Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Vitamin D along with Splint therapy in the Vit D deficient patients with Temporomandibular disorder-A Randomized, double-blind, placebo-controlled clinical trial

Aim: The purpose of this study is to comparatively evaluate the Vitamin D supplementation and stabilization splint therapy in patients exhibiting temporomandibular disorders (TMD). Settings and Design The study design was double-blinded, parallel-group, randomized and placebo-controlled trial conducted in patients with low Vitamin D and TMDs, which were allocated to two groups, Study group S + D (Stabilization splint with Vitamin D supplementation) and Control Group S (Stabilization Splint with placebo drug). Subjects and Methods: Thirty-six participants of 18–45 years of age gap with Vitamin D deficiency and TMD were included in the study. Preoperative values of Vitamin D levels in ng/ml, comfort mouth opening (CMO) in mm, maximum mouth opening (MMO) in mm, temporomandibular joint (TMJ) tenderness (grading 0–3), Visual analog scale score (VAS Score 0–10 cm), and total energy (TE) integral values of both left and right TMJ's in Hertz (Hz) were recorded using joint vibration analysis All the values of CMO, MMO, TMJ Tenderness and VAS were recorded at each follow-up at 1st week, 1st month, 2nd month, and 3rd month, respectively. Postoperative Vitamin D levels and TE of both TMJs were recorded at end of 3 months. Statistical Analysis Used: For intergroup comparison, Mann–Whitney U-test and Pearson Chi-square tests were done. For Intragroup comparison, Wilcoxon signed rank test was used for comparison. Results: In Intergroup comparison, a significant difference was seen in CMO, VAS score and MMO (P < 0.05) but not among mean values of TE of right and left TMJ, and Vitamin D levels (P < 0.05). In both groups, there were significant statistical variations in CMO, VAS score, MMO, and TE integral before and after treatment in the right and left TMJs (P < 0.05). Conclusions: The study concludes centric stabilization splint helps in improving symptoms of TMD patients and Vitamin D supplementation provided faster relief in those cases.
New Phaselet-based Algorithm of Fast Distance Relay

This paper describes a new algorithm of fast distance relay based on the concept of phaselets. The principle is proposed by means of directly comparing phase difference between the operating voltage phaselet and the polarizing phaselet on the basis of new phaselet equation of transmission line and new definition of operating voltage. Additionally, the exponential decaying DC component matches the new phaselet equation. The proposed algorithm, therefore, needs neither converting the phaselets into phasors, nor removing the decaying DC offsets. EMTP simulation tests associated with a simple transmission system and 500 kV transmission system in Shandong power system show that the algorithms are of high speed with simple calculation, not influenced by DC decaying component, and high frequency components are effectively restrained.
Dynamic Pain-Related Changes in Pulse-Graph Measurements in Patients with Primary Dysmenorrhea before and after Electroacupuncture Intervention and Its Correlation with TCM Pattern

Objective To explore the dynamic changes recorded in pulse graph related to the changes in the severity of pain before and after electroacupuncture (EA) intervention among young women suffering from primary dysmenorrhea (PD). Methods A total of 147 female college students were recruited in this study. Based on participants' symptoms associated with menstruation, they were divided into the PD group and the healthy control group. In addition, participants in the PD group were further sorted into the Cold Coagulation and Blood Stasis Pattern (CCBSP) and Qi Stagnation and Blood Stasis Pattern (QSBSP) based on TCM diagnoses and their pulses differences. Participants in the PD group received EA at maximal pain during menstruation. The primary acupuncture points selected were SP 6 and RN 3, additional RN 4 for CCBSP, and LR 3 for QSBSP. Four observation time points were 7–10 days before menstruation (T0), maximal pain during menstruation (T1), immediately after EA (T2), and 30 mins after EA (T3). The severity of pain was assessed by a visual analog scale (VAS) along with a pulse analyzer to record the variations of the pulse graph throughout the changes of pain level. Results (1) The average VAS score in the PD group decreased from 5.44 ± 1.46 at T1 to 1.72 ± 1.27 at T2 and 1.59 ± 1.30 at T3. The average VAS score in participants of CCBSP at T1, T2, and T3 was higher than that of QSBSP. (2) At T1, h2, h3, h4, and w1/t were all significantly increased, compared with those at T0. At T2, t and t5 were both significantly increased, and w1/t, t1, and t1/t were all significantly decreased, compared with those at T1. At T3, w1/t, t1, and t1/t were all significantly increased, and t and t5 were both significantly decreased, compared with those at T2. (3) Comparing the pulse graphs between the healthy control and the PD groups, h1 was significantly lower at T0; w1/t was significantly higher at T1; t was significantly higher at T2; and t1 and t1/t were both significantly higher at T3 in PD group. (4) When comparing the pulse graphs between QSBSP and CCBSP, t4/t5 was significantly higher at T0 and t1 was significantly higher at T1 in the CCBSP group. Conclusion EA is effective in relieving primary dysmenorrhea. Our results showed the opposite changing of the pulse graph recorded before the onset of pain to the maximum pain and that from maximum pain to pain relief. Indeed, there were differences in the recorded pulse graphs between CCBSP and QSBSP (two patterns of PD) as described in traditional Chinese pulses diagnosis. The study has been registered in the Chinese Clinical Trial Registry (registered number: ChiCTR2000040065; registered date: 2020/11/19).
High Order Deep Neural Network for Solving High Frequency Partial Differential Equations

This paper proposes a high order deep neural network (HOrderDNN) for solving high frequency partial differential equations (PDEs), which incorporates the idea of “high order” from ﬁnite element methods (FEMs) into commonly-used deep neural networks (DNNs) to obtain greater approximation ability. The main idea of HOrderDNN is introducing a nonlinear transformation layer between the input layer and the ﬁrst hidden layer to form a high order polynomial space with the degree not exceeding p , followed by a normal DNN. The order p can be guided by the regularity of solutions of PDEs. The performance of HOrderDNN is evaluated on high frequency function ﬁtting problems and high frequency Poisson and Helmholtz equations. The results demonstrate that: HOrderDNNs( p > 1) can efﬁciently capture the high frequency information in target functions; and when compared to physics-informed neural network (PINN), HOrderDNNs( p > 1) converge faster and achieve much smaller relative errors with same number of trainable parameters. In particular, when solving the high frequency Helmholtz equation in Section 3.5, the relative error of PINN stays around 1 with its depth and width increase, while the relative error can be reduced to around 0.02 as p increases (see Table 5).
Theoretical optimization and experimental verification of a non-contact damper for high temperature superconducting Maglev systems

High-temperature superconducting (HTS) maglev has excellent potential in high-speed transportation due to its capability of passive stabilization levitation. However, in applications, the vibration of maglev vehicle with a large amplitude will be triggered for some external incentives or interference owing to its weak damping characteristics. Hence, it is preferable and necessary to find a way to reduce the vibration without mechanical contact. In terms of mechanical structure, the HTS maglev system mainly consists of HTS bulks and permanent magnets guideway (PMG). Utilizing the magnetic field provided by PMG, we designed and assessed an electromagnetic shunt damper (EMSD) for the HTS maglev system, by which the vibrations can be attenuated without contact. At first, the parameters of EMSD were theoretically optimized, respectively. Second, according to the structure of the Halbach PMG, the experimental EMSD was presented. In this damper, two groups of coils in reverse series were set above the two magnetic poles of the PMG to get a large induced electromotive force. Eventually, the experimental results show that the vibrations of the HTS maglev model vehicle can be attenuated more than 80% in the resonance region when the designed EMSD was employed in the system. Namely, this non-contacting damper will highly enhance the dynamic characteristics of HTS maglev systems.
Vision-Based System for Automated Estimation of the Frontal Area of Swimmers: Towards the Determination of the Instant Active Drag: A Pilot Study

