Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Recurrent Aphthous Stomatitis and Metabolic Syndrome

Metabolic syndrome or X syndrome includes a group of metabolic and non-metabolic disorders, that increase the risk of some disease. This study aimed to determine the prevalence of metabolic syndrome in healthy subjects and patients with recurrent aphthous stomatitis (RAS) was conducted. In this cross-sectional study 33 patients with recurrent aphthous stomatitis which were referred to Department of Oral Medicine of Tabriz dental school were studied. The inclusion criteria included consent to participates in the study, lack of underlying disease associated with RAS such as Behcet’s syndrome and lack of systemic diseases. RAS patients were examined in terms of risk of the metabolic syndrome and 33 age and sex matched healthy individuals were selected and examined in terms of risk of metabolic syndrome. Data samples from check list contain demographic and laboratory findings were collected. Results showed that the prevalence of metabolic syndrome in patients with RAS (21.2%) was significantly higher than in healthy people (9.1%). (p<0.05) Female percentage of RAS patients with metabolic syndrome were 71.4 and Male percentage of RAS patients with metabolic syndrome were 28.6. Given the high prevalence of metabolic syndrome in patients with RAS, this syndrome can be considered as a risk factor for RAS.
Combining Artificial Intelligence planning advantages to assist preliminary formal analysis on Industrial Control System cybersecurity vulnerabilities

This paper provides a novel and practical approach, to check the correctness and completeness of functional specifications (the completeness means complete for one specified function), especially for complex safety critical systems. This approach combines Artificial Intelligence (AI) planning technique, together with formal functional specification, to assist further cybersecurity vulnerability analysis on safety critical Industrial Control System (ICS). The effectiveness of this combination is represented by examples, which are based on a safety critical ICS-Refueling Machine (RM). RMs are widely used in Nuclear Power Plants (NPPs). The outcome of this idea is a correct and complete functional specification of specific functions, which is the key foundation for our further safety and security vulnerability analysis, especially for performing analysis based on model checking. This practical checking approach is applicable to further Instrumentation & Control (I&C) systems and platforms.
An Overview of Modular Multilevel Converters in HVDC Transmission Systems With STATCOM Operation During Pole-to-Pole DC Short Circuits

Fault-current handling capability of the modular multilevel converters (MMCs) under dc-cable short-circuit conditions is a major concern for the MMC applications on the high-voltage direct-current (HVDC) transmission systems, where the MMCs based on half-bridge submodules (SMs) cannot block the fault currents to protect the converter devices. In this paper, a comprehensive review for the fault-ride-through capability of the HVDC transmission systems based on the MMCs adopting different SM schemes is presented, where the MMCs can block the fault currents and compensate the reactive currents to the electric grid during the dc faults. An analysis of the dc short-circuit faults in the MMC is introduced and then the operation principle of different SM circuits building the MMC for blocking the fault currents is highlighted. The fault-tolerant operation of these MMC schemes as static synchronous compensator to enhance the ac grid stability during the dc faults is also investigated. A comparison in terms of investment cost, loss, volume, and controllability for various MMC topologies is performed. Comprehensive simulation results for the most promising topologies of the MMC with a capability of fault-ride through under dc-fault conditions are finally presented.
Additive Consistency of Hesitant Fuzzy Linguistic Preference Relation With a New Expansion Principle for Hesitant Fuzzy Linguistic Term Sets

Hesitant fuzzy linguistic preference relation (HFLPR) is a new preference structure that the decision makers (DMs) are hesitant about several possible linguistic terms of preference information for pairwise comparison between alternatives. This paper examines the additive consistency of HFLPR with a new expansion principle for hesitant fuzzy linguistic term sets (HFLTSs). In order to normalize HFLTSs with different numbers of linguistic terms, a least common multiple expansion (LCME) principle is proposed. According to the LCME principle, the additive consistent index of an HFLPR is defined to measure the consistency level of the HFLPR. For improving the unacceptable additive consistency of an HFLPR, a pure integer programming model is constructed to derive an acceptable additive consistent HFLPR. In group decision making (GDM) with HFLPRs, the similarities between DMs are calculated based on their individual HFLPR with acceptable additive consistency. Subsequently, the confidence degrees of DMs are defined to derive DMs’ weights, and some examples including an investment project management problem are analyzed to verify the effectiveness of the proposed method.
The Performances of the Fixed Constraints Transform Applied in Text Compression Experimental Results and Comparisons

