Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-Test Probability Assessment and d-Dimer Based Evaluation in Patients with Previous Acute Aortic Syndrome

Background and Objectives. Acute aortic syndromes (AASs) are emergencies burdened by high morbidity and mortality. Guideline-recommended diagnostic workup is based on pre-test probability assessment (PPA) and d-dimer testing. However, the performance of PPA and d-dimer has never been studied in individuals with previous AAS (pAAS), which represent a challenging population. Materials and Methods. We analyzed a registry of patients with pAAS evaluated in two Emergency Departments (EDs) for suspected novel AAS (nAAS). Enrolment criteria were history of pAAS and the presence of truncal pain, syncope or perfusion deficit. All patients underwent advanced imaging. Clinical data were registered prospectively and PPA was performed by applying the aortic dissection detection (ADD) and an aorta simplified (AORTAs) score. Results. A total of 128 patients were enrolled, including 77 patients with previous Stanford type A aortic dissection and 45 patients with previous Stanford type B aortic dissection. The final diagnosis was nAAS in 40 (31%) patients. Clinical variables associated with nAAS were: aortic valve disease, thoracic aortic aneurysm, severe pain, sudden pain, ripping/tearing pain and hypotension/shock. ADD score ≥ 2 had a sensitivity of 65% and a specificity of 83% for nAAS; AORTAs score ≥ 2 had a sensitivity of 48% and a specificity of 88%. d-dimer (cutoff ≥ 500 ng/mL or age-adjusted cutoff) had a sensitivity of 97% and a specificity of 13%/14.7%, for diagnosis of nAAS. Patients that were candidates for guideline-compliant PPA/d-dimer integrated rule-out were: 5 (4.9%) with ADD ≤ 1/d-dimer and 8 (7.8%) with AORTAs ≤ 1/d-dimer < age-adjusted cutoff. None of them had a nAAS. Conclusions. Patients with pAAS evaluated in the ED for red-flag symptoms showed intermediate-to-high pre-test probability of nAAS. The ADD score had lower sensitivity and specificity than in unselected patients. d-dimer, alone and integrated with PPA, was highly sensitive for nAAS, but very unspecific. PPA/d-dimer integrated strategies are unlikely to significantly reduce the number of patients with pAAS undergoing advanced imaging.
Research on the detection method of alignment beacon based on the spatial structure feature of train bogies

The high-speed train inspection robot needs to perform alignment parking and image acquisition at the position of the train bogie, and reliable alignment identification marks are the basis for the inspection robot to achieve highprecision alignment parking. The existing alignment method is mainly based on the way of artificial beacons, in the inspection workshop through manual laying of additional identification marks such as QR codes, magnetic stripes, RFID electronic tags, auxiliary robot alignment parking, but this type of method in beacon maintenance, laying costs and other aspects have certain limitations. Aiming at the problem, this paper proposes a vision-based bogie beacon detection method, which combines the target detection technology with the unique bogie spatial structure feature of the train, and provides reliable identification marks for the alignment and parking of the inspection robot. This method improves the alignment accuracy, reduces the cost of laying and maintaining artificial beacons, and has high application value.
Vitamin D Status and Psoriatic Arthritis: Association with the Risk for Sacroiliitis and Influence on the Retention Rate of Methotrexate Monotherapy and First Biological Drug Survival—A Retrospective Study

A growing body of evidence on the importance of vitamin D in immune modulation has increased the interest in its possible impact on the course of rheumatological diseases. The scope of our study is to assess if the presence of different statuses of vitamin D could interfere in the clinical subsets, in methotrexate monotherapy discontinuation, and biological drug (b-DMARDs) survival in psoriatic arthritis patients (PsA). We conducted a retrospective study on PsA patients and split them into three groups based on their vitamin D status: the group with 25(OH)D ≤ 20 ng/mL, the group with levels of 25(OH)D between 20 and 30 ng/mL, and the group with serum levels of 25(OH)D ≥ 30 ng/mL. All patients were required to fulfill the CASPAR criteria for psoriatic arthritis and to have the evaluation of vitamin D serum levels at baseline visit and at clinical follow-up visits. The exclusion criteria were ages less than 18 years old, the presence of HLA B27, and satisfaction of rheumatoid arthritis classification criteria (during the study time). Statistical significance was set at p ≤ 0.05. Furthermore, 570 patients with PsA were screened and 233 were recruited. A level of 25(OH)D ≤ 20 ng/mL was present in 39% of patients; levels of 25(OH)D between 20 and 30 ng/mL presented in 25% of patients; 65% of patients with sacroiliitis presented 25 (OH)D ≤ 20 ng/mL. Methotrexate monotherapy discontinuation for failure was higher in the group with 25 (OH)D ≤ 20 ng/mL (survival time: 92 ± 10.3 weeks vs. 141.9 ± 24.1 weeks vs. 160.1 ± 23.6 weeks; p = 0.02) with higher discontinuation risk (HR = 2.168, 95% CI 1.334, 3.522; p = 0.002) than those with 25(OH)D between 20 and 30 ng/mL and those with 25(OH)D ≥ 30 ng/mL. Significantly shorter survival of first b-DMARDs was assessed in the group with 25 (OH)D ≤ 20 ng/mL versus the other groups (133.6 ± 11 weeks vs. 204.8 ± 35.8 weeks vs. 298.9 ± 35.4; p = 0.028) (discontinuation risk 2.129, 95% CI 1.186, 3.821; p = 0.011). This study highlights significant differences in clinical presentation, in particular sacroiliac involvement and on drug survival (methotrexate and b-DMARDs) in PsA patients with vitamin D deficiency. Further prospective studies, including a larger sample of patients, are needed to validate these data and to assess if the supplementation of vitamin D could improve the b-DMARDs response in PsA patients.
Van der Pol networks with symmetry

