Miracle-dz/UniFAR-FTUs huggingface dataset:
On SMC-based indirect adaptive fuzzy control

In this paper, a fuzzy sliding mode controller (FSMC) based on the sliding mode control (SMC) scheme is proposed. Two rule bases are utilized to represent the controlled system. The membership functions of THEN-part are changed according to adaptive law to construct a suitable equivalent control of SMC. Especially, only one adaptive factor is characterized to adapt the membership functions instead of several ones in conventional adaptive approaches. Under the design scheme, we do not only maintain the distribution of membership functions over state space but also considerably reduce computing time. We apply this FSMC to control a nonlinear inverted pendulum system for confirming the validity of the proposed approach.
Streptococcus ( GBS ) Urinary Tract Infection Involves Binding of GBS to Bladder Uroepithelium and Potent but GBS-Specific Induction of Interleukin 1 a

Glen C. Ulett, Richard I. Webb, Kimberly B. Ulett, Xiangqin Cui, William H. Benjamin, Michael Crowley, and Mark A. Schembri Departments of Microbiology, Medicine, Genetics, Biostatistics, and Pathology and Heflin Center for Human Genetics, University of Alabama at Birmingham, Birmingham; School of Medical Sciences, Centre for Medicine and Oral Health, Griffith University, Southport, Queensland, and Centre for Microscopy and Microanalysis and School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
TMD symptoms and vertical mandibular symmetry in young adult orthodontic patients in North Sumatra, Indonesia: a cross-sectional study

Background: Temporomandibular joint disorder (TMD) includes symptoms of pain and dysfunction in the muscles of mastication and the temporomandibular joint. Differences in vertical condylar height, observed in the assessment of mandibular asymmetry, is a structural alteration that represents a risk factor for TMD. The study aimed to evaluate the association between TMD symptoms and vertical mandibular symmetry in young adult orthodontic patients in North Sumatra, Indonesia. Methods: The cross-sectional study included 18-25-year-old (mean ± SD, 21.9 ± 2.0 years) old orthodontic patients admitted to the Dental Hospital of Universitas Sumatera Utara, Medan, between June 2016 and March 2017. Vertical mandibular asymmetry was assessed from all 106 subjects using Kjellberg’s technique from pre-treatment panoramic radiographs. The TMD symptoms were assessed by structural interviews using modified questionnaires based on Temporomandibular Disorder Diagnostic Index and Fonseca’s Anamnestic Index. Results: Of the 106 subjects, 26 (24.5% of the total) with vertical mandibular symmetry and 39 (36.8%) with vertical mandibular asymmetry were positive for TMD symptoms. By contrast, 17 patients (16.0% of the total) with vertical condylar symmetry and 24 patients (22.6%) with vertical mandibular asymmetry were regarded negative for TMD symptoms. There was no significant difference (p=0.520) in TMD symptoms based on vertical mandibular symmetry. Conclusion: The results from this studied Sumatran population indicate that there are common TMD symptoms in young adult orthodontic patients, but there is no significant association between vertical mandibular asymmetry and TMD symptoms. Further study on the development of TMD, mandibular asymmetry and treatment planning for growing patients is suggested, using longitudinal and transitional approaches.
The anatomy and causal structure of a corporate myth: Nokia by the book

In this paper we conceptualize explanations of company-specific commercial performance as corporate myths. To improve our understanding of anatomy and causal structure of corporate myths, we analyze publications that deal with Nokia's historical transformation from a loss-making 1980s conglomerate to a focused and successful telecommunications company in the early 1990s. From a corpus of related literature, 89 causal arguments are identified and analyzed in terms of the logic of the arguments employed. The analysis shows that: (1) most existing analyses offer either a specific or a biased explanation for Nokia's success; (2) very few explanations are either plausible or logical; and (3) it is most unlikely that another company would achieve the same outcomes even if exactly the same decisions were made. Even though combining and comparing different explanations does not enhance the validity of any specific historical interpretation of Nokia's evolution or commercial success, it does offer an improved conceptual understanding of the ingredients found in corporate myths.
Preliminary assessment on the vulnerability of NMA-based Galileo signals for a special class of record & replay spoofing attacks

