Similar Distances Research Articles

Amiodarone-related thyroid dysfunction and associated outcomes in patients with heart failure - a matched case-control study

Abstract Aim The association between amiodarone-related thyroid dysfunction and the risk of worsening heart failure (HF) or death has been reported with conflicting results. Methods The study cohort was patients with HF and without a history of thyroid dysfunction (defined by medical treatment or diagnosis) who initiated treatment with amiodarone in the period 1996-2021. Within this cohort, those who developed thyroid dysfunction (cases) were identified and matched 1:3 on age, sex, calendar year, duration of HF, and time since initiation of amiodarone with patients (controls) from the same cohort and followed for the primary outcome of HF hospitalization or all-cause death at 1-year, assessed by Kaplan Meier curves and Cox regression models adjusted for ischemic heart disease, chronic kidney disease, malignancy, atrial fibrillation and ventricular tachycardia. Results The study cohort comprised 21,947 patients, of whom 4,137 (19%) developed thyroid dysfunction following initiation of amiodarone. After matching, the study population consisted of 4,063 cases (mean age 69 years, 68% men, 41.6% developed thyroid dysfunction within the first year after initiation of amiodarone) and 10,940 matched controls, (Figure 1). The index date was the date of thyroid dysfunction for cases, and for matched controls a date at a similar distance since amiodarone initiation. At 1-year follow-up, the primary outcome occurred in 1,543 (38.0%) cases and 3,681 (33.6%) matched controls (reference), adjusted hazard ratio (HR) 1.13 (95%, confidence interval CI 1.07-1.20), although this was solely driven by HF hospitalization (28.9% vs 21.8%; HR 1.32 (95% CI 1.23-1.41), whereas all-cause death was lower among cases (18.2% vs. 20.4%, HR 0.85 (95% CI 0.78-0.93), (Figure 2). Conclusions Among HF patients treated with amiodarone, the development of thyroid dysfunction was associated with an increased risk of HF hospitalization or all-cause death, but this was solely driven by the excess risk of HF hospitalization, whereas it was associated with a lower risk of all-cause death. The absence of excess mortality indicates that thyroid complications are manageable in this vulnerable population of patients with HF.Figure 1 Flowchart of the populationFigure 2 Cumulative risk

Facial expression morphing: enhancing visual fidelity and preserving facial details in CycleGAN-based expression synthesis

Recent advancements in facial expression synthesis using deep learning, particularly with Cycle-Consistent Adversarial Networks (CycleGAN), have led to impressive results. However, a critical challenge persists: the generated expressions often lack the sharpness and fine details of the original face, such as freckles, moles, or birthmarks. To address this issue, we introduce the Facial Expression Morphing (FEM) algorithm, a novel post-processing method designed to enhance the visual fidelity of CycleGAN-based outputs. The FEM method blends the input image with the generated expression, prioritizing the preservation of crucial facial details. We experimented with our method on the Radboud Faces Database (RafD) and evaluated employing the Fréchet Inception Distance (FID) standard benchmark for image-to-image translation and introducing a new metric, FSD (Facial Similarity Distance), to specifically measure the similarity between translated and real images. Our comprehensive analysis of CycleGAN, UNet Vision Transformer cycle-consistent GAN versions 1 (UVCGANv1) and 2 (UVCGANv2) reveals a substantial enhancement in image clarity and preservation of intricate details. The average FID score of 31.92 achieved by our models represents a remarkable 50% reduction compared to the previous state-of-the-art model’s score of 63.82, showcasing the significant advancements made in this domain. This substantial enhancement in image quality is further supported by our proposed FSD metric, which shows a closer resemblance between FEM-processed images and the original faces.

Ultra-high-energy gamma-ray bubble around microquasar V4641 Sgr.

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension measurement of the spatial morphology of a microquasar, SS 433, to TeV gamma rays1 localizes the acceleration of electrons at shocks in the jet far from the black hole2. V4641 Sagittarii (V4641 Sgr) is a similar binary system with a black hole and B-type main-sequence companion star and has an orbit period of 2.8 days (refs. 3,4). It stands out for its super-Eddington accretion5 and for its radio jet, which is one of the fastest superluminal jets in the Milky Way. Previous observations of V4641 Sgr did not report gamma-ray emission6. Here we report TeV gamma-ray emission from V4641 Sgr that reveals particle acceleration at similar distances from the black hole as SS 433. Furthermore, the gamma-ray spectrum of V4641 Sgr is among the hardest TeV spectra observed from any known gamma-ray source and is detected above 200 TeV. Gamma rays are produced by particles, either electrons or protons, of higher energies. Because energetic electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron-production mechanism or points to the acceleration of high-energy protons. This suggests that large-scale jets from microquasars could be more common than previously expected and that they could be a notable source of galactic cosmic rays7-9.

