Miracle-dz/UniFAR-FTUs huggingface dataset:
The Fermilab Holometer

We propose an experiment at Fermilab to study a conjectured effect called "holo­ graphic noise" that may arise from new Planck scale physics: the measured posi­ tions of bodies may wander randomly from ideal geodesics of classical relativity, in measurement-dependent directions, by about a Planck length per Planck time. The experiment will search for this holographic jitter in the relation of mass-energy and space-time by looking for correlated phase noise between two neighboring 40 meter interferometers. The goal of the experiment is to provide convincing evidence for or against the hypothesis that relative transverse positions of bodies display this particu­ lar new kind of quantum noise, whose power spectrum is independent of frequency and has a spectral density determined only by the Planck time. A positive result of the experiment would be a major step forward in understanding the emergence of space­ time and mass-energy from a unified theory of spacetime and quantum mechanics. A negative result will impact the macroscopic interpretation of unified theories.
Observation and modelling of main sequence star chromospheres - VIII. High resolution observations of M and K dwarf chromospheric lines

Aims. We report on high resolution observations of chromospheric lines for M and K dwarfs. Methods. We observed up to 13 spectral lines per star: the Hα ,H β ,H � ,P a � ,P a 8, CaII H, CaII IR triplet, CaI 6572A, NaI D1/D2 and HeI D3 lines. We observed two dMe stars, one dM(e) star, 5 dM stars and 3 dK stars. Results. We observed a self-reversal in the emission core of the CaII H line for the brightest stars only, indicating a rather optically thick region of formation. We present original spectra of the NaI doublet and the CaII IR triplet for active dMe stars and less active dM stars. Core emission is detected in the Sodium lines and the CaII IR triplet lines for the most active M dwarf AU Mic. We investigate the difference spectra between active dMe stars and dM stars and show that these provide interesting new constraints for the NLTE-radiation transfer modelling of the chromospheres. In our sample, emission Hα profiles have a rather homogeneous FWHM of about 1.5 A. This, according to our previous modelling, can be interpreted as the signature of a rather constant temperature break in the chromosphere. We found that one of our targets (MCC 332) is a binary with a faint but active Hα emission component. – –
FORECAST PROBLEMS DURING THE LIFE CYCLE OF A CUT-OFF LOW, JANUARY 20–29, 1962

Abstract The life cycle of a cut-off Low at the 500-mb. level in the southwestern United States, January 20–29, 1962, is examined. The forecast problem is analyzed with the numerical barotropic predictions used as the first approximation. The effects of the mountains on the forecasts are discussed. The relative success of modifications to the barotropic forecasts, along with reasons for these modifications, is shown. Some aspects of the surface developments with respect to events at the 500-mb. level are examined.
Presence capture cameras - a new challenge to the image quality

Commercial presence capture cameras are coming to the markets and a new era of visual entertainment starts to get its shape. Since the true presence capturing is still a very new technology, the real technical solutions are just passed a prototyping phase and they vary a lot. Presence capture cameras have still the same quality issues to tackle as previous phases of digital imaging but also numerous new ones. This work concentrates to the quality challenges of presence capture cameras. A camera system which can record 3D audio-visual reality as it is has to have several camera modules, several microphones and especially technology which can synchronize output of several sources to a seamless and smooth virtual reality experience. Several traditional quality features are still valid in presence capture cameras. Features like color fidelity, noise removal, resolution and dynamic range create the base of virtual reality stream quality. However, co-operation of several cameras brings a new dimension for these quality factors. Also new quality features can be validated. For example, how the camera streams should be stitched together with 3D experience without noticeable errors and how to validate the stitching? The work describes quality factors which are still valid in the presence capture cameras and defines the importance of those. Moreover, new challenges of presence capture cameras are investigated in image and video quality point of view. The work contains considerations how well current measurement methods can be used in presence capture cameras.
Canine Parvovirus in Turkey: First Whole-Genome Sequences, Strain Distribution, and Prevalence

