Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Khat chewing habit a risk factor for occlusal caries progression?

Backgrounds People in Yemen and in East African countries chew khat more than five hours daily. Objectives The aim of this study was to assess the relationship between khat and occlusal caries progression. Methods A cohort study was carried out among 98 Yemeni khat chewers and 101 non-chewers aged 18–35 years old with early occlusal caries lesions. All participants answered questions on socio-demographic, khat , oral hygiene , sugar intake, and oral health knowledge at baseline. All posterior teeth with an early enamel lesion on occlusal surfaces detected by visual inspection at baseline were also subjected to DIAGNOdent assessment to confirm early lesion (DIAGNOdent reading 13–24). Participants were re-examined after 12 weeks. Caries progression was considered to occur when the DIAGNOdent reading was >25. Data were analyzed using Relative risk, Mann-Whitney U test, a Wilcoxon Signed-Rank test and logistic regression analysis. Results Occlusal caries progression incidence between khat chewers and non-chewers, with the relative risk was 1.68. There was no significant difference in occlusal caries progression on chewing side and non-chewing side among khat chewers. Khat chewing was a statistical predictor for those with low income. Conclusion Khat is a risk factor for occlusion caries progression among low income group.
Power Minimization Resource Allocation for Underlay MISO-NOMA SWIPT Systems

The combination of cognitive radio and non-orthogonal multiple access (NOMA) has tremendous potential to achieve high spectral efficiency in the IoT era. In this paper, we focus on the energy-efficient resource allocation of a cognitive multiple-input single-output NOMA system with the aid of simultaneous wireless information and power transfer. Specifically, a non-linear energy harvesting (EH) model is adopted to characterize the non-linear energy conversion property. In order to achieve the green design goal, we aim for the minimization of the system power consumption by jointly designing the transmit beamformer and the receive power splitter subject to the information transmission and EH harvesting requirements of second users (SUs), and the maximum tolerable interference constraints at primary users. However, the formulated optimization problem is non-convex and hard to tackle. By exploiting the classic semi-definite relaxation and successive convex approximation, we propose a penalty function-based algorithm to solve the non-convex problem. The convergence of the proposed algorithm is further proved. Finally, simulation results demonstrate that the non-linear EH model is able to strongly reflect the property of practical energy harvester and the performance gain of the proposed algorithm than the baseline scheme.
A Study on the Suitability of Visual Languages for Non-Expert Robot Programmers

A visual programming language allows users and developers to create programs by manipulating program elements graphically. Several studies have shown the benefits of visual languages for learning purposes and their applicability to robot programming. However, at present, there are not enough comparative studies on the suitability of textual and visual languages for this purpose. In this paper, we study if, as with a textual language, the use of a visual language could also be suitable in the context of robot programming and, if so, what the main advantages of using a visual language would be. For our experiments, we selected a sample of 60 individuals among students with adequate knowledge of procedural programming, that was divided into three groups. For the first group of 20 students, a learning scenario based on a textual object-oriented language was used for programming a specific commercial robotic ball with sensing, wireless communication, and output capabilities, whereas for the second and the third group, two learning scenarios based on visual languages were used for programming the robot. After taking a course for programming the robot in the corresponding learning scenario, each group was evaluated by completing three programming exercises related to the robot features (i.e. motion, lighting, and collision detection). Our results show that the students that worked with visual languages perceived a higher clarity level in their understanding of the course exposition, and a higher enjoyment level in the use of the programming environment. Moreover, they also achieved an overall better mark.
Error Probability Analysis of Non-Orthogonal Multiple Access Over Nakagami- $m$  Fading Channels

Non-orthogonal multiple access (NOMA) is currently considered as a promising technology for the next-generation wireless networks. In this paper, the error rate performance of NOMA systems is investigated over Nakagami- $m$ fading channels, while considering imperfect successive interference cancelation. In particular, this paper focuses on the pairwise error probability (PEP) analysis, where exact PEP expressions are derived to characterize the performance of all users under different fading conditions. The obtained PEP expressions are then used to derive an exact union bound on the bit error rate (BER). Through the derived PEP expressions, the asymptotic PEP analysis is presented to investigate the maximum achievable diversity gain of NOMA users. Moreover, using the derived BER bound, the power allocation problem for all users in NOMA systems is considered under average power and users BER constraints, which allows realizing the full potential of NOMA. Monte Carlo simulation and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error rate performance of each user and the achievable diversity gain.
±0.5 V 15  $\mu$ W Recycling Folded Cascode Amplifier With 34767 MHz·pF/mA FOM