The authentication of GNSS signals becomes more and more important in recent years. The Navigation Message Authentication (NMA) is one approach to provide authentication of the GNSS data level. In this work, the capability and vulnerability of NMA-authenticated Galileo E1B INAV signals are evaluated. To this end, different case studies emulating spoofing attacks are investigated. The results of these tests provide a first assessment on the capability and vulnerability of NMA-authenticated Galileo signals.
COPD stands for complex obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) has extensively been reported as a complex disease affecting patients' health beyond the lungs with a variety of intra- and extrapulmonary components and considerable variability between individuals. This review discusses the assessment of this complexity and underlines the importance of transdisciplinary management programmes addressing the physical, emotional and social health of the individual patient. COPD management is challenging and requires advanced, sophisticated strategies meeting the patient's individual needs. Due to the heterogeneity and complexity of the disease leading to non-linear and consequently poorly predictable treatment responses, multidimensional patient profiling is crucial to identify the right COPD patient for the right treatment. Current methods are often restricted to general, well-known and commonly used assessments neglecting potentially relevant (interactions between) individual, unique “traits” to finally ensure personalised treatment. Dynamic, personalised and holistic approaches are needed to tackle this multifaceted disease and to ensure personalised medicine and value-based healthcare. Dynamic and holistic approaches are needed to ensure personalised medicine and value-based healthcare http://ow.ly/iawH30jGFSU
Design and Fabrication of Full Wheatstone-Bridge-Based Angular GMR Sensors

Since the discovery of the giant magnetoresistive (GMR) effect, GMR sensors have gained much attention in last decades due to their high sensitivity, small size, and low cost. The full Wheatstone-bridge-based GMR sensor is most useful in terms of the application point of view. However, its manufacturing process is usually complex. In this paper, we present an efficient and concise approach to fabricate a full Wheatstone-bridge-based angular GMR sensor by depositing one GMR film stack, utilizing simple patterned processes, and a concise post-annealing procedure based on a special layout. The angular GMR sensor is of good linear performance and achieves a sensitivity of 0.112 mV/V/Oe at the annealing temperature of 260 °C in the magnetic field range from −50 to +50 Oe. This work provides a design and method for GMR-sensor manufacturing that is easy for implementation and suitable for mass production.
Calculation of Cramer Rao maximum a posteriori lower bounds from training data

A neural network approach is presented to estimate the Cramer Rao maximum a posteriori (CRM) lower bounds on estimation error variances. First, from training data an additive statistical signal model is obtained by a feedforward neural network which maps output vectors back to input vectors. The CRM lower bounds are then calculated through the signal model. In neural network applications, the CRM lower bounds can be used: 1) to help determine when to stop training the network; and 2) to determine the importance of input features according to their contributions to the bounds. The convergence of the modeling procedure is shown. Examples are given to illustrate the proposed approach.
Wireless wavelength hopping with AWG/optical switch implemented secure audio/digital signals

In contrast to wired fiber transmission, wireless optical communication (WOC) had made more and more attraction because of convenient installation. Unfortunately, the WOC result in the intercepted crisis. By placing optical switch before arrayed waveguide grating router (hereinafter referred to as AWG), the proposed integrated AWG and optical switch configuration was made to implement wavelength hopping over wireless wavelength division. A pair of master-slave pseudo random noise generator (PRNG) algorithm was embedded into microprocessor such as Arduino chip to trigger optical switch and then convert transmission path of wavelength inside AWG. By motoring wavelength hopping with optical spectrum analyzer (OSA), the experiment results showed that audio and digital signals were correctly identified and retrieved with oscilloscope (OSC) to prove possible protecting against interception and interference.
Time series prediction using Lyapunov exponents in embedding phase space

In this paper, a multidimension chaotic time series prediction method using Lyapunov exponents in phase space is proposed. The method is based on the fundamental characteristic of chaotic behaviour that sensitive dependence upon initial conditions (SDUIC) and Lyapunov exponents are the measure of the SDUIC in a chaotic system. The fundamental characteristic of chaotic time series is analyzed, the proposal method is deduced. Numerical experimental results show that the proposed method is simple, practical and effective.
An intelligent predictive control approach to path tracking problem of autonomous mobile robot

An intelligent predictive control approach to the path tracking problem of an autonomous mobile robot is presented based on the analysis of the characteristics of the vehicle model and the traditional predictive control algorithm. A neural network model of the vehicle is used to predict future vehicle posture according to the current posture and control variables. The future tracking error between this predictive posture and the planned ideal path created by the local path planning module can be calculated. The optimal control variables in the next control instant are computed by the online optimization algorithm. The above process is updated and solved repeatedly. The intelligent predictive controller is composed of four principal components: a reference path, a predictive model, a set of online optimization algorithms and a feedback tuning model, which are discussed in detail in the paper. The characteristic of this method is analyzed and the result is provided.
Caterpillar Climbing: Robust, Tension-Based Omni-Directional Locomotion