Canine parvovirus (CPV) is a significant pathogenic virus with up to 100% morbidity and 91% mortality rates, especially in unvaccinated puppies. The emergence of new strains, interspecies transmission, and vaccine effectiveness can be enabled by just a few base changes in the CPV genome. Therefore, to cope with CPV disease, it is important to identify the viral agent and regularly monitor vaccine effectiveness against new strains. The present study has investigated CPV’s genetic profile in Turkey by collecting 80 samples from dogs in Turkey between 2020 and 2022. These samples and all sequences previously studied for CPV in Turkey were analyzed for whole-genome sequences, nationwide strain distribution over the two years, and the central Turkey prevalence rate. Next-generation sequencing was used for the genome study, Sanger sequencing for strain detection, and PCR for the prevalence analyses. The CPV-2 variants circulating in Turkey form their own cluster while being closely related to Egypt variants. Substantial amino acid changes were detected in antigenically important regions of the VP2 gene. Moreover, CPV-2b has become the most frequent genotype in this region, while the incidence of CPV-2c is predicted to increase gradually over the coming years. The prevalence of CPV in central Turkey was 86.27%. This study thus provides powerful insights to further our understanding of CPV’s genetic profile in Turkey and suggests that up-to-date vaccination efficacy studies are urgently needed.
Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to non-random selection, or there is self selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.
Sk-Conv and SPP-based UNet for lesion segmentation of coronary optical coherence tomography

BACKGROUND: Coronary artery disease (CAD) manifests with a blockage the coronary arteries, usually due to plaque buildup, and has a serious impact on the human life. Atherosclerotic plaques, including fibrous plaques, lipid plaques, and calcified plaques can lead to occurrence of CAD. Optical coherence tomography (OCT) is employed in the clinical practice as it clearly provides a detailed display of the lesion plaques, thereby assessing the patient’s condition. Analyzing the OCT images manually is a very tedious and time-consuming task for the clinicians. Therefore, automatic segmentation of the coronary OCT images is necessary. OBJECTIVE: In view of the good utility of Unet network in the segmentation of medical images, the present study proposed the development of a Unet network based on Sk-Conv and spatial pyramid pooling modules to segment the coronary OCT images. METHODS: In order to extract multi-scale features, these two modules were added at the bottom of UNet. Meanwhile, ablation experiments are designed to verify each module is effective. RESULTS: After testing, our model achieves 0.8935 on f1 score and 0.7497 on mIOU. Compared to the current advanced models, our model performs better. CONCLUSION: Our model achieves good results on OCT sequences.
Shared Decision Making for Choosing renAl Replacement Therapy in Chronic Kidney Disease Patients (SDM-ART trial): study protocol for randomized clinical trial

Background Patients with chronic kidney disease (CKD) should be educated about their condition so that they can initiate dialysis at the optimal time and make an informed choice between dialysis modalities. Shared decision-making (SDM) empowers patients to select their own treatment and improves patient outcomes. This study aimed to evaluate whether SDM affects the choice of renal replacement therapy among CKD patients. Methods This is a multicenter, open-label, randomized, pragmatic clinical trial. A total of 1,194 participants with CKD who are considering renal replacement therapy were enrolled. The participants will be randomized into three groups in a 1:1:1 ratio: the conventional group, extensive informed decision-making group, and SDM group. Participants will be educated twice at months 0 and 2. Videos and leaflets will be provided to all patients. Patients in the conventional group will receive 5 minutes of education at each visit. The extensive informed decision-making group will receive more informed and detailed education using intensive learning materials for 10 minutes each visit. Patients in the SDM group will be educated for 10 minutes each visit according to illness perception and item-based analysis. The primary endpoint is the ratio of hemodialysis to peritoneal dialysis and kidney transplantation among the groups. Secondary outcomes include unplanned dialysis, economic efficiency, patient satisfaction, patient evaluation of the process, and patient adherence. Discussion The SDM-ART is an ongoing clinical study to investigate the effect of SDM on the choice of renal replacement therapy in patients with CKD.
Fatigue driving detection method based on deep learning

In order to avoid fatigue driving, the driver fatigue detection technology is studied by extracting facial fatigue feature parameters. Use the optimized SSD to extract facial features, use PFLD to detect key points of the face, and detect the key points and spatial attitude angles of the eyes, mouth, and head of the face; calculate the face fatigue feature parameters based on time series The matrix is input to GRU for fatigue driving detection. Compared with other eight methods in the case of low computing power, it has a high accuracy rate and detection speed, which meets the needs of the fatigue driving detection system.
Synchronous Bilateral Breast Cancer: A Case Report Piloting and Evaluating the Implementation of the AI-Powered Large Language Model (LLM) ChatGPT

