Miracle-dz/UniFAR-FTUs huggingface dataset:
A Networked SIS Disease Dynamics Model with a Waterborne Pathogen

This paper proposes a distributed continuous-time epidemic model, called networked SIWS (Susceptible-Infected-Water-Susceptible) model, for an SIS type waterborne disease spreading over a network of multiple groups of individuals sharing a water source. A sufficient condition is obtained for the healthy state, at which all individuals are not infected and the water is not contaminated, to be globally asymptotically stable. The effects of the shared water source on the disease spreading are analyzed through the comparison of the basic reproduction number with the networked SIS model without water and demonstrated via simulations.
Dynamic Coordination Architecture and Mobility Management for Next Generation Cooperative Cellular Networks

The fundamental challenges of existing cellular wireless networks are the exponential demand of mobile data traffic, higher data rates, massive numbers of user-coverage and lower latency. Moreover, the next generation of wireless cellular networks also consider potential use cases, such as autonomous vehicle control, smart cities, remote surgery and eHealth, tactile internet, etc. To address these challenges and potential use cases, network densification such as ultra-dense heterogeneous networks (UDHetNet) and multi-cell cooperation are considered as the foundation to support the 1000× capacity challenge in the next generation wireless cellular networks. In this thesis, we study the coordination architecture and mobility management of multi-cell cooperative communications and present novel algorithms to improve the performance of multi-cell cooperative cellular networks. We propose DCEC: Direct CSI-feedback to Elected Coordination-station, a CoMP coordination architecture for cooperative communication to improve the performance of cellular networks, reducing the signaling overhead and latency. We extended the DCEC approach to heterogeneous cellular networks named DCEC-HetNet as well. We also propose a handover procedure for heterogeneous multi-cell cooperative cellular networks named EHoLM: Enhanced Handover for Low and Moderate speed UEs. The goal of the EHoLM handover procedure is to improve the system performance and user experience, reducing the number of handovers, handover oscillation and handover failure rate. To examine the performance of the proposed algorithms we use the discrete event system specifications (DEVS) for modeling and simulation of cellular networks employing the DCEC and EHoLM methods. Simulation results show that the proposed algorithms have potentials to improve the performance of cooperative cellular networks compared to the conventional methods. We also study the verification and validation (V&V) process of simulation models. A revised lifecycle of modeling and simulation (M&S) has also been presented that accommodates both formal and conceptual approaches of the verification and validation (V&V) process. Finally, how we validated the simulation models we developed for analyzing the proposed algorithms has been presented.
Cloud Assisted Live Video Streaming over DHT Overlay Network

Many researches have been proposed to solve the scalability, the availability and the low-latency problems of peer-to-peer live video streaming; however, the problems are still not yet completely solved. In this paper, we propose a cloud assisted live video streaming over the distributed hash table (DHT) overlay network to tackle these problems. Our system is based on a scalable DHT network, which structures into a mesh-based overlay, to efficiently share the video stream. Moreover, cloud assist is introduced in order to maintain the availability, the low-latency and the scalability of the system. In this work, cloud server is also part of the DHT network. The role of the cloud server is to assist other nodes when the number of nodes which received video segment is less than the specific threshold. The cloud servers directly receive video segments from video server so the quality and the availability of the video segments can be guaranteed. The evaluation results show that with the help of cloud assist, the availability and the low-latency of the video segments are greatly improved if cloud has enough bandwidth. Keywords-video live streaming; cloud computing; peer-topeer; DHT overlay network.
A Deep Transfer Learning Solution for Food Material Recognition Using Electronic Scales

In this article, we present a novel solution to automating the procurement of food materials by using electronic scales, which can automatically identify the food materials along weighing them. Although the CNN model is regarded as one of the most effective solutions to image recognition, the traditional techniques cannot handle the mismatch problem between the lab training data and the real world data. To solve the problem, we propose to embed a partial-and-imbalanced domain adaptation technique (tree adaptation network) in the deep learning model, which can borrow knowledge from sibling classes, to overcome the imbalance problem, and transfer knowledge from the source domain to the target domain, to fight the mismatch problem between the lab training data and the real world data. Experiments show that the proposed approach outperforms state-of-the-art algorithms. Furthermore, the proposed techniques have already been used in practice.
KOORDINASI NONPLAYER CHARACTER FOLLOWER MENGGUNAKAN ALGORITMA POTENTIAL FIELDS BERBASIS MULTIBEHAVIOUR