Abstract Animals that must transition from horizontal to inclined or vertical surfaces typically change their locomotion strategy to compensate for the relative shift in gravitational forces. The species that have been studied have stiff articulated skeletons that allow them to redistribute ground reaction forces (GRFs) to control traction. Most also change their stepping patterns to maintain stability as they climb. In contrast, caterpillars, most of which are highly scansorial, soft-bodied, and lack rigid support or joints, can move with the same general kinematics in all orientations. In this study, we measure the GRFs exerted by the abdominal prolegs of Manduca sexta (Linnaeus) during locomotion. We show that, despite the orthogonal shift in gravitational forces, caterpillars use the same tension-based environmental skeleton strategy to crawl horizontally and to climb vertically. Furthermore, the transition from horizontal to vertical surfaces does not seem to require a change in gait; instead gravitational loading is used to help maintain a stance-phase body tension against which the muscles can pull the body upwards.
When Deep Neural Networks Meet Job Offers Recommendation

The purpose of this work is to present the recent results that we have obtained on a new job board recommendation system. Firstly, the job applicant clickstreams history on various job boards are stored in a large learning database, and then represented as time series. Secondly, a deep neural network is trained to predict future values of the clicks on the job boards. Third, and in a parallel way, dimensionality reduction techniques are used to transform the clicks multivariate numerical time series into temporal symbolic sequences. Ngrams are then used to predict future symbols for each sequence. Finally, a list of top ranked job boards are kept by maximizing the clickstreams forecasting in both representations. Our experiments are tested on a real dataset, coming from a job-posting database of an industrial partner. The promising results have shown that using deep learning, the recommendation system outperforms standard multivariate models.
Tenacity of Collateral Perfusion in Proximal Cerebral Arterial Occlusions 6–12 h after Onset

Background: Clinical trials have shown that benefits of endovascular recanalization (EVT) for acute ischemic stroke patients with sizable penumbral tissues seems plausible even beyond 6 h after their last seen normal (LSN). Persistency of ischemic penumbra remains unclear in delayed periods. Methods: From a prospective stroke registry database, we identified 111 acute ischemic stroke patients who had internal carotid artery or middle cerebral artery occlusion with baseline National Institutes of Health Stroke Scale scores ≥6 points and arrived 6–12 h after LSN. Baseline information and functional outcomes were prospectively collected as a clinical registry. Attending physicians made treatment decisions for EVT based on the current guidelines and institutional protocols. MR image parameters, including the volume of diffusion-restricted lesions and mapping of the hypoperfused area, were quantified using automated commercial software. Binary logistic regression analysis models, with modified Rankin Scale (mRS) scores of 0–1 at 3 months after stroke included as a dependent variable, were constructed. Results: Between 6 and 12 h after onset, 58% had a mismatch ratio of ≥1.8 at baseline and 42% had favorable imaging profiles as determined by DEFUSE 2 study. After 9 h, there was a mismatch ratio of ≥1.8 in 47 and 38% favorable profiles. EVT was performed in 54% of cases. A 3-month mRS score of 0–1 was found in 19% (25% in EVT and 12% in medical treatment groups) of cases. EVT was associated with an increased OR of having a mRS score of 0–1 at 3 months after stroke (adjusted OR 7.59 [95% CI 1.28–61.60]). Conclusions: Penumbral tissues were persistent in a substantial proportion of anterior circulation occlusion cases 6–12 h after LSN. EVT at 6–12 h in a predominantly Asian cohort resulted in better outcomes.
Leveraging Large Face Recognition Data for Emotion Classification

In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous year's best result on the test set by about 1%, achieving a 60.03% classification accuracy without any use of visual temporal information, showing a top-2 result in this challenge.
Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm.
Challenges of Muslim Women Entrepreneurs in Malaysian SMEs

This paper tries to study the challenges faced by the Muslim women entrepreneurs in Malaysian SMEs. At present, only 15.9% of businesses are owned by women entrepreneurs. This is far way behind of what has the Government of Malaysia targeted in 2011 that is 30%. The finalized questionnaires were distributed by post to 250 Muslim women entrepreneurs in Malaysian SMEs by using simple random sampling technique. The response rate is 44% which is good. It is found out that the most frequent type of challenges faced by the respondents are lack of finance (79.2%), lack of demand (50.9%) and location problem (50.9%). Meanwhile, their proposed solutions to the challenges are a mix of observing their religion (to give tithe and alms, to do recommendatory prayers and to hire orphans), doing business (to have good relationship with customers, businesswomen organizations, suppliers, competitors and workers, to obtain more information on business development and personal (to apply work life balance).
An Exploration of Applying Gate-Length-Biasing Techniques to Deeply-Scaled FinFETs Operating in Multiple Voltage Regimes

