Miracle-dz/UniFAR-FTUs huggingface dataset:
Infecundity, infertility, and childlessness in developing countries

For many couples the inability to bear children is a tragedy. The conflux of personal interpersonal social and religious expectations brings a sense of failure loss and exclusion to those who are infertile. Relationships between couples can become very strained when children are not forthcoming. One partner may seek to blame the other as being defective or unwilling. Socially most societies are organized especially in the developing countries such that children are necessary for care and maintenance of older parents. Even in developed countries with social support systems children and family are expected to provide much of the care for the elderly. Childless couples are also excluded from taking leading roles in important family functions and events such as birthdays christenings confirmations bar mitzvahs and weddings of their children. Moreover many religions assign important ceremonial tasks to the couple’s children. The incidence of infertility in a population has important demographic and health implications as well. Because high infertility has a dampening effect on overall fertility and the rate of population growth improvements in the ability to bear children may impede efforts to lower the fertility rate. For example it has been estimated that a reduction in infertility in sub-Saharan Africa to “normal” levels would increase fertility in that region by 15 percent. Similarly Bongaarts Frank and Lesthaeghe (1984) found that infertility accounts for 60 percent of the variation in total fertility in 18 sub-Saharan countries and that fertility decreases by one birth for each increase in 9 percentage points in the proportion of women age 45 to 49 who have no children. In Cameroon a country with an unusually high level of infertility Larsen and Menken estimated that the then current total fertility rate of 5.5 children would rise to 7.3 in the absence of sterility. Reports from Demographic and Health Surveys (DHS) data have provided up-to-date measurements of levels and trends in fertility in conjunction with data on important fertility determinants such as first marriage and union first birth contraception breastfeeding and infant and child mortality. Outside of sub-Saharan Africa no major attention has been paid to the DHS data relating to infecundity although there is no doubt that infecundity is also an important determinant of fertility there. (excerpt)
Beamwidth-Enhanced Low-Profile Dual-Band Circular Polarized Patch Antenna for CNSS Applications

A low-profile dual-band circular polarized (CP) patch antenna with wide half-power beamwidths (HPBWs) is presented for CNSS applications. Simple stacked circular patches are used to achieve dual-band radiation. To enhance the HPBW for the two operation bands, a dual annular parasitic metal strip (D-APMS) combined with reduced ground plane (R-GP) is presented. A single-input feed network based on the coupled line transdirectional (CL-TRD) coupler is also proposed to provide two orthogonal modes at the two frequency bands simultaneously. Experimental results show that the 10 dB impedance bandwidth is 32.7%. The 3 dB axial ratio (AR) bandwidths for the lower and upper bands are 4.1% and 6.5%, respectively. At 1.207 GHz, the antenna has the HPBW of 123° and 103° in the xoz and yoz planes, separately. And the values are 127° and 113° at 1.561 GHz.
Visualization Analysis for Aqueduct Researches Based on CiteSpace

In order to learn the aqueduct researches situation in China, we analyze the published literature about aqueduct from 1988 to 2017 in the CNKI database, and process on the CiteSpace visual analysis software. The scientific knowledge maps are obtained to tease out the changes of the literature about aqueduct researches, the cooperation relationship between authors and the institutions, and the keywords about hot spots. They visually reflect the complex relationships among the knowledge groups of aqueduct researches, such as network, structure and crossover, and clarify the current research situation and development trend of aqueduct researches. This study indicates that there are relatively few research literature about aqueduct; The number of authors researching on aqueduct is large, but the number of prolific ones is relatively small. Meanwhile, the collaboration between authors is relatively dispersive, since it does not form close relation. The distribution of research institutions is centralized which is not beneficial to mutual cooperation between research institutions and research institutions. The analysis of keywords shows that the researches about aqueduct are mainly in the aspects of internal stress, external stress, structure and construction technology.
The effect of green tea and its possible interactions with relevant factors on lung cancer in Dafeng county,Jiangsu province,China