An enhanced super class AB recycling folded cascode (RFC) operational transconductance amplifier is presented. It combines various adaptive biasing and transconductance enhancement techniques. Measurement results of a CMOS 130 nm test chip prototype show an improvement in slew rate and gain-bandwidth product by factors 100 and 27, respectively, compared to the conventional RFC topology for the same supply voltage and bias currents.
Stock Market Prediction Analysis by Incorporating Social and News Opinion and Sentiment

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced strategies have been used to improve the performance of the learning based predictors. However, performing successful stock market prediction is still a challenge. News articles and social media data are also very useful and important in financial prediction, but currently no good method exists that can take these social media into consideration to provide better analysis of the financial market. This paper aims to successfully predict stock price through analyzing the relationship between the stock price and the news sentiments. A novel enhanced learning-based method for stock price prediction is proposed that considers the effect of news sentiments. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using the real stock price data set with an improvement of performance in terms of reducing the Mean Square Error (MSE). The research work and findings of this paper not only demonstrate the merits of the proposed method, but also points out the correct direction for future work in this area.
Quality-Driven Detection and Resolution of Metamodel Smells

In model-driven engineering, analogously to any software development practice, metamodel design must be accurate and performed by considering relevant quality factors, including maintainability, reusability, and understandability. The quality of metamodels might be compromised by the introduction of smells that can be the result of inappropriate design decisions. Detecting and resolving metamodel smells are a complex task. The existing approaches deal with this problem by supporting the identification and resolution of smells without providing the means to explicitly trace them with the quality attributes that can be potentially affected. In this paper, we present an approach to defining extensible catalogues of metamodel smells. Each smell can be linked to the corresponding quality attributes. Such links are exploited to automatically select only those smells that have to be necessarily resolved for enhancing the quality factors that are of interest for the modeler. The implementation of the approach is based on the Edelta language, and it has been validated on a corpus of metamodels retrieved from a publicly available repository.
Interferon-γ receptor-1 gene promoter polymorphisms and susceptibility for brucellosis in Makkah region

Background Genetic polymorphisms that affect the production levels of certain cytokines and/or their receptors may determine the risk, severity or protection in some infectious diseases like brucellosis. Objectives The aim of this study was to investigate the association of certain known Interferon-γ Receptor-1 (IFN-γ R1) gene promoter polymorphisms and the susceptibility to infection with Brucellosis in Saudi population. Methods A cases-control association study was conducted in 69 individuals with human brucellosis and 94 healthy individuals. Genotyping of IFN-γ R1 — 56 C>T and IFN-γ R1 — 611 A>G polymorphism in both patients and healthy controls was done by PCR- restriction enzyme length polymorphisms (PCR-RFLP) and PCR- confronting two primer pairs (PCR-CTPP) methods and were assessed for potential associations with susceptibility for human brucellosis and their mode of penetrance. Results Interestingly, we have designed a PCR-CTPP system to be used for genotyping of IFN-γ R1 — 611 A > G polymorphism. The PCR-CTPP is an accurate method for genotyping of SNPs. Moreover, it is time-saving, inexpensive and easy to perform. Conclusion Both tested polymorphisms, IFN-γ R1 — 56 C>T and IFN-γ R1 -611 A>G polymorphism had no role in genetic susceptibility to human brucellosis in the study population. The PCR-CTPP can be used for genotyping IFN-γ R1 — 611 A > G polymorphism and other types of mutation.
A Single-Stage LED Driver Based on Half-Bridge  $CLCL$  Resonant Converter and Buck–Boost Circuit

A single-stage light-emitting diode driver based on interleaving buck–boost circuit and CLCL resonant circuit is proposed in this paper. The two circuits achieved an effective integration as well as kept their respective working conditions through the switch integration. Interleaving buck–boost circuit with symmetrical structure worked in discontinuous-conduction mode, achieving high power factor and low harmonic distortion. This structure can reduce the input current ripple and balance the current stress of switch at the same time. Besides, the bus voltage was also reduced. The CLCL resonant circuit has a soft-switching characteristic, the primary side operates in zero voltage mode and the secondary side operates in zero current mode. In addition, due to the small turn-OFF current, the turn-off loss of switch was also reduced. Therefore, the system efficiency was high. This paper elaborated the working mode, analysis and design method of the proposed topology. Finally, a 100-W experimental prototype was built, and the experimental results were corresponded with the theoretical analysis.
Deep Neural Networks with Mixture of Experts Layers for Complex Event Recognition from Images