Swimmers take great advantage by reducing the drag forces either in passive or active conditions. The purpose of this work is to determine the frontal area of swimmers by means of an automated vision system. The proposed algorithm is automated and also allows to determine lateral pose of the swimmer for training purposes. In this way, a step towards the determination of the instantaneous active drag is reached that could be obtained by correlating the effective frontal area of the swimmer to the velocity. This article shows a novel algorithm for estimating the frontal and lateral area in comparison with other models. The computing time allows to obtain a reasonable online representation of the results. The development of an automated method to obtain the frontal surface area during swimming increases the knowledge of the temporal fluctuation of the frontal surface area in swimming. It would allow the best monitoring of a swimmer in their swimming training sessions. Further works will present the complete device, which allows to track the swimmer while acquiring the images and a more realistic model of conventional active drag ones.
Spatio-Temporal Analysis of Highly Pathogenic Avian Influenza (H5N1) Outbreaks In Poultry In Egypt (2006 To 2017)

Background: In Egypt, the highly pathogenic avian influenza (HPAI) subtype H5N1 is endemic and possesses a severe impact on the poultry. To provide a better understanding of the distributional characteristics of HPAI H5N1 outbreaks in Egypt, this study aimed to explore the spatiotemporal pattern and identify clusters of HPAI H5N1 outbreaks in Egypt from 2006 to 2017. Results: The Epidemic curve (EC) was constructed through time series analysis; in which six epidemic waves (EWs) were revealed. Outbreaks mainly started in winter peaked in March and ended in summer. However, newly emerged thermostable clades (2.2.1.1 and 2.2.1.2) during the 4th EW enabled the virus to survive and cause infection in warmer months with a clear alteration in the seasonality of the epidemic cycle in the 5th EW. The endemic situation became more complicated by the emergence of new serotypes. As a result, the EC ended up without any specific pattern since the 6th EW to now. The spatial analysis showed that the highest outbreak density was recorded in the Nile Delta considering it as the 'Hot spot' region. By the 6th EW, the outbreak extended to include the Nile valley. From spatiotemporal cluster epidemics, clustering in the Delta was a common feature in all EWs with primary clusters consistently detected in the hot-spot region, but the location and size varied with each EW. The highest Relative Risk (RR) regions in an EW were noticed to contain the primary clusters of the next EW and were found to include stopover sites for migratory wild birds. They were in Fayoum, Dakahlia, Qalyobiya, Sharkia, Kafr_Elsheikh, Giza, Behera, Menia, and BeniSuef governorates. Transmission of HPAI H5N1 occurred from one location to another directly resulted in a series of outbreaks forming neighboring secondary clusters. The absence of geographical borders between the governorates in addition to non-restricted movements of poultry and low vaccination and surveillance coverage contributed to the wider spread of infection all over Egypt and to look like one epidemiological unit Conclusion: Our findings may be helpful to better understand the characteristics of HPAI H5N1 outbreaks and the distribution of outbreak risk, which can be used for effective disease control strategies.
The complete chloroplast genome sequence and phylogenetic analysis of Nanhaia speciosa (Fabaceae)

Abstract Nanhaia speciosa (Fabaceae) is a woody perennial vine used as an important traditionally Chinese medicine. In this study, the complete chloroplast genome of Nanhaia speciosa was sequenced and assembled. The chloroplast genome of N. speciosa was 132,551bp in length including only one copy of the inverted repeat (IR). It encoded a total of 110 genes, containing 76 protein-coding genes, 30 tRNA and 4 rRNA. The overall GC content was 34.1%. Phylogenetic analysis using a matrix of 69 protein-coding genes illustrated that N. speciosa is most closely related to Wisteriopsis reticulata of tribe Wisterieae.
Luminance Level of Histogram-Based Scene-Change Detection for Frame Rate Up-Conversion

Scene change detection is an essential process of frame rate up-conversion (FRUC). The performance of FRUC highly dependents on the accuracy of scene change detection. This paper proposes a new scene-change detection method that uses analysis of luminance level of the histograms for FRUC. The histogram luminance level refers to the statistical average luminance value obtained from the generated histograms for each region. Existing histogram-based scene change methods calculate the difference between optimal threshold values using an automatic thresholding technique or extract the difference between the histogram shape to detect the scene change. The automatic thresholding method uses iterative operations—the difference between the histogram shape is simply a method of calculating the luminance difference for the current and previous frames. Thus, it requires many computational resources and incorrectly detects a scene change because calculating the histogram shape cannot reflect regional image characteristics. The proposed method addresses these problems using histogram luminance levels for each region in the given frames. It calculates the level differences between the previous and current frames to detect the initial scene change regions. Moreover, the proposed method refines the initial scene change regions by analyzing the distribution of surrounding detected regions and uses refinement to enhance scene-change detection accuracy. In the experimental results, the proposed method increased the average F1 score to 0.4816 (a 122.51% improvement) compared with the benchmark methods. The average computation time per pixel of the proposed method also decreased to $13.5323 \,\,\mu \text{s}$ (a 87.06% reduction) compared with the benchmark methods.
IN-SITU MEASUREMENT METHOD OF RADIATION EMISSION BASED ON NON-UNIFORM ARRAY AND ADAPTIVE NOISE CANCELLING

For the coexistence of SUT (System Under Test) radiative emission signal and ambient interference signal, the amplitude of SUT signal will be submerged by the amplitude of interference signal, so it is difficult to accurately measure the amplitude of SUT signal. In this paper, a two-level nested array is used as the receiving array antenna, and the mixed matrix estimation method based on Blind Source Separation (BSS) is used to separate the coherent groups of the signal. Then the Sparse Reconstruction method is used for the DOA (Degree Of Arrival) estimation of each coherent group of the signal. After the DOA information of each signal is obtained, beamforming method is used to form beams of the main channel and auxiliary channel. The beam of the main channel outputs without distortion in the direction of the SUT signal and forms zero traps in the direction of the coherent signals, while the beam of the auxiliary channel forms zero traps in both the direction of the SUT signal and the direction of the coherent signal. The data received by the array are respectively multiplied by the weights of the main channel and auxiliary channel to obtain the output signals of the two channels. The output signals of the two channels are respectively fed into the Adaptive Noise Cancellation (ANC) system, and the ANC method is used to suppress the ambient interference signals and restore the SUT signal. Simulation and experiment results show that this method can accurately estimate DOA of radiation emission signals, effectively suppress ambient signals and restore the signal of SUT in field measurement of radiation emission.
Influence of Structural and Physical Parameters on Working Harmonic of Flux-Modulated Linear Actuator for Energy Storage System Fire Hazard Detection Robot

This paper proposes the design and analysis of a flux modulated (FM) permanent magnet (PM) linear actuator for the long track gantry robot system used to detect fire hazards in an electric energy storage system (ESS). A detailed design framework for the FM-linear actuator inspired by the magnetic gearing effect is proposed which includes the selection of slot-pole pair numbers and assigning optimal geometric parameters based on working harmonic characteristics. Using the optimal model, the electromagnetic performances both under no-load and full load conditions are performed to check the feasibility of the actuator for the gantry system. A prototype is built to validate the results obtained using the 2D and 3D finite element analysis (FEA).
Improved Model Predictive Control With Signal Correction Technique of LC-Coupling Hybrid Active Power Filter

In this article, an improved model predictive control (MPC) with signal correction technique for an LC-coupling hybrid active power filter (LC-HAPF) is proposed, which can track the reference compensating current with fast dynamic response and low steady-state error. The conventional MPC causes a certain degree of steady-state error due to the error from discretization and prediction. To solve this problem, a signal correction technique is proposed to improve the compensation performance of the MPC for the LC-HAPF. First, the model of the LC-HAPF for implementing the MPC is deduced in this article. Then, the control objective and the design methodology of the improved MPC are presented. After that, the stability and parameter design of the improved MPC is analyzed and discussed. Finally, the compensation performance of the proposed improved MPC for the LC-HAPF is verified by simulation and experimental results in comparison with the conventional MPC and other representative current controllers.
Robust Fault Detection for Hydraulic Servo System Using Disturbance Compensation and Adaptive Threshold

