Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio Structures of Compact Quasars with Broad Absorption Lines

Abstract Broad absorption lines (BALs), seen in a small fraction of both the radio-quiet and radio-loud quasar populations, are probably caused by the outflow of gas with high velocities and are part of the accretion process. The presence of BALs is due to a geometrical effect and/or it is connected with the quasar evolution. Using the final release of FIRST survey combined with a catalog of BAL QSOs from SDSS/DR3, we have constructed a new sample of compact radio-loud BAL QSOs, which constitutes the majority of radio-loud BAL QSOs. The main goal of this project is to study the origin of BALs by analysis of the BAL QSOs radio morphology, orientation, and jet evolution using the European VLBI Network (EVN) at 1.6 GHz and the Very Long Baseline Array (VLBA) at 5 and 8.4 GHz.
Well-posedness and energy decay estimates for the damped wave equation with lr localizing coefficient

In a bounded domain Ω, we consider the wave equation with a local dissipation ay′. Tlw function a belongs to with r greater than 2. We prove that the system is well-posed and we also prove the polynomial decay of its energy. The results obtained improve the existing results proved for Key words.wave equation, decay estimates, local dissipation, degenerate dissipation.integral inequalities.
Real-Valued Extended Propagator Method for Estimating DOAs of Noncircular Signals with Polynomial Rooting

A computationally efficient direction-of-arrival(DOA) estimation algorithm for noncircular signals is proposed in this paper.Based on the real-valued extended propagator method and polynomial rooting technique,the algorithm is computationally efficient. Firstly,the real-valued array extension matrix and its covariance matrix are constructed by utilizing the noncircularity of the signals. Secondly,the noise subspace is achieved with extended propagator method without eigendecomposition.Thirdly,the estimated DOAs are obtained by the polynomial rooting method applying to uniform linear arrays.The simulation results indicate that the performance of the algorithm is close to those of Euler-root-MUSIC and NC-root-MUSIC.The analysis of the complexity shows that the algorithm is computationally efficient in comparison to the above mentioned algorithms.The proposed algorithm is of practical value for its good performance and computational efficiency.
KATEGORIZACIJA I ANALIZA SENTIMENTA DOKUMENATA UPOTREBOM VIŠEJEZIČNOG TRANSFORMER MODELA SA VIŠE IZLAZA

U radu je predstavljen model za kategorizaciju i analizu sentimenta teksta na sto jezika upotrebom transformer neuronskih mreža. Takođe je pokazan način za optimizaciju takvog modela postupkom destilacije modela.
Artificial intelligence to predict the need for mechanical ventilation in cases of severe COVID-19

Objective To determinate the accuracy of computed tomography (CT) imaging assessed by deep neural networks for predicting the need for mechanical ventilation (MV) in patients hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019 (COVID-19). Materials and Methods This was a retrospective cohort study carried out at two hospitals in Brazil. We included CT scans from patients who were hospitalized due to severe acute respiratory syndrome and had COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR). The training set consisted of chest CT examinations from 823 patients with COVID-19, of whom 93 required MV during hospitalization. We developed an artificial intelligence (AI) model based on convolutional neural networks. The performance of the AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results For predicting the need for MV, the AI model had a sensitivity of 0.417 and a specificity of 0.860. The corresponding area under the ROC curve for the test set was 0.68. Conclusion The high specificity of our AI model makes it able to reliably predict which patients will and will not need invasive ventilation. That makes this approach ideal for identifying high-risk patients and predicting the minimum number of ventilators and critical care beds that will be required.
Ensemble modeling of black pomfret (Parastromateus niger) habitat in the Taiwan Strait based on oceanographic variables

The location, effort, number of captures, and time of fishing were all used in this study to assess the geographic distribution of Parastromateus niger in the Taiwan Strait. Other species distribution models performed worse than generalized linear models (GLMs) based on six oceanographic parameters. The sea surface temperature (SST) was between 26.5 °C and 29.5 °C, the sea surface chlorophyll (SSC) level was between 0.3–0.44 mg/m3, the sea surface salinity (SSS) was between 33.4 °C and 34.4 °C, the mixed layer depth was between 10 °C and 14 °C, the sea surface height was between 0.57 °C and 0.77 °C, and the eddy kinetic energy (EKE) was between 0.603 °C. According to the statistical findings, SST is merely a small effect compared to SSS, SSC level, and EKE in terms of impacting species distribution. By combining four effective single-algorithm models with no obvious bias, an ensemble habitat model was created. The ranges of 117°E–119°E and 22°N–24°N have the highest annual distributions of S.CPUE and nominal CPUE.
Democratic Confidence Intervals for Adjusted Means and Incidence Rates

This paper presents confidence intervals for adjusted means using linear regression with weighted sum contrasts and applies the method to a study comparing blood lead concentrations among Pattani school children. These intervals are “democratic” in the sense that they compare the mean for each factor level with the overall mean, rather than selecting a referent. Confidence intervals based on negative binomial GLM models with sum contrasts are also recommended for comparing incidence rates for levels of a study factor after adjusting for biases due to associations with covariate factors. This method is applied to the terrorism events in regions of Southern Thailand that occurred over the period from 2004 to 2009.
Retrieving the stellar content in distant starbursts

Abstract In starburst galaxies, the light emitted by the young and massive stars dominates the photon budget along most of the SED and hides the old and intermediate stellar populations. The fraction of old stars and the stellar mass are systematically underestimated by current methods (Wuyts et al. (2009)). We have implemented a new method to retrieve stellar masses and stellar populations in distant galaxies from photometry and spectral features. The method uses a complex SFH description and a new constraint has been introduced: the star-formation rate (SFR).
The “Commitment Trap” Revisited: Experimental Evidence on Ambiguous Nuclear Threats

In this paper, we provide an empirical test for the theoretical claim that ambiguous nuclear threats create a “commitment trap” for American leaders: when deterrence fails, supposedly they are more likely to order the use of nuclear weapons to avoid domestic audience costs for backing down. We designed an original survey experiment and fielded it to a sample of 1,000 U.S. citizens. We found no evidence of a commitment trap when ambiguous nuclear threats are made. Unlike explicit threats, ambiguous ones did not generate domestic disapproval when the leader backed down; the decision to employ nuclear weapons led to more public backlash for the leader than being caught bluffing; and the threats did not influence public preference for nuclear use across our scenarios. Our findings contribute to the scholarly literature on nuclear crisis bargaining and policy debates over the future of US declaratory policy.
Estrogen Receptor Knockout Mice and Their Effects on Fertility

Estrogens play a crucial role in sexual development and fertility as well as many other physiological processes, and it is estrogen receptors that mediate the physiological responses. To study the role of the estrogen receptors in these processes, several genetic mouse models have been developed using different strategies, which also in some cases yield different results. Here, we summarize the models that have been made and their impact on fertility in relation to known cases of human estrogen receptor mutations.
Curve Fitting by Regression on Smoothed Singular Vectors

Summary A technique is presented for modeling data from an n-way classification where levels of one factor are quantitative and levels of the other n-1 factors are qualitative. The procedure consists off tting main effiects of and interactions among the qualitative factors, arranging the residuals in a matrix, Z, where levels of the quantitative factor index the columns and the various combinations of levels of the qualitative factors index the rows, and then obtaining a singular value decomposition of Z. The singular vectors associated with the quantitative factor are smoothed by cubic spline smoothing, atad smooth curves arefitted to the original data by regressing the data on the smoothed singular vectors and their interactions with qualitative factors. A formula for the degree of smoothing is suggested which, along with a suitable choice of significance levelfor regressor variable selection, appears to rather consistently result in afitted model with a lack-of-fit mean square approximately equal to the error mean square for the experiment. A 2 x 2 x 11 example (the thirdfactor quantiative) is presented. Mandel (1969) has noted that "the central problem of data analysis is the achievement of just the right balance between prior assumptions on the one hand and the findings based on observation of the data on the other hand. " In a relatively few instances a completely specified mathematical model is available prior to analysis. However, in many cases, the best information available is that contained in the data themselves, and choosing a mathematical function to model the data may serve only to force them into an inappropriate mold. In this paper, we present a technique for modeling data from a n-way classification where levels of one factor are quantitative and levels of the other n-1 qualitative. Our method involves a singular value decomposition, cubic spline smoothing of the singular vectors associated with the quantitative factor, and regression analysis of the data on the smoothed singular vectors. This combination of methods has given us a general approach for fitting em
Modeling and Analysis of Spike Signal Sequence for Memristor Crossbar Array in Neuromorphic Chips