Primary breast carcinoma is the most common cancer type in women, and although bilateral synchronous breast cancers (s-BBC) remain quite rare, the reported incidence may increase with the adoption of more sensitive imaging modalities. Here, we present a case of histomorphological and clinically distinct s-BBC, together with a discussion of clinical management decisions, prognosis, and treatment standards and how these relate to outcomes vis-à-vis more established standards in unifocal breast carcinoma. The case report also constitutes a pilot and formal evaluation of a large language model (LLM) of ChatGPT as a tool to aid in generating a single patient case report.
A Framework Combining Diagrammatic and Symbolic Problem Solving

We introduce Vivid, a domain-independent framework for mechanized heterogeneous natural deduction that combines diagrammatic and symbolic reasoning. The framework is presented in the form of a family of denotational proof languages (DPLs). We present novel formal structures, called named system states, that are specifically designed for modeling potentially underdetermined diagrams. These structures allow us to deal with incomplete information, which is a pervasive feature of heterogeneous problem solving. We introduce a notion of attribute interpretations that enables us to interpret first-order signatures into named system states, and develop a formal semantic framework based on a three-valued logic. We extend the assumption-base semantics of DPLs to accommodate diagrammatic reasoning by introducing general inference mechanisms for the valid extraction of information from diagrams and for the incorporation of sentential information into diagrams. A rigorous big-step operational semantics is given, on the basis of which we prove that our framework is sound. We present examples of particular instances of Vivid, and discuss related work.
The outflow from the class 0 protostar HH 25MMS: methanol enhancement in a well-collimated flow

We present millimetre and submillimetre observations of HH 25MMS made with the James Clerk Maxwell Telescope (JCMT). From greybody fits to the continuum data we can confirm that HH 25MMS is a class 0 source, with a bolometric luminosity of similar to 6 L. and T-bol = 34 +/- 2 K. The ratio of submillimetre-to-bolometric luminosity is similar to 0.05, mon than 10 times higher than the defining value specified by Andre, Ward-Thompson & Barsony. The radio continuum source lies at the centre of a highly collimated, jet-driven, bipolar molecular outflow which lies close to the plane of the sky. We have mapped the southern lobe in the emission of CO J = 3-->2 and find that the CO peaks upstream of the bright H-2 knot HH25C, indicative of a prompt entrainment mechanism. A plot of the momentum per unit distance along the outflow axis confirms this. Furthermore, the jet shows evidence of a time-varying direction. The southern lobe is the site of significant methanol enhancement. Maps are presented of the J(K) = 5(K)-->4(K) group of lines which show emission solely within the confines of the southern outflow lobe. HH 25MMS is thus only the second class 0 source to have the distribution of methanol mapped within its outflow. Eight further methanol lines have been observed at the position of one of the H-2 knots (HH25C) to constrain better an estimate of the methanol column density and excitation temperature. The methanol at this position is slightly warmer than the ambient gas with an excitation temperature of 22-34 K, and the abundance of methanol is enhanced by a factor of at least 200 over that in the ambient gas. Statisitical equilibrium calculations confirm this, although the results are not particularly well constrained. The gas kinetic temperature is somewhat higher than the excitation temperature at similar to 60 K, the H-2 number density lies within a factor of 5 of 10(5) cm(-3), and the abundance is within a factor of 10 of 2.6 x 10(-7) relative to H-2. The SO J(N) = 8(7)-->7(6) and SiO J = 7-->6 lines are also observed within the outflow, emission from the latter being predominantly redshifted relative to the rest velocity.
The Mechanical Properties of Blended Fibrinogen:Polycaprolactone (PCL) Nanofibers

Electrospinning is a process to produce versatile nanoscale fibers. In this process, synthetic and natural polymers can be combined to produce novel, blended materials with a range of physical, chemical, and biological properties. We electrospun biocompatible, blended fibrinogen:polycaprolactone (PCL) nanofibers with diameters ranging from 40 nm to 600 nm, at 25:75 and 75:25 blend ratios and determined their mechanical properties using a combined atomic force/optical microscopy technique. Fiber extensibility (breaking strain), elastic limit, and stress relaxation times depended on blend ratios but not fiber diameter. As the fibrinogen:PCL ratio increased from 25:75 to 75:25, extensibility decreased from 120% to 63% and elastic limit decreased from a range between 18% and 40% to a range between 12% and 27%. Stiffness-related properties, including the Young’s modulus, rupture stress, and the total and relaxed, elastic moduli (Kelvin model), strongly depended on fiber diameter. For diameters less than 150 nm, these stiffness-related quantities varied approximately as D−2; above 300 nm the diameter dependence leveled off. 50 nm fibers were five–ten times stiffer than 300 nm fibers. These findings indicate that fiber diameter, in addition to fiber material, critically affects nanofiber properties. Drawing on previously published data, a summary of the mechanical properties for fibrinogen:PCL nanofibers with ratios of 100:0, 75:25, 50:50, 25:75 and 0:100 is provided.
THE ESTIMATION OF DISCRETE CHOICE MODELS WITH LARGE CHOICE SET