The paper studies model observer based robust fault detection system for an aircraft hydraulic servo system, presents the disturbance compensation and adaptive threshold design methods, and enhances the robustness and sensitivity using them. Simulations demonstrate that the detection method proposed can robustly and rapidly detect the small abrupt and slow drift faults of the servo system.
Analysis techniques to estimate the overhead energy for industrial facilities and case studies

In this study, four important techniques for estimating the non-process energy (heating, cooling, lighting and ventilation) in industrial and manufacturing buildings were examined. The building energy data for two industrial facilities were collected and analysed. The building non-process energy includes lighting, heating, cooling and ventilation. The power intensity (kW/m2) for each energy type use was estimated using two or more methods (direct measurement of non-process energy, direct measurement of process energy, regression and simulation) and then analysed. The results of these estimates were close to each other which indicate that any method can be used based on available information. This non-process energy needs to be clearly defined to allow more quantitative improvements. The utilization of percentage of non-process energy in industrial and manufacturing plants is an important potential source of energy and cost improvements. Previous analysis of industrial energy use often expressed non-process energy as a percentage of total energy, but without clear values of actual non-process energy. This information is a low value since the actual non-process energy is then dominated by the dominator which is unspecified.
Modelling by Simulations Monte Carlo of First Historical Zika Outbreak in Salta, Argentina, Occurred in 2017

Scopes and Objectives: We analyzed some aspects of the first historical outbreak of Zika that occurred in Salta, Argentina, in the year 2017. We obtained elementary estimates, such as the prevalence ratio and describe the probabilistic behavior of the outbreak by simulation of type Monte Carlo. Study Design: Retrospective-descriptive studies and stochastic computational experiment analysis. Place and Duration of Study: Department of Mathematic, Faculty of Exact Sciences. National University of Salta, Argentina, from December 2020 to September 2021. Methodology: Descriptive and computational experiment analysis. Estimates of parameters and Simulation of type Monte Carlo. Results: We describe the probabilistic behavior through Monte Carlo simulation of the first historical outbreak of Zika in Salta Argentina, 2017. Based on the data of registered Zika cases, we estimate a probabilistic model for it. We also spatially describe the outbreak by estimating the prevalence ratio. Finally, by computational experiment we generate epidemic outbreaks with 10, 20 and 30 runs, determining the intrinsic growth rate and estimating the basic reproductive number R0 for a generation time that takes into account both man and mosquito. We find that the estimates are significantly affected for the simulation type factor of 10 runs. The computational experiment shows that the descriptions of the outbreak and the estimates of R0 obtained, if the number of repetitions of the experiment corresponds to 20 and 30 runs, are qualitatively acceptable.
Modulation Recognition of Underwater Acoustic Signals using Deep Hybrid Neural Networks

It is a huge challenge for the receiver to correctly identify the modulation types due to the complex underwater channel environment and severe noise interference. Additionally, the real-time communications have strict requirements in terms of time. In order to solve this well-known issue, in this work, we combine the automatic feature extraction and learning ability of recurrent neural network (RNN) and convolutional neural network (CNN) for designing a modulation recognition model for underwater acoustic signals. The proposed model is based on deep hybrid neural networks called recurrent and convolutional neural network (R&CNN). As compared with the traditional modulation recognition techniques, this method achieves higher recognition accuracy without manual feature extraction. The experimental results show that the validation accuracy of the proposed R&CNN’s on the Trestle data set is 98.21%. Similarly, the validation accuracy of the proposed R&CNN’s on the South China Sea data set is 99.38%. The average recognition time is 7.164ms. As compared with the conventional deep learning methods, the proposed R&CNN not only has a higher recognition accuracy, but also greatly reduces the recognition time.