The Fixed Constraints Transform (FCT) encodes the text based on a dictionary. This dictionary is used to accomplish the connections between the words in the text and their corresponding transforms. The dictionary is generated one time and it is saved in a binary form for a better word-indexing speed. This method is strictly designed for text compression and it has maximum performances when the text has normal formatting - in a phrase, only the first word starts with upper case, and it continues with lower case. Because the algorithm is based on modification of the words in the text, on unaltered signs of punctuation, on spaces and other special characters, the algorithms performance is given by the ratio between the number of letters in the text and the total number of characters. FCT has close performances with other frequently used transforms - Star, Burrows-Wheeler, etc. - in terms of compression, but it has better execution speed. The applied algorithms of lossless data compression for testing are: RLE (Run-Length Encoding), arithmetic, PPMd (Prediction by Partial Matching), BZip2, Deflate (WinZip), LAMA, and RAR. The following indicators of compression performance were measured: the requested time for transform generation, the compression rate, and the requested time for compression. The text files used for evaluating the performance are from the Calgary Corpus. FCT leads to compression performances close to the ones obtained by the usual transforms used as pre-compression methods (BWT, Star Transform and derivatives). FCT is suited for the use of a chain of processors that have as purpose lossless data compression. The transform itself does not do a performing compression, but - most important - it helps a compression algorithm applied after it with the fact that it eliminates some redundant information and specific features to the idioms written in a certain language. FCT is an efficient method of data processing with notable results that can be very easily implemented and used in a lossless compression chain both for stream sequences and files in usual applications.
Compact Tunable Balanced Bandpass Filter With Constant Bandwidth Based on Magnetically Coupled Resonators

In this letter, a compact high-selectivity and common-mode (CM) suppression tunable balanced microstrip bandpass filter (BPF) with constant bandwidth is designed by adopting the magnetically coupled resonators with step-impedance parallel coupling feedline (SIPCF) structure. The desired coupling coefficient <inline-formula> <tex-math notation="LaTeX">$K_{12}$ </tex-math></inline-formula> and external quality factor <inline-formula> <tex-math notation="LaTeX">$Q_{e}$ </tex-math></inline-formula> can be obtained by the mixed coupling between the two resonators and SIPCF structure, respectively. Two differential-mode (DM) transmission zeros (TZs) near the lower and upper passband edges are created by the mixed coupling and source–load coupling, respectively. Furthermore, intrinsic high CM suppression in the DM tunable operating passband can be maintained because of the magnetically coupled resonator structure. Finally, a compact tunable balanced BPF prototype with constant fractional bandwidth of 6.5% is implemented and fabricated. The measured results agree well with the simulated ones.
Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained From FRA Signature

Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude–frequency signature, while phase–frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude–frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis

The electroencephalogram (EEG) signal is a medium to realize a brain–computer interface (BCI) system due to its zero clinical risk and portable acquisition devices. Current EEG-based BCI research usually requires a subject-specific adaptation step before a BCI can be employed by a new user. In contrast, the subject-independent scenario, where a well trained model can be directly applied to new users without precalibration, is particularly desired. Considering this critical gap, the focus in this letter is developing an effective EEG signal analysis adaptively applied to subject-independent settings. We present a convolutional recurrent attention model (CRAM) that utilizes a convolutional neural network to encode the high-level representation of EEG signals and a recurrent attention mechanism to explore the temporal dynamics of the EEG signals as well as to focus on the most discriminative temporal periods. Extensive experiments on a benchmark multiclass EEG dataset containing four movement intentions indicate that the proposed model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches by at least eight percentage points. The implementation code is made publicly available.11https://github.com/dalinzhang/CRAM.
Wavelet Transform for Seizures Detection in EEG Records

In this paper, we propose an algorithm based on wavelet transform for seizures detection in EEG records. Epilepsy is a disorder that affects about 1% of the population. Seizure detection is important in patient monitoring and in diagnostic. For this work were used records from 23 channels EEG scalp. The method consists in signal processing by using DWT (discrete wavelet transforms). The signals were passed through low pass filter and decomposed using high-pass filter. The outputs are the detail coefficients and approximation coefficients. The decomposition was repeated for increasing the frequency resolution, approximation coefficients were decomposed with high and low-pass filters. After signal processing for seizure detection we used neural network for classify the EEG seizure segments by finding the onset and offset points.
A 2.3-mW 26.3-GHz  $G_{m}$ -Boosted Differential Colpitts VCO With 20% Tuning Range in 65-nm CMOS

This paper presents an architecture for differential Colpitts voltage-controlled oscillators (VCOs) in complementary metal–oxide–semiconductor (CMOS) that utilizes three design techniques to extend the tuning range (TR) of the VCO, while maintaining a low phase noise (PN) and a low power consumption. First, a switched-capacitor bank based on a variable capacitive feedback technique is introduced to achieve a wide TR with a minimal PN degradation. Second, a <inline-formula> <tex-math notation="LaTeX">$G_{m}$ </tex-math></inline-formula>-boosting technique using interstage inductors is employed to lower the power consumption and relax VCO startup issues. Third, a dynamic forward-body self-biased technique is used to further reduce the power consumption and PN of the proposed structure. As a proof of concept, a 26.3-GHz differential Colpitts VCO is designed and fabricated in a 65-nm CMOS process. Based on the measurement results, the VCO achieves a PN of −122.1 dBc/Hz at 10-MHz offset from the center frequency, and a TR of 20%. The circuit consumes 2.3 mW from a 1-V supply and excluding the pads occupies a 0.22 mm<sup>2</sup> of silicon area. Compared to the recently published CMOS VCOs within the same frequency range, the proposed VCO simultaneously achieves a wide TR, low power dissipation, and low PN, resulting in a figure of merit (FOM) and FOM incorporating the TR (FOM<inline-formula> <tex-math notation="LaTeX">$_{T}$ </tex-math></inline-formula>) of −187 and −193 dBc/Hz at 10-MHz offset from the center frequency, respectively.
Effect of Shielding with Electromagnetic Absorbing Materials on Specific Absorption Rate Reduction

In this paper, a new specific absorption rate reduction technique is introduced. The shielding technique is performed by different electromagnetic absorbing materials. Electromagnetic absorbers used in shielding are glass, FR-4, teflon, Nickel, Gallium Arsenide and Silicon. Specific absorption rate reduction factor of 48.38% averaged over 1 gm of mass is achieved. The infinity-shaped structure is a new metamaterial structure which is used in shielding. The infinity-shaped structure achieves specific absorption rate reduction factor of 53.8% averaged over 1 gm of mass tissue. The standard anthropomorphic mannequin phantom human head model at 900 MHz is presented. Specific absorption rate distribution and radiation pattern are analyzed, as well, to verify the performance of the model and the antenna. Simulations show the effect of changing the thickness and the width of the shield on the specific absorption rate value.
Detection Probability Calculation Model of Visible and Infrared Fusion Method in Composite Photoelectric Detection Target

To improve the detection probability and detection performance of photoelectric detection target under low environment luminance, this paper puts forward a new method to design its detection system by using the visible and infrared photoelectric detectors and form composite photoelectric detection target; sets up their detection probability calculation model of visible light and infrared photoelectric detector in single wavelength based on the spectral contrast between target and environment luminance. According to the binary decision theory, deduces the total detection probability calculation function by utilizing the visible light and infrared detector fusion method, and lead into the target distance factor to analyze its influence factor. Through experiment and analysis, the results found when the environment luminance is larger than 300 lx, the detection probability of composite photoelectric detection target reach 91% within the limit detection distance, which can improve the detection performance and capture rate of photoelectric detection target under low luminance conditions.
A Comparison of Supervised and Unsupervised Dimension Reduction Methods for Hyperspectral Image Classification

Hyperspectral images are extensively used, now-a-days, for governing several geo-spatial fields. Due to the high number of dimensions or spectral bands the classification accuracy demotes which is known as the “Hughes Phenomenon” or the “Curse of Dimensionality”. To overcome this obstacle dimensionality reduction approaches need to be performed or simply the number of the spectral bands needs to be reduced. As a preprocessing step feature extraction or feature selection can be performed that reduces the computational complexity of the hyperspectral data classification. Highly correlated features are omitted and only the informative ones are considered for the classification. In this paper, we considered the feature extraction approaches, namely, the Principal Component Analysis, the Linear Discriminant Analysis and both of them combined. We have applied these feature extraction methods on the dataset individually and then classified the dataset using Support Vector Machine classifier. The experimental results show that the LDA approach provides the best accuracy (86.53%) among the three dimension reduction techniques applied. All the files and codes used in our work can be found at https://github.com/joybiS31/multiclassSVM classification u sing PCA LDA/
Personalized Mio-electrical Monitoring System Based on ADS1298 and MIM Electrodes

The paper depicts a versatile solution for noninvasive electrophysiological signals recording, by a specialized ADS1298 bio-medical platform, equiped with signal acquisition on laptop. The novelty degree of this paper comes from a new acquisition platform, specialized in bio-electrical signal cropping, besides to some Metal-Insulator-Metal - MIM capacitive electrodes, specially fabricated for this purpose. The capacitive electrodes avoid the disadvantage of high series resistance, encountered to metallic electrodes and are more sensitive to variable biosignal that can overlap. The cropped electrical signals at skin level are converted into a digital format as a vector data and then stored and post-processed by optimal computing resources. This paper presents the final stage of a personalized biomedical signal system for many myo-electrical scopes. Therefore, some tests on subjects are available.
Trust model for cloud providers using Linear equations