With the aggressive downscaling of process technologies and the importance of battery-powered systems, reducing leakage power consumption has become a crucial design challenge for IC designers. In addition, the traditional bulk CMOS technologies face significant challenges related to short-channel effects and process variations. FinFET devices have attracted a lot of attention as an alternative to bulk CMOS in sub-32nm technology nodes. This paper presents a device-circuit cross-layer framework to utilize fine-grained gate-length biased FinFETs for circuit leakage power reduction in near- and super-threshold (<inline-formula><tex-math notation="LaTeX">$V_T$</tex-math><alternatives> <inline-graphic xlink:href="cui-ieq1-2640185.gif"/></alternatives></inline-formula>) operation regimes. The impacts of cell-level and transistor-level Gate-Length Biasing (GLB) on circuit speed and leakage power are studied using a 7 nm FinFET technology.
Gaussian Weighting Reversion Strategy for Accurate On-Line Portfolio Selection

Considering the drawbacks of existing reversion based on-line portfolio selection (PS) strategies, we propose a new on-line learning and reversion based strategy that is called Gaussian Weighting Reversion (GWR in short). On the one hand, to exploit the "time validity" of historical market data, which means that the more recent market data are more valuable to market prediction than the less recent market data, we use the Gaussian function to weight data in a moving window. On the other hand, for each time point we average two predictions to alleviate the impact of noise and outliers. We conduct extensive evaluation on six real market datasets and compare our strategy with nine existing methods, including the state of the art ones. Experimental results show that our method outperforms the existing methods.
Roselle Anthocyanins: Antioxidant Properties and Stability to Heat and pH

Roselle is rich in anthocyanins and is traditionally used to prepare a bright red beverage by decoction. However, heat treatment and different pH environments are often encountered during food processing, and these factors are often detrimental to anthocyanins. Therefore, it is very important to understand the influence of pH and heat treatment on anthocyanins for the application of roselle. This study determined the antioxidant properties of roselle extract, explored changes in the color and anthocyanin content in different pH environments, and evaluated the thermal stability of roselle anthocyanins using kinetic equations. The results showed that the roselle extract is rich in anthocyanins and has good antioxidant capacity (DPPH IC50 = 4.06 mg/mL, ABTS IC50 = 3.7 mg/mL). The anthocyanins themselves exhibited a certain degree of heat resistance and good color stability in an acidic environment. In contrast, they degraded very quickly and exhibited significant changes in color in a low-acid environment. The activation energy (Ea) ranges of the anthocyanins in the acidic and low-acid environments were quite different at 55.8–95.7 and 31.4–74.9 kJ/mol, respectively. Thus, it can be concluded that roselle anthocyanins are susceptible to heat treatment in a low-acid environment, affecting their quality and appearance; however, they can serve as a good source of functional ingredients and color in an acidic environment.
How to manage coverability graphs construction: an overview

To analyze the behavior of Petri nets, the reachability graph permits us to verify qualitative properties. However, even for small nets, the reachability graph can become infinite if the studied Petri net is unbounded. So, to deal with the state space explosion problem, the coverability graph must be used. The paper aims to expose the main ways of construction of coverability graphs and specifies how some qualitative properties can be checked in these different approaches.
GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach

In silico investigation of biological systems requires the knowledge of numerical parameters that cannot be easily measured in laboratory experiments, leading to the Parameter Estimation (PE) problem, in which the unknown parameters are automatically inferred by means of optimization algorithms exploiting the available experimental data. Here we present MS 2 PSO, an efficient parallel and distributed implementation of a PE method based on Particle Swarm Optimization (PSO) for the estimation of reaction constants in mathematical models of biological systems, considering as target for the estimation a set of discrete-time measurements of molecular species amounts. In particular, such PE method accounts for the availability of experimental data typically measured under different experimental conditions, by considering a multi-swarm PSO in which the best particles of the swarms can migrate. This strategy allows to infer a common set of reaction constants that simultaneously fits all target data used in the PE. To the aim of efficiently tackling the PE problem, MS 2 PSO embeds the execution of cupSODA, a deterministic simulator that relies on Graphics Processing Units to achieve a massive parallelization of the simulations required in the fitness evaluation of particles. In addition, a further level of parallelism is realized by exploiting the Master-Slave distributed programming paradigm. We apply MS 2 PSO for the PE of synthetic biochemical models with 10, 20 and 30 parameters to be estimated, and compare the performances obtained with different GPUs and different configurations (i.e., numbers of processes) of the Master-Slave.