Concerns the modelling of biological control with rhythm, especially the mammalian circadian rhythm. Van der Pol oscillator networks have been proposed as a template to produce abstract circuit models that are capable of producing complex oscillatory and spiking patterns of behavior. Here, a particular case of a symmetrically coupled network is treated for its global behavior. It is shown that for a certain connection pattern the circuit is capable of producing globally asymptotically stable oscillations.
On the Complexity of the Web’s PKI: Evaluating Certificate Validation of Mobile Browsers

Digital certificates are frequently used to secure communications between users and web servers. Critical to the Web’s PKI is the secure validation of digital certificates. Nonetheless, certificate validation itself is complex and error-prone. Moreover, it is also undermined by particular constraints of mobile browsers. However, these issues have long been overlooked. In this article, we undertook the first systematic and large-scale study of the certificate validation mechanism within popular mobile browsers to highlight the necessity of reassessing it among all released browsers. To this end, we first compile a comprehensive test suite to identify security flaws in certificate validation from various aspects. By designing and implementing a generic, automated testing pipeline, we effectively evaluate 30 popular browsers on two mobile OS versions and compare them with five representative desktop browsers. We found the latest mobile browsers Accept as many as 33.2% invalid certificates and Reject merely 5.4% invalid ones on average, leaving the majority of them to be decided by users who usually have little expertise. Our findings shed light on the severity and inconsistency of certificate validation flaws across mobile browsers, which are likely to expose users to MITM attacks, spoofing attacks, and so forth.
1x2 printed element-based MIMO antenna with UWB and multiband response for airborne and naval radar communication

We have proposed a multiband operated superlative MIMO antenna structure. The simple T-shaped resonator-based design is numerically investigated for the 4 GHz to 20 GHz frequency range. The performance of the design is done in terms of reflectance coefficient, transmission coefficient, radiation pattern, gain, and electric field. The design provides the four-band behaviour. The results represent a minimum S11 of -27.27 dB, a peak gain of 9.66 dB, an E-field of 20041 v/m, and broad directivity. The design is fabricated using the Roger RT Duroid 5880. The presented structure's performance also examines ECC, TARC, DG, and CCL. The reliability of the design is checked by comparison among simulated and measured results. The design helps the airborne, Naval radar and many wireless communication applications.
Global existence of small solutions to semilinear Schroedinger equations

We present global existence theorem for semilinear Schrodinger equations. In general, Schrodinger-type equations do not admit the classical energy estimates. To avoid this difficulty, we use S. Doi`s method for linear Schrodinger-type equations. Combining his method and L{sup p}-L{sup q} estimates, we prove the global existence of solutions with small initial data.
A Predictor-Corrector Method for Multi-objective Optimization in Fair Machine Learning

Issues of fairness often arise in graphical neural networks used for misinformation detection. However, improving fairness can often come at the cost of reducing accuracy and vice versa. Therefore, we formulate the task of balancing accuracy and fairness as a multi-objective optimization (MOO) problem where we seek to find a set of Pareto optimal solutions. Traditional first-order approaches to solving MOO problems such as multi-gradient descent can be costly, especially with large neural networks. Instead, we describe a more efficient approach using the predictor-corrector method. Given an initial Pareto optimal point, this approach predicts the direction of a neighboring solution and refines this prediction using a few steps of multi-gradient descent. We show experimentally that this approach allows for the generation of high-quality Pareto fronts faster than baseline optimization methods.
A New Microstrip UWB Bandpass Filter Using Defected Microstrip Structures