In this paper we discussed the estimation of choice model with correlation and/or heteroscedasticity across alternatives when the number of alternatives was large. We examined the performance of randomly drawn choice set approach and Poisson regression approach. We designed 6 Monte Carlo experiments. 1000 observations with 30 alternatives each were generated for each experiment. We found that the randomly drawn choice set approach is only applicable to independent and homoscedastic alternatives. When there are correlated factors among alternatives, this approach can not get good fit for the correlated factors even when the number of draw is quite large. We proved theoretically and empirically that Poisson regression model can get the same estimates as some logit type models that showed heteroscedasticity and correlation across alternatives. Poisson regression model can also get estimates close to those of logit kernel model with less computing time.
General smoothing techniques for estimating deterministic sinusoidal frequencies from noisy data

General high-resolution smoothing techniques for estimating deterministic sinusoidal frequencies from short-record noisy data are presented. These techniques are general in the sense that methods such as the modified least squares Prony method, as well as those which are based on eigenvector decompositions, may be considered as special cases of them. The theoretical basis of these smoothing techniques is discussed, and their performance in the presence of white Gaussian noise at low signal-to-noise ratio (SNR) is examined. It is shown that close to the threshold of the maximum-likelihood method (SNR approximately=3 dB) these symmetric smoothing techniques provide better accuracy than any other current method. <<ETX>>
Design improvement method of elderly household blood glucose meter integrating KANO model and Kansei engineering: take the Chinese elderly as an example

With the continuous development of Chinese society and the gradual increase of the aging population, the product development of elderly products has also received more and more attention. In order to respond to the development of the market and the needs of users, product designers need to continuously optimize products according to the real needs of users, so as to improve the competitiveness of products. Based on the KANO model, this research has an in-depth understanding of the inner needs of users, combined with the research method of Kansei Engineering, extracts relevant design elements, and finally establishes the connection between user needs and product samples, so as to provide designers with help in product design and development. People-oriented design concept is the core concept of product design. Through the analysis and summary of this study, it is worth looking forward to improving the humanization of blood glucose meter products.
The Explanatory Power of Network Models

Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.
The Influence of Individual Differences of Chinese Learners of German on Their Choices of Internship

This study aims to explore the influence of individual differences on the internship choices of Chinese undergraduate students in German major and to provide better career service suggestions targeting at foreign language students. This study has significant implications, considering the challenges that final-year undergraduate students in German are faced with and the limited number of previous studies which address these issues. The current study interviewed 23 third-year undergraduates majoring in German to investigate their internship choices and experience, as well as individual differences including language learning motivation, anxiety and character. Only four of them were engaged in German-related internships, eleven were engaged in internships that are not German-related, and eight had no internships yet. Fifteen of them believe that motivation can improve their academic performance. It can be seen that students with integrative motivation will be engaged in German-related internships. Twelve people believe that anxiety has a positive impact on German learning, and one third of whom believe that anxiety improves their German performance to a certain extent, which promotes them to be engaged in German-related internships. However, the influence of character on German learning is uncertain. The results revealed a direct influence of learning motivation on interviewees choice of German-related internships, indirect influence of learning anxiety, and no relationship between character and internship choices. This study has implications for better career support to German-major undergraduate students.
Genetic Algorithm for the Optimization of Mine Ventilation Network

A general non-linear mathematics programming model for optimizing the mine ventilation network was established and solved by genetic algorithm.The penalty function method was used in treating and transforming the constraint equations contained in the optimizing model so as to be suitable for the genetic algorithm.Non-differentiable accurate penalty functions were chosen in treating the constraint equation and the advantages of simulated annealing algorithm was used for reference in determining penalty parameters.On the basis of this,a dynamic penalty function was designed.Then the genetic algorithm was used to solving the optimized model for a simple ventilation network.The result shows that the overall energy consumption of the optimized ventilation system has a decrease of 7.78 kW,with a decreasing rate of 3%.