Miracle-dz/UniFAR-FTUs huggingface dataset:
IRE: Improved Image Super-Resolution Based on Real-ESRGAN

Image super-resolution (SR) is a research field focusing on image degradation techniques. The High-order Deterioration Model (HDM) implemented in Real-ESRGAN has proven more effective in simulating the degradation of real-world images compared to conventional bicubic kernel interpolation. However, images reconstructed by Real-ESRGAN suffer from two significant weaknesses. Firstly, the rebuilt image is overly smooth and suffers from substantial texture information loss, resulting in a worse performance than classical models such as SRGAN and ESRGAN. Secondly, the reconstructed images exhibit better visualization effects but are entirely different from the original image, violating the principle of image reconstruction. To address these issues, this paper presents an improved image SR model based on the HDM implemented in Real-ESRGAN. The first-order degradation modeling of HDM was removed, and only the second-order degradation modeling was kept to reduce the degree of visual deterioration. PatchGAN was used as the fundamental structure of the discriminator, and a channel attention mechanism was added to the generator’s dense block to enhance texture details in the reconstructed images. The L1 loss function was also replaced with the SmoothL1 loss function to improve convergence speed and model performance. The proposed model, IRE, was evaluated on various benchmark datasets and compared to Real-ESRGAN. The results show that the proposed model outperforms Real-ESRGAN regarding visual quality and measures such as RankIQA and NIQE. The study also indicates that PatchGAN, as the discriminator, reduces the average training time by approximately 28%.
Kinetics of lossy grazing impact oscillators

The equation of motion of a grazing impact oscillator is generalized to include compliant boundaries and impact energy dissipation, yielding the phase diagram, indentation and force. Good agreement with experimental results using tapping-mode atomic force microscopy is demonstrated. The relationship between phase and set-point amplitude is discussed in terms of the dissipation, showing that for a parameter space for which the system is highly nonlinear a chaotic response is possible.
Scaling laws and accurate small-amplitude stationary solution for the motion of a planar vortex filament in the Cartesian form of the local induction approximation.

We provide a formulation of the local induction approximation (LIA) for the motion of a vortex filament in the Cartesian reference frame (the extrinsic coordinate system) which allows for scaling of the reference coordinate. For general monotone scalings of the reference coordinate, we derive an equation for the planar solution to the derivative nonlinear Schrodinger equation governing the LIA. We proceed to solve this equation perturbatively in small amplitude through an application of multiple-scales analysis, which allows for accurate computation of the period of the planar vortex filament. The perturbation result is shown to agree strongly with numerical simulations, and we also relate this solution back to the solution obtained in the arclength reference frame (the intrinsic coordinate system). Finally, we discuss nonmonotone coordinate scalings and their application for finding self-intersections of vortex filaments. These self-intersecting vortex filaments are likely unstable and collapse into other structures or dissipate completely.
Individualizing Fluid Management in Patients with Acute Respiratory Distress Syndrome and with Reduced Lung Tissue Due to Surgery—A Narrative Review

Fluids are the cornerstone of therapy in all critically ill patients. During the last decades, we have made many steps to get fluid therapy personalized and based on individual needs. In patients with lung involvement—acute respiratory distress syndrome—finding the right amount of fluids after lung surgery may be extremely important because lung tissue is one of the most vulnerable to fluid accumulation. In the current narrative review, we focus on the actual perspectives of fluid therapy with the aim of showing the possibilities to tailor the treatment to a patient’s individual needs using fluid responsiveness parameters and other therapeutic modalities.
On Generalized Score Tests

Abstract Generalizations of Rao's score test are receiving increased attention, especially in the econometrics and biostatistics literature. These generalizations are able to account for certain model inadequacies or lack of knowledge by use of empirical variance estimates. This article shows how the various forms of the generalized test statistic arise from Taylor expansion of the estimating equations. The general estimating equations structure unifies a variety of applications and helps suggest new areas of application.
Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.
Cosmological tests of models for the accelerating universe in terms of inhomogeneities

We study cosmological tests of models that can explain the apparent accelerated expansion of the present universe. In this thesis, we provide methods of testing these models by particularly focusing on inhomogeneities of the universe, because, practically, our universe is inhomogeneous. First, we consider the effective gravitational stress-energy tensor for short-wavelength perturbations in modified gravity theories in the cosmological context. We address this problem in a simple class of f(R) gravity theories on the assumptions that (i) the background, or coarse-grained metric averaged over several wavelengths, has the FriedmannLemâıtre-Robertson-Walker symmetry and that (ii) when our f(R) theory reduces to Einstein gravity, the field equations of Einstein gravity should be reproduced. We show by explicit computation that the effective gravitational stress-energy tensor for a cosmological model in our f(R) theories, as well as that obtained in the corresponding scalar-tensor theory, takes a similar form to that in general relativity and is in fact traceless, hence acting again like a radiation fluid as in the case of general relativity. If the assumption (ii) above is dropped, then an undetermined integration constant appears and the resultant effective stress-energy tensor acquires a term that is in proportion to the background metric, hence being, in principle, able to describe a cosmological constant. Whether this effective cosmological constant term is positive and whether it has the right magnitude as dark energy depends upon the value of the integration constant. Second, we discuss temperature anisotropies of cosmic microwave background (CMB) in local void models. We derive analytic formulae for the dipole and quadrupole moments of the CMB temperature anisotropy that hold for any spherically symmetric universe model and can be used to compare consequences of this model with observations of the CMB temperature anisotropy rigorously. We check that our formulae are consistent with the numerical studies previously made for the CMB temperature anisotropy in the void model. We also update the constraints concerning the location of the observers in the void model by applying our analytic dipole formula with the latest WMAP data.
Improving Thermal Conductivity and Tribological Performance of Polyimide by Filling Cu, CNT, and Graphene

The thermal conductivity, mechanical, and tribological properties of polyimide (PI) composites filled by copper (Cu), carbon nanotube (CNT), graphene nanosheet (GNS), or combination were investigated by molecular dynamics simulation (MD). The simulated results suggested that Cu can improve thermal stability and thermal conductivity, but it reduces mechanical properties and tribological properties. CNT and GNS significantly improved the thermal and tribological properties at low content, but they decreased the properties at high content. In this study, the modification mechanism, friction, and wear mechanism of different fillers on polyimide were revealed by observing the frictional interface evolution process from the atomic scale, extracting the atomic relative concentration, the temperature and velocity distribution at the friction interface, and other microscopic information.
Numerical and Experimental Analysis of Drug Inhalation in Realistic Human Upper Airway Model

The demand for a more efficient and targeted method for intranasal drug delivery has led to sophisticated device design, delivery methods, and aerosol properties. Due to the complex nasal geometry and measurement limitations, numerical modeling is an appropriate approach to simulate the airflow, aerosol dispersion, and deposition for the initial assessment of novel methodologies for better drug delivery. In this study, a CT-based, 3D-printed model of a realistic nasal airway was reconstructed, and airflow pressure, velocity, turbulent kinetic energy (TKE), and aerosol deposition patterns were simultaneously investigated. Different inhalation flowrates (5, 10, 15, 30, and 45 L/min) and aerosol sizes (1, 1.5, 2.5, 3, 6, 15, and 30 µm) were simulated using laminar and SST viscous models, with the results compared and verified by experimental data. The results revealed that from the vestibule to the nasopharynx, the pressure drop was negligible for flow rates of 5, 10, and 15 L/min, while for flow rates of 30 and 40 L/min, a considerable pressure drop was observed by approximately 14 and 10%, respectively. However, from the nasopharynx and trachea, this reduction was approximately 70%. The aerosol deposition fraction alongside the nasal cavities and upper airway showed a significant difference in pattern, dependent on particle size. More than 90% of the initiated particles were deposited in the anterior region, while just under 20% of the injected ultrafine particles were deposited in this area. The turbulent and laminar models showed slightly different values for the deposition fraction and efficiency of drug delivery for ultrafine particles (about 5%); however, the deposition pattern for ultrafine particles was very different.
Quasi maximum likelihood estimation for simultaneous spatial autoregressive models

This paper considers the problem of estimating a simultaneous spatial autoregressive model (SSAR). We propose using the quasi maximum likelihood method to estimate the model. The asymptotic properties of the maximum likelihood estimator including consistency and limiting distribution are investigated. We also run Monte Carlo simulations to examine the finite sample performance of the maximum likelihood estimator.
Methods for Computing Wagner Trees

Abstract Farris, J. S. (Biol. Sci., State Univ., Stony Brook, N. Y.) 1970. Methods for computing Wagner Trees. Syst. Zool. , 19:8342. -The article derives some properties of Wagner Trees and Networks and describes computational procedures for Prim Networks, the Wagner Method, Rootless Wagner Method and optimization of hypothetical intermediates ( HTUs). The Wagner Ground Plan Analysis method for estimating evolutionary trees has been widely employed in botanical studies (see references in Wagner, 1961) and has more recently been employed in zoological evolutionary taxonomy (Kluge, 1966; Kluge and Farris, 1969). Wagner Trees are one possible generalization of the most parsimonious trees of Camin and Sokal (1965). The Wagner technique is of con-siderable interest for quantitative evolution- ary taxonomists because it is readily pro- grammable and because the type of tree produced can tractably be extended to ap- plications in a variety of novel quantitative phyletic techniques. In this paper I shall formalize the concept of a Wagner Network and discuss a number of algorithms for calculating such networks. The rationale for the methods described will not be treated extensively here, as it is published elsewhere (Kluge and Farris, 1969).
Generating Tensor Representation from Concept Tree in Meaning Based Search