This paper presents a new ultra wideband bandpass filter which is implemented using defected microstrip structures (DMS). This wideband bandpass filter consists of a cascade connection of new bandpass DMS resonators. Transmission line model for proposed resonator is introduced, and detailed analysis using method of lines is investigated. Properly selected impedances of transmission lines achieve broadband filter. The theoretically predicted performance is in good agreement with experimental results.
Prediction of Landslide Deformation Region Based on the Improved S-Growth Curve Model

Quantitative research on and the prediction of a landslide deformation area is an important point to accurately and comprehensively understand the failure mechanism of landslides and the degree of slope failure. This study uses image processing techniques to quantitatively identify the area and volume of deformation regions during rainfall-type landslide destabilization under multifactor conditions. The findings revealed that (1) an increase in rainfall intensity and slope angle, as well as the existence of slope crest, will accelerate the process of slope instability. In our study, when the rainfall intensity was 140 mm/h and the landslide volume reached 35.68%, the landslide failure was the most serious. (2) Slopes with high compaction of subsoil as well as those without perimeter pressure are relatively more damaged. (3) The higher the density of vegetation cover, the stronger the protection ability of the slope, and the higher the wind speed, the greater the failure to the slope. Furthermore, an improved S-growth curve model was proposed to predict landslide volumes in 16 sets of experiments. In detail, the proposed S-growth curve model predicted landslide volumes with an average absolute percentage error of 4.34–16.77%. Compared with the time series analysis moving-average method (average absolute percentage error of 6.39–68.89%), the S-growth curve model not only has higher prediction accuracy but also can describe the three stages of deformation region development from a physical perspective and can be applied to the volume during landslide change prediction.
Sliding mode control for ball mill load based on prescribed performance

To solve the problem of mill current fluctuations caused by sudden input changes in the cement combined grinding system, this paper built a mathematical model of the ball mill load and designed a sliding mode control for ball mill load based on prescribed performance. The prescribed performance formula is introduced into the controller to guarantee the error of mill current can quickly astrict according to the preset curve. Next, the sliding mode surface of mill current error is defined and controller is solved. Through simulation, and by comparing with model-free adaptive sliding control strategy, the designed control strategy can stabilize mill current, reduce adjustment time, and the effectiveness and robustness are verified.
Sexting Behavior Predictors Vary With Addressee and the Explicitness of the Sexts

The aim of this research is to examine associations of adolescent sexting with gender, being in a romantic relationship, and other online or offline sexual behaviors, using the adolescent sexting scale A-SextS as a standardized measure. Data were collected from a sample of 579 Spanish secondary school pupils (47.3% girls), between the ages of 11 and 18 (interquartile range: 13–15 years; median = 14 years). Multinomial regression models revealed that having had sexual intercourse was the most relevant predictor variable, especially for high-frequency sexting. Adolescents involved in a romantic relationship were more likely to engage in high-frequency sexting with someone known in person. Pornography consumption was mainly associated with high-frequency explicit sexting with someone known only on the internet. Lastly, females were more likely to engage in low-frequency, non-explicit sexting as well as high-frequency explicit sexting with someone known only on the internet.
Comparative analysis and application of deep learning polyp detection in colonoscopy

Colonoscopy is the most common method for early diagnosis of colorectal cancer. The traditional detection method mainly relies on manual detection by doctors, and the detection efficiency is low. By contrast, the deep learning method has high detection precision and accuracy, which is an ideal tool for the disease detection of colonoscopy. Based on this, this paper compares the six models of YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, Faster RCNN with and without "fpn" structure, and finds out the most effective colonoscopy polyp detection among the six models. Comparing and integrating various factors, it is found that the YOLOv5l model has the best comprehensive detection effect, and its mAP value is generally higher than 98.5%, which is the largest mAP value among the above six models, and the detection time is relatively short, about 0.018s per image. The balance of accuracy and rapidity in colonoscopy can be achieved.
Effect of coolant pulsation on film cooling performance on turbine vane

Abstract Combined with infrared thermography measurements, large eddy simulation was used to study the square-wave pulsating film cooling on turbine vane at the time-averaged blowing ratios of 0.5 and 1.5. On suction surface, the introduction of coolant pulsation results in the decrease of cooling effectiveness. On pressure surface, coolant pulsation at high blowing ratio can improve coolant coverage. Starting vortex is the typical structure in pulsating film cooling. However, on suction surface, due to fully-turbulent mainstream, plenty of small-scale vortices play a dominating role in flow fields, and the effect of starting vortex is unobvious. The statistic characteristics and power spectrum density (PSD) of velocity fluctuation were also analyzed in detail. The results show that the disturbance of coolant jet on suction-side mainstream boundary layer is very weak, However, on pressure side, coolant pulsation causes notable increase of PSD and turbulent kinetic energy.
Development of Parametric Reinforcement Learning for different operation preferences

