Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Hidden Aneurysms on the Posterior F1ow: Computational Fluid Dynamic Study

This paper addresses changes in blood flow parameters in the area behind a saccular aneurysm. The hemodynamics modelling was conducted with the ANSYS software using the Lattice Boltzmann method. We performed a computational fluid dynamics simulation of the blood flow in two vessels of 8.4 mm in diameter, one of which contained an aneurysm of 23 mm in diameter, and the other had no abnormalities. The maximum relative deviation of the blood flow velocity registered 35 mm away from the aneurysm, provided the inflow velocity was 86 cm/s at $60^{\mathrm{t}\mathrm{h}}$ ms from the start of the simulation, was 38.5% compared to the flow velocity in the normal vessel.
Multi-Sensory Guidance and Feedback for Simulation-Based Training in Robot Assisted Surgery: A Preliminary Comparison of Visual, Haptic, and Visuo-Haptic

Nowadays, robot assisted surgery training relies more and more on computer-based simulation. However, the application of such training technologies is still limited to the early stages of practical training. To broaden the usefulness of simulators, multi-sensory feedback augmentation has been recently investigated. This study aims at combining initial predictive (guidance) and subsequent error-based (feedback) training augmentation in the visual and haptic domain. 32 participants performed 30 repetitions of a virtual reality task resembling needle-driving by using the surgeon console of the da Vinci Research Kit. These trainees were randomly and equally divided into four groups: one group had no training augmentation, while the other groups underwent visual, haptic and visuo-haptic augmentation, respectively. Results showed a significant improvement, initially introduced by guidance, in the task completion capabilities of all the experimental groups against control. In terms of accuracy, the experimental groups outperformed the control group at the end of training. Specifically, visual guidance and haptic feedback played a significant role in error reduction. Further investigations on long term learning could better delineate the optimal combination of guidance and feedback in these sensory domains.
Performance indicators of models of non-binary control automates

Models of control automata and types of their behavior in the system are considered. The concept of functional and technological behavior of the automata is defined. Indicators for evaluating the effectiveness of control automata by the number of possible types of behavior and the number of elements of the FSM with equivalent behavior are proposed. Examples of a comparative evaluation of models of automata according to the proposed indicators are shown. They showed that control automata with non-binary elements of their sets are more efficient. A nonbinary automata simulator program has been developed and used to model behavior.
Collaborative Truck-Drone Routing Optimization Using Quantum-Inspired Genetic Algorithms

Today, e-commerce has become more common than ever. The time for customers to receive the product they ordered is directly dependent on the speed of the shipment companies. In addition to operating and operator costs, traditional deliveries also increase delivery times. That’s why the distribution with truck-drone cooperation method emerged. In this study, it is aimed to determine the less costly route for parcel delivery with the cooperation of truck-drone. Quantuminspired genetic algorithms are used to optimize the route. The proposed method is tested with truck-drone simulation developed with Python programming language. Simulation results are obtained and compared exclusively for truck-based distribution and distribution with truck-drone collaboration. A cost calculation is made by taking into account factors such as the total distance traveled, the waiting time of the customers, and the working time of the staff. The simulation results show that despite the increasing total traveling distance in the truck-drone collaboration, cost savings are achieved. As a result of the tests carried out, it is seen that 4-8% cost savings are achieved from 3 different scenarios created for 100 customers.
Reversible lane control system with low emission load based on VISSIM simulator

A reversible lane control system with low emission load is designed based on VISSIM simulation software. This system aims at the problem that the traffic flow of different directions at the intersection has the imbalance of time and space. Through the VISSIM simulation based on Python language, the second lane from the central separation belt is taken as the variable lane, and the lane control under different traffic flow conditions is realized. The system takes the queuing length, traffic delay time and carbon emissions of the intersections as the traffic efficiency evaluation indexes of the variable lane, and analyse the actual benefits of setting variable lanes to relieve the time and space resources of the intersections. Simulation results by using VISSIM and the designed Python interface show that, compared with the settings without variable lane control system, the average queue length at the intersection decreases by 38.6%, the maximum queue length by 36.4%, the average delay time by 20.7%, the maximum delay time by 15.1%, and the average carbon emission by 17.3%. It can be seen that the variable lane control system at intersections based on VISSIM has a significant effect on easing traffic congestion at intersections and reducing total carbon emissions.
Biomass-Gasifier/Aeroderivative Gas Turbine Combined Cycles: Part B—Performance Calculations and Economic Assessment

