Miracle-dz/UniFAR-FTUs huggingface dataset:
A combined TSVM model and GLRT detector for a roll invariant target detection

Abstract In this paper, a novel method is proposed for detecting Polar imetric Synthetic Aperture Radar (PolSAR) targets. Theproposed method is a combination of the Target Scattering Ve ctor Model (TSVM) and the Generalized Likelihood RatioTest - Linear Quadratic (GLRT-LQ) detector. The TSVM provid es an unique and roll-invariant decomposition of theobserved target vector by means of four independent paramet ers. The combination of those two methods will allow thedetection of any oriented targets. 1 Introduction The new generation of recently launched Synthetic Aper-ture Radar (SAR) sensors are able to produce high qualityimages of the Earth's surface with meter resolution. Thenumber of scatterers present in each resolution cell de-creases considerably and homogeneous hypothesis of thePolarimetric SAR (PolSAR) clutter can be reconsidered.Heterogeneouscluttermodelshavethereforerecentlybeenstudied. In 1973, Kung Yao has r st introduced the use of Spheri-cally Invariant Random Vectors (SIRV) and their applica-tions to estimation and detection in communication. Froma PolSAR point of view, the observed target vector k isden ed as the product of a square root of a positive ran-dom variable (representingthe texture) with an indepen-dent complex Gaussian vector z with zero mean. SIRVsdescribe a whole class of stochastic processes. This classincludes the conventional clutter models having Gaussian,K ,G
Sprites/Halo Influence on the Mesosphere Chemistry: Self-Consistent Modeling, Electric Field Dynamics

We have developed a new plasma-chemical model which describes perturbation dynamics of the chemical composition of the mesosphere at altitudes between 60 and 90 km due to sprite/halo generation for night conditions, and apply it for the self-consistent 1D modeling for sprite and axial-symmetry modeling for halo. In our model we use a simple representation for electric field, where it is related to the flash current (uncompensated electric charge in the troposphere) and depends on the conductivity perturbation in the mesosphere. The perturbed concentrations of electrons, neutrals, ions, cluster ions, excited atoms, and molecules have been estimated for typical flash parameters as altitude dependent.
Comparative in vitro and in vivo Evaluation of Different Iron Oxide-Based Contrast Agents to Promote Clinical Translation in Compliance with Patient Safety

Introduction One of the major challenges in the clinical translation of nanoparticles is the development of formulations combining favorable efficacy and optimal safety. In the past, iron oxide nanoparticles have been introduced as an alternative for gadolinium-containing contrast agents; however, candidates available at the time were not free from adverse effects. Methods Following the development of a potent iron oxide-based contrast agent SPIONDex, we now performed a systematic comparison of this formulation with the conventional contrast agent ferucarbotran and with ferumoxytol, taking into consideration their physicochemical characteristics, bio- and hemocompatibility in vitro and in vivo, as well as their liver imaging properties in rats. Results The results demonstrated superior in vitro cyto-, hemo- and immunocompatibility of SPIONDex in comparison to the other two formulations. Intravenous administration of ferucarbotran or ferumoxytol induced strong complement activation-related pseudoallergy in pigs. In contrast, SPIONDex did not elicit any hypersensitivity reactions in the experimental animals. In a rat model, comparable liver imaging properties, but a faster clearance was demonstrated for SPIONDex. Conclusion The results indicate that SPIONDex possess an exceptional safety compared to the other two formulations, making them a promising candidate for further clinical translation.
Static investigation of martensitic nucleation criticality in an atomistic model material

ABSTRACT This contribution investigates the nucleation of martensitic transformations in the bulk of austenitic single crystals. The transformation is associated with a decrease in the potential energy that is the driving force for the transformation. The coordinated atom displacements, which generate the martensite, produce large elastic strains in the material, which are associated with an elastic energy that opposes the transformation. A thermodynamic nucleation criterion for this type of transformations is derived. The criterion is evaluated by the method of static lattice relaxation for homogeneous nucleation in an atomistic 2D model material. The potential energy of martensitic clusters of different sizes and shapes, embedded in a stabilised austenitic matrix are calculated. The results exhibit non-monotonous progressions of the potential energy with the cluster size. This indicates that the elastic strain energy scales non-linearly with the size to allow the formation of local energy maxima at critical cluster sizes, interpreted as nucleation barriers. These barriers separate domains of stability from domains of instability in the configurations space, similar to Kelvin's classical theory of spherical fluid phase nucleation. The critical nuclei sizes are in the order of 100–150 unit cells, and their shapes are plate-like with long dimension parallel to the shear direction. The critical size of twinned clusters decreases with the twinning multiplicity.
How Can We Best Measure Frailty in Cardiosurgical Patients?

Background: Frailty is gaining importance in cardiothoracic surgery and is a risk factor for adverse outcomes and mortality. Various frailty scores have since been developed, but there is no consensus which to use for cardiac surgery. Methods: In an all-comer prospective study of patients presenting for cardiac surgery, we assessed frailty and analyzed complication rates in hospital and 1-year mortality, as well as laboratory markers before and after surgery. Results: 246 included patients were analyzed. A total of 16 patients (6.5%) were frail, and 130 patients (52.85%) were pre-frail, summarized in the frail group (FRAIL) and compared to the non-frail patients (NON-FRAIL). The mean age was 66.5 ± 9.05 years, 21.14% female. The in-hospital mortality rate was 4.88% and the 1-year mortality rate was 6.1%. FRAIL patients stayed longer in hospital (FRAIL 15.53 ± 8.5 days vs. NON-FRAIL 13.71 ± 8.94 days, p = 0.004) and in intensive/intermediate care units (ITS/IMC) (FRAIL 5.4 ± 4.33 days vs. NON-FRAIL 4.86 ± 4.78 days, p = 0.014). The 6 min walk (6 MW) (317.92 ± 94.17 m vs. 387.08 ± 93.43 m, p = 0.006), mini mental status (MMS) (25.72 ± 4.36 vs. 27.71 ± 1.9, p = 0.048) and clinical frail scale (3.65 ± 1.32 vs. 2.82 ± 0.86, p = 0.005) scores differed between patients who died within the first year after surgery compared to those who survived this period. In-hospital stay correlated with timed up-and-go (TUG) (TAU: 0.094, p = 0.037), Barthel index (TAU-0.114, p = 0.032), hand grip strength (TAU-0.173, p < 0.001), and EuroSCORE II (TAU 0.119, p = 0.008). ICU/IMC stay duration correlated with TUG (TAU 0.186, p < 0.001), 6 MW (TAU-0.149, p = 0.002), and hand grip strength (TAU-0.22, p < 0.001). FRAIL patients had post-operatively altered levels of plasma-redox-biomarkers and fat-soluble micronutrients. Conclusions: frailty parameters with the highest predictive value as well as ease of use could be added to the EuroSCORE.
Heart Valve Involvement in Patients with Antiphospholipid Syndrome: A Long-Term Follow-Up Study of a Single Centre

Background: Valve involvement is the most common cardiac manifestation in antiphospholipid syndrome (APS). The objective of the study was to describe the prevalence, clinical and laboratory features, and evolution of APS patients with heart valve involvement. Methods: A retrospective longitudinal and observational study of all APS patients followed by a single centre with at least one transthoracic echocardiographic study. Results: 144 APS patients, 72 (50%) of them with valvular involvement. Forty-eight (67%) had primary APS, and 22 (30%) were associated with systemic lupus erythematosus (SLE). Mitral valve thickening was the most frequent valve involvement present in 52 (72%) patients, followed by mitral regurgitation in 49 (68%), and tricuspid regurgitation in 29 (40%) patients. Female sex (83% vs. 64%; p = 0.013), arterial hypertension (47% vs. 29%; p = 0.025), arterial thrombosis at APS diagnosis (53% vs. 33%; p = 0.028), stroke (38% vs. 21%; p = 0.043), livedo reticularis (15% vs. 3%; p = 0.017), and lupus anticoagulant (83% vs. 65%; p = 0.021) were more prevalent in those with valvular involvement. Venous thrombosis was less frequent (32% vs. 50%; p = 0.042). The valve involvement group suffered from higher mortality (12% vs. 1%; p = 0.017). Most of these differences were maintained when we compared patients with moderate-to-severe valve involvement (n = 36) and those with no or mild involvement (n = 108). Conclusions: Heart valve disease is a frequent manifestation in our cohort of APS patients and is associated with demographic, clinical and laboratory features, and increased mortality. More studies are needed, but our results suggest that there may be a subgroup of APS patients with moderate-to-severe valve involvement with its own characteristics that differs from the rest of the patients with mild valve involvement or without valve involvement.
A NOTE ON NONPARAMETRIC INFERENCE FOR CAPTURE-RECAPTURE EXPERIMENTS WITH HETEROGENEOUS CAPTURE PROBABILITIES

The estimation of the size of a population using capture-recapture data if the capture probabilities are heterogeneous is essentially an unsolved problem in that no nonparametric estimator has been shown to be uniformly applicable. More- over, often the conditions under which the various nonparametric estimators give reasonable estimates are either unknown or uncheckable. Here a feasible nonpara- metric extension of a parametric empirical Bayes method used by Huggins (2001) is examined. It is concluded that assumptions concerning the distribution of the capture probabilities are required to successfully estimate the population size. A byproduct of the procedure is a conditional likelihood based test for time dependent capture probabilities in the presence of heterogeneous capture probabilities.
A Novel Hybrid Membrane Process Coupled with Freeze Concentration for Phosphorus Recovery from Cheese Whey

The ever-increasing demand for phosphorus fertilisers for securing global food production, coupled with finite phosphate rock reserves, is one of the emerging problems in the world. Indeed, phosphate rock is listed as an EU critical raw material, triggering attention to find an alternative source to substitute the use of this limited resource. Cheese whey, characterized by a high content of organic matter and phosphorus, represents a promising feedstock for phosphorus recovery and recycling. An innovative application of a membrane system coupled with freeze concentration was assessed to recover phosphorus from cheese whey. The performances of a microfiltration membrane (0.2 µm) and an ultrafiltration (200 kDa) membrane were evaluated and optimized under different transmembrane pressures and crossflow velocities. Once the optimal operating conditions were determined, a pre-treatment including lactic acid acidification and centrifugation was applied to increase the permeate recovery. Finally, the efficiency of progressive freeze concentration for the treatment of the permeate obtained from the optimum conditions (UF 200 kDa with TMP of 3 bar, CFV of 1 m/s and lactic acid acidification) was evaluated at specific operating conditions (−5 °C and 600 rpm of stirring speed). Finally, 70% of phosphorus could be recovered from cheese whey using the coupled technology of the membrane system and freeze concentration. A phosphorus-rich product was obtained with high agronomic value, which constitutes a further step towards establishing a broader circular economy framework.
Bi-cubic non-uniform B-spline surface reconstruction for slice contours

A surface reconstruction method from the slice contours was proposed.First,feature points were extracted based on curvature feature,and they were resampled in order to get a unification of sampling points in each line(column). Then,the sampling points were interpolated to get a bi-cubic non-uniform B-spline surface.Finally,nodes were inserted on the surface based on distance feature at a certain control accuracy,and the new control points through the least-squares approximation method were calculated to get approximate surface within the permissible range error.Based on the characteristics of slice contours,B-spline cycle and non-cycle B-spline combined,and the calculation of closed and non-closed surface was discussed.It was found that the combination of interpolation and approximation makes the algorithm more rapid and practical.
MSRPPnet: an automatic ICD coding method for clinical records based on a deep neural network

The International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. In this work, we proposed an automatic ICD coding method for clinical records based on a co-occurrence disease perception network. It uses a multi-scale residual network to capture text patterns of different lengths of clinical text and Personalized PageRank to capture co-occurrence between codes to enhance the ability of automatic coding (MSRPPnet). As shown in the experimental results on the real medical dataset MIMIC-III, the P@N and Micro-F1 of this method are 72.2% and 55.1%, respectively, which significantly improves the prediction performances.
An iterative solution of a variational inequality for certain monotone operators in Hilbert space

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The primary instability of falling films in the presence of soluble surfactants

Abstract We investigate the linear stability of a film flowing down a solid substrate in the presence of soluble surfactants. The Navier–Stokes equations for the liquid motion are considered, together with advection–diffusion equations for the concentrations of the species involved, which include monomers dissolved in the bulk and adsorbed at the liquid–air and at the liquid–substrate interfaces. The adsorption–desorption kinetics of the surfactant at both interfaces is explicitly accounted for. An Orr–Sommerfeld eigenvalue problem is formulated, and solved analytically in the limit of long-wave disturbances and numerically for arbitrary wavelength using a finite element method. An extensive parametric study is performed to reveal the role of surfactant solubility and adsorption–desorption kinetics. The results quantify the stabilizing effect of soluble surfactants due to the presence of Marangoni stresses, and indicate that moderately soluble surfactants may be more effective than insoluble ones. Disturbances of finite wavelength are stabilized by more than an order of magnitude, and their detailed behaviour depends in a non-monotonic way on the amount of surfactant and on its solubility and kinetics. The above predictions provide insights for the interpretation of recent experimental findings on the primary instability and on the ensuing unstable dynamics of liquid films doped with soluble surfactants.
Longitudinal SARS-CoV-2 Seroprevalence among Employees in Outpatient Care Services in Hamburg

The risk of SARS-CoV-2 infection is particularly high for healthcare workers during the pandemic. Home care workers visit many different households per shift. Encounters with mostly elderly patients and their relatives increase the potential for the undetected spread of SARS-CoV-2. In order to gain insight into the seroprevalence of SARS-CoV-2 antibodies and possible transmission risks in outpatient care, this follow-up study was conducted with nursing services in Hamburg. The aim was to estimate the dynamics of seroprevalence in this occupational group over a 12-month period, to identify occupation-specific risk factors, and to collect information on the vaccination status of the surveyed nursing staff. Antibody testing for SARS-CoV-2 IgG against the S1 domain (EUROIMUN Analyser I® Lübeck, Germany) was performed on participating healthcare workers with patient contact at a total of four time points within one year from July 2020 to October 2021 (baseline, follow-up after three, six and twelve months). The data were mostly analysed descriptively. Differences in IgG titres were analysed using variance analysis methods, particularly Tukey’s range test. The seroprevalence was 1.2% (8/678) at baseline and 1.5% (9/581) at the three-month follow-up (T1). At the second follow-up (T2) after six months, vaccination against SARS-CoV-2 was available from January 2021 onwards. The prevalence rate of positive IgG antibodies relative to the S1 domain of the spike protein test among unvaccinated individuals was 6.5%. At (T3) after twelve months (July to October 2021), 482 participants were enrolled, and 85.7% of the workers were considered fully vaccinated at this time point, while 51 individuals were unvaccinated. The prevalence was 13.7% (7/51). In our study, a low seroprevalence was found among home care workers, which was lower than in our studies conducted in the clinical setting. Therefore, it can be assumed that the occupational risk of infection is rather low for both the nursing staff and the patients/clients cared for in the outpatient setting. The good provision of protective equipment and the high vaccination rate of the staff probably had a positive influence.
An Empirical Spatial Network Model Based on Human Mobility for Epidemiological Research: A Case Study