With the industry process becoming more and more complex, there are more and more different working conditions and operation policy requirements. Accordingly, operators with different operation preferences are needed in different conditions. Some are conservative and some are aggressive. At the same time, Deep Reinforcement Learning (DRL) is becoming notable for its excellent performance in exploring controlling policies in various kinds of problems in the past few years. However, although DRL can help solve the problem in some condition, it can be difficult for one agent to adapt to all situations, and it is also a waste of time to train a new agent when new operation requirements come. In this paper, a method called Parametric Reinforcement Learning is proposed to solve the problem, using a parameter to represent the policy characteristic of a single agent, so that the target operation can be fitted with some base agents trained ahead of time. Also, Shell benchmark is used to simulate the effectiveness of this method.
A Formal Theory of Knowledge and Action

Abstract : Most work on planning and problem solving within the field of artificial intelligence assumes that the agent has complete knowledge of all relevant aspects of the problem domain and problem situation In the real world, however, planning and acting must frequently be performed without complete knowledge. This imposes two additional burdens on an intelligent agent trying to act effectively. First, when the agent entertains a plan for achieving some goal, he must consider not only whether the physical prerequisites of the plan have been satisfied, but also whether he has all the information necessary to carry out the plan. Second, he must be able to reason about what be can do to obtain necessary information that he lacks. In this paper, we present a theory of action in which these problems are taken into account, showing how to formalize both the knowledge prerequisites of action and the effects of action on knowledge.
Near-Field Metallic Metasurfaces for Enhancing Antenna Capabilities

This paper reviews and highlights the potential of near-field metallic metasurfaces (MMs) in developing high-performance antenna systems. The proposed MMs are realized based on near-field phase translation and successfully employed to enhance the gain and steer the beam in a large 3D conical space. The dielectric-free, low-cost, and lightweight attributes of MMs can be leveraged for usage in strict operating conditions.
Experimental analysis of a broadly tunable InGaAsP laser with compositionally varied quantum wells

Custom-designed InGaAsP lasers have been fabricated, tested, and show a broad spectral output at a bias current of 240 mA. The Fabry-Perot ridge waveguide lasers were grown with one 80-/spl Aring/ and five 100-/spl Aring/ quantum wells in the active region. A different material composition was used for each well and this provided contributions to the gain profile over a broad wavelength range. A 1400-/spl mu/m cavity length laser was found to operate in the spectral region from 1475 to 1650 nm and single-mode operation on the individual Fabry-Perot modes of the uncoated laser was achieved over a 172-nm tuning range using a diffractive optical element short external cavity. The side-mode suppression ratio was measured to be above 30 dB at all wavelengths within the tuning range. Complete spectral coverage, in overlapping short segments, with the device is possible using temperature tuning.
Significant increases in Donghong kiwifruit yield by a novel umbrella-shaped trellis system and identification of associated molecular mechanisms

China is the largest kiwifruit producer in the world, accounting for more than half of the total. However, in terms of yield per unit area, China is much lower than the global average and lags behind that of other countries. Yield improvement is of critical importance for the current kiwifruit industry in China. In this study, an improved overhead pergolas trellis (OPT) system, namely, the umbrella-shaped trellis (UST) system, was developed for Donghong kiwifruit, which is now the second most popular and widely cultivated red-fleshed kiwifruit in China. Surprisingly, the estimated yield on the UST system was more than two times higher than that with a traditional OPT, while the external fruit quality was maintained and the internal fruit quality was improved. One of the mechanisms contributing to the yield improvement was the significant promotion of the vegetative growth of canes at 6 ~ 10 mm in diameter by the UST system. The upper canopy of the UST treatment served as a natural shading condition for the lower fruiting canopy and thus had positive effects on the accumulation of chlorophylls and total carotenoids in the fruiting canopy. The most productive zones on the fruiting canes (6 ~ 10 mm in diameter) contained significantly higher (P < 0.05) levels of zeatin riboside (ZR) and auxin (IAA) and ratios of ZR/gibberellin (GA), ZR/abscisic acid (ABA), and ABA/GA. A relatively high carbon/nitrogen ratio may promote the flower bud differentiation process of Donghong kiwifruit. The outcomes of this study provide a scientific basis for manifold increase in production of kiwifruit and contribute to the sustainability of the kiwifruit industry.
Atmospheric Gravity Wave Detection Using Transfer Learning Techniques

Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy.