Miracle-dz/UniFAR-FTUs huggingface dataset:
OPTH_A_251218 1079..1090

Ho Duc Hung 1 Duong Dinh Chinh Pham Van Tan Nguyen Viet Duong Nguyen Quoc Anh Nguyen Huu Le Ho Xuan Tuan 5 Nguyen Tuan Anh 6 Nguyen Thi Thuy Duong Vu Duy Kien 8 1Quynh Lap National Leprosy Dermatology Hospital, Hoang Mai Town, Nghe An, Vietnam; 2Nghe An Department of Health, Vinh City, Nghe An, Vietnam; 3Vietnam National Eye Hospital, Hanoi, Vietnam; 4Nghe An Eye Hospital, Vinh City, Nghe An, Vietnam; 5School of Medicine and Pharmacy, The University of Da Nang, Da Nang City, Vietnam; 6Hanoi Medical University, Hanoi, Vietnam; 7National Institute of Hygiene and Epidemiology, Hanoi, Vietnam; 8OnCare Medical Technology Company Limited, Hanoi, Vietnam Purpose: To assess the prevalence of myopia and associated factors among secondary school children in a rural area of Vietnam. Methods: A school-based cross-sectional study of children in grades six to nine was conducted in four secondary schools in Hoang Mai town, Nghe An Province, Vietnam, during December 2018 and January 2019. The status of myopia was defined as a spherical equivalent objective refractive error of −0.50 D or worse in either eye. A case–control study was conducted to explore factors associated with myopia, where children with myopia were considered to be cases, and children without myopia were considered to be controls. Factors associated with myopia were analyzed using univariate and multivariate logistic regression. Results: The prevalence of myopia among secondary school children was 14.2% (95% CI: 12.7–15.7%) and tended to increase with grade, from 10.5% in grade six to 17.7% in grade nine. Myopia prevalence in girls was significantly higher than in boys. Factors associated with myopia were a mother with a college/university education (OR = 2.5, 95% CI = 1.2–5.3), parents who wore spectacles (OR = 2.0, 95% CI = 1.1–3.8), distance from near work (OR = 5.2, 95% CI = 3.5–7.9), and taking breaks after 30 minutes of continued reading (OR = 1.6, 95% CI = 1.1–2.5). However, there were inverse associations with myopia for children belonging to the wealthiest households (OR = 0.2, 95% CI = 0.1–0.5) and time spent performing outdoor activities (OR = 0.6, 95% CI = 0.4–0.9). Conclusion: Our study showed that the prevalence of myopia is considerable among secondary children in rural areas of Vietnam. The prevalence of myopia tended to increase among children in higher grade levels. Thus, appropriate interventions should be developed and conducted to deal with the issue of school-age myopia.
Terminal Virtualization Framework for Mobile Services

- Terminal virtualization focuses on applying Information Technology (IT) virtualization technology to the terminals, and realizes the full or parts of terminal functions extension or migration to other devices on the network, such as resource reducing, information sharing, data synchronization, etc. It is becoming increasingly clear that more and more features of terminal virtualization and mobile computing on the edge will be used in practice. However, some issues are raised with terminal virtualization, such as security, privacy, Quality of Service (QoS), efficient transmission, computation/functions offloading management, etc. In this paper, after analyzing above issues, a mobile terminal virtualization framework is proposed to solve them. Then the implementation methods of the proposed framework modules are presented in detail, which are based on popular the terminal Operating System (OS) and some current technologies. This framework provides a better transparent experience to users from free handover on network to unified sensors usage.
Process Design: Adipic Acid from Glucose via Rennovia Process

Among recently proposed production routes for mass chemicals from renewable resources the preparation of oxidized C6 chemicals, such as adipic acid, from glucose seems particularly attractive: On the one hand the natural C6 chain including functionality is preserved. On the other hand, linear C6 chemicals that are currently produced from mineral oil, require high efforts and inefficiencies if produced from alternative resources, such as natural gas. From patent literature it can be concluded that Rennovia Inc. owns a leading technology for manufacturing adipic acid from glucose. In a previous project a Aspen Plus® Simulation for the two-step synthesis of adipic acid was built upon the Rennovia patent. The aim of this project is to complement the previous one, particularly regarding separation and purification of adipic acid (Chapter 3.3.1), recovery of acetic acid (Chapter 3.3.2), and an overall optimization of heat integration (Chapter 3.3.3). Separation of adipic acid seems feasible by crystallization, conserving a high process yield of 98.1 % respect the previous one (99.4 %). Moreover, a conceptual process design of adipic acid separation including solvent (acetic acid) recovery is presented. However, high capital cost must be invested for the construction of the new sections of the process plant.
An Overview of the Method of Smoothed Particle Hydrodynamics