With the need for the real-world applications, event recognition from static images has become more and more popular in these years. Although there remain good achievements, recognizing events from images with a complex background like WIDER dataset is still very hard to get good results. In this paper, we show this gap is probably caused by the large discrepancy of data. Most of the existing methods choose to use various modifications on pre-trained CNN network model to solve the problem. Although we follow this thought, after a review of existing methods, we choose two other ways to solve this problem. Firstly, we reveal that a deep one-channel model with end-to-end structure is more suitable to this problem than other multi-channel or multi-task models, which leads we to propose a model under this rule by modifying on one single pre-trained ResNet channel. Secondly, we propose a Mixture of Experts (MoE) neural network layer to overcome the large discrepancy of data. To increase the performance and enhance the specialization of the MoE layer, we also involve a simple neural network transfer method, Elastic Weight Consolidation, to transfer knowledge from SocEID dataset. The result shows that we enhance the accuracy of the WIDER dataset from the state-of-the-art by 9.4% with lower computational time and memory consumption. And some experiments are also listed there to proof the validation of our method.
Image-to-Image Learning to Predict Traffic Speeds by Considering Area-Wide Spatio-Temporal Dependencies

Spatio-temporal dependencies are the key to predicting the traffic parameters of an urban arterial network. However, their inclusion in forecasting traffic states has been hampered due to both the absence of a robust model and the computational burden. Recently, an innovative way to tackle the problem was developed by adopting a convolutional neural network (CNN) to deal with map images representing traffic states. Unlike previous studies that utilized map images only for input, the present study adopted images for both the input and the output of a CNN model to predict traffic speeds. The results show that the performance of the proposed model based on image-to-image learning is superior to that of the existing models.
Practical Adaptive Fuzzy Control of Nonlinear Pure-Feedback Systems With Quantized Nonlinearity Input

This paper investigates the fuzzy adaptive practical tracking problem for a class of nonlinear pure-feedback systems with quantized input signal. In the control scheme design process, the considered system is transformed into a plant with a strict-feedback form by borrowing the mean value theorem of differential, then fuzzy logic systems are used to compensate for some uncertain nonlinearities in the considered plant and the classical adaptive technique is employed to handle some unknown parameters. In the backstepping design, some non-negative switching functions are introduced to develop the desired fuzzy controller, and Barbalat’s lemma is used to analyze the stability and the control performance of the closed-loop system. It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori. A simulation example is presented to verify the effectiveness of the proposed control method.
Strictly Prioritized Transmission of Urgent Data Based on MAC Coordination in WSNs

Recently, by the spread of Internet of Things (IoT), wireless sensor networks and their applications are becoming more diverse. Future tasks in IoT include healthcare and factory networks, in which urgent data, in case of sudden condition change of elderly people or disaster due to machine failure, must be transferred with minimum delay and loss. However, existing methods for such prioritized transmission support only relative prioritization among different data traffic. In this paper, we propose a strictly prioritized transmission method of urgent data for transmission with minimum delay and loss. Urgent data are transmitted with strictly prioritized manner in MAC coordination between neighbor nodes along the route.
Understanding Path Reconstruction Algorithms in Multihop Wireless Networks

Low-power and multihop wireless networking is envisioned as a promising technology to achieve both energy efficiency and easy deployment for many Internet of Things (IoT) applications. Measuring packet-level path is crucial for managing large-scale multihop wireless networks. Packet-level path information encodes the routing path, a packet that takes through a network. The availability of packet-level path information can greatly facilitate many network management tasks. It is challenging to reconstruct packet-level paths using a small overhead, especially for large-scale networks. While there is a long list of existing path reconstruction algorithms, these algorithms focus on specific network scenarios, e.g., periodic monitoring networks or event detection networks. There lacks a unified model for systematically understanding and comparing the performance of these algorithms in different network scenarios. In this paper, we fill this gap by proposing an abstract model. Using this model, it is possible to derive a decision space for selecting the best algorithm for different networks. Furthermore, this model also guides us to devise better path reconstruction algorithms (cPath<inline-formula> <tex-math notation="LaTeX">$_{T}$ </tex-math></inline-formula>, cPath<inline-formula> <tex-math notation="LaTeX">$_{S}$ </tex-math></inline-formula>, and cPath<inline-formula> <tex-math notation="LaTeX">$_{ST}$ </tex-math></inline-formula>) with respect to path reconstruction ratio. Extensive experiments demonstrate the prediction power of our model as well as the advantages of our proposed algorithms. The results show that our algorithm (cPath<inline-formula> <tex-math notation="LaTeX">$_{ST}$ </tex-math></inline-formula>) improves a path reconstruction ratio from 94.4%, 34.3%, and 30.8% to 98.9%, 99.9%, and 60.1% on average in three network scenarios, respectively, compared with the best state-of-the-art algorithms.
Maneuver Guidance and Formation Maintenance for Control of Leaderless Space-Robot Teams