Constructing a data-driven spatial contact network model is challenging in epidemiological research. In this study, we examine the applicability of geotagged Twitter data as an instrumental data source for tackling such a challenge. Geotagged Twitter data carrying geolocations of the account users have the strength for longitudinal data collection at a massive scale. Still, the unstructured nature of the data exerts significant methodological and computational difficulties. We focus on methodological solutions and develop a novelty approach that lets a spatial contact network emerge naturally from the massive amount of geospatial tweets. We show that such a data-driven network has reflected the assumptions made by network models regarding human behaviors and has the potential of being used for epidemiological research. To this end, we investigate the network properties and study the spread of pathogens on the proposed spatial contact network by using the homogeneous and heterogeneous susceptible–infectious–recovered (SIR) network models and the event-driven Gillespie’s algorithm. Our simulation results strongly suggest that it is feasible to explicitly construct data-driven spatial models using massive longitudinal Twitter data for public health research.
Solar and Convective Drying: Modeling, Color, Texture, Total Phenolic Content, and Antioxidant Activity of Peach (Prunus persica (L.) Batsch) Slices

Peach is a fruit highly appreciated by consumers; however, it is highly perishable, so drying is an alternative to preserve its physical and chemical properties. In this study, the effect of drying in natural and forced convection at three different temperatures (40 °C, 45 °C and 50 °C) and solar drying with two air velocities (1 m/s and 3 m/s) on the color, texture, total phenol content and antioxidant capacity of peach (Prunus persica (L.) Batsch), were evaluated. The experimental data of the drying kinetics were adjusted to five different mathematical models (Page, Logarithmic, Two-term exponential, Wang and Singh, and Verma et al.). The model that best represented the experimental data in natural convection was the Wang and Singh model (r2 > 0.998; RMSE < 0.016; χ2 < 2.85 × 10−4); in forced convection (45 °C and 50 °C), it was the Verma et al. model (r2 > 0.997; RMSE < 0.025; χ2 < 8.12 × 10−4); and finally, for solar drying, it was the Logarithmic model at 3 m/s (r2 = 0.999; RMSE < 0.012; χ2 < 1.12 × 10−4) and Wang and Sing model (1 m/s) (r2 = 0.998; RMSE = 1.31 × 10−4; χ2 = 1.92 × 10−4). The highest color difference was in samples dried by the natural convection method. The highest values of hardness were obtained by the solar drying method. The value of chlorogenic acid increased with the temperature of natural convection, while the concentration of neochlorogenic acid increased with the temperature at forced convection. For solar drying, the values of chlorogenic acid were greater at 3 m/s; in contrast, the neochlorogenic acid was greater at 1 m/s.
The Impact of Digital Technology Innovation Network Embedding on Firms’ Innovation Performance: The Role of Knowledge Acquisition and Digital Transformation

In the digital economy context, enterprises’ competitive environment is changing rapidly. Historically, enterprises rely on a solitary fight to occupy the market. Now, enterprises should actively embed into digital technology innovation networks to maximize access to external digital technology knowledge resources through organizational cooperation and achieve the absorption of digital resources and technologies. However, the relationship between digital technology innovation network embedding and innovation performance still needs to be clarified. Therefore, this study adopts the “structure–behavior–performance” research paradigm to extend innovation network research to the digital technology innovation network context, aiming to explore the impact of digital technology innovation network embedding on enterprise innovation performance and to analyze the mediating effect of knowledge acquisition and the moderating effect of digital transformation. This study conducts an empirical study based on Chinese A-share listed firms that undertook digital technology innovation from 2010–2021. The findings show that digital technology innovation networks’ relational and structural embedding positively affects firm innovation performance. Knowledge acquisition mediates digital technology innovation network embedding and innovation performance. Digital transformation has a moderating role between digital technology innovation network embedding and innovation performance, and different levels of digital transformation will have different effects on firms’ innovation performance. Overall, the relational and structural embedding of digital technology innovation networks can encourage enterprises to acquire more social capital and tacit knowledge and reduce R&D costs, thus improving their innovation performance. Firms should focus on building external cooperation networks, actively establishing an excellent corporate image, strengthening communication and cooperation with network members, establishing mutually beneficial cooperation beliefs, and promoting digital transformation. The present results will help companies understand the impact of digital technology innovation networks and provide a reference for companies to utilize in digital transformation to improve their innovation performance.