This paper presents the efficient systematic methods for modeling and analysis of spike signal sequence in crossbar arrays for neuromorphic computing chips. A novel spike signal sequence is proposed, where the ideal spike sequence with only spike time information in the original spiking neural network (SNN) algorithm is mapped onto actual spike waveform by stitching neighboring sequential spikes together with certain overlaps. We thoroughly investigate and analyze the performance of the input encoding as well as the implementation of spike timing dependent plasticity (STDP)-based SNN on memristor crossbar arrays with the proposed spike signal sequence. A detailed circuit model of a crossbar array, consisting of resistance, capacitance and inductance derived by the partial equivalent element circuit (PEEC) method, is created to simulate the training process of SNN. The proposed spike signal sequence is demonstrated that is able to achieve accurate input encoding as well as high recognition accuracy when it is used to perform the classification task on MNIST handwritten digits. The spike signal sequence is further analyzed and assessed in terms of the main factors affecting its encoding accuracy and the parasitic effects of crossbar arrays on its robustness.
Regenerative Potential of Granulation Tissue in Periodontitis: A Systematic Review and Meta-analysis

Methods Electronic searches were conducted in five databases including CENTRAL, MEDLINE, EMBASE, Web of Science, and Dentistry & Oral Sciences Source using a combination of MeSH terms and keywords up to 21 June 2022. Human studies including patients aged over 18 years with all forms of periodontitis were included. Following the risk of bias assessment, both qualitative and quantitative analyses were performed. Results A total of twelve studies were included in qualitative analysis and six of them in quantitative analyses. The evidence suggested that cells derived from periodontitis granulation tissue have osteogenic, adipogenic, chondrogenic, neurogenic, and angiogenic differentiation abilities as well as immunoregulatory properties. In particular, CD44+, CD73+, CD90+, CD105+, and CD146+ cells were found widely in granulation tissue whilst the only meta-analysis confirmed that CD90+ cells were present in lower numbers within the granulation tissue when compared with healthy periodontal tissue (WMD = −23.43%, 95% CI -30.43 to -16.44, p < 0.00001). Conclusions This review provided further evidence that granulation tissue from patients with periodontitis can be a potential stem cell source for regenerative therapy.
Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.
ADHDCoach—a virtual clinic for parents of children with ADHD: Development and usability study

Attention-deficit/ hyperactivity disorder (ADHD) is one of the most common mental health problems in childhood. Despite the fact that evidence-based treatments exist, behavioral parent training programs are the gold standard in the care of children with ADHD, a significant percentage of parents of children with ADHD do not access such interventions. Internet-delivered interventions are effective for a range of mental health problems, however, there is limited research conducted on the efficacy of such interventions in the treatment of ADHD. Objective: The aim of this study is to present the development and feasibility of an Internet-delivered intervention for parents of children with ADHD. Methods: The intervention was based on Behavioral Parent Training and Rational Emotive Behavior Therapy. Participants were mental health specialists (N = 16) and parents of children diagnosed with ADHD (N = 24). Results: Our results indicated high usability and parental satisfaction with the intervention. Conclusion: In conclusion, an Internet-delivered intervention addressed to parents of children diagnosed with ADHD is a promising approach. Future research should investigate the efficacy of this Internet-delivered intervention in a randomized controlled trial.
High-Resolution Slow-Light Fiber-Bragg-Grating Microphone and Hydrophone

This article reports a slow-light fiber-Bragg-grating (FBG) pressure sensor that functions both as a microphone and a hydrophone and pushes the limit of acoustic pressure resolution of conventional FBG sensors operated without external diaphragms. The sensor utilizes a narrow slow-light resonance to enhance its optical response (greater transmission change per wavelength shift), a soft polymeric coating to enhance its acoustic response (greater wavelength shift per pressure), and an ultranarrow linewidth laser to reduce its laser frequency noise (which often dominates in such sensors). The coated fiber microphone exhibits an average resolution of <inline-formula> <tex-math notation="LaTeX">$\sim 210 ~\mu $ </tex-math></inline-formula>Pa/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz (100 Hz–10 kHz), which is ~6 times better than the previous record. When used as a hydrophone, the resolution is <inline-formula> <tex-math notation="LaTeX">$\sim 880 ~\mu $ </tex-math></inline-formula>Pa/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz (1–100 kHz), which is better than the previous record for a passive FBG hydrophone by a factor of ~23 over the same acoustic frequency range. The wavelength shift per unit pressure is larger for the coated FBG than for the bare FBG by a measured factor of ~16 in air and ~3.7 in water. This last value differs markedly from a published ~300-fold enhancement using a conventional FBG hydrophone coated with the same polymer. It is shown that most of this discrepancy is due to a referencing error in the publication that reported this improvement. With further improvement, slow-light FBGs could be utilized for high-resolution applications, such as in the deep sea and seismic research.
Studying the shelf life of butter containing fucoidan, by evaluating sensory and chemical properties

Abstract Fucoidan powder was added in amounts of 0.05, 0.1,0.3, and 0.5% to sour cream butter and sensory and chemical properties were tested on their shelf life for 60 days during storage. Peroxide levels initially increased until day 40 of storage and then decreased. Butter samples from the control group had the highest amount of peroxide on day 40 (15.25 ± 1.41 meq/kg butter), while samples treated with fucoidan 0.5% had the lowest amount of peroxide (6.35 ± 0.53 meq/kg butter). The acidity of butter treatments increased during storage (p < .05). Butter samples from the control group had the highest acidity at 60 days of storage (0.40 ± 0.033 mg KOH / g butter), while samples treated with 0.5% fucoidan had the lowest acidity (0.17 ± 0.013 mg KOH / g butter). The treated butter samples showed the highest stability. Fucoidan, as an antioxidant, reduces the taste, odor, and discoloration of butter added with fucoidan during storage because it completely removes odorless tasteless powder, and the free radical chain is involved in oxidation and improves product properties. The results showed that there are no significant changes in the acceptance rate of butter treated with fucoidan during 60 days of storage in the refrigerator (p > .05). The sensory scores of the treated butter showed that the sensory properties during the storage period were similar to the control samples, but on day 40 of storage, they decreased. In general, a concentration of 0.5% fucoidan delays the oxidative process and increases shelf life and is selected as a superior treatment in terms of sensory evaluation, and is introduced as a functional food.
Strain and Pressure Sensors Based on MWCNT/PDMS for Human Motion/Perception Detection

Flexible wearable devices have attracted wide attention in capacious fields because of their real-time and continuous monitoring of human information. The development of flexible sensors and corresponding integration with wearable devices is of great significance to build smart wearable devices. In this work, multi-walled carbon nanotube/polydimethylsiloxane-based (MWCNT/PDMS) resistive strain sensors and pressure sensors were developed to integrate a smart glove for human motion/perception detection. Firstly, MWCNT/PDMS conductive layers with excellent electrical and mechanical properties (resistivity of 2.897 KΩ · cm, elongation at break of 145%) were fabricated via a facile scraping-coating method. Then, a resistive strain sensor with a stable homogeneous structure was developed due to the similar physicochemical properties of the PDMS encapsulation layer and MWCNT/PDMS sensing layer. The resistance changes of the prepared strain sensor exhibited a great linear relationship with the strain. Moreover, it could output obvious repeatable dynamic response signals. It still had good cyclic stability and durability after 180° bending/restoring cycles and 40% stretching/releasing cycles. Secondly, MWCNT/PDMS layers with bioinspired spinous microstructures were formed by a simple sandpaper retransfer process and then assembled face-to-face into a resistive pressure sensor. The pressure sensor presented a linear relationship of relative resistance change and pressure in the range of 0–31.83 KPa with a sensitivity of 0.026 KPa−1, and a sensitivity of 2.769 × 10−4 KPa−1 over 32 KPa. Furthermore, it responded quickly and kept good cycle stability at 25.78 KPa dynamic loop over 2000 s. Finally, as parts of a wearable device, resistive strain sensors and a pressure sensor were then integrated into different areas of the glove. The cost-effective, multi-functional smart glove can recognize finger bending, gestures, and external mechanical stimuli, which holds great potential in the fields of medical healthcare, human-computer cooperation, and so on.
First-passage failure of single-degree-of-freedom nonlinear oscillators with fractional derivative

The first-passage failure of single-degree-of-freedom nonlinear oscillators with fractional derivative under Gaussian white noise excitations is studied. First, the term associated with fractional derivative is approximately equivalent to amplitude-dependent quasi-linear damping and stiffness forces by using the generalized harmonic balance technique and the given system is replaced by an equivalent nonlinear stochastic system without fractional derivative. Then, the equivalent nonlinear stochastic system state is approximately represented by a one-dimensional diffusive process through stochastic averaging. The backward Kolmogorov equation governing the conditional reliability function and the Pontryagin equation governing the conditional mean of first-passage time are established from the averaged Itô equation of the total energy and solved numerically, respectively. Finally, two examples are worked out in detail and the analytical results are validated by those from the Monte Carlo simulation of original systems.