Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband High-Gain Circularly Polarized Antenna for Conformal Applications

A broadband high-gain circularly polarized (CP) antenna with low profile for conformal applications is proposed. The antenna is composed of a complete ground plane, a loop patch, a feeding patch, and four parasitic patches. The loop patch with a sequential phase difference of 90<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> is designed to achieve circular polarization resonating in TM<inline-formula><tex-math notation="LaTeX">$_{11}$</tex-math></inline-formula> mode. The four parasitic patches surrounding the loop patch are designed to excite another CP resonant mode, which will widen the bandwidth and improve the gain. To verify the conformal capability of the proposed antenna, the performance under different bending radii is compared and discussed. Finally, the proposed antenna with a profile of 0.054<inline-formula><tex-math notation="LaTeX">$\lambda _{0}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$\lambda _{0}$</tex-math></inline-formula> is the free-space wavelength at the operation frequency of 5.5 GHz) is fabricated and measured. The measured <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>10 dB impedance bandwidth is 34.8%, 3 dB axial-ratio (AR) bandwidth is 20%, and peak gain is 11.2 dBic.
Selective area doping of GaN toward high-power applications

Selective area doping in GaN, especially p-type, is a critical and inevitable building block for the realization of advanced device structures for high-power applications, including, but not limited to, current-aperture vertical electron transistors, junction termination extensions, junction barrier Schottky diodes, junction field-effect transistors (JFETs), vertical-channel JFETs, U-shaped metal–oxide–semiconductor field-effect transistors (U-MOSFETs), and Fin MOSFETs. This paper reviews and summarizes some of the recent advances in the fields of selective area etching and regrowth, ion implantation, and polarity-dependent doping that may lead to the practical realization of GaN-based power devices.
Intraday trend prediction of stock indices with machine learning approaches

Abstract In recent years, as research at the intersection of machine learning and finance has grown, predicting stock price movements has become a particularly intriguing issue. Current research focuses primarily on using historical data of the previous day to predict stock movements for the following day, whereas fewer studies use the trading day’s opening data to predict market movements for the current day. We predict intraday price movements of the SSE-50 (Shanghai Securities 50 Index) using stock market opening data as input. Specifically, decision tree, extreme gradient boosting (XGBoost), random forest, support vector machines (SVM), and long-short-term memory are developed to predict the movements of the SSE-50 index utilizing opening price data of various time intervals. We also design three trading strategies when different time frequencies of data are used. At the same time-frequency, the results demonstrate that SVM with Gaussian and linear kernels outperform others. The forecasting accuracy at 10-min frequency approaches 70%, which is close to the results at longer time intervals, indicating that intraday trend can be determined by opening price fluctuations and the first 10-min data contains sufficient information to predict the trend for the entire trading day. In addition, trading methods based on the forecast of daily, weekly, and monthly SSE-50 price movement outperform buy-and-hold strategies. Daily trading performs better than the other two strategies. The outcomes of this research can expand the use of machine learning in quantitative trading and enrich intraday trading techniques further.
Out of steady state: Tracking canopy gap dynamics across Brazilian Amazon

Canopy gaps are evidence of disturbances on forest landscapes. A forest stand is in constant flux, with long stretches of biomass accumulation punctuated by episodic disturbances. We used multitemporal airborne laser scanning data to compare the gap dynamics of four Amazon forest sites. We assessed gap dynamics over 1.9–3.8 years between 2017 and 2020 at sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon, over areas ranging from 590 to 1205 ha at each site. Gap size ranged from a minimum of 10 m2 to a maximum of about 10,000 m2. We analyzed four stages of gap dynamics: formation, expansion, persistence, and recovery based on two consecutive airborne laser scanning surveys. The gap fraction at our study sites varied between 1.26% and 7.84%. All the sites have similar proportion of gaps among gap size classes. What notably differed among sites was not the gap size‐distribution, but the relative importance of stages of gap dynamics. Expansion and persistence rates ranged from 12 to 118 m2 ha−1. The gap formation rate (formation + expansion) was lower than the recovery rate for three of the four study sites. In contrast, the southeastern site has 1.44 times more area in formation and expansion compared to gap recovery. Over the 2–4 years interval of our study, no site was close to steady state. Multitemporal analyses of large areas over many years are needed to improve our understanding of tropical forest dynamics.
Attempts to Influence the Value of Alcohol by Manipulating Social Influence and Context