Uncovering disparities in water-based outdoor recreation using cell phone mobility data

Abstract Outdoor recreation plays a pivotal role in improving people’s physical and mental health, serving as a popular form of entertainment and a significant economic contributor. Limited access to these resources not only exacerbates health disparities but also deprives underserved areas of essential benefits like stress relief and community bonding, both of which are crucial for enhancing overall quality of life. This paper provides one of the first detailed analyses of water-based recreation at over 61 000 inland and coastal sites across the United States. We aim to explore disparities in recreational behavior across race, ethnicity, income, and socioeconomic status. Using Advan cellphone data from more than 70 million outdoor trips, representing 215 000 census block groups, we find that communities of color, rural areas, and socioeconomically disadvantaged groups are significantly underrepresented in water-based recreational visits. Despite living similar distances from recreational sites, these groups show notably different patterns in travel distance for water-based recreation. Additionally, we find Native Americans from underserved areas have to travel 3–5 times longer distances than other groups for water-based recreation. Our findings show that the extensive and frequent cellphone mobility data could reveal policy-relevant patterns especially those made by underserved Americans often overlooked in traditional household surveys.

Chemical abundances for a sample of FGK dwarfs in the Pleiades open cluster from APOGEE

ABSTRACT This paper presents chemical abundances of 12 elements (C, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, and Fe) for 80 FGK dwarfs in the Pleiades open cluster, which span a temperature range of $\sim$2000 K in T$_{\rm eff}$, using the high-resolution (R$\sim$22 500) near-infrared SDSS (Sloan Digital Sky Survey)-IV/APOGEE (Apache Point Observatory Galactic Evolution Experiment) spectra ($\lambda$1.51–1.69 $\mu$m). Using a 1D local thermodynamic equilibrium abundance analysis, we determine an overall metallicity of [Fe/H] = +0.03 $\pm$ 0.04 dex, with the elemental ratios [$\alpha$/Fe] = +0.01 $\pm$ 0.05, [odd-z/Fe] = –0.04 $\pm$ 0.08, and [iron peak/Fe] = –0.02 $\pm$ 0.08. These abundances for the Pleiades are in line with the abundances of other open clusters at similar galactocentric distances as presented in the literature. Examination of the abundances derived from each individual spectral line revealed that several of the stronger lines displayed trends of decreasing abundance with decreasing $T_{\rm eff}$. The list of spectral lines that yield abundances that are independent of $T_{\rm eff}$ are presented and used for deriving the final abundances. An investigation into possible causes of the temperature-dependent abundances derived from the stronger lines suggests that the radiative codes and the APOGEE line list we employ may inadequately model van der Waals broadening, in particular in the cooler K dwarfs.

Development of a Children’s Educational Dictionary for a Low-Resource Language Using AI Tools

Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, Mongolian, and others, due to the lack of qualitative and accessible resources, morphological complexity, and the semantics of agglutinative languages. This article presents research on early childhood learning resources for the low-resource Kazakh language. Generally, a dictionary for children differs from classical educational dictionaries. The difference between dictionaries for children and adults lies in their purpose and methods of presenting information. A themed dictionary will make learning and remembering new words easier for children because they will be presented in a specific context. This article discusses developing an approach to creating a thematic children’s dictionary of the low-resource Kazakh language using artificial intelligence. The proposed approach is based on several important stages: the initial formation of a list of English words with the use of ChatGPT; identification of their semantic weights; generation of phrases and sentences with the use of the list of semantically related words; translation of obtained phrases and sentences from English to Kazakh, dividing them into bigrams and trigrams; and processing with Kazakh language POS pattern tag templates to adapt them for children. When the dictionary was formed, the semantic proximity of words and phrases to the given theme and age restrictions for children were taken into account. The formed dictionary phrases were evaluated using the cosine similarity, Euclidean similarity, and Manhattan distance metrics. Moreover, the dictionary was extended with video and audio data by implementing models like DALL-E 3, Midjourney, and Stable Diffusion to illustrate the dictionary data and TTS (Text to Speech) technology for the Kazakh language for voice synthesis. The developed thematic dictionary approach was tested, and a SUS (System Usability Scale) assessment of the application was conducted. The experimental results demonstrate the proposed approach’s high efficiency and its potential for wide use in educational purposes.

