Miracle-dz/UniFAR-FTUs huggingface dataset:
Soy isoflavones ameliorate the cognitive dysfunction of Goto-Kakizaki rats by activating the Nrf2-HO-1 signalling pathway

Soy isoflavones (SIF) are soybean phytochemicals that are considered to be biologically active components that protect from neurodegenerative diseases. In this study, the therapeutic effect of SIF was evaluated in a diabetic Goto-Kakizaki (GK) rat model. Twenty male GK rats were randomly divided into diabetes mellitus (DM) model group and SIF+DM group (n=10 in each group). Twenty age-matched male Wistar rats were randomly divided into control group (CON group) and CON+SIF group, with 10 rats in each group. The learning and memory functions of the animals were determined by the Morris water maze (MWM) test. Hematoxylin-eosin staining (HE) was performed to examine pyramidal neuron loss in the CA1 area of the hippocampus. Markers of oxidative stress (OS) were measured to evaluate oxidative stress-mediated injury. RT-PCR and western blotting were used to analyze the expression of nuclear factorerythroid2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1) and NAD(P)H dehydrogenase quinone1 (NQO1). Treatment with SIF for 4 weeks alleviated the cognitive dysfunction of the GK rats as determined by the MWM test. Moreover, SIF treatment also reduced diabetes-related oxidative reactions. In addition, SIF enhanced the expression of Nrf2, HO-1 and NQO1, suggesting a potential antioxidation mechanism for the effect of SIF. These findings suggest that SIF can be considered candidates for inhibiting the progression of diabetes-induced cognitive dysfunction, provide novel insights into the antioxidant effect of SIF and further strengthen the link between oxidative stress and diabetes.
Implanted blood vessel external support device (VasQ™) for creation of hemodialysis arteriovenous fistula: A single-center experience

Introduction: the external support device VasQ is intended to promote arteriovenous fistula (AVF) maturation by maintaining the optimal anastomotic angle in order to minimize blood flow disturbances around the anastomotic area. The aim of the study is to assess efficacy and safety of the VasQ device both in brachiocephalic and radiocephalic fistulae. Methods: a single institution prospective study was conducted with implantation of the VasQ device during AVF creation. Clinical and Doppler ultrasounds evaluations were performed at day-1, 1, 6, and 12 months for assessment of device-related adverse events, AVF maturation and patency. Moreover, volume flow rate and diameter of outflow vein were measured. A total of 16 patients were enrolled. Ten brachiocephalic and 6 radiocephalic AVFs were created with VasQ. Preoperatively cephalic vein diameter was 3.6 ± 0.9 mm. Results: our population included 13 male and 3 females patients, 9 end stage kidney disease in conservative therapy, 4 dialysis treated patients, and 3 transplanted patients; mean age was 74.0 ± 8.1 years; no severe device-related adverse events were observed. Primary patency at 1, 6, and 12 months was 100%, 87.5%, and 67.7%, respectively, while secondary patency was 100%, 100%, and 78.3%, respectively. Comparing brachiocephalic to radiocephalic AVFs no significant differences in patency rates were seen. Overall maturation rate was 94% (15/16). Mean vein diameter measured with Doppler ultrasound at postoperative day-1 and at 1, 6, and 12 months was 5.0 ± 1.0, 5.9 ± 0.9, 7.2 ± 1.6, and 7.9 ± 1.4 mm, respectively, with a mean flow rate at the brachial artery of 841 ± 176, 1052 ± 224, 1261 ± 490, and 1348 ± 477 ml/min, respectively. Conclusions: in our limited experience VasQ was safe, with high maturation and patency rates. Positive results suggest a potential benefit for VasQ in AVF.
Circularly Polarized Folded Transmitarray Antenna for Multi-beam Applications

A circularly polarized multibeam folded transmitarray antenna (CP-MFTA) with a lower profile and high gain is proposed in this paper. It has a lower profile than normal reflectarray and transmitarray antennas as well as folded reflectarray antennas, with linearly-polarized (LP) sources and circularly-polarized radiation beams. As an example, a CP-MFTA operating at 29 GHz is designed and simulated. The numerical results successfully demonstrate the CP multibeam functionality, with appropriate bandwidths for the impedance matching and the 3-dB axial ratio.
Global Optimization based on Mixed H2 and Hinf Approach for Placement of Piezoelectric Actuators and Sensors on Curved Surfaces in Actively Controlled Structures

The presence of actuators and sensors characterizes inevitably all operating processes in engineering, industry or applied research. Careful selection of the actuator/sensor placement can substantially improve performance of systems and contribute in turn to considerable savings. With respect to suppression of unwanted vibrations in lightweight structures by applying appropriate control, piezoelectric films represent convenient solution since they can be easily integrated with structures and due to their lightweight character they do not influence the overall mass of structures, but may contribute to changing stiffness in accordance with applied control and in that way actively perform adapting the structure’s properties to changing environmental conditions. In this paper a global optimization procedure for placement of piezoelectric actuators and sensors on lightweight structures prone to vibration is presented. Optimization is model-based and assumes software-in-the-loop coupling of finite element numeric software with corresponding programming solutions to provide iterative exploration of a predefined set of candidate locations with respect to defined objective functions. The objective here is defined in terms of H2 and H∞ norms constructed upon the eigenforms of structures, which provides maximization of the control influence or maximal sensing effect. Implementation is illustrated by two examples of structures with curved surfaces – cylindrical and funnel shaped structure.
River Flooding Forecasting and Anomaly Detection Based on Deep Learning

Pluvial floods are rare and dangerous disasters that have a small duration but a destructive impact in most countries. In recent years, the deep learning model has played a significant role in operational flood management areas such as flood forecasting and flood warnings. This paper employed a deep learning-based model to predict the water level flood phenomenon of a river in Taiwan. We combine the advantages of the CNN model and the GRU model and connect the output of the CNN model to the input of the GRU model, called Conv-GRU neural network, and our experiments showed that the Conv-GRU neural network could extract complex features of the river water level. We compared the predictions of several neural network architectures commonly used today. The experimental results indicated that the Conv-GRU model outperformed the other state-of-the-art approaches. We used the Conv-GRU model for anomaly/fault detection in a time series using open data. The efficacy of this approach was demonstrated on 27 river water level station datasets. Data from Typhoon Soudelor in 2015 were investigated by our model using the anomaly detection method. The experimental results showed our proposed method could detect abnormal water levels effectively.
Sum Rate Maximization for UAV-Enabled Wireless Powered NOMA Systems

In this paper, we consider the sum rate maximization problem of uplink non-orthogonal multiple access (NOMA) system with unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT). The sum rate problem is a non-convex problem, which is difficult to handle directly because the energy harvesting time and position of UAV are coupled in the objective function. We propose an iterative algorithm to search the energy harvesting time and the position of UAV. The energy harvesting time problem is solved by the bisection search method. Then, the position optimization problem is solved by the quadratic transform method. Finally, based on the above two methods, we propose a two-tier iterative algorithm to jointly optimizing the energy harvesting time and position of the UAV. Simulation results show that the proposed algorithm has a better performance compared with the benchmark algorithms.
Knowledge Distillation for Real-time On-Road Risk Detection

On-road risk detection is one of the most important applications of Deep Neural Network (DNN). A good on-road risk detection system can accurately detect dangerous obstacles on the road and provides drivers with early warning information to avoid danger. However, current high-precision detection systems usually use a cumbersome DNN, which require a large number of computing resources. Due to the limitations of mobile devices such as limited computing power and power consumption problem, it is difficult to apply the high-precision detection model to such devices for running real-time applications. Therefore a fast and accurate model is needed to solve the on-road risk detection problem for low-cost mobile devices. In this paper, we propose a real-time on-road risk detection framework for the low computational platform. The proposed framework uses a tiny neural network model that improves accuracy by leveraging the knowledge distillation technique. By using the proposed method, we conduct our experiments in a Raspberry Pi using one Intel Neural Compute Stick-2 (NCS-2). The practical results show that our detection system can successfully detect the position and type of road obstacles in real-time while maintaining performance comparable to more complex DNN.
Understanding metal concentration and speciation in motorway runoff

ABSTRACT Although highway runoff has historically been extensively studied, the increasing complexity of stormwater management means that there are still significant gaps regarding the reduction of soluble metals. The work reported in this paper addresses these challenges by analysing the presence and behaviour of iron, copper and zinc in runoff from junction 24 of the M1 motorway in the UK (peak traffic flow: 30,000 vehicles per hour) and comparing it with other urban sources of metals found in the same catchment (a local brook and sewage treatment works). The sampling site included an interceptor and a treatment lagoon and the event monitoring indicated a trend by which the metals did not change their concentration or particulate soluble proportion immediately, hence showing that pre- and post-storm conditions are important factors when analysing the solubility of metals and their behaviour. The data provided further evidence of the important influence of storm characteristics on metal concentrations in highway runoff, in particular the effects of an antecedent dry weather period (ADWP). In addition, this study also helped us to better understand how the release of sodium the application of de-icer for road maintenance in winter affects the availability of zinc. GRAPHICAL ABSTRACT
An Approach of The Analytic Hierarchy Process to Acquire The Weighting Factor of High Voltage Circuit Breaker Health Index