Games have become popular among the people, as a form of entertainment, social support interaction between them . NPC behavior modeling is an important issue in realizing the intelligence of computer games . In NPC team - mate, AI needed to help regulate the behavior of team - mate who played alongside or under the command of a human player to assist players in achieving the goal. Potential fields is described as the iron particles are moving towards the object through the magnetic field created by the target object . This movement depends on the existing magnetic field , the particles will be drawn towards the goal , or just the opposite of the iron particles will be rejected by the magnetic field at the time met an obstacle . In this study , the data obtained by reading the references relating to the title to find out the problems faced in coordinating the team in the game . From the study , analyzed the needs of the games that will be made to the AI model that will be used for team coordination . Only then designed a game that can resolve the issue . After the game was made , the game will be tested by several methods , so it will look the difference . The expected outcome of this study is to model the NPC behavior Follower and adjust the position of the player in accordance with the AI have been made . So players will not quickly lose the game and can finish coordinate with the NPC Follower followed by adjusting the movement of NPC Follower to the players during the attacks , NPC Follower still within range radius of the player to protect the player
PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS

In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model.
A Calculation Method for the On-Load Cogging Torque of Permanent Magnet Synchronous Machine

This paper presents a new method to calculate the cogging torque of the permanent magnet synchronous machine under load condition. Combined with the mathematical expression of the cogging torque, it is found that the cogging torque will be influenced by the load. In order to accurately analyze the torque ripple of the motor at low speed and high torque operation and make it possible to reduce the cogging torque under load condition, it is necessary to calculate the cogging torque accurately. A calculation method for the on-load cogging torque of the PMSM, which the cross magnetization and core saturation have been taken into consideration, is proposed in this paper. The finite element analysis (FEA) and field-oriented control (FOC) contribute to the method, which mainly consists of three parts: elimination of the reluctance torque, calculation of the average torque, and the ripple torque and the separation of the cogging torque. By using this method, the cogging torques of a 16-pole/24-slot PMSM under different load conditions has been calculated. The result shows a very good agreement between simulation and experiment, which verifies the feasibility and effectiveness of the proposed method.
Peak load scheduling in smart grid using cloud computing

In this paper present peak, energy management attainable is feasible by monitoring real-time readings of whole loads within the college premises victimization this schedule loads so energy saving is possible. Currently, cloud computing technology offer on-line real-time monitoring knowledge, we have a tendency to create project supported cloud computing application for energy management that is employed for monitoring real time consumption of electricity and load planning. With respect to monitoring knowledge, we have a tendency to be able to plot the load curves so it'll be useful in achieving optimum energy consumption for educational institute.
Achieving Neural Compatibility With Human Sensorimotor Control in Prosthetic and Therapeutic Devices

Prosthetic and therapeutic devices have been developed to ameliorate the quality of daily living for people with amputation or neurological disorders. However, many of them fall short of functional benefits, and therefore, are frequently rejected by users due to awkward control, or no awareness of interaction during tasks. Traditional wisdom in the design of prosthetic and therapeutic devices may have emphasized the need to provide users with apparatus that replace or assist motor ability. Rather, the notion to achieve neural compatibility with the existing sensorimotor system has not been well recognized. We argue that providing biomimetic control and sensing capacity to prosthetic and therapeutic devices can enhance their neural compatibility, and therefore, can yield greater functionality in performing activities of daily lives, or in rehabilitation training. In this paper, the authors will present a range of neural technologies that may allow implementation of biomimetic sensorimotor control, including natural sensory feedback, neuromuscular like compliant control, natural module of synergy-based control, as well as advanced neural signal processing techniques. Based on the evidence in our research and in literature, we propose that achieving neural compatibility with the existing human sensorimotor system should be the ultimate goal of prosthetic and therapeutic devices.
74-dBc SFDR 71-MHz Four-Stage Pipeline ROM-Less DDFS Using Factorized Second-Order Parabolic Equations

In this brief, a four-stage pipeline read only memory (ROM)-less direct digital frequency synthesizer (DDFS) with equal division interpolation is proposed. To attain higher spurious-free dynamic range (SFDR) and faster clock rate, the hardware cost and delay using different segments with various interpolation equations are analyzed systematically to explore the optimal solution. The second-order parabolic equations with proper coefficients and factorized operation orders based on optimized hardware cost and delay are finally utilized to enhance SFDR. The proposed design is demonstrated by the physical implementation using the TSMC 0.18- $\rm {\mu }\text{m}$ CMOS technology cell library and on-silicon measurements, where the maximum SFDR is 74 dBc, 0.018-mW/MHz power dissipation, and the maximal clock frequency is 71.9 MHz.
The Cost of Big Brains in Groups