Application for Predicting Vessel Motion Enhances Operability in Offshore Drilling

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35341, “Enhancing Energy Efficiency and Operability in Offshore Drilling Operations: Validation and Benefits of a Machine-Learning-Based Vessel-Motion-Prediction Application,” by Daniel A.C. Canache, Massimiliano Russo, and Rune Haakonsen, Kongsberg Maritime, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ This paper delves into the framework of a new application that leverages advanced machine-learning (ML) techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads. The discussion encompasses the key role of diverse input factors in shaping prediction accuracy and outlines strategies to address inherent uncertainties associated with methods, weather forecasts, and the stochastic nature of the underlying problem. Methods, Procedures, and Process Traditional Physics-Based Vessel Model. A physics-based model of the vessel is used to train the ML model. As such, the accuracy of the physics-based model is critical to the success of the ML model. The model is built using vessel-specific hydrodynamic data, mass data, and thruster-control software. The thruster-control software is extracted from the actual vessel to ensure that the same settings are being used in the model. The model is used to execute time-domain simulations, which include second-order effects, from which prediction targets [six degrees-of-freedom (DOF) motions and thruster usage] are extracted. As shown in Fig. 1, four environmental components are typically considered: wind waves, swell waves, wind, and current. To train an ML model capable of predicting in any given weather condition, environmental parameters are varied over thousands of simulations. Physics-Informed Neural Network (PINN) Architecture. The adopted architecture for the ML model is a PINN, a model architecture that offers several advantages. By integrating vessel-specific hydrodynamic data, the model becomes grounded firmly in the underlying physical phenomena. Furthermore, the architecture facilitates the model’s comprehension and assessment of the environment by enabling the compilation of individual loads into an overall load acting on the vessel. Ultimately, the analysis of resultant loads on the vessel provides a means to compare various environmental load cases directly in relation to the vessel’s response. Optimizing the Training Matrix. The optimal training matrix should span the environmental parameter space up to the vessel’s station-keeping capacity while minimizing the number of necessary load cases. Given the computational expense and prolonged execution times associated with time-domain simulations, it is important to maintain a manageable number of cases. While resultant loads facilitate the comparison across different environment load cases, a systematic algorithm is needed for case selection. To address this requirement, a similarity metric incorporating cosine similarity and Euclidean distance was implemented. The downside to the Euclidean distance is that, at low magnitudes, the distance will be low regardless of direction. This is mitigated to some extent by combining the measure with the cosine similarity. The cosine similarity will be high if two vectors are close in direction regardless of their magnitudes. For the specific problem described in the complete paper (vessel responses), it is desirable that the similarities at low loads stay high because the response also will likely be low.

Unveiling Similarities in the Code of Life: A Detailed Exploration of DNA Sequence Matching Algorithm

Identifying similar DNA sequences is crucial in various biological research endeavors. This paper delves into the intricate workings of a specific algorithm designed for this purpose. We provide a systematic explanation, exploring how the algorithm handles user input, reads stored DNA sequences, utilizes the Word2Vec model for vector representation, and calculates sequence similarity using diverse metrics like Cosine Similarity and Neutrosophic Distance. Additionally, the paper explores the incorporation of neutrosophic values to account for uncertainty in the comparisons. Finally, we discuss the extraction of results, including matched sequences, similarity scores, and accuracy measures. This in-depth exploration provides a clear understanding of the algorithm's capabilities and fosters its effective application in DNA sequence analysis.

Exploring language relations through syntactic distances and geographic proximity

Languages are grouped into families that share common linguistic traits. While this approach has been successful in understanding genetic relations between diverse languages, more analyses are needed to accurately quantify their relatedness, especially in less studied linguistic levels such as syntax. Here, we explore linguistic distances using series of parts of speech (POS) extracted from the Universal Dependencies dataset. Within an information-theoretic framework, we show that employing POS trigrams maximizes the possibility of capturing syntactic variations while being at the same time compatible with the amount of available data. Linguistic connections are then established by assessing pairwise distances based on the POS distributions. Intriguingly, our analysis reveals definite clusters that correspond to well known language families and groups, with exceptions explained by distinct morphological typologies. Furthermore, we obtain a significant correlation between language similarity and geographic distance, which underscores the influence of spatial proximity on language kinships.

Prehospital computed tomography in a rural district for rapid diagnosis and treatment of stroke.