Gas turbines fueled by integrated biomass gasifiers are a promising option for base-load electricity generation from a renewable resource. Aeroderivative turbines, which are characterized by high efficiencies in small units, are of special interest because transportation costs for biomass constrain conversion facilities to relatively modest scales. Part A of this two-part paper reviewed commercial development activities and major technological issues associated with biomass integrated-gasifier/gas turbine (BIG/GT) combined cycle power generation. Based on the computational model also described in Part A, this paper (Part B) presents results of detailed design-point performance calculations for several BIG/GT combined cycle configurations. Emphasis is given to systems now being proposed for commercial installation in the 25-30 MW e power output range. Three different gasifier designs are considered: air-blown, pressurized fluidized-bed gasification; air-blown, near-atmospheric pressure fluidized-bed gasification; and near-atmospheric pressure, indirectly heated fluidized-bed gasification. Advanced combined cycle configurations (including with intercooling) with outputs from 22 to 75 MW are also explored. An economic assessment is also presented, based on preliminary capital cost estimates for BIG/GT combined cycles and expected biomass costs in several regions of the world.
A Method for Surface Defect Detection of Printed Circuit Board Based on Improved YOLOv4

The quality detection of printed circuit board (PCB, Printed Circuit Board) is a very important task. However, due to the diversity, complexity and smallness of PCB defects, traditional surface defect detection methods are still difficult to deal with. Therefore, this paper proposed a method for detecting surface defects of printed circuit board based on improved YOLOv4. Firstly, we used the multistage residual hybrid attention module (MRHAM) to improve feature learning to enhance the feature expression ability of the shallow network, so that the receptive field pays more attention to the object feature and ignores irrelevant features; Then, we used the K-means++ clustering algorithm perform cluster analysis on the experimental dataset, determined the anchor boxes value of the PCB defects dataset, and improved the accuracy of the model for small object defect positioning; Finally, we used online and offline data augmentation, transfer learning and multi-scale training methods to enhance the adaptability of the model to images of different input scales and improved the generalization ability of the model. Experimental results showed that compared with other traditional and deep learning object detection models, the improved model has improved detection accuracy and detection rate. The mAP value reaches 99.71% and the detection rate reaches 68 f.s-1. It showed that the improved YOLOv4 has high application value in PCB surface defect detection.
Stable grasping control of robot based on particle swarm optimization

In order to improve the compensation and stability control ability of handling robot’s stable grasping and positioning control, a method of stable grasping and positioning control for handling robot based on particle swarm optimization is proposed. Combining visual information fusion and sensitive sensor information fusion, the constraint parameters of stable grasping control for handling robot are analyzed, and sensitive mechanical sensors are used to collect and identify the information of grasping posture parameters of handling robot. The feature quantity of spatial distribution information of grasping posture parameters of the handling robot is extracted, and the compensation and optimization control of posture parameters in the process of grasping posture determination of the handling robot are carried out by using the particle swarm optimization control method. The adaptive positioning error compensation and feedback correction method are combined to carry out particle optimization parameter fusion and optimal solution analysis in the process of grasping posture determination of the handling robot, and the adaptive parameter correction and error feedback fusion method are combined to realize stable grasping positioning control of the handling robot. The simulation results show that the output stability of this method is better and the positioning error is lower, which improves the output stability of the handling robot in the grasping process.
First principles investigation and design of fluorophosphate sodium-ion battery cathodes

Lithium-ion batteries are currently the most widely used consumer energy storage technology. Recently, lithium-ion batteries have been evaluated for use in mitigating the intermittent power supply of leading renewable energy technologies, thereby enabling their use on the electric grid. In order to facilitate the widespread adoption of electric vehicles and renewable energy technologies, the energy-densities, lifetimes, and cost of batteries must be improved. Due to concerns over long-term lithium availability, sodium-ion batteries are currently being investigated as an alternative to lithium-ion batteries in grid-level applications. In this thesis, we use ab inritio methods to characterize th high-voltage sodium-ion fluorophosphate with formula NaxV2(PO4)2O2yF3-2y as an alternative chemistry to Li-ion batteries. In Chapter 3 we investigate the sodium-extraction limitations in the NaxV2(PO4)2O2yF3-2 fluorophosphate. Specifically, we focus on the potential to reversibly extract sodium beyond the 1 </= x </= 3 range. We find that the capacity limitation arises from a combination of the high voltage of the V 4+/'+ oxidation reaction in the 0 </= x </= 1 region, coupled with a strong sodium-vacancy ordering at x = 1, which prevents the formation of mobile defects in the structure. We deduce that the accessible capacity of Na)V2 (PO4 )2F3 can potentially be expanded to 0 </= x </= 3 by introducing defects into the material and reducing the voltage of the active redox couple in the 0 </= x K 1 range. In Chapter 4, we investigate the stability and voltage characteristics of transition metal substitutions on the fluorophosphate framework. We demonstrate that the inferior performance associated with non-vanadium fluorophosphates is the result of a thermodynamic driving force to release oxygen gas upon charging, in tandem with high voltages. From our calculations, we demonstrate that molybdenum is simultaneously stable in the fluorophosphate framework and capable of reducing the sodium extraction voltage in the 0 K x </= 1 range. We conclude with an analysis of the phase stability and voltage curves of mixed transition metal fluorophosphates along the NaxV 2 (PO4) 202yF 3-2y NaxMo 2 (PO4)202yF3-2y composition line. From the results of this study, we identify NaxV2(PO4)2O2yF3-2 as the most promising candidate system, with the potential to improve the capacity of current fluorophosphate cathodes by 37%.
Development of the Neural-Based Navigation System for a Ground-Based Mobile Robot