Generating Tensor Representation from Concept Tree in Meaning Based Search. (May 2010) Jagannath Panigrahy, B.Tech., National Institute of Technology, Tiruchirapalli, India Chair of Advisory Committee: Dr. Rabi N Mahapatra Meaning based search retrieves objects from search index repository based on user’s search Meanings and meaning of objects rather than keyword matching. It requires techniques to capture user’s search Meanings and meanings of objects, transform them to a representation that can be stored and compared efficiently on computers. Meaning of objects can be adequately captured in terms of a hierarchical composition structure called concept tree. This thesis describes the design and development of an algorithm that transforms the hierarchical concept tree to a tensor representation using tensor algebra theory. These tensor representations can capture the information need of a user in a better way and can be used for similarity comparisons in meaning based search. A preliminary evaluation showed that the proposed framework outperforms the TF-IDF vector model in 95% of the cases and vector based conceptual search model in 92% of the cases in adequately comparing meaning of objects. The tensor conversion tool also was used to verify the salient properties of the meaning
Erratum: “High-Mass Protostellar Candidates. II. Density Structure from Dust Continuum and CS Emission” (ApJ, 566, 945 [2002])

This approximation stems from R. Hildebrand (QJRAS, 24, 267 [1983]; Table 1, row 6, col. [4]) and was used the same way in the work of T. Hunter (Ph.D. thesis, Caltech [1997]) and T. Hunter et al. (AJ, 119, 2711 [2000]), to which we compared our sample. This approximation implicitly assumes that the emissivity between 125 and 250 m has the same index, , as at submillimeter wavelengths, for which we assumed 1⁄4 2. However, Hildebrand advocates 1⁄4 1 for the former number. Using 1⁄4 1 between 125 and 250 m and changing the reference wavelength to 250 m, longward of which is left as a free parameter, we derive the following expression for Q , which is consistent with the relation given in the Hildebrand’s Table 1 (last row, col. [2]):
Anomaly Detection Module for Network Traffic Monitoring in Public Institutions

It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statistics and detecting possible anomalies in the network traffic description. The developed solution, called the anomaly detection module, is mostly dedicated to public institutions as the additional component of the network security services. Despite the use of well-known anomaly detection methods, the novelty of the module is based on providing an exhaustive strategy of selecting the best combination of models as well as tuning the models in a much faster offline mode. It is worth emphasizing that combined models were able to achieve 100% balanced accuracy level of specific attack detection.
DNA assays for genetic discrimination of three Phragmites australis subspecies in the United States

Abstract Premise To genetically discriminate subspecies of the common reed (Phragmites australis), we developed real‐time quantitative (qPCR) assays for identifying P. australis subsp. americanus, P. australis subsp. australis, and P. australis subsp. berlandieri. Methods and Results Utilizing study‐generated chloroplast DNA sequences, we developed three novel qPCR assays. Assays were verified on individuals of each subspecies and against two non‐target species, Arundo donax and Phalaris arundinacea. One assay amplifies only P. australis subsp. americanus, one amplifies P. australis subsp. australis and/or P. australis subsp. berlandieri, and one amplifies P. australis subsp. americanus and/or P. australis subsp. australis. This protocol enhances currently available rapid identification methods by providing genetic discrimination of all three subspecies. Conclusions The newly developed assays were validated using P. australis samples from across the United States. Application of these assays outside of this geographic range should be preceded by additional testing.
Levitation properties of superconducting magnetic bearings using superconducting coils and bulk superconductors

We have been developing a flywheel energy storage system (FESS) with 36 MJ energy capacity for a railway system with superconducting magnetic bearings (SMBs). We prepared two kinds of models using superconducting coils and bulk superconductors (SCs). One model demonstrated SMB load capacity of 20 kN and the other model proved non-contact stable levitation and non-contact rotation with SMBs. Combining these results, the feasibility of a 36 MJ energy capacity FESS with SMBs completely inside a cryostat has been confirmed. In this paper, we report the levitation properties of SMBs in these models.
Nitric oxide promotes adventitious root formation in cucumber under cadmium stress through improving antioxidant system, regulating glycolysis pathway and polyamine homeostasis

Cadmium (Cd) as a potentially toxic heavy metal that not only pollutes the environment but also interferes with plant growth. Nitric oxide (NO) regulates plant growth and development as well as abiotic stress response. However, the mechanism underpinning NO-induced adventitious root development under Cd stress remains unclear. In this study, cucumber (Cucumis sativus ‘Xinchun No. 4’) was used as the experimental material to investigate the effect of NO on the development of adventitious roots in cucumber under Cd stress. Our results revealed that, as compared to Cd stress, 10 μM SNP (a NO donor) could considerably increase the number and length of adventitious roots by 127.9% and 289.3%, respectively. Simultaneously, exogenous SNP significantly increased the level of endogenous NO in cucumber explants under Cd stress. Our results revealed that supplementation of Cd with SNP significantly increased endogenous NO content by 65.6% compared with Cd treatment at 48 h. Furthermore, our study indicated that SNP treatment could improve the antioxidant capacity of cucumber explants under Cd stress by up-regulating the gene expression level of antioxidant enzymes, as well as reducing the levels of malondialdehyde (MDA), hydrogen peroxide (H2O2) and superoxide anion ( O 2 · − ) to alleviate oxidative damage and membrane lipid peroxidation. Application of NO resulted in a decrease of the O 2 · − , MDA, and H2O2 level by 39.6%, 31.4% and 60.8% as compared to Cd-alone treatment, respectively. Besides that, SNP treatment significantly increased the expression level of related genes involved in glycolysis processes and polyamine homeostasis. However, application of NO scavenger 2-(4-carboxy -2-phenyl)-4, 4, 5, 5-tetramethy limidazoline -1-oxyl -3-oxide (cPTIO) and the inhibitor tungstate significantly reversed the positive role of NO in promoting the adventitious root formation under Cd stress. These results suggest that exogenous NO can increase the level of endogenous NO, improve antioxidation ability, promote glycolysis pathway and polyamine homeostasis to enhance the occurrence of adventitious roots in cucumber under Cd stress. In summary, NO can effectively alleviate the damage of Cd stress and significantly promote the development of adventitious root of cucumber under Cd stress.
Research on Named Entity Recognition Method Based on Language Pre-Training Model

: Aiming at the problem that the existing named entity recognition models have insufficient ability to recognize common unknown words in data, this paper proposes a text vectorization representation method based on language pre-training model. The program can't understand the text directly, and it can only be understood by the program after the text is converted into a numerical value. Firstly, this paper introduces the methods of word vector representation, including discrete representation and distributed representation. The traditional word vector representation method can't deal with the problem of polysemy and can't fully express semantic features. Aiming at the defects of word vector method, this paper proposes a text vectorization method based on language pre-training model. The idea of fine-tune is introduced, and the pre-training model, which completed training on massive data sets, is transferred to the People's Daily data set, and the parameters are optimized. Finally, this paper designs a comparative experiment on the People's Daily data set, compares it with the traditional word embedding methods using CBOW, Skip-gram and GloVe, analyzes the results, and verifies the effectiveness of the proposed method.
TOX regulates T lymphocytes differentiation and its function in tumor

Thymocyte selection-associated high mobility group box protein (TOX) is expressed differently at all T lymphocytes development stages. Owing to more advanced scientific and technological means, including single-cell sequencing technology, heterogeneity of T lymphocytes and TOX has gradually been revealed. Further exploration of such heterogeneity will help us comprehend the developmental stage and functional characteristics of T lymphocytes in greater detail. Emerging evidence supports its regulation not only in exhausting, but also in activating T lymphocytes, thereby verifying TOX heterogeneity. TOX can be used not only as a latent intervention target for tumor diseases and chronic infections, and a therapeutic strategy for autoimmune diseases, but also as a critical factor predicting the drug response and overall survival of patients with malignant tumors.
The k‐ary Montgomery modular inverse over nonbinary computers

This paper presents a k‐ary Montgomery modular inverse algorithm over nonbinary computers by using Sedjelmaci's right shift k‐ary greatest common divisor scheme. Over traditional binary computers, Kaliski's scheme can output Montgomery modular inverse Q − 12n mod P, where P is coprime to Q and n is the bit length of P. Over k‐ary computers, our algorithm can discover the k‐ary Montgomery inverse Q − 1km mod P, where P, Q, and k are pairwise relatively prime positive integers and m = log kP. In the worst case, the computational cost of our algorithm is O(m2)k‐ary digit operations. Copyright © 2013 John Wiley & Sons, Ltd.