Early diagnosis and triage of patients with ischemic stroke is essential for rapid reperfusion therapy. The prehospital delay may be substantial and patients from rural districts often arrive at their local hospital too late for disability-preventing thrombolytic therapy due to prolonged transport times. Hallingdal District Medical Centre (HDMC) is located in a rural area of Norway and is equipped with a computed tomography (CT) scanner. We established emergency pathways of CT imaging and thrombolytic treatment of patients with acute ischemic stroke at HDMC. During office hours these pathways were managed by a radiographer and a general physician supported by videoconference from the Primary Stroke Centre. Outside office hours we remotely controlled the CT exam and supported telestroke guided paramedics handling and examining the patients. With a primary aim of demonstrating the feasibility of this de novo concept we enrolled patients in the period 2017-2021 into a comparative cohort observational study. We compared patients treated at HDMC (the Rural CT group) to patients from two other rural regions in Norway with similar distances to their local hospital but without access to a rural CT scanner (the Reference group). A total of 86 patients were included in the Rural CT group (mean age 74, 52% male, 43% stroke mimics), and 69 patients were included in the Reference group (mean age 70, 42% male, 28% stroke mimics). Median time from onset of symptoms to completed CT examination was 93 min in the Rural CT group as compared to 240 min in the Reference group (p < 0.05). In patients receiving intravenous thrombolysis time from onset of symptoms to treatment was median 124 min in the Rural CT group and 213 min in the Reference group, p < 0.05. The frequency of thrombolysis for ischemic stroke did not significantly differ between the two groups. Combining prehospital rural CT examination with telestroke guided diagnosis and thrombolytic treatment by paramedics may facilitate earlier initiation of thrombolysis for patients with ischemic stroke.

Extracting Features from Text Flows based on Semantic Similarity for Text Classification: an Approach Inspired by Audio Analysis

Text classification is a mainly investigated challenge in Natural Language Processing (NLP) research. The higher performance of a classification model depends on a representation that can extract valuable information about the texts. Aiming not to lose crucial local text information, a way to represent texts is through flows, sequences of information collected from texts. This paper proposes an approach that combines various techniques to represent texts: the representation by flows, the benefit of the word embeddings text representation associated with lexicon information via semantic similarity distances, and the extraction of features inspired by well-established audio analysis features.In order to perform text classification, this approach splits the text into sentences and calculates a semantic similarity metric to a lexicon on an embedding vector space. The sequence of semantic similarity metrics composes the text flow. Then, the method performs the extraction of twenty-five features inspired by audio analysis (named Audio-Like Features). The features adaptation from audio analysis comes from a similitude between a text flow and a digital signal, in addition to the existing relationship between text, speech, and audio. We evaluated the method in three NLP classification tasks: Fake News Detection in English, Fake News Detection in Portuguese, and Newspaper Columns versus News Classification. The approach efficacy is compared to baselines that embed semantics in text representation: the Paragraph Vector and the BERT. The objective of the experiments was to investigate if the proposed approach could compete with the baselines methods improve their efficacy when associated with them. The experimental evaluation demonstrates that the association between the proposed and the baseline methods can enhance the baseline classification efficacy in all three scenarios. In the Fake News Detection in Portuguese task, our approach surpassed the baselines and obtained the best effectiveness (PR-AUC = 0.98).

Two-sided matching theory-based second-hand house transaction evaluation and recommendation by the modified PLC-DEMATEL method

To enhance the two-sided matching efficiency in a multi-source heterogeneous environment, this paper takes the randomness and unstructured features of the online comments into consideration, and proposes a new matching mechanism by introducing the complex information representation tool. Firstly, the concept of the probabilistic linguistic normal cloud (PLNC) model is introduced to preserve information cohesion and characteristic distribution. Next, the basic operation laws and corresponding operators are given. Then, an innovative maximum boundary concept skipping the indirect approach is presented to update the similarity degree and distance measures, also the correlation coefficient. Furthermore, for the multi-indicator systems with interactions, the peer experts are invited to evaluate the relationship between indicators, a modified algorithm based on the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method is applied to obtain the subjective weights of indicators. After that, a whole matching process and a correlation coefficient cluster method-based recommendation algorithm are presented. A case study is provided to illustrate the method, wherein a new indicator system is constructed by analyzing the correlation of multiple indicators based on online linguistic evaluations. The random forest model is combined to obtain the objective weights and balance its reliability. Finally, sensitivity analysis and comparative analysis are employed to validate the effectiveness and applicability.

