Relevant Codes Research Articles

A unified stochastic particle method with spatiotemporal adaptation for simulating multiscale gas flows

Recently, the Unified Stochastic Particle (USP) method has emerged as a promising approach for multiscale particle simulations, tailored specifically for non-equilibrium gas flow scenarios. However, in typical simulations of multiscale flow fields, the time step and grid size of the original USP method remain constrained by the smallest spatiotemporal scale of local flow field, limiting its true multiscale simulation potential. To overcome this critical issue, this work introduces a spatiotemporal adaptive USP method. Initially, we analyze the sources of errors in the traditional single-scale stochastic particle methods under large time steps, which leads to the derivation of the governing equations of USP. Building upon this foundation, we propose principles that dictate the rational time step and grid size for an arbitrary USP simulation. Subsequently, we introduce a spatiotemporal adaptive approach primarily based on the local gradient length, leading to the development of a USP solver endowed with spatiotemporal adaptability. The universality, reliability, and efficiency of the algorithm have been verified through an examination of three cases: two-dimensional flow over a cylinder, three-dimensional flow over a blunted body, and flow over a scaled X38-like model. This work forms an integral component of the development initiative for the general USP solver—SPARTACUS—with relevant source code being open-source in an online repository under the GNU General Public License (GPL).

Low-value MRI of the knee in Norway: a register-based study to identify the proportion of potentially low-value MRIs and estimate the related costs

ObjectivesThe objective of this study is to investigate the proportion of potentially low-value knee MRI in Norway and to provide an estimate of the related costs.DesignRegister study based on conditional...

FloripaSat-2: An Open-Source Platform for CubeSats

This paper presents the FloripaSat-2 platform, an open-source service platform already on its second version, including, but not limited to, On-Board Data Handling (OBDH), Telemetry, Tracking and Command (TTC), and Electrical Power System (EPS) modules. The platform is conceived to be general-purpose, i.e., easily adaptable for missions with different payloads and requirements. In this context, an overview of the generic nanosatellite architecture, along with the ongoing missions in which it is being employed, is discussed, including the embedded systems, functionalities, and components. Also, the relevant source code is available in an online repository and can be used and expanded to different kinds of CubeSat missions. Finally, good practices regarding the development and a FlatSat proposal for faster testing of the subsystems are presented to aid engineers in testing and adapting the systems to their mission requirements.

Insights on obstetric outcomes in pregnant individuals with Marfan Syndrome: evidence from the National Inpatient Sample.

We investigated the potential association between Marfan Syndrome (MFS) and adverse obstetric outcomes using the National Inpatient Sample (NIS) database. We utilized the International Classification of Diseases (ICD-10) system to identify relevant codes and extracted data from the NIS database covering the period 2016-2019. Descriptive statistics and χ2 tests were employed to summarize and compare baseline characteristics. Univariate and multivariate regression analyses (adjusted for age, race, hospital region, smoking status, and alcohol misuse) were conducted to evaluate association between MFS and adverse obstetric outcomes. The regression analyses were summarized as Odds Ratios (OR) with 95% confidence intervals (CI). Among the 2,854,149 pregnant individuals, 179 had MFS. Baseline characteristics revealed significant associations between MFS and age, race, and hospital location. Univariate analysis showed MFS individuals had significantly increased risks of amniotic fluid/membrane abnormalities (AFAs, OR=1.64, 95% CI: 1.01-2.68, P=0.045) and postpartum hemorrhage (PPH, OR=3.73, 95% CI: 2.41-5.78, P<0.001). Several obstetric outcomes showed some trends towards increased (multiple gestation, placenta previa, and preterm labor) and decreased (premature rupture of membrane, gestational diabetes, and preeclampsia) obstetric risks with MFS; however, they were not statistically significant. Multivariate analysis showed MFS was significantly associated with increased risks of AFAs (adjusted OR=1.68, 95% CI: 1.03-2.74, P=0.037) and PPH (adjusted OR=3.62, 95% CI: 2.31-5.68, P<0.001). MFS is associated with increased risks of adverse obstetric outcomes, specifically AFAs and PPH. These results highlight the importance of monitoring these specific pregnancy outcomes in MFS individuals to ensure optimal maternal-fetal health.

CircRNA identification and feature interpretability analysis

BackgroundCircular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation.ResultsWe developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification.ConclusionsCircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git.

Validating the International Classification of Functioning, Disability and Health Core Sets for Autism in a Sample of Australian School-Aged Children on the Spectrum.

Assessing functioning of children on the autism spectrum is necessary to determine the level of support they require to participate in everyday activities across contexts. The International Classification of Functioning, Disability and Health (ICF) is a comprehensive biopsychosocial framework recommended for classifying health-related functioning in a holistic manner, across the components of body functions, activities and participation, and environmental factors. The ICF Core Sets (ICF-CSs) are sub-sets of relevant codes from the broader framework that provide a basis for developing condition-specific measures. This study combined the ICF-CSs for autism, attention deficit hyperactivity disorder (ADHD) and cerebral palsy (CP) to validate the ICF-CSs for autism in an Australian sample of school-aged children. This cross-sectional study involved caregivers of school-aged children on the spectrum (n = 70) completing an online survey and being visited in their homes by an occupational therapist to complete the proxy-report measure based on the ICF-CSs for autism, ADHD and CP. Absolute and relative frequencies of ratings for each of the codes included in the measure were calculated and reported, along with the number of participants who required clarification to understand the terminology used. Findings indicate that the body functions and activities and participation represented in the ICF-CSs for autism were the most applicable for the sample. However, findings relating to environmental factors were less conclusive. Some codes not currently included in the ICF-CSs for autism may warrant further investigation, and the language used in measures based on the ICF-CSs should be revised to ensure clarity.