Cloud computing is a vital environment for the real time business activities. Even though cloud booms up to high level in IT industries, still its back end processing are threatening the cloud customers. This paper specifies the importance of trust in cloud computing. The cloud customers are not aware of the processing techniques through which the cloud providers provide services. As well as they do not know how the customer data are handled confidentially by the cloud providers. In this paper the quantitative and qualitative measures of the service providers are analysed and based on that the trust model is created, which makes the cloud customer to believe service providers.
Small-Signal Model of Switched Inductor Boost Converter

Equivalence between the switched inductor boost (SIB) converter and the tapped inductor boost (TIB) converter is suggested and the required conditions are derived. Based on the equivalence conditions, it is further shown that dynamic model of the SIB converter is merely a private case of the more general TIB converter. The model is derived using the tapped inductor switcher switched flow graph modeling approach. Simulation and experimental results are also reported.
228-Spatial-Channel Bi-Directional Data Communication System Enabled by 39-Core 3-Mode Fiber

We demonstrated simultaneous usage of 228 spatial channels (effectively) within a strand of fiber. We fabricated a 13-km 39-core 3-mode fiber and constructed a bi-directional space division multiplexing (SDM) transmission setup by using cascaded spatial multiplexers on both fiber ends. Aggregated intercore crosstalk and return loss of the system were below −35 and above 31 dB, respectively, over the C and L bands. The feasibility of using bi-directional SDM transmission was investigated using 10 GBaud, 37.38-Gb/s dual-polarization QPSK signals at 1550 nm and coherent detection with a 6 × 6 multi-input-multi-output equalizer. Excellent transmission properties were obtained for most spatial channels.
Experimental Investigation of Linear Cumulative Damage Theory With Power Cycling Test

In this paper, several power cycling tests under single or combined test conditions were undertaken to investigate the applicability of linear cumulative damage theory in the lifetime prediction of power semiconductor devices. The validity of this theory was verified by an experimental method for one lifetime limit, which is an increase of forward voltage at load current by 5%. The corresponding failure mechanism is the degradation of bond wire contacts.
A Fiber-Wireless Sensor Networks QoS Mechanism for Smart Grid Applications

Smart grid applications, such as teleprotection, synchrophasors, remote condition monitoring, and control of assets, require timely and reliable data communication systems. Many communication architectures and solutions have been proposed to provide support for these smart grid applications, including fiber networks, wireless mobile networks, wireless sensor networks (WSNs), and so on. The use of heterogeneous solutions is an attractive architecture option since it combines the advantages of two or more communication systems to meet the delay and reliability requirements of the smart grid. Fiber-wireless sensor networks (Fi-WSNs) are gaining popularity as a reliable communication infrastructure in many other applications. This is due to the low cost, reliability, availability, the distributed nature of the WSNs, and the high bandwidth and reliability of the optical fiber networks. Although the Fi-WSNs systems can provide a good delay performance, they may not meet the requirements of the above smart grid delay-critical applications. In this paper, we propose a novel adaptive and cross-layer service differentiation mechanism for the Fi-WSNs. The proposed mechanism implements an adaptive scheduling mechanism and allows WSNs to cooperate with the optical network unit (ONU) to reduce the delay for high priority traffic. We determine the effects of the proposed mechanism on the quality of service (QoS) of delay critical smart grid monitoring applications in terms of the end-to-end delay and reliability. We show through simulations that our proposed QoS mechanism can reduce the end-to-end delay in the Fi-WSN system and in the long-reach passive optical networks (LR-PONs). We also show that our mechanism can outperform the existing techniques under the same traffic and network conditions.
A Biometric System with Hierarchical Feature-level Fusion

The paper approaches the feature-level fusion for biometric authentication, a big challenge for the actual biometric security systems design. A hierarchical inter-modal fusion method is proposed and evaluated for a reduced feature space. The feature generation and fusion are performed using regions of interest manually defined within the original images. The features are extracted using co-occurrence matrices, providing a common framework for feature generation among several human traits. The fusion is based on a functional combination of the features, an approach that is feasible if the feature vectors are homogeneous. The functional fusion avoids the concatenation that increases the feature space size, leading to curse of dimensionality and additional computational complexity costs.