s Objective:To study the effects of green tea and its possible interactions with relevant factors on the risk of lung cancer in Dafeng County,an area with high cancer incidence in Jiangsu Province,China. Methods:A population-based case-control study was carried out in Dafeng Country,523 newly diagnosed lung cancer cases and 1 924 health control subjects were recruited. Epidemiology data were collected by interviews using a standard questionnaire. Unconditional Logistic regression was applied for calculating crude odds ratio(ORCru) and adjusted odds ratio(ORAdj). Results:A decreasing association between regular green tea drinking and lung cancer risk was observed with ORAdj of 0.520(95% CI:0.392～0.691). Moreover,dose-response relationship between the age of first green tea drinking and risk of lung cancer was found(Pfor Trend Adj 0.000 1). There were interactions between regular green tea drinking and smoking history,total cigarettes consumptions(≥ 30 pack·year),alcohol drinking and continual cooking oil exposure,respectively. Furthermore,there were significant interactions between regular green tea drinking and smoking history(ORAdj for interaction:0.575;95% CI:0.425～0.777) and total cigarettes consumptions(≥30 pack·year)(ORAdj for interaction:0.706;95% CI:0.500～0.999),which reduced the risk of lung cancer. Conclusion:Regular green tea drinking could reduce the risk of lung cancer in high cancer incidence areas and could be used for lung cancer chemoprevention.
Filtering approaches for online train motion estimation with onboard power measurements

Future railway systems will need the support of more precise and reliable information on train motion during operation, to fully exploit the potential of new technologies. Train motion information are here intended as time series of train state characteristics, that is, position, speed, and acceleration. This paper investigates the use of two different filtering formulations for better online estimations of train state along the track. Specifically, extended Kalman filters (EKFs) and particle filters (PFs) are used to fuse kinematic measurements collected by means of Global Navigation Satellite Systems (GNSSs) with information collected by the trains on the used tractive power. The EKFs need linearity assumptions and are based on statistical evaluations of the train state, the PFs are instead conceived for nonlinear models and they are based on probability functions related to train state measurements and estimates. A set of experiments with real data of trains operating on a Swiss line is presented to analyze the capabilities of these two filters when applied to rail operation in non‐urban environments. We discuss the extent by which the proposed filtering approaches match the latest technical requirements for implementation. We expect such an enhanced train motion estimation to enable reliable and continuous positioning, available at train level and at the traffic control center, while trackside equipment will be gradually reduced.
Personal navigation system using ultrawideband technology

This paper describes a high-accuracy personal navigation system based on measurements of distances between ultrawideband (UWB) radio modules. The paper contains a description of the physical model of the presented system. Furthermore, positioning algorithms implemented in the proposed system are discussed with emphasis on the Extended Kalman Filter. Subsequently, the process of experiments is described, and chosen results are given. Streszczenie. W artykule przedstawiono system nawigacji personalnej o wysokiej dokładności pozycjonowania, oparty na pomiarach odległości pomiędzy modułami ultraszerokopasmowymi (UWB). Artykuł zawiera opis fizycznego modelu systemu. Ponadto, omówione zostały algorytmy pozycjonujące zaimplementowane w systemie, przy czym najwięcej uwagi poświęcono rozszerzonemu filtrowi Kalmana. Następnie, omówiono przebieg badań oraz podano ich wybrane wyniki. (System nawigacji personalnej wykorzystujący technikę ultraszerokopasmową).
Characteristic mode analysis and design of a slot‐loaded low‐profile wideband microstrip patch antenna

In this letter, a slot‐loaded low‐profile wideband microstrip patch antenna is presented and analyzed. The theory of characteristic modes (TCM) is applied to study the bandwidth and the radiation performance of the proposed antenna. We find that the resonant frequencies of two characteristic modes, with similar characteristic current distributions and far field radiation patterns, are close to each other. By exciting these two modes simultaneously, the proposed microstrip patch antenna attains wide band properties. In addition, the radiation patterns are stable over the whole passband, which is consistent with the prediction by the TCM. The measurements reveal that the antenna's operating frequency ranges from 2.275 to 2.705 GHz with standing wave ratio less than 2, which exhibits a wide impedance bandwidth of 17.3%. The thickness of the proposed antenna is only 0. 04λ0. The realized gain is stable and varies between 8.2 and 9.7 dBi within the whole passband. Consequently, the TCM shows potential applications in the area of the design of a high‐performance slot‐loaded microstrip patch antenna.
A New Error Estimator for Reduced-Order Modeling of Linear Parametric Systems

