scale integration due to the substantial parasitic effects. Further the increase in Joule heating and significant increase of grain boundary scattering posed a harsh challenge for future technologies. Subsequently the VLSI industry started a searching the alternative of conventional copper interconnect to get rid of these issues. Here the surprise innovation, graphene, came in picture. Graphene is the material with high electron mobility and high mean free path, so the high current density and lowest resistivity. For interconnect application, due to lower resistivity, graphene nano ribbon (GNR), further multi-layer GNR (MLGNR) has been considered to the most suitable for nano-interconnect application. Further intercalation doping improves the conductivity for MLGNR interconnect. This article demonstrates the basic structural properties and depicts the electrical models of single and multi-layer and intercalation doped GNR. A preliminary discussion on production methods for structuring pristine and intercalated GNR interconnect has also been discussed in this article.
Economic Evaluation of Electric Vehicle Charging Infrastructure Integration in Microgrids

The operation of charging infrastructure is often not profitable yet. However, the integration of charging infrastructure in microgrids enables the introduction of innovative business models, e. g. by using PV generation and storage units. This study presents several business models, i. a. based on self-supply of electricity and smart charging algorithms. These are evaluated by means of real-world transaction data of the EUREF campus microgrid in Berlin, Germany. The dataset comprises data of 3346 transactions of foremost commercial fleet vehicles from the year 2018. The microgrid configurations evaluated in this study involve the integration of PV generation, battery storage, individually as well as combined. A reference is provided by a comparison to the conventional operation of charging infrastructure. The evaluation shows that the integration of PV generation in combination with smart charging is profitable, due to lower expenditures by local energy supply utilization. Additional integration of a stationary battery is less profitable. However, it increases autarky and flexibility by charge during evening and night hours.
An IIoT Based ICS to Improve Safety Through Fast and Accurate Hazard Detection and Differentiation

Safety and security of Industrial Control Systems (ICS) applied in many critical infrastructures is essential. In these systems, hazards can be due either to system failure or cyber-attacks factors. Accurate hazard detection and reducing reconfiguration time after hazard is one of the most important objectives in these systems. One of the procedures that can reduce the reconfiguration time is determining the cause of hazards and, based on the aforementioned factors, adopting the best commands in reconfiguration time. However, it is difficult to differentiate between different types of hazard because their effects on the system can be similar. With the advent of IoT into ICS, known as IIoT, it has become possible to differentiate the hazards through the adoption of data from different IIoT sensors in the environment. In this article, we propose a risk management approach that identifies hazards based on the physical nature of these systems with the support from the IIoT. The identified hazards fall into four categories: stealthy attack, random attack, transient failure, and permanent failure. Then, the reconfiguration process is run based on the proposed differentiation, which provides a better performance and reconfiguration time. In the experimental section, a fluid storage system is simulated, showing 97% correct differentiation of hazards and reducing in 60% the lost time in the system recovery reconfiguration.
Predicting Revenues and Expenditures Using Artificial Neural Network and Autoregressive Integrated Moving Average

The Saudi government has now set up several strategic strategies to predict the country's future, such as Saudi Vision 2030. Mathematical model and forecasting methods are significant instruments to achieve superior development in the country's economy. In this research, the Kingdom of Saudi Arabia's revenues and expenditures are predicted using models of the Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA). This article uses statistical software to forecast a time series data using ANN and ARIMA models on Kingdom of Saudi Arabia's total revenues and expenses from 1969 to 2018. The models ANN and ARIMA (1, 0, 0), ARIMA (0, 1, 1) and ARIMA (1,1,2) are found to be suitable for predicting the full revenue and expenditure of the Kingdom of Saudi Arabia.
Thin Core Fiber and Thin Fiber Based Inline Mach-Zehnder Interferometer for Temperature Measurement

In this paper, a thin core fiber (TCF) and thin fiber (TF) based inline Mach-Zehnder interferometer is proposed and demonstrated experimentally. The proposed sensor is fabricated based on SMF-TCF-TF-SMF structure and the TF is core offset spliced with the TCF. Three sensors have been designed and the transmission spectral responses versus temperatures have been investigated. The experimental results show that the temperature sensitivity can reach up to 60.71 pm$/^{\circ}\mathrm{C}$ and the temperature response linearity is as high as 99.63 %. The proposed sensor is expected to have good application prospects in biological and chemical fields due to simple fabrication, compactness and low cost.
Design and Implementation of an Efficient Dadda Multiplier Using Novel Compressors and Fast Adder

Fast multipliers play a significant role in digital signal processing (DSP) and Arithmetic Logic Unit (ALU) systems. Delay and area are cardinal factors that limit the performance of a VLSI design circuit. The paper focuses on new approaches to Dadda Multiplier using Novel compressor designs. Two novel 4-2 compressors and modified higher order compressors are introduced. Three multiplier designs are proposed and compared with existing multiplier designs. Proposed design is found to be more optimal with the existing design in terms of delay and area, and can be used for exact multiplier applications. The designs are simulated using Xilinx ISE tool.
Multi-Model Convolutional PFC for the Control of Activated Sludge Reactors

This paper investigates the use of Predictive Functional Control algorithm by replacing the classical internal model with an impulse response of the process under control as Convolutional Predictive Functional Control Conv-PFC. Convolution functional control has been successfully applied in industry. The use of the Conv-PFC to a reduced bio-reactor activated sludge ASM1 model, which describes the biological degradation of an activate sludge reactor, is designed based on several simplifications, as a TS model, witch structure is based on a set of linear sub models, covering the process input-output space, interpolated by a nonlinear weighting function. The Conv-PFC controller is compared to a benchmark PID in terms of error and response dynamics.
Data collection on Shipper’s Preferences Using Simulation Games: : A Synchromodality Case Study

The development of new more efficient freight transportation services requires in depth understanding of the engaged stakeholders behavior. Simulation Games (SG) have been used to study decisions and raise awareness over complex transport problems. This paper investigates the possibility of applying a simulation game as an innovative data collection tool for choices related to freight transport. Games sessions were organised with Dutch logistics managers and data on their choices for synchromodal services were collected. Model results revealed reliability, costs and reduced work load as key factors making shippers to opt for more synchromodal services.
Cross-Modal Interference-Control Is Reduced in Childhood but Maintained in Aging: A Cohort Study of Stimulus- and Response-Interference in Cross-Modal and Unimodal Stroop Tasks

Interference-control is the ability to exclude distractions and focus on a specific task or stimulus. However, it is currently unclear whether the same interference-control mechanisms underlie the ability to ignore unimodal and cross-modal distractions. In 2 experiments we assessed whether unimodal and cross-modal interference follow similar trajectories in development and aging and occur at similar processing levels. In Experiment 1, 42 children (6–11 years), 31 younger adults (18–25 years) and 32 older adults (60–84 years) identified color rectangles with either written (unimodal) or spoken (cross-modal) distractor-words. Stimuli could be congruent, incongruent but mapped to the same response (stimulus-incongruent), or incongruent and mapped to different responses (response-incongruent); thus, separating interference occurring at early (sensory) and late (response) processing levels. Unimodal interference was worst in childhood and old age; however, older adults maintained the ability to ignore cross-modal distraction. Unimodal but not cross-modal response-interference also reduced accuracy. In Experiment 2 we compared the effect of audition on vision and vice versa in 52 children (6–11 years), 30 young adults (22–33 years) and 30 older adults (60–84 years). As in Experiment 1, older adults maintained the ability to ignore cross-modal distraction arising from either modality, and neither type of cross-modal distraction limited accuracy in adults. However, cross-modal distraction still reduced accuracy in children and children were more slowed by stimulus-interference compared with adults. We conclude that; unimodal and cross-modal interference follow different life span trajectories and differences in stimulus- and response-interference may increase cross-modal distractibility in childhood.
Privacy Protection of VANET Based on Traceable Ring Signature on Ideal Lattice

With the development of the Vehicular Ad-Hoc Network (VANET), protecting user privacy while ensuring security has become an immense challenge. A ring signature has the characteristics of no manager and unconditional anonymity, which can substantially protect user privacy. However, two problems exist in practical applications: first, unconditional anonymity cannot track the signer, which has potential security risks; and second, ordinary ring signatures cannot resist quantum attacks. In response to these problems, this paper proposes a Traceable Ring Signature (TRS) scheme on ideal lattice. In the scheme, the traceability of the ring signature is achieved by adding information to the ring signature. Selecting the algorithm on the ideal lattice to generate the master key can effectively resist quantum attacks. The scheme gives a detailed security proof and efficiency analysis. The new scheme has strong unforgeability for the difficult problem of Short Integer Solution (SIS) Question and a smaller signature size.
Impact of Upper Body Anthropometrics on Spatiotemporal Parameters during Manual Wheelchair Propulsion in Able-bodied Users

for everyday manual wheelchair mobility, the stroking motion is a bilateral cyclic motion. Anthropometrics are still limited for the able-bodied population, but it is even scarcer in terms of the structural and functional characteristics of wheelchair users. A marker-based three-dimensional 3D motion analysis protocol was employed to investigate the interrelationship between manual users’ upper body anthropometric characteristics and the biomechanics of wheelchair propulsion in terms of spatiotemporal parameters such as propulsion angles, time, velocity and stroke cadence. These outcomes have been analysed according to the anthropometrics of fifteen novice able-bodied male individuals using the same hand-rim wheelchair with their steady self-selected speed and propulsion pattern. The findings of this study indicated a significant correlation that quantified the association of the user’s upper body anthropometrics with the biomechanical behaviour during the manual wheelchair mobility. This supports the initial hypothesis of varying anthropometric characteristics of the users affects potentially their propulsion biomechanics during manual wheelchair mobility. Such information could potentially provide a realistic guidance for designers and manufacturers to better optimize wheelchair prescription.
Proxy Haptics for Surgical Training