Abstract Background : Recent cognitive neuroscience models of value-based decision-making suggest value-based choices for alcohol are sensitive to various inputs, such as context and social influence. In two online experiments, we tested whether manipulating these inputs influenced proxies for alcohol value. Experiment 1 : 157 social drinkers were presented with 4 hypothetical scenarios (drinking alone, with friends who are also drinking, with friends but trying to “cut-down” for health reasons, with friends who aren’t drinking) in a within-subjects design, and completed the Brief Assessment of Alcohol Demand after each as a measure of value. Value for alcohol (number of drinks purchased) was greatest when drinking with friends who were also drinking compared to drinking alone (d = 0.95), friends not drinking (d = 1.49) and friends drinking/health related (d = 1.59). Value for alcohol was also greater when drinking alone compared to with friends who were not drinking (d = 0.55), and also with friends drinking/health related (d = 0.62). Experiment 2 : 241 participants were randomly allocated to see one of four categories of images in a 2 (context: bar vs house) x 2 (social influence: enjoy vs not enjoy) design, before completing a Concurrent Choice Task for alcohol and Visual Analog Scales. There were no significant effects found on either task, both taken as proxies for value. Conclusion : There was inconclusive evidence that the value for alcohol could be manipulated by social context. This could be explained by greater saliency of the manipulation in asking participants to imagine themselves in a hypothetical situation as opposed to presenting images depicting drinking scenarios.
Poverty Reduction through Federal and State Policy Mechanisms: Variation over Time and across the United States

The efficacy of the US antipoverty policy is shaped both by its reliance on categorical sorting and by its decentralized structure. To examine the implications of these features, this study introduces a novel disaggregation of poverty reduction instruments into four mechanisms: federal taxes and federal transfers (centralized) and state taxes and state transfers (decentralized). Using the Current Population Survey’s Annual Social and Economic Supplement data and a sequence-independent decomposition, this analysis assesses the relative effectiveness of the mechanisms at the national level between 1996 and 2016 and across the states in 2016. The study finds that absolute and relative poverty reduction is greater and has increased over time for working-age households with children compared with those without children. We also find cross-state variation in market- and disposable-income poverty and in the poverty reduction attributable to each of the redistributive mechanisms, highlighting the importance of examining poverty and antipoverty policy subnationally.
Decoupling control of asynchronous motor based on feedback linearization theory

For the five-order affine nonlinear model of an asynchronous motor under the d-q rotating coordinate system,a complete linearization method was realized by differential geometry theory and method.And then the state feedback control law was designed to realize the decoupling of both rotational speed and rotor flux subsystems.The state feedback control law was simplified further in order to ensure its realization in practical engineering.The controllers of the linearized rotational speed and rotor flux subsystems were separately designed.The simulation results show that the controllers designed by the proposed method can make the system possess the good dynamic and steady performances.And the high performance control for the asynchronous motor has been realized.
The number of working periods of a repairable Markov system during a finite time interval

In reliability analysis, continuous parameter homogeneous irreducible finite Markov processes are used to model repairable systems with time-independent transition rates between individual states. The state space is then partitioned into the set of up states and the set of down states. The number of completed repair events during a finite time interval is an important (undiscounted) cost measure for such a system; it can be expressed in terms of the number of working periods during the same time interval. This paper derives a closed-form expression for the PMF of this latter quantity. The tool used is a recent result on the joint distribution of sojourn times in finite Markov processes. The MatLab implementation of the Markov model of a 2-unit parallel power transmission system is used to demonstrate the utility of the formula. The quantity examined is the number of completed repairs during a finite time interval. The method is viable in this case whereas the more usual randomization technique is not. >
Research on Customer Retention Prediction Model of VOD Platform Based on Machine Learning

—Advanced wireless technology and smart mobile devices allow users to watch Internet video from almost anywhere. The major VOD platforms are competing with each other for customers, slowly shifting from a "product-centric" strategic goal to a "customer-centric" one. At present, existing research is limited to platform business model and development strategy as well as user behavior research, but there is less research on customer retention prediction. In order to effectively solve the customer retention prediction problem, this study applies machine learning methods to video-on-demand platform customer retention prediction, improves the traditional RFM model to establish the RFLH theoretical model for video-on-demand platform customer retention prediction, and uses machine learning methods to predict the number of customer retention days. The Optuna algorithm is used to determine the model hyperparameters, and the SHAP framework is integrated to analyze the important factors affecting customer retention. The experimental results show that the comprehensive performance of the LightGBM model is better than other models. The total number of user logins in the past week, the length of video playback in the same day, and the time difference between the last login and the present are important features that affect customer retention prediction. This study can help companies develop effective customer management strategies to maximize potential customer acquisition and existing customer retention for maximum market advantage.
A Deep Neural Network Based Wake-After-Sleep-Onset Time Aware Sleep Apnea Severity Estimation Scheme Using Single-Lead ECG Data