Efficient error estimation is important for reliable reduced-order modeling with guaranteed accuracy. We propose an error estimator for reduced-order modeling of linear parametric dynamical systems. The error estimator estimates the error of the reduced transfer function in the frequency domain and can be easily extended to the output error estimation of the reduced-order models (ROMs) for linear steady parametric systems. It is tight and cheap to compute. Using the error estimator, the ROM can be adaptively obtained with high reliability. Numerical results show that the error estimator can accurately estimate the true error even for transfer functions with many resonances. Compared with an existing error bound, the proposed error estimator can be orders of magnitudes sharper and needs much less computational time.
A Self-Starting Class-E Power Oscillator With an Inverting Gate Driver

This article presents a simple Class-E power oscillator (PO) which autonomously starts oscillating without requiring an additional start-up circuit. This has been achieved through the use of an inverting gate driver that drives the switching transistor. A resistor, connecting the input to the output of the gate driver, biases the driver at its switching threshold voltage, at which it has high ac gain. In addition, the inverting driver introduces a minimum of −180° phase shift. As a result, the Barkhausen oscillation criteria are readily satisfied and the PO starts oscillating autonomously. In order to allow the use of an inverting gate driver, a new and simple feedback network, consisting of only two resistors and a capacitor, is proposed. Due to the feedback network being a low-Q nonresonant circuit, the efficiency and the output power of the proposed circuit have negligible sensitivities to the variations in the components. A semianalytical design procedure is also presented for the proposed circuit. A prototype circuit was built and tested to prove the proposed concepts. At VDD = 4.5 V, the measured output power, efficiency, and oscillation frequency of the prototype PO are 1.02 W, 92%, and 800 kHz, respectively.
Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.
Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.
Technologies and Fabrication of Intelligent Packaging for Perishable Products

The preservation of perishable products to maintain their quality is of paramount importance for food safety and security, and is attracting more attention due to increasing concerns regarding food quality, healthcare, and quality of life. Advances in technology and materials in recent years have led to the development and implementation of intelligent packaging for perishable products that can monitor or even control their quality in a supply chain. In this paper, the techniques used in intelligent packaging (i.e., indicators, sensors, and identification technology) and the major printing methods for fabricating electronics (i.e., inkjet printing, screen printing, and gravure printing) are reviewed with a focus on the packaging of perishable products. Although the high manufacturing costs pose a major challenge the commercialization and large-scale deployment of perishable products, it is expected that the technological progresses in printing electronics will significantly reduce the manufacturing cost of intelligent packaging to a threshold of acceptance by markets. In addition, the broad applications of intelligent packaging can facilitate the traction and monitoring of perishable products for better control of the product quality and improved management of the supply chain.
Reviewing the Role of Mathematical Models in Enhancing Regional Pavement Management

Pavement management refers to a process through which roadway networks or facility maintenance and repairs are planned. According Abdulaziz, Taha, Kenawi and Kamel (2013), the main aim of this process is to ensure that an entire network’s pavement conditions are optimized. According to Aiswarya and Deepthi (2015), pavement management also refers to a program through which the performance and quality of pavements are improved; with Alawi and Rajab (2013) documenting further that the practice strives to ensure that through good management practices costs are minimized.
Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification

Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate different targets effectively by the characteristic information. This paper has constructed the infrared signature model of spatial point targets and obtained the infrared radiation intensity sequences dataset of different types of targets. This paper aims to design an algorithm for the classification problem of infrared radiation intensity sequences of spatial point targets. Recurrent neural networks (RNNs) are widely used in time series classification tasks, but face several problems such as gradient vanishing and explosion, etc. In view of shortcomings of RNNs, this paper proposes an independent random recurrent neural network (IRRNN) model, which combines independent structure RNNs with randomly weighted RNNs. Without increasing the training complexity of network learning, our model solves the problem of gradient vanishing and explosion, improves the ability to process long sequences, and enhances the comprehensive classification performance of the algorithm effectively. Experiments show that the IRRNN algorithm performs well in classification tasks and is robust to noise.
Evaluation Algorithms for Multiple Function of Dispersed Electrical Energ y Storage Systems

– With the increase of electrical consumption and the unbalance of power demand and supply, power reserve rate is getting smaller and also the reliability of the power supply is getting deteriorated. Under this circumstance, the electrical energy storage (EES) System is considered as one of essential countermeasure for demand side management. This paper proposes efficient evaluation algorithms of multiple functions for EES systems, especially the secondary battery energy storage systems, in the case where they are interconnected with the power distribution systems. It is important to perform the economic evaluation for the new energy storage systems in a quantitative manner, because they are very costly right now. In this paper, the multiple functions of EES systems such as load levelling, effective utilization of power distribution systems and uninterruptible power supply are classified, and then the quantitative evaluation methods for their functions are proposed. From the case studies, it is verified that EES systems installed at distribution systems in a dispersed manner have multiple functions involved with direct and indirect benefits and also they can be expected to introduce to distribution systems with respects to economical point of view.
An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method

The zinc ion is the second richest metal ion in organisms. The proteins binding to zinc ions have important biological functions. However, few scholars have integrated the existing tools to predict the zinc-binding sites in proteins. To make up for this gap, this paper combines three well-known prediction tools into an improved model called IBayes_Zinc to predict the zinc-binding sites, and utilizes the advantages of the Bayesian method in handling incomplete or partial missing data. Specifically, the prediction scores of three existing sequence-based prediction tools were adopted, and the missing values were padded, forming an integrated classification tool. Then, the probabilities of positive and negative samples were computed and categorized as the class with higher probabilities. Experiments were conducted on a non-redundant training dataset and an independent testing dataset. The results show that our method surpassed the other three methods by nearly 5–13% in Matthew correlation coefficient (MCC) and outperformed the latter in recall and precision. The research findings promote the detection of zinc-binding sites in protein sequence and the identification of metalloprotein functions.
A DPSK demodulator for biomedical application

In this study, a differential phase‐shift‐keying (DPSK) demodulator, which is implemented by digital circuit, is proposed. A differential voltage clipper is used to generate narrow pulses at the extremum of the received modulated signal. Regarding the DPSK signal, the outputs of the clipper have double frequency. In order to halve the frequency, a frequency divider is applied to get synchronous clock. Two binary counters are used to detect phase variation; the data are recovered after two times sampling. This technique removes the need for voltage controlled oscillator (VCO). The proposed demodulator is postlayout simulated in a 0.18‐μm CMOS process, with a power of 64 μW and active area of 68 × 70 μm2. The demodulator recovers 10 Mb/s data rate at 10‐MHz carrier frequency using a 1.8‐V power supply.
Research on Binocular Visual System of Robotic Arm Based on Improved SURF Algorithm

With the increasingly extensive application of robots in industrial production, visual of the robotic arm has become an important research direction in the field of robots, which is also recognized as believed to widely considered to the most vital directions of modern high-tech development. In the robot vision system, the binocular visual system is most widely applied. When the robotic arm based on binocular vision captures the object, SURF algorithm is adopted to realize feature point extraction and matching of images. Because of the influence of the objective factors, there is false match in the process of SURF matching of feature points. In this paper, SURF algorithm is improved and reverse feature vectors matching strategy is used to initialize the matching points set. Then the RANSAC algorithm is adopted to select the initial matching points set in accordance of the polar geometric model to get the final good match set. The experiment results show that the improved SURF algorithm has higher registration accuracy and faster speed than SURF.