The social brain hypothesis posits that the evolution of big brains (neural complexity) in groups of social organisms is the evolutionary result of cognitive challenges associated with various complex interactions and the need to process and solve complex social tasks. This study aims to investigate the environmental and evolutionary conditions under which neural complexity evolves without sacrificing collective behavioral efficacy. Using an evolutionary collective robotics system this research evaluates the impact of imposing a fitness cost on evolving increased neural complexity in robot groups that must operate (accomplish cooperative tasks) in environments of varying complexity. Results indicate that for all environments tested, imposing a cost on neural complexity induces the evolution of smaller neural controllers that are comparably effective to more complex controllers.
A 9.1-ENOB 6-mW 10-Bit 500-MS/s Pipelined-SAR ADC With Current-Mode Residue Processing in 28-nm CMOS

This paper introduces a current-mode residue processing technique in a pipelined-successive-approximation register (SAR) analog-to-digital converter (ADC), which extends the operation speed of a single-channel ADC utilizing low-impedance-based signaling. A 10-bit pipelined-SAR ADC with featured building blocks such as a degenerated gm-cell as an open-loop residue amplifier, a switched-current mirror for sample-and-hold (S/H) function, and a split current digital-to-analog converter (DAC) for current-domain SAR conversion achieves a 500-MS/s conversion-rate under a 1.0-V supply. With background inter-stage mismatch calibration, a prototype ADC fabricated in a 28-nm CMOS process achieves 56.6-dB signal-to-noise-and-distortion ratio (SNDR) at a Nyquist input, resulting in a Walden figure of merit (FoM) of a 21.7-fJ/conversion-step.
Wearable Ankle Robots in Post-stroke Rehabilitation of Gait: A Systematic Review

Background: Stroke causes weak functional mobility in survivors and affects the ability to perform activities of daily living. Wearable ankle robots are a potential intervention for gait rehabilitation post-stroke. Objective: The aim of this study is to provide a systematic review of wearable ankle robots, focusing on the overview, classification and comparison of actuators, gait event detection, control strategies, and performance evaluation. Method: Only English-language studies published from December 1995 to July 2018 were searched in the following databases: PubMed, EMBASE, Web of Science, Scopus, IEEE Xplore, Science Direct, SAGE journals. Result: A total of 48 articles were selected and 97 stroke survivors participated in these trials. Findings showed that few comparative trials were conducted among different actuators or control strategies. Moreover, mixed sensing technology which combines kinematic with kinetic information was effective in detecting motion intention of stroke survivors. Furthermore, all the selected clinical studies showed an improvement in the peak dorsiflexion degree of the swing phase, propulsion on the paretic side during push-off, and further enhanced walking speed after a period of robot-assisted ankle rehabilitation training. Conclusions: Preliminary findings suggest that wearable ankle robots have certain clinical benefits for the treatment of hemiplegic gait post-stroke. In the near future, a multicenter randomized controlled clinical trial is extremely necessary to enhance the clinical effectiveness of wearable ankle robots.
Noise effect and parameter estimation in excitable dynamic media

Stochasticity in gene expression arises from fluctuations in transcription and translation. This phenomenon has implications for cellular regulation. The novel techniques for single-cell analysis have provided new experimental and theoretical investigations. As a result, a coherent picture of stochasticity in prokaryotic and eukaryotic gene expression has been obtained. In this paper, we analyze the behavior of a stochastic process applied to Brusselator, investigating the noise affecting this system. Once the noise has been retrieved, the maximum likelihood estimation may be used to retrieve the parameters of the system itself. Although these techniques have been applied to simple reaction processes as those hypothesized with Brusselator, the methodology is general and can be used to any reaction and related linear and nonlinear ODEs.
Economics of Fog Computing: Interplay Among Infrastructure and Service Providers, Users, and Edge Resource Owners

Fog computing is a paradigm which brings computing, storage, and networking closer to end users and end devices for better service provisioning. One of the crucial factors in the success of fog computing is on how to incentivize the individual users’ edge resources and provide them to end users such that fog computing is economically beneficial to all involved economic players. In this paper, we model and analyze a market of fog computing, from which we aim at drawing practical implications to uncover how the fog computing market should operate. To this end, we conduct an economic analysis of such user-oriented fog computing by modeling a market consisting of Infrastructure and Service Provider (ISP), end Service Users (SUs), and Edge Resource Owners (EROs) as a non-cooperative game. In this market, ISP, which provides a platform for fog computing, behaves as a mediator or a broker which leases EROs’ edge resources and provides various services to SUs. In our model, a two-stage dynamic game is used where in each stage, there exists a dynamic game, one for between ISP and EROs and another for between ISP and SUs, to model the market more practically. Despite this complex game structure, we provide a closed-form equilibrium analysis which gives an insight on how much economic benefit is obtained by ISP, SUs, and EROs from user-oriented fog computing under what conditions, and we figure out the economic factors that have a significant impact on the success of fog computing.
ReHand: Secure Region-Based Fast Handover With User Anonymity for Small Cell Networks in Mobile Communications