The circuit breaker (CB) health condition greatly affects the reliability of CB, which eventually will affect the reliability of the power system. A method that can be used to determine CB health conditions based on multi criteria is the calculation the health index. The critical issue in developing health index for a circuit breaker is the weighting factor. There are many techniques to formulate the weighting factor, this paper proposes a new model of the circuit breaker health index weighting factor determination by utilizing the Analytical Hierarchy Process (AHP). The weighting factor of the proposed circuit breaker health index model is generated using the AHP based on experts’ interviews and surveys. The result presents that the condition assessment factor is judged to be more important than the profile assessment factor to reflect the circuit breaker health index. The contact resistance is considered as the most significant compared to the insulation quality, leakage current, Open Close Timing on CB, and insulation resistance parameters. The AHP approach has proven to be able to capture both subjective and objective evaluation measures of the circuit breaker health index weighting factor.
A Comparative Analysis of Different Regression Models on Predicting the Spread of Covid-19 in India

According to the World Health Organization (WHO) Situation Reports of Corona Virus Disease(Covid-19), as on 15th May 2020, India has 81,970 totals confirmed cases, 2649 total deaths and is still within the limit of community transmission phase. In this study, we analyze the spread of the disease and the fatalities caused up to 15th May 2020, as per the data obtained. A granular computing based regression model, namely Granular Box Regression is used along with Linear Regression for comparative analysis to study the increase in the number of confirmed cases and deaths based on days and an increase in the number of samples tested per day. A separate analysis is also conducted to evaluate the performance of Polynomial Regression on the same dataset. The performance of the different models has been evaluated using R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Squared Error values.
Least Squares Criterion Adaptive Filtering Algorithms as Tools for Calibration of Arrays with Digital Beamforming

This paper explores the application of the adaptive filtering algorithms, based on Least Squares (LS) criterion for the calibration of the receiving arrays with digital beamforming. The digital beamforming assumes the access to the signals in the array channels as a requirement, which has to be provided if the adaptive signal processing is used. In this case, the adaptive filtering algorithms, based on the minimization of the LS error between the desired and array output signals, are able to estimate the channel parameter spread, which values can be stored and used for correction of the array weights during the beam steering. If a set of steered beam positions is known, for example in scanning arrays, the proposed algorithm allows to create a codebook of the array weights, which ensures the random variation of channel’s parameters compensation and correct beam steering. The architecture of the proposed solution and mathematical description of a Recursive Least Squares (RLS) adaptive filtering algorithm, on which the calibration is based on, are presented. The algorithm uses a dynamic regularization of the array input signal correlation matrix. No specific correlation properties are required for the algorithm training signal. The simulation results demonstrate the ability of the calibration algorithm to compensate the channel’s parameters spread. If the array channel Signal-to-Noise Ratio (SNR) is 0 … 30 dB, then the achieved spread of the calibrated channels gain errors is about –20 dB. The norm of the ideal and calibrated array difference is also about –20 dB. The results are obtained with the Phase Shift Keying (PSK) and Quadrature Amplitude Modulated (QAM) training signals. The ability of the proposed calibration algorithm to operate in interference conditions is also demonstrated
Modeling and Control of $N$-Paralleled Virtual Synchronous Machines in Island Mode

Most of new power sources connected to electrical grids are interfaced by using power electronics converters. These converters can be used to operate parts of the grid as electrical islands and this property has a great potential to increase the system reliability in case of an outage. A comprehensive solution to achieve these goals is to replicate the operation principle of synchronous generators by using the converter control system. This can be done by using a control technique called virtual synchronous machine (VSM), and many alternatives to select the control parameters have already been proposed in the literature. However, a modeling and design procedure to select and analyze a large number of VSMs in island mode has not been studied before. In this work, a method for modeling the parallel operation of $N$ VSMs and designing their control parameters is presented. The proposed design methodology makes it possible to adjust the active and reactive power dynamics accurately according to the number of VSMs. The theoretical developments were validated in an experimental setup, which included four 15 kVA converters operated as VSMs.
Percentiles and Reference Values for Accelerometric Gait Assessment in Women Aged 50–80 Years

Background: The identification of factors that alter postural stability is fundamental in the design of interventions to maintain independence and mobility. This is especially important for women because of their longer life expectancy and higher incidence of falls than in men. We constructed the percentile box charts and determined the values of reference for the accelerometric assessment of the gait in women. Methods: We used a cross-sectional study with 1096 healthy adult women, who were asked to walk a distance of 20 m three times. Results: In all of the variables, a reduction in the magnitude of accelerations was detected as the age of the group advanced. The box charts show the amplitude of the interquartile ranges, which increases as the age of the participants advances. In addition, the interquartile ranges were greater in the variables that refer to the maximum values of the accelerations. Conclusions: The values obtained can be used to assess changes in gait due to aging, trauma and orthopaedic alterations that may alter postural stability and neurodegenerative processes that increase the risk of falling.
Buffer-Aided Cooperative Non-Orthogonal Multiple Access for Downlink Transmission