This report is intended to provide an introduction to the method of SmoothedParticle Hydrodynamics or SPH. SPH is a very versatile, fully Lagrangian, particle based code for solving fluid dynamical problems. Many technical aspects of the method are explained which can then be employed to extend the application of SPH to new problems.
Exploring Cross-National Differences in Online Review Topics between China and the United States

The fast growing cross-border e-commerce makes it imperative for online merchants to deeply understand the cross-national differences in consumers’ preferences and online shopping behaviors. Using a data-driven topic model, this study plans to investigate the semantic differences in online product reviews posted by consumers from China and the United Sates. The preliminary results from a pilot study of online reviews of books show that Chinese reviewers focus more on a product’s concrete attributes while American reviewers prefer to express their general evaluations of the product
Human Cytomegalovirus–Speciﬁc Memory CD8 (cid:1) and CD4 (cid:1) T Cell Differentiation after Primary Infection

Background. The development of human cytomegalovirus (HCMV)–speciﬁc T cell immunity after primary infection and its correlation with virus transmission to the fetus were investigated. Methods. The membrane phenotype (CCR7 and CD45RA expression) of and intracellular cytokine (interferon [IFN]– (cid:1) and interleukin-2) production by HCMV-speciﬁc T cells (stimulated with HCMV-infected dendritic cells) were investigated in 21 immunocompetent pregnant women (12 transmitters and 9 nontransmitters) and in 5 nonpregnant subjects during the ﬁrst year after infection. Results. IFN- (cid:1) –producing CD4 (cid:1) and CD8 (cid:1) T cells were readily detected during the ﬁrst month, and their levels did not signiﬁcantly change with time. CCR7 expression was negligible during both the early and the late stage of infection. Among CCR7 (cid:2) cells, those reexpressing CD45RA progressively increased until they reached median levels of 33% (range, 7%–51%) and 51% (range, 22%–76%) for HCMV-speciﬁc CD4 (cid:1) and CD8 (cid:1) T cells, respectively, similar to those observed in subjects with remote infection. CD45RA reexpression correlated with HCMV disappearance from blood. The level of HCMV-speciﬁc CD45RA (cid:1) T cells during the ﬁrst months after infection was signiﬁcantly lower in mothers who were transmitters than in those who were nontransmitters. Conclusions. After primary infection, circulating HCMV-speciﬁc effector
Supervised pattern recognition of archers’ relative psychological coping skills as a component for a better archery performance

The current study explores the most significant psychological skills needed for the accomplishment of high archery scores and determines the discriminating psychological coping skills needed for archery performance. 32 archers completed psychological coping skills inventory before their shooting tests. Multivariate techniques of principal component analysis, hierarchical agglomerative cluster analysis and discriminant analysis were applied. The rotated PCA indicates 3 parameters containing 6 and 3 principal components (Pcs). The first Pcs reveal high loadings from three psychological skills. The second Pcs disclose high loadings from two skills, while the third Pcs demonstrate a high loading from only one skill. HACA classified the archers into two classes viz. High-performance group and low performance group. DA discriminates the classes with an excellent accuracy of 93.55%. Keywords: archery; pattern recognition; psychological coping skills; multivariate analysis
Lateral Control of a Vehicle using Reinforcement Learning

In the current state of the art, autonomous driving is implemented by controlling the longitudinal and lateral dynamics of a vehicle based on a predefined or online calculated reference trajectory. In this context, the longitudinal and lateral controllers of the vehicle are mostly independent of each other and are based on standard controller approaches such as nonlinear adaptive controllers, cascaded control loops or LQ controllers. The application of these controllers on the real vehicle is a laborious and imprecise process due to the simplification of the vehicle models used. In this paper, lateral control of a vehicle is implemented using reinforcement learning. Here, the longitudinal motion of the vehicle is given by a trajectory-dependent velocity profile. The Soft Actor Critic is an off-policy reinforcement learning algorithm which we used in this paper. It features a continuous action space of the agent, which is the case with a steering angle of a vehicle. In the training process, the agent learns to control the lateral dynamics of the vehicle in a performant manner according to the reward function of the environment which is an easier process for the development and calibration of the controllers. We show that the reinforcement learning agent achieves a slightly improved performance with respect to the relevant error variables for the lateral control compared to a standard controller approach. These error variables are the lateral distance to the reference trajectory and the yaw angle error to the reference trajectory.
Fault Modeling and Testing of Spiking Neural Network Chips

Spiking neural network (SNN) is a very promising low-power neural network that can be implemented in asynchronous circuits. However, it is hard to test SNN chips since they are inherently probabilistic and fault tolerant. So far, there is no good fault model and test methodology suitable for SNN chips. In this paper, we propose seven behavior fault models for SNN based on the function of neurons and synapses. We also propose a test methodology, which considers the output response as a distribution rather than specific values. The experiment results on a MNIST dataset show that although SNN is fault tolerant, two fault models are still critical for SNN chips. Given the digit recognition application, the accuracy of chips that passed our test is 88.90%, which is indistinguishable from that of good chips, even in the effects of random seeds.
Use of Electrical Resistance to Modeling the Phase Transformation of Shape Memory Alloy