In this paper, the proposed collision avoidance D* algorithm is trained, which enables the mobile robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a convolution neural network. The trained neural network architecture is capable of choosing an action directly based on the input images using the convolution 2D layers (CNN) architecture. A convolution neural network analyzes the exact situation using maps 30 × 30 information on its environment and the mobile robot navigates based on the situation analyzed through Deep learning. The main result of this article is a new iterative algorithm of the training set development. In the first iteration the start training set is developed, and initial learning of a neural network is made. In the next iterations trained at the previous stage neural network is used as filter for the next training sets. The filter selects the trajectories with collisions reasoned by errors of the neural network. Moreover, the numbers of convolution and fully connected layers are increased iteratively. Thus, proposed algorithm allows develop both training set and neural network architecture. Comparison of the training results for filtered and unfiltered sets is performed. A high effectiveness of the filtering is conformed. The algorithm can be used to develop the planning unit of the control system of mobile ground-based robots. The developed algorithm could be used for supervised and reinforcement learning. Modeling results of the mobile robot control system with trained neural network is presented.
Random Fourier Features based Post-Distortion for Massive-MIMO Visible Light Communication

Massive multiple input multiple output (m-MIMO) visible light communication (VLC) has emerged as a viable technology to enhance the throughput of an existing VLC based systems in order to serve the high speed data requirement for beyond 5G and 6G communication systems. However, performance of m-MIMO VLC is severely impaired by (1) columns of m-MIMO VLC channel are highly correlated which makes the channel matrix ill-conditioned, and (2) nonlinear transfer-characteristics of light emitting diode (LED). The aforementioned channel impairments collectively degrade the overall bit error rate (BER) at the receiver. In this paper, finite memory budget adaptive precoder is proposed to decrease the channel correlation/condition number of MIMO channel matrix. Reproducing kernel Hilbert space (RKHS) based post-distorters have been proposed in the literature for VLC which rely on growing dictionary of kernel evaluations, and hence it is difficult to practically implement them under finite memory budget. In this paper, random Fourier features (RFF) based kernel least mean square (RFF - KLMS) algorithm is proposed for post-distortion over m-MIMO VLC channels which alleviates the requirement of dictionary and facilitates post-distortion under finite memory budget. Computer simulations performed over m-MIMO VLC channels show that the proposed RFF based algorithm exhibit similar BER performance with reduced computational complexity as compared to the dictionary based post-distortion algorithm.
Construction of Radio Transmission Equipment Type Approval Knowledge Graph and its Application

Domain knowledge graph has become a research focus in the era of artificial intelligence. Radio transmission equipment type approval is the core function of radio equipment management. This paper introduces construction process of the radio transmission equipment type approval knowledge graph, and analyses algorithms of the knowledge graph by category, which is a great tool for knowledge mining. The question answering construction over the knowledge graph is also discussed as an application, which has laid the foundation for further research in radio regulatory knowledge graph.
Millimeter-Wave GaN Devices for 5G: Massive MIMO Antenna Arrays for Sub-6-Ghz and mm-Wave Bandwidth

Semiconductor technology plays an important role in realizing the 5G mobile communication system. One of the technical objectives for 5G is to significantly boost cellular network throughput-by 100 times compared with 4G (to achieve a peak data rate of > 10 Gb/s) [1]. Achieving this goal will require massive multiple input/multiple output (MIMO) antenna technology [2], [3].
Mobile and personalized learning system for computer science students