Comparisons of Different Cluster Analysis Methods with Application to Mizoram Rainfall Data

Analysing rainfall patterns in Mizoram from 1998 to 2017 reveals diverse trends. The highest average rainfall occurred in 2004, reaching 292.8 mm, while 2014 marked the lowest at 151.77 mm. Siaha District experienced the highest average rainfall (3020.2 mm), while Champhai District had the lowest (1663 mm). In 2017, the Kendall method showed correlations between temperature and relative humidity, rainfall and relative humidity, but not between rainfall and temperature. Cluster analysis, a technique partitioning datasets into cohesive groups, was applied to Mizoram’s district-wise rainfall data using single, complete, and average linkage methods. The Single Linkage Method formed one large cluster with under 26% similarity and the shortest distances between data points. The complete linkage method divided districts into two clusters with under 26% similarity and maximal inter-cluster distance. The Average Linkage Method merged all districts into one cluster with under 26% similarity and minimised inter-cluster distances. Comparing the techniques, Single and Complete Linkage Methods proved most effective for Mizoram’s district-wise rainfall data. With only eight districts, forming additional clusters remained limited. This analysis highlights the significance of rainfall patterns in agricultural ecosystems and the utility of statistical methodologies like cluster analysis in understanding long-term trends.

The electric double layer structure and interface energy of uranium electrode in molten LiCl-KCl-UCl3 mixture: A molecular dynamics simulation study

The electric double layer structure and interface energy of uranium electrode in molten LiCl-KCl-UCl3 mixture, as well as the surface energies of metallic uranium and molten salts, were investigated using molecular dynamics simulations. The surface energies were calculated, respectively, from the pressure tensor and the energy difference for molten salt and uranium, and interface energy was evaluated from the Gibbs free energy difference between systems with and without interface. The surface energies of molten alkali halides are between 76.4 and 201.8 mJ·m−2 and decrease with the increase of radius of the halide anion or the alkali cation for molten salt with the same alkali cation or the same halide anion. The surface energy of uranium depends on the allotrope morphology and crystal facet, and the surface energies of α-U, β-U, and γ-U are 2.36, 1.45, and 1.69 J·m−2 in average for the seven low index crystal facets. The interface energy of uranium and molten LiCl-KCl-UCl3 mixture is about 2.71 J·m−2 and increases with the increase of charge density on the uranium slabs. The molten LiCl-KCl-UCl3 mixture in anodic zone has higher coordination number than that in cathodic zone but similar coordination distance.

Source-load coordinated dispatching model taking into account the similarity between renewable energy and load power

With the deployment of renewable energy, the load curve is expected to follow the renewable energy output curve to minimize the fluctuation of thermal power output in the source-load coordinated dispatching. The traditional indicators for the load curve are no longer enough to describe the load characteristics. A new load indicator called the source-load similarity distance is proposed by improving the similarity measurement method of the time series and calculating the similarity distance between the renewable energy output curve and the load curve. By combining the Euclidean distance with the improved dynamic time warping, the source-load similarity distance is obtained and the data distribution and morphological fluctuation characteristics can be simultaneously considered. The source-load coordinated dispatching model is also established to minimize the source-load similarity distance. The simulation results show that the source-load similarity distance can effectively describe the similarity characteristics of the renewable energy output curve and the load curve. Increasing the source-load similarity distance can reduce the thermal power operation cost by 56.2 % and the cost of demand response by 25.3 %, and increase the utilization rate of wind power by 4.6 % compared to the dispatching model with the standard deviation indicator.

An Enhancement of Multi-Factor Weighted Approach Technique in Prioritizing Test Cases by Comparing Similarity Distance

An Enhancement of Multi-Factor Weighted Approach Technique in Prioritizing Test Cases by Comparing Similarity Distance

Enhancing water transportation capacity by asymmetrical patterned surface with super-wettability

Spontaneous and directional water droplet transportation based on patterned surface with super-wettability is crucial for the development of frontier science technology. However, water droplet transportation cannot meet both long distance and fast transportation simultaneously. Here, we overcame this limitation by proposing an asymmetric serial brachistochrone-shaped pattern (ASBP). Water droplet could be transported on the ASBP with a transportation distance of 72.52 mm and a transportation velocity of 158 mm/s after a series of single-factor experiments, orthogonal design optimization, and junction transition optimization. In addition, the water droplet could be transported on a curved ASBP, a super-long ASBP for multi-droplet scenarios, and an ASBP at an inclination angle. Moreover, acidic and alkaline aqueous solution droplet showed similar transportation distance and transportation velocity on the ASBP. Based on the aforementioned superb water transportation capacity, this ASBP can be applied in the fields of fog collection, solution mixing and reaction, and reagent detection. This work has strong implications for promoting the application of patterned surface with super-wettability in the field of high-performance fluid transportation systems.