Obstructive sleep apnea (OSA) is a prevalent yet potentially severe sleep disorder. Polysomnography (PSG) is most commonly used to assess the severity of OSA. However, there have been numerous studies to find OSA patients more effectively since running a PSG test is expensive and time-consuming. The existing studies, however, raise four major concerns, such as (i) the use of inaccurate sleep time data to calculate the apnea-hypopnea index, (ii) the use of poor preprocessing techniques for real patient clinical datasets, (iii) the lack of multi-stage classification capability, and (iv) the absence of experiments on sufficiently large data sets. To address these concerns, we propose a novel OSA severity classification scheme based on single-lead electrocardiogram (ECG) data, as well as a novel deep learning model, CLNet, to perform apnea/hypopnea and sleep stage classification. By identifying apnea/hypopnea events from a patient’s ECG data and computing AHI using “pure” sleep duration via CLNet, our method improves patient OSA severity degree estimation. CLNet was trained and evaluated using two different real-world datasets containing 286 OSA patient records and a total of 2,155 hours of ECG data. In our experiments, the proposed scheme outperforms existing approaches by up to 10% in total accuracy and AUC on the public PhysioNet dataset. In terms of apnea classification sensitivity, we show that the proposed CLNet model outperforms the state-of-the-art model by up to 41.8% for our clinical dataset. Our scheme can be used as a successful, high-quality pre-screening tool by more effectively prioritizing prospective OSA patients. We will be able to perform PSG on only the most severe patients, saving both time and money. Our algorithms are publicly available on GitHub.
Linguistic Models: Optimization With the Use of Conditional Fuzzy C-Means

Most fuzzy models are just numeric. In this study, we revisit, explore and augment a concept of linguistic models, viz., fuzzy models producing results that are information granules, and, specifically, intervals or fuzzy sets. The proposed architecture is formed by constructing a network of linked fuzzy sets (information granules) ininput and output spaces with the aid of a context-based Fuzzy C-Means clustering method. The user centricity of such clustering method is implied by the explicit formulation of fuzzy sets in the output space. The resulting information granules constructed in the input space are conditioned by the corresponding fuzzy sets in the output space. This arrangement can increase the interpretability of the model and represent the model as a collection of logically arranged associations among information granules. The model's overall design process is discussed along with a detailed algorithmic structure. Its experimental evaluations are provided by using both synthetic and publicly datasets. For the former, the model brings the performance improvement ranging from 91% to 250% over the models with information granules uniformly distributed in output space. For the latter, such improvement ranges from 6% to 94%. Finally, a thorough discussion is provided together with guidelines on how to develop such a linguistic model in different contexts.
Growth, Yield Components, and Yield Parameters of Maize (Zea mays L) as Influenced by Unified Use of NPSZnB Blended Fertilizer and Farmyard Manure

A two-year field experiment was carried out to scrutinize the integrated use of NPSZnB blended fertilizer and farmyard manure (FYM) on maize growth and yield components at Koga and Bachima villages in Mecha district and Geray and Jiga villages in Jabi Tehran district. Factorial combinations have five levels of NPSZnB (100, 150, 200, 250, and 300 kg·ha−1), and four rates of FYM (0, 12, 16, and 20 t·ha−1), plus one blanket recommendation (200 kg·ha−1 DAP and 150 kg·ha−1 Urea). A randomized complete block design with three replications was used to set up the studies. Except for days to silking and leaf area index at both locations, and days to physiological maturity, and ear length at Mecha, main and interaction effects on all parameters were detected at both locations. The only main effect of NPSZnB was detected on the harvest index at Jabi Tehnan. Generally, the results achieved from the interaction effect were better and greater than those obtained from the blanket recommendation although this was not the case for the main effects. Grain yields at Jabi Tehnan and Mecha were 5618.5 and 5421 kg·ha−1, respectively, as a result of the unified use of 250 kg·ha−1 NPSZnB and 20 t·ha−1 FYM and shortened days to 50% tasselling by 3.46 days and delayed 90% physiological maturity by 2.95 days than the blanket recommended fertilizer at Jabi Tehnan. As the main effect, compared to the blanket recommendation, 250 kg·ha−1 at Jabi Tehnan and 300 kg·ha−1 NPSZnB at Mecha reduced the days to 50% silking by 0.225 and 0.292 days, respectively. Contrasted with the conventional recommendation, the application of 300 kg·ha−1 NPSZnB minimizes days to 90% physiological maturity by 0.89 at Mecha, and the rise in FYM level from 0 to 20 t·ha−1 caused 0.832 and 0.279 extra days to reach 50% silking at Jabi Tehnan and Mecha, respectively. The yield had a very strong correlation that is positive with growth and yield components. Economic study: the use of 200 kg·ha−1 NPSZnB with 20 t·ha−1 FYM resulted in the maximum net benefit of 36221.06 and 35431.04 ETB ha−1 at Jabi Tehnan and Mecha, respectively. Thus, 250 kg·ha−1 NPSZnB with 20 kg·ha−1 FYM and 200 kg·ha−1 NPSZnB with no FYM application were the most acceptable rates at both locations with a low cost of production to small-scale farmers. For resource-rich farmers, 200 kg·ha−1 NPSZnB with 20 t·ha−1 FYM was also favorable, with the best net benefit at both locations.
Prediction model for urban multi-attraction tourist flow based on spatiotemporal multi-graph convolution