A downlink buffer-aided cooperative nonorthogonal multiple access (C-NOMA) system is investigated in this paper. Both the direct transmission from the base station to the users and the buffer-aided cooperative transmission between two users are considered. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the system outage probability of the proposed scheme is theoretically derived with a closed-form expression. Furthermore, the full diversity order of three is demonstrated to be achieved if the buffer size is not less than three, which is larger than conventional non-buffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system.
Fennel for Reducing Pain in Primary Dysmenorrhea: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Fennel is used as an alternative treatment for primary dysmenorrhea. This review aims to evaluate the effectiveness and safety of fennel for reducing pain in primary dysmenorrhea. Twenty databases, including English, Korean, Chinese, Japanese, Iranian, and Spanish databases, were searched from inception to 20 October 2020. All randomized controlled trials (RCTs) investigating the effectiveness of fennel for treating primary dysmenorrhea were considered. Two reviewers conducted the data extraction and risk of bias assessment independently. Any discrepancies were resolved through discussion with a third reviewer. A total of 12 studies were included in this review. The pooled results of seven trials showed that the effect of fennel is similar to that of conventional drug therapies in alleviating pain (n = 502, standardized mean difference (SMD): 0.07, 95% confidence interval(CI): −0.08 to 0.21, p < 0.37, I2 = 0%). In comparison with placebo, fennel was seen to have favorable effects on reducing pain in primary dysmenorrhea (n = 468, SMD: −3.27, 95% CI: −5.28 to −1.26, p = 0.001, I2 = 98%). Only three studies assessed adverse events (AEs), and one study reported minor AEs. Although the risk of bias for all the included studies was moderate, potential publication bias was observed due to the presence of a greater number of small studies with favorable effects. This systematic review concludes that fennel is as effective as conventional drug therapies in alleviating pain in primary dysmenorrhea. More studies that include more diverse populations and robust evidence of fennel’s effects will be needed in future research endeavors.
Course Model Research of OBE in “Internet Plus” Era

Aiming at solving the problems of emphasizing theory and neglecting practice in the courses on financial management, this paper introduces the concept of OBE(Outcome Based Education) into the course of financial management in higher continuing education. This paper collects the data of 8 classes have had financial management course by questionnaire survey, and divides the data into two groups for comparative analysis: the traditional group and the OBE group. Our result shows that: (1) the teaching effect under the OBE is significantly different from that of the traditional teaching method; (2) the effect of OBE on students' classroom behavior and learning effect is greater than that of the traditional teaching method.
Vaccines and vitriol: an anthropological commentary on vaccine hesitancy, decision-making and interventionism among religious minorities

Abstract This commentary addresses the issue of vaccine hesitancy and decision-making among religious minority groups in high-income country settings. Recent measles outbreaks have been attributed to lower-level vaccination coverage among religious minorities, which has inspired targeted as well as wholesale public health interventions and legislation in a range of jurisdictions. The commentary takes the case of self-protective ethnic and religious minority groups, especially Haredi or ‘ultra-Orthodox’ Jews in the United Kingdom, to address two key aims. First, this commentary flags how damaging representations of religious minorities in recent measles outbreaks can be avoided by better understanding inner processes of vaccine decision-making and acceptance, which can, in turn, help to address hesitancy sustainably and trustfully. Second, the commentary advocates for addressing vaccine hesitancy as part of a broader re-visioning of public health relations with minority groups. This commentary calls on public health services to improve confidence in childhood vaccinations rather than resorting to compulsory (and coercive) vaccination policies in order to address lower-level vaccination coverage. The commentary signposts how essential it is to carefully navigate relationships with minority groups amidst the new forms of public health preparedness that will emerge from the 2020 Coronavirus pandemic (COVID-19).
Exploring the Role of Contactins across Psychological, Psychiatric and Cardiometabolic Traits within UK Biobank

Individuals with severe mental illness have an increased risk of cardiometabolic diseases compared to the general population. Shared risk factors and medication effects explain part of this excess risk; however, there is growing evidence to suggest that shared biology (including genetic variation) is likely to contribute to comorbidity between mental and physical illness. Contactins are a family of genes involved in development of the nervous system and implicated, though genome-wide association studies, in a wide range of psychological, psychiatric and cardiometabolic conditions. Contactins are plausible candidates for shared pathology between mental and physical health. We used data from UK Biobank to systematically assess how genetic variation in contactin genes was associated with a wide range of psychological, psychiatric and cardiometabolic conditions. We also investigated whether associations for cardiometabolic and psychological traits represented the same or distinct signals and how the genetic variation might influence the measured traits. We identified: A novel genetic association between variation in CNTN1 and current smoking; two independent signals in CNTN4 for BMI; and demonstrated that associations between CNTN5 and neuroticism were distinct from those between CNTN5 and blood pressure/HbA1c. There was no evidence that the contactin genes contributed to shared aetiology between physical and mental illness