Application and corrosion characteristics of chrome-free SiC-MgAl2O4 refractory linings serviced in an entrained flow gasifier

This study investigated the degradation mechanisms of SiC-MgAl2O4 refractory materials in a bench-scale entrained flow gasifier. Layered degradation and protective SiO2 formation on SiC aggregates were observed by macroscopic, microscopic, phase analysis, and thermodynamic calculations. A comparative investigation of erosion rates revealed that SiC-MgAl2O4 materials located away from the burner had an unusually low erosion rate of 0.0017 mm/h, which was significantly lower than that reported in their counterparts situated near the burner (0.0167 mm/h). This performance surpassed even that of high-chrome refractory materials positioned at a similar distance from the burner. The findings underscore the remarkable durability of SiC-MgAl2O4, demonstrating its significant potential as a sustainable and chrome-free alternative for application in harsh gasification conditions. The environment-specific degradation patterns, characterized by the formation of mullite and cordierite, revealed the complex interaction between refractory materials and gasification environments. This understanding offers valuable insights that can inform the development and implementation of chrome-free refractory materials under industrial gasification conditions.

Calibrating the WSA Model in EUHFORIA Based on Parker Solar Probe Observations

We employ Parker Solar Probe (PSP) observations during the latest solar minimum period (years 2018–2021) to calibrate the version of the Wang–Sheeley–Arge (WSA) coronal model used in the European Heliospheric Forecasting Information Asset (EUHFORIA). WSA provides a set of boundary conditions at 0.1 au necessary to initiate the heliospheric part of EUHFORIA, namely, the domain extending beyond the solar Alfvénic point. To calibrate WSA, we observationally constrain four constants in the WSA semiempirical formula based on PSP observations. We show how the updated (after the calibration) WSA boundary conditions at 0.1 au are compared to PSP observations at similar distances, and we further propagate these conditions in the heliosphere according to EUHFORIA’s magnetohydrodynamic (MHD) approach. We assess the predictions at Earth based on the dynamic time-warping technique. Our findings suggest that, for the period of interest, the WSA configurations that resembled optimally the PSP observations close to the Sun were different from the ones needed to provide better predictions at Earth. One reason for this discrepancy can be attributed to the scarcity of fast solar wind velocities recorded by PSP. The calibration of the model was performed based on unexpectedly slow velocities that did not allow us to achieve generally and globally improved solar wind predictions compared to older studies. Other reasons can be attributed to missing physical processes from the heliospheric part of EUHFORIA but also the fact that the currently employed WSA relationship, as coupled to the heliospheric MHD domain, may need a global reformulation beyond that of just updating the four constant factors that were taken into account in this study.

Pencil push-up training compared with binocular vision training in the management of slight post-operative under-correction of intermittent exotropia: A prospective study.

This study aims to compare the effectiveness of pencil push-up training and binocular vision training in treating post-operative mild under-correction in patients with intermittent exotropia. A prospective cohort study was conducted, including patients who underwent surgery for intermittent exotropia at Children's Hospital of Nanjing Medical University between June 2022 and January 2023 and experienced post-operative mild under-correction (-8∆ to -15∆). Patients were divided into two groups: pencil push-up training group and binocular vision training group. All patients underwent measurements of exodeviation and stereoacuity at distance and near, sensory fusion, and fusion convergence amplitude. The data were analyzed using independent sample t-tests, repeated measures analysis of variance, and Chi-square tests. There were no statistically significant differences in exodeviation at distance and near between the two training groups before the training. After 6 months of training, the exodeviation at distance and near achieved a significant decrease in both groups (P < 0.05), and the pencil push-up training group showed a similar distance and near exodeviation compared to the binocular vision training group (t = 1.58, P ＞ 0.05; t = 0.43, P ＞ 0.05). After 6 months of training, the binocular vision training group exhibited significantly superior stereoacuity and fusion convergence amplitude compared to the pencil push-up training group (P < 0.001). Both pencil push-up training and binocular vision training are effective in reducing exodeviation in patients with post-operative mild under-correction of intermittent exotropia. However, binocular vision training demonstrates superior efficacy in restoring stereopsis and fusion convergence amplitude compared to pencil push-up training.