Due to the fact that the higher density of mobile devices is expected, the fifth generation (5G) mobile networks introduce small cell networks (SCNs) to prevent exhausting radio resources. SCNs improve radio spectrum utilization by deploying more base stations (BSs) in the networks. Even though authenticated key exchange (AKE) is still essential to ensure entity authentication and confidentiality in mobile communications. Besides, user anonymity is required to guarantee the footprints of mobile communications being concealed. However, AKE with user anonymity may increase the latency of communications dramatically in total due to several times more frequency in SCNs. The increase of latency will be more serious when an AKE protocol supports user anonymity, where traceability and revocability to users are necessary. Thus, this paper presents a secure region-based handover scheme (ReHand) with user anonymity and fast revocation for SCNs. ReHand greatly reduces the communication latency when user equipments (UEs) roam between small cells within the region of a macro BS, i.e., eNB, and the computation costs due to the employment of symmetry-based cryptographic operations. Compared to the three related prior arts, ReHand dramatically reduces the latency from 82.92% to 99.99% by region-based secure handover. Nevertheless, this paper demonstrates the security of ReHand by theoretically formal proofs.
Bacteriological Analysis and Public Health Impact of Broiler Meat: A Study on Nalitabari Paurosova, Sherpur, Bangladesh

Broiler meat is one of the most important protein sources for Bangladeshi people. Food-borne diseases associated with the consumption of poultry meat and its processed products are of public health concern worldwide. An investigation was conducted to assess the bacteriological quality of poultry meat from some poultry farms and its health impact on consumer of Nalitabari paurosova, Sherpur district, Bangladesh. Total 15 samples were randomly selected and collected from different poultry farms on the basis of farms level and size. Bacteriological quality of the samples was assessed by following the standard microbiological methods. The health impact was evaluated with the help of semi-structured based questionnaire of 400 peoples. The average value of TVC and TCC were found as 4.3 × 106 CFU/g and 3.6 × 104 CFU/g respectively. In this study, the prevalence of fecal coliform was recorded as 33% and the presence of E. coli in 53% samples. The mean value of Salmonella spp. of meat samples was 4.6 × 103 CFU/g. No Shigella spp. , Vibrio spp. and fungal species were detected in any sample. Some selected isolates were tested for their sensitivity against some commercially available common antibiotics used in Bangladesh. E. coli was 80% resistance to Ampicillin and 90% sensitive to Ciprofloxacin whereas Salmonella spp. showed 100% resistance to Ampicillin and 80% sensitivity to Ciprofloxacin. The antibacterial activity of renowned medicinal plant Azadirachta indica was also evaluated against some multidrug resistance bacteria. The inhibitory zone of both 30% methanolic and ethanolic extracts of Azadirachta indica was 12 mm and 12.3 mm, where 40% methanolic and ethanolic extracts were 14 mm and 16.3 mm against E. coli. The 40% ethanolic extract showed the better activity between them. The plant extract has no activity against Salmonella spp. Awareness and health impact of broiler meat was determined among the people of different sectors on the basis of educational qualification, socio-economic condition, income source, broiler meat intake pattern, BMI range and food related diseases they have suffered. The peoples who eat broiler meat are much more prone to complicated diseases than the peoples who never eat it. So broiler meat intake pattern must be changed for better health. The widespread occurrence of Salmonella spp. and E. coli in poultry meat also reinforces the need for effective control measures.
Distributed power system

PURPOSE: To realize a distributed power system which can economically reduce unbalance in a three-phase or single-phase three-wire load. CONSTITUTION: In a distributed power system which is constituted of a dc supply 1, a single-phase inverter 3, current transformers 5, 7, etc., and is connected to a commercial power system 6 and then is connected to a load 8 in a small consumer such as a housing, three sets of single-phase inverter 3 are used when the commercial power system 6 is three-phase and two sets are used when the commercial power system 6 is single-phase three-wire and an output adjuster 9 is installed which supplies power to the load 8 and adjusts the output standard of the single-phase inverter 3 based on a value of the current of the load 8 and compensates for an unbalance between lines. COPYRIGHT: (C)1993,JPO&Japio
Chinese grammatical error detection using a CNN-LSTM model

: In this paper, we proposed a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) model for Chinese grammatical error detection. The TOCFL learner corpus is adopted to measure the system performance of indicating whether a sentence contains errors or not. Our model performs better than other neural network based methods in terms of accuracy for identifying an erroneous sentence written by Chinese language learners.