The era of Information and Communication Technology (ICT) allows people to learn without any limits “anywhere and anytime”. Mobile learning is considered as new and interesting form of learning due to the high spread and development of mobile technologies. In E-learning, the educational content is delivered through electronic technologies such as internet (mobility of learning content). Whereas, in M-learning, the mobility covers learning content, learners, and learning process. So, The M -learning is known as learning using mobile devices: smartphones, tablet computer∆ etc. However, the mobile learning systems have some challenges or difficulties in term of the design, development and the technology usability. In this paper we develop a mobile system that enhances the learning process of the students of computer science. Moreover, our system provides several adaptation strategies to meet the needs of heterogeneous learners. The results of the tested system (on a group of second year license students) show the improvement in learning achievement of students and the big influence of our Mobile learning system on the degree of understanding of the courses
Physical Principles of Nanotube Thermal Properties Modeling Using the Cauchy Number Analogue

in this work, basing on a comparative analysis of a carbon nanotube thermal conductivity carried by excitation of acoustic phonons in it, the Cauchy similarity number analogue is introduced, which makes possible to carry out preliminary qualitative-theoretical analysis of nanosystems, where definitive criteria are affected by dimensional effect. This allows one to properly organize the setting of an experiment and to carry out preliminary results processing which brings the practical value.
Social User Influence Measurement Algorithm Based on Multi-perspectives Analysis

With the rapid development of social networks, user influence plays an increasingly vital role in commodity marketing and public opinion monitoring. Therefore, analyzing and identifying users with high influence has become a hot research topic. Aiming at the lack of comprehensive analysis in existing influence measurement methods, a novel user influence ranking method ASIRank (Actively Strength and Interest based Rank) is proposed to measure influence of social users. First, in order to fully consider the contribution of different relationships to user's influence, the user topological networks are constructed from multiple perspectives such as follow, retweet, comment and mention. Secondly, for the sake of quantifying influence more accurately, factors such as user activeness, interaction intensity and interest preferences are considered in different relational networks. Then, user's influence from different perspectives is integrated to obtain the influence ranking of users in the global scope. Through experiments on real microblog data set, the results show that the proposed method is superior to existing methods in terms of accuracy, correlation and coverage.
Lightweight Deep Neural Network for Real-Time Instrument Semantic Segmentation in Robot Assisted Minimally Invasive Surgery

Vision-based detection and tracking of surgical instrument is attractive because it relies purely on existing setup already in the operating scenario for Robot assisted minimally invasive surgery (RMIS). While significant advanced approaches have been made in recent years, how to effectively carry out the efficient algorithm in the abdominal environment for real-time surgical instrument semantic segmentation is still a fundamental challenging topic. For this purpose, this letter reports the development of a surgical instrument semantic segmentation method for RMIS scenario. We have demonstrated a novel lightweight deep neural network to exploit surgical instrument semantic segmentation that meets real-time, accuracy and robustness. Specifically, our model contains an innovative design of two stages lightweight network by integrating the Ghost modules with MobileNetV3. Given that reducing computation cost with holding high accuracy is crucial for the task, in the first stage, Ghost module is explored to model the bottleneck for extracting the semantic features. We also design the lightweight Segmentation head as the second stage to further reduce computation cost, the model outputs a pixel-by-pixel mask to represent the surgical instrument parts semantic segmentation results. Promising validation has been conducted to evaluate the proposed lightweight network, the results report that the method can exhibit the outstanding surgical instrument semantic segmentation and surpass existing algorithms in the real-time performance with holding the high accuracy. The letter demonstrates the feasibility to perform real-time surgical instrument detection by taking advantage of the components of surgical robot system, contributing to the reference for further surgical intelligence.
Mathematical Modeling of Noise Attenuation in Dissipative Silencers of Irregular Shape

Analyzed the using of mathematical modeling of noise silencers for the power industry. Results allow to determine its acoustic characteristics. The most important characteristics of silencers are their acoustic efficiency and aerodynamic drag. These two factors need to consider together. Aerodynamic drag imposes restrictions on the use of silencers. Improvement in the design of industrial silencers leads to the sophistication of their shapes. Difficulties arise to use known formulas for calculating the acoustic efficiency of such silencers. Analyzed the mathematical models and the physical characteristics of materials. Also, information on material properties depending on the selected model. Discussing the information about the limits of applicability dissipative silencers models. Carried out a mathematical simulation of a silencer. Results compared with known acoustic efficiency from the experiment for verification. Presented the results of modeling a silencer with variable plate thickness.