Accurate tourist flow forecast can provide important support and guarantee for scenic spot staff and tourists to make decisions or plans. Aiming at the current situation that most of the existing forecasting methods are tourist flow forecasting of a single scenic spot and cannot fully mine spatial dependency information between various scenic spots, this paper proposes AMGCN model, a multi-graph convolution-based tourist flow prediction model for multiple scenic spots. First, AMGCN defines different non-Euclidean correlations between attractions, encoded as different graphs respectively, and uses multi-graph convolution to extract spatial correlation features. Then, a channel-wise attention mechanism incorporating global spatial context information is used to improve the performance of a long short-term memory network (LSTM), which is used to learn temporal correlation features. AMGCN is evaluated on a real-world large-scale urban multi-spot tourist flow historical dataset, the results show that the model outperforms the baseline model on regression evaluation metrics such as MAE, RMSE, MAPE, and SMAPE.
Singularité du spectre de l'opérateur de Schrödinger aléatoire dans un ruban ou un demi-ruban

- Let H be the finite différence Schrödinger operator with random potential in a strip of width d. Goldseid has announced in [7] ] that H has almost surely pure point spectrum, but this result has been definitely established only for d = 1. Hère we prove that the spectral measure of H is almost surely orthogonal to any fixed measure on [RL In particular H has almost surely no absolutely continuous spectrum. For that purpose, we adapt a method of Pastur [6] for the case d = 1, which consists in comparing two types of almost sure increase of the images of a vector under the action of a random walk with values in the symplectic group.
Theory of radiative electron attachment to molecules: Benchmark study of CN−

Benchmark study of CN− Nicolas Douguet, Samantha Fonseca dos Santos, Maurice Raoult, Olivier Dulieu, Ann E. Orel1∗, and Viatcheslav Kokoouline, Department of Chemical Engineering and Materials Science, University of California, Davis, CA 95616, USA; Laboratoire Aime Cotton, CNRS/Univ Paris-Sud/ENS-Cachan, Bât. 505, Campus d’Orsay, 91405 Orsay Cedex, France; Department of Physics, University of Central Florida, Orlando, Florida 32816, USA Abstract We have developed an approach based on first-principles to study the process of radiative electron attachment (REA) to linear molecules of astrophysical interest in collisions between the molecules and electrons at energies below 1 eV. The approach is based on accurate ab initio calculations of electronic bound and continuum states of the negative ion. The electronic continuum states are obtained with the complex-Kohn variational method. The benchmark calculation for the REA to the simplest negative ion CN−, which was recently observed in the interstellar medium, has produced a relatively low rate coefficient α(T ) = 7.3 × 10−16 cm3/s at T=30 K. Moreover, our results are shown to agree well with microscopic reversibility applied on a recent photodetachment experiment on CN−. Finally, the study confirms a previous assessment that the CN− ion is unlikely be formed by REA in the interstellar medium.
Laparoscopic radical hysterectomy without uterine manipulator or vaginal tube use

In the Laparoscopic Approach to Cervical Cancer trial, minimally invasive surgery (MIS) has been associated with significantly lower disease-free survival and overall survival rates. The proposed reasons for the increased recurrence and mortality associated with MIS are uterine manipulation, the effect of insufflation gas (CO2), and intracorporeal colpotomy. We applied 2 techniques during surgery to reduce tumor spillage in laparoscopic radical hysterectomy (LRH), which included avoiding using a uterine manipulator and containing the colpotomy using an endoscopic stapler. We aimed to introduce an easy and comfortable traction method with tagged uterine sutures instead of a manipulator or vaginal tube for minimally invasive radical hysterectomy (RH). The patient underwent LRH. After entering the peritoneal cavity, tubal ligation was performed with an endoscopic clip to prevent tumor spillage via the fallopian tubes. Then, the uterine fundus was tied with needle-straightened multifilament Vicryl 2-0, and the tagged uterus was manipulated. Thereafter, pelvic lymphadenectomy was performed before RH. Thereafter, we performed intracorporeal colpotomy by resecting the vagina twice using an endoscopic stapler. Finally, the stapled vaginal stump was resected to retrieve the specimen via the vaginal opening using monopolar scissors after the vagina was washed several times with sterile water. After removing the specimen, the vaginal stump was endoscopically closed with a barbed suture. LRH can be feasibly performed in patients with uterine cervical neoplasm by retracting tagged uterine sutures without the use of a uterine manipulator.