Constitutive models are widely used to characterize the behavior of Shape Memory Alloys (SMA). These models use the physical properties to mathematically describe the phase transformation and the thermo-mechanical relation between the systems components driven by SMA. However it is often disregarded the fact that some of the physical properties undergo changes throughout the thermal cycle. Taking these variations into account will assure not only better model accuracy but also reliability. In this work is proposed the usage of the SMA electrical resistance behavior to enhance the constitutive models accuracy. To do so, an experimental structure was developed to subject a SMA wire through cooling and heating cycles, through electrical current variation, and collect the required data. The constitutive model is divided in thermal, phase change and mechanical dynamics. The SMA electrical parameters are used as inputs of the thermal model, to estimate the wire temperature. Besides, with the resistance behavior analysis versus temperature, it is possible to obtain the phase changes temperatures. As a result, the model presents good performance to predict the hysteresis behavior of the phase transformation under different load conditions.
A Multi-Parameter Intelligent Communication Anti-Jamming Method Based on Three-Dimensional Q-Learning

Aiming at the deficiency of the traditional intelligent communication anti-jamming algorithms, a multi-parameter intelligent anti-jamming method for wireless communication based on three-dimensional Q-learning (3DQL) is proposed. In the case of multi-channel random jamming, the wireless communication system guides the transmitter to choose the optimal channel and power for communication or keep silent in each timeslot by learning the random variation law of channel, timeslot and power of jamming, achieving the anti-jamming effect of optimal decision accuracy and timeslot utilization. Simulation results show that the 3DQL algorithm is better than the traditional two-dimensional Q-learning (2DQL) algorithm in decision accuracy and slot utilization.
OptTopo: Automated set-point optimization for coupled systems using topology information

The manufacturing sector has witnessed a rapid rise in the importance of energy-efficient operation. For finding optimal set-points for industrial facilities, optimization problems of increasing complexity occur. Key challenges are the leak of derivative information and the curse of dimensionality. For systematic reduction of the search-space by decomposition of the model, a methodology for the inclusion of topology knowledge in the optimization procedure is developed. An implementation of OptTopo (Optimization based on Topology), embedded in a testbed, demonstrates its advantages compared to popular out-of-the-box-optimization. OptTopo could be integrated in energy management software offering advanced set-point control for complex facilities.
High-Density Motor Drive Development for Electric Aircraft Propulsion: Cryogenic and non-Cryo Solutions

This paper presents an overview for the power electronics converter development effort for Electric Aircraft Propulsion (EAP) Systems, in both non-cryogenic and cryogenic power system configurations, from the authors' research group. The first part of the paper presents an architectural comparison for motor drives in such systems. The second part of the presentation will use examples from the author's development to explain on co-design/co-optimization efforts to achieve high power-density and efficiency for the propulsion motor drives. These efforts include converter-level and component-level optimization/advancement, as well as power module packaging. The last part of the paper will briefly introduce the challenges from modeling, testing, and analysis of the “side-effects” for the power electronics in electric aircraft propulsion systems, including partial discharge, EMI/EMC, and reflected wave influences, in such a compact, high-power, high-altitude application environment.
AMSER-FF: Area-Minimized Soft-Error-Recoverable Flip-Flop for Radiation Hardening

Among various radiation hardening by de-signs (RHBD), triple-modular redundancy (TMR) is frequently used to correct soft errors. One of the well-known TMR solutions is called $\Delta{\mathrm TMR}$, which makes three copies of flop-flops (FFs) and inserts different delay buffers in front of data inputs of the second and third FFs for capturing soft errors. However, such solution has shortcomings in the area/power overhead and the imbalanced rise/fall delay. As a result, a novel TMR flip-flop design called area-minimized soft-error-recoverable flip-flop (AMSER-FF) is proposed in this paper to reduce the area/power overhead and timing degradation. Each AMSER-FF embeds a reference voltage generator (RVG), which generates balanced rise/fall delay and has fixed area utilization instead of inserting delay buffers for correcting errors. Experimental results show that AMSER-FF achieves lower area overhead, power overhead and less timing degradation than $\Delta\mathrm{TMR}$.
Towards Software Support for Situation-specific Cross-organizational Design Thinking Processes