This paper propose a solution to the realism of haptic perception in VR by using the concept called “proxy haptics. ” In proxy haptics, real physical props are placed around the real environment to match their virtual counterparts. If the physical props are co-registered with the virtual world, a compelling sense of tactile sensation can be achieved. A prototype proxy haptic system was developed and a pilot study was performed to determine the effects of our system for simple surgical training tasks. Our results show that from a user perspective, our system is more believable and closer to the real world than the standard VR interface.
Privacy Challenges With Protecting Live Vehicular Location Context

Future Intelligent Transport Systems (ITS) will require that vehicles are equipped with Dedicated Short Range Communications (DSRC). With these DSRC capabilities, new privacy threats are emerging that can be taken advantage of by threat actors with little experience and cheap components. However, the origins of these privacy threats are not limited to the vehicle and its communications, but extend to non-vehicular devices carried by the driver and passengers. A shortcoming of existing work is that it tends to focus on a specific aspect of privacy leakage when attempting to protect location privacy. In doing so, interactions between privacy threats are not considered. In this work, we investigate the privacy surface of a vehicle by considering the many different ways in which location privacy can be leaked. Following this, we identify techniques to protect privacy and that it is insufficient to provide location privacy against a single threat vector. A methodology to calculate the interactions of privacy preserving techniques is used to highlight the need to consider the wider threat landscape and for techniques to collaborate to ensure location privacy is provided against multiple sources of privacy threats where possible.
Multistage Active Distribution Network Integrated Planning Incorporating Energy Storage Systems and Active Network Management

Active distribution network (ADN) expansion planning by modeling network active management is addressed in this paper. In this regard the expansion of distributed energy resources (DERs) and distribution network assets are jointly planned. In the proposed model active management is applied to efficiently utilize the DERs in the planning problem and alleviated the uncertainties. In this paper energy storage systems (ESSs) as an important DER are utilized in active management framework. To model active management of the network, based on load and renewable energy resources’ behavior, some operating levels are regarded for each planning time stage. The objective is to minimize the net present worth of investment, maintenance and operation costs for all planning stages and operating levels. As the problem is solved the expansion plans of network alternatives including feeders, transformers, and substations as long as DERs including micro turbines, wind turbines and ESSs are extracted. Furthermore, the optimal operations of the network and DERs are obtained through the operating levels. Associated uncertainties are considered using a correlated scenario based approach. The problem is mathematically modeled in the form of mixed integer linear programming (MILP).
Multi-threshold Voltage and Dynamic Body Biasing Techniques for Energy Efficient Ultra Low Voltage Subthreshold Adders

This paper designs and reports energy efficient subthreshold adders using 22 nm FDSOI technology. The dynamic body biasing technique and multi-threshold voltage devices have been used to match Pull up/Pull down networks (PUN/PDN). The post-layout simulation results show that the logic gates and full adder circuit based on dynamic body biasing are more robust than those with conventional body bias against process, voltage, and temperature (PVT) variations at ultra low supply voltages. The adder based on the conventional and dynamic body biasing techniques have achieved energy per addition of 0.23 fJ at Vdd =300 mV and 0.56 fJ at Vdd =140 mV, respectively. Compared to the other published subthreshold adders in [1] and [2], the energy per addition for our designed adders improved by 2.82X, 28.1X, respectively. The minimum operating supply voltage for dynamic and conventional body bias adders based on Monte Carlo simulations taking into account both mismatch and process variations are 140 and 200 mV, respectively. The area for conventional body biased adder has been reduced by 43.9 and 38.8 percent compared to those of the adders in [1] and [3], respectively.
Deep Reinforcement Learning for Collision Avoidance of Autonomous Vehicle

To save training efforts, reinforcement learning approach is applied to the autonomous vehicle for obstacle avoidance. Therefore, this study is aimed to let the autonomous vehicle to learn from mistakes and readdress its movement accuracy for collision avoidance in working environment. An enhanced learning method Q-learning is used to record and update the Q values for different movement through a table that the autonomous vehicle can use it to determine how and where to move. The Q table is learned through the deep learning neural network which may encounter innumerable situations from the environments and the different actions performed by the autonomous vehicle. In the experiments, the depth camera is adopted as the input device to be not affected by light intensity and road color. The Q table is ready to use after 9000 epochs or about 3.5 hours training. Let the autonomous vehicle run for 3 minutes at a time in three different environments with lights on and off 10 times each. The success rate of obstacle avoidance is as high as 95% which proves the feasibility of proposed approach.
Real-time personalized cardiovascular monitoring system with arrhythmia classification method

This study proposes a personal healthcare system to monitor, identify ectopic heartbeats, and provide services to patients with heart diseases. The proposed system includes three subsystems: (1) an electrocardiography (ECG) signal acquisition device to acquire ECG signals accurately; (2) an Android-based smartphone to display the actual ECG waveform and upload the signal to the Web platform for analysis of cardiac disorder events; and (3) a Web platform for feature selection and arrhythmia classification with the support vector machine algorithm. The classification algorithm can identify three types of heartbeats, namely, normal, atrial premature beat, and premature ventricular contraction. The average sensitivity, specificity, and accuracy of the classification algorithm over the training set are 98.5%, 96.7%, and 97.7%, respectively.