A basic problem for a group of space-based robots is how to maintain a rigid formation during orbital maneuvers. To a leaderless team with homogeneous space-based robots, a multirobot controller architecture is proposed in which information flow is included. For each robot, there are two coupled feedback loops with different control objectives. One feedback loop is for guidance and is designed so that each robot can rapidly maneuver and rendezvous at the target point in a neighboring orbit. The other feedback loop is for formation control and is designed so that all robots in the group maintain a desired formation during a maneuver. A modified linear quadratic regulator method is used to design the guidance and control laws, where separate performance indexes are used for the two loops. By analyzing the interaction between the formation control and the maneuver guidance, a new restriction on the communication topology is proposed. An estimator for the state of the formation center is included in this control loop, and to guarantee a consensus on the formation center state estimates among all of the robots, a distributed Kalman filter based on the consensus algorithm is developed. Simulation results demonstrate the approach.
Within-Network Classification in Temporal Graphs

Recent results indicate that static graph features might not be adequate to solve challenges in graphs involving a temporal dimension. We analyze several classification problems using already established temporal metrics, and we propose label-sensitive and recency-sensitive variants of these metrics that capture labeling information and additional temporal patterns in the data. We test all new and old metrics, and a baseline based on a standard disease-spreading model, using tuned off-the-shelf classifiers on 9 datasets of varying size and usage domain. Our experiments indicate that usage of label-and recency-sensitive metrics on real-world data provides more accurate results than static approaches and approaches based on temporal metrics alone.
A New Timit Benchmark for Context-Independent Phone Recognition Using Turbo Fusion

In this work, we apply the recently proposed turbo fusion in conjunction with state-of-the-art convolutional neural networks as acoustic models to the standard phone recognition task on the TIMIT database. The turbo fusion operates on posterior streams stemming from standard filterbank features and from group delay (phase) features. By the iterative exchange of posterior information, the phone error rate is decreased down to 16.91% absolute, which is to our knowledge the best reported result on the TIMIT core test set so far using context-independent acoustic models, outperforming the previous respective benchmark by 4.4% relative.
Learning via Social Preference: A Coarse-to-Fine Training Strategy for Style Transfer Systems

Neural style transfer represented a paradigm shift in image artistic rendering and applications in digital entertainment. Generative adversarial networks are considered as a general solution for style transfer problems. Researchers have explored multiple learning algorithms to increase the learning ability of style transfer networks. However, such research has overlooked an important outside motivator: social preference. Classical style transfer networks are trained offline and worked as a stationary model to transfer photos into stylized images, without any interaction with the environment. Based on the ideas from online training, we propose a new coarse-to-fine training strategy for neural style transfer systems to adapt to social preference change. In coarse stage, a primary model is learned via normal training method. In fine stage, the model is updated with online learning approach and sequentially added new data. We show that our approach exhibits improved performance compared to stationary model from visual effect and reflection of social preference. We conclude that the coarse-to-fine training strategy can improve the output of the generative model in social media environment.
Stabilization of Stochastic Uncertain Complex-Valued Delayed Networks via Aperiodically Intermittent Nonlinear Control

This paper concerns the stabilization problem of stochastic uncertain complex-valued delayed networks (SUCVDNs) via aperiodically intermittent nonlinear control. It is worth noting that the intermittent control is aperiodic and nonlinear. As a special case, when each control width tends to zero, intermittent control becomes impulsive control. Hence, the stabilization problem of SUCVDNs via impulsive control is also studied. The main method is the combination of the Lyapunov method and Kirchhoff’s matrix tree theorem in graph theory, which does not require us to solve any linear matrix inequalities. Different from previous results on complex-valued system, we consider the complex-valued system on complex space directly by using complex version Itô’s formula and (conjugate) ${\mathbb {R}}$ derivative. Then we give some sufficient criteria, which have a close relationship with the control interval and the topological structure of the considered network. The main results are applied to a class of stochastic complex-valued coupled oscillators, and numerical examples are also presented to show the effectiveness of the control strategies.