To deal with uncertain user needs and the development of cross-organizational solutions, Design Thinking (DT) processes are one general problem-solving approach. However, DTs actual implementation within organizations depends highly on the situation (e.g., goal to achieve) which affects the conducted process itself (e.g., different tasks to conduct), the used places (e.g., different visualization on whiteboards), and the involved people (e.g., different stakeholders for specific tasks). Currently, the situation needs to be covered manually by the DT moderator during the process construction and is insufficiently addressed by existing software tools during execution. Therefore, we propose a design science study to develop abstract design knowledge and a specific software tool for supporting situation-specific DT processes based on the application domain on cross-organizational development of business models for business ecosystems. Within this workshop paper, we provide preliminary design principles together with a software tool based on the practical experience in existing research projects.CCS CONCEPTS• Human-centered computing → Interaction design; Collaborative and social computing.
Mapping of Project Manager Competences with Project Complexity - a Case Study of IT Sector in Pakistan

Projects are at the core of organizations that undertake them for various reasons including revenue generation. The IT sector projects in Pakistan are no exception. Due to several reasons such as technological advancements, and organizational and environmental factors, the projects have evolved into complex projects. The increasing complexity associated with the projects has also increased the risk associated with the failure of projects. This study is divided into phases. The first phase deals with the understanding of complexity elements that exist in Pakistan's IT sector projects. The second phase deals with the competences associated with the complexity elements identified in the first phase. In the third phase, mapping has been built correlating the elements of project complexity and the project manager's competences. The implications of this study shall help project managers to focus on the elements which contribute to the complexity in projects of the IT sector in Pakistan. Moreover, it will help them to keep their attention on the competences that will be helpful to deal with project complexities.
Research on Post Earthquake Public Opinion Analysis Based on XLNet-BiGRU-A Algorithm

Because the traditional public opinion sentiment analysis can only be one-way analysis according to the direction of semantic information, it is impossible to fully obtain the semantics of the above and below and extract the deep semantic information in the sentence; and the public opinion comments after the earthquake are different from the general e-commerce, movie and other comments with the characteristics of short sentences and few extractable features. Aiming at the above problems, this paper proposes an emotional trend analysis model that combines XLNet, BiGRU and attention mechanism (XLNetBiGRU-A). The results show that on the self-dataset, the XLNetBiGRU-AT public opinion sentiment trend analysis model has certain advantages, and the accuracy rate is 79.96%. It was increased to 90.42%.
A Comparative Study of Various Intrusion Detections In Smart Cities Using Machine Learning

Smart Cities demand highly scalable and connected technologies to operate at multiple distributed locations. Despite the enormous potential it brings to citizens' lives, security as well as privacy concerns must still be addressed. A huge volume of data is formed as a result of technological advancements, and machine learning techniques are used to understand useful patterns. Recent advances in ML methods make it possible to handle the huge amount of complex data, enable fast processing, robustness by decentralization, and scalability of real-world systems. In recent decades, cyber security has appeared as an important consideration throughout scientific investigation. According to the World Internet Statistics Report, the Internet grew by 1,114 % from 2000 to 2019, with more than 2 quintillion bytes of data formed daily basis. It also demonstrates that rate of data advancement out of multiple sources is incredibly quick, while hacking tools as well as techniques are also developing at a rapid pace. Machine Learning techniques identify network intrusions and threats by forecasting hazard utilizing data training. The demand for the internet is growing by the day, raising concerns about network security. By blocking malicious things in the network system, an Intrusion Detection System (IDS) can furnish solutions to several rapidly growing network attacks (e.g., DDoS attack, Ransom ware attack, Botnet attack, and so on). This systematic review summarizes latest framework studies in sensing DDoS threats and investigates a comprehensive evaluation of numerous Ml techniques employed throughout Smart City applications. Furthermore, the goal of this work is to categorize the intrusions utilizing ML algorithms.
Average Consensus Problem in Multi-Agent System in an Environment with Obstacle: Track number: Track 3

A multi-agent system (MAS) is composed of multiple agents, and the local interaction between the agents determines the behavior of the complete system [1]. In recent years, the tasks and performance requirements of individual robots have rapidly increased in complexity and sophistication, and thus, it has become difficult to meet those demands efficiently. Collaborative operation using a MAS has gained attention as a solution to this issue [2].
Service Selection Based on Bat Algorithm in Hybrid Cloud-Edge Computing

Handing over edge network data and computation to cloud platforms often results in high bandwidth costs and processing delays, which makes hybrid cloud-edge computing a hot topic for research and application in recent years. If reasonable service selection can be performed on mobile edge gateways, not only can this problem be well solved, but also the energy cost of mobile devices can be reduced. In this paper, we first design a time and energy cost model that considers the latency and energy cost of three components: edge devices, cloud servers, and data transmission, and convert the service selection of minimizing the overall latency under the completion of energy constraints into a nonlinear programming problem. Then we design a bat algorithm to solve the above problem. Furthermore, we design an adaptive chaos bat algorithm to optimize the solution space so that it avoids falling into local optimal solutions. Eventually, simulation results show that the proposed algorithm is superior to other algorithms in terms of overall time delay optimization and has a better stability.