Association of Cannabis Use Disorder with Major Adverse Cardiac and Cerebrovascular Events in Older Non-Tobacco Users: A Population-Based Analysis.

Tobacco use disorder (TUD) adversely impacts older patients with established cardiovascular disease (CVD) risk. However, CVD risk in chronic habitual cannabis users without the confounding impact of TUD hasn't been explored. We aimed to determine the risk of major adverse cardiac and cerebrovascular events (MACCE) in older non-tobacco smokers with established CVD risk with vs. without cannabis use disorder (CUD). We queried the 2019 National Inpatient Sample for hospitalized non-tobacco smokers with established traditional CVD risk factors aged ≥65 years. Relevant ICD-10 codes were used to identify patients with vs. without CUD. Using multivariable logistic regression, we evaluated the odds of MACCE in CUD cohorts compared to non-CUD cohorts. Prevalence of CUD in the sample was 0.3% (28,535/10,708,815, median age 69), predominantly male, black, and non-electively admitted from urban teaching hospitals. Of the older patients with CVD risk with CUD, 13.9% reported MACCE. The CUD cohort reported higher odds of MACCE (OR 1.20, 95% CI 1.11-1.29, p < 0.001) compared to the non-CUD cohort. Comorbidities such as hypertension (OR 1.9) and hyperlipidemia (OR 1.3) predicted a higher risk of MACCE in the CUD cohort. The CUD cohort also had higher unadjusted rates of acute myocardial infarction (7.6% vs. 6%) and stroke (5.2% vs. 4.8%). Among older non tobacco smokers with known CVD risk, chronic cannabis use had a 20% higher likelihood of MACCE compared to those who did not use cannabis.

Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations

Physics-Informed Neural Networks (PINNs) has provided a novel direction for solving partial differential equations (PDEs) and has achieved significant advancements in the field of scientific computing. PINNs effectively incorporate the physical constraints of equations into the loss function, enabling neural networks to learn and approximate the behavior of physical systems by optimizing the loss function. According to the existing research, we propose a transferable pretrained neural networks framework based on temporal domain decomposition to solve partial differential equations. Specifically, we divide the domain into multiple subdomains according to time, each of which is solved using an individual neural network. Subsequently, the trained subnetwork and the predicted values at the common boundary are used as the pretrained model and initial values for the next subdomain, respectively. This not only improves the prediction accuracy of the subnetwork, but also reduces the training time. Finally, we present a series of classical numerical experiments including one-dimensional, two-dimensional, and three-dimensional partial differential equations. The experimental results indicate that the proposed method outperforms existing approaches in terms of accuracy and efficiency. Relevant data and code can be accessed on GitHub via https://github.com/karry-Zhang/Trans-Net.

Investigation of the Structural Behavior of Reinforced Concrete Beams at Elevated Temperatures.

Reinforced concrete structures encounter a range of detrimental external factors over their operational lifespan. One of them is the elevated temperature effect due to fires. Conversely, due to the influence of global warming, temperatures are on the rise worldwide, leading to an increase in fire incidents. Owing to the increasing rates of construction and fire incidents, it becomes imperative to investigate the durability of reinforced concrete members when exposed to high temperatures. This experimental study aims to assess the structural behavior of reinforced concrete beams following exposure to elevated temperatures. To accomplish this goal, concrete cube specimens, steel rebars, pull-out specimens, and reinforced concrete beams were exposed to elevated temperatures of up to 800 °C and then allowed to cool in air. Following this, all specimens were subjected to testing in accordance with the relevant codes and standards. Test results were analyzed through comparison. In a comprehensive examination of the results, it is evident that the concrete compressive strength experiences an approximately 55% reduction at 600 °C. Meanwhile, there is no notable decrease in the yield strength of the steel at this temperature. However, at 800 °C, steel yield strength decreases by nearly 30%, while the compressive strength of the concrete decreases by a significant 82%. This indicates a substantial reduction in the load-bearing capacity of the beam specimens due to concrete deterioration and the subsequent decline in the bonding performance between concrete and steel rebars.

The Impact of Australian Bushfires on Asthma Medicine Prescription Dispensing.

Air pollution can cause numerous health problems and increase the need for medicines to treat and prevent asthma in affected areas. There is limited evidence about the association between airborne particles with a diameter of 2.5 micrometres or smaller (PM2.5) and asthma medicine usage. This study examined the potential association between the levels of PM2.5 and the supply of prescription asthma medicines in the Australian Capital Territory (ACT), Australia, during the severe bushfire season between November 2019-January 2020. Daily data was obtained from an ACT air quality monitoring station from November 2019 to January 2020 (study period) and November 2018 to January 2019 (control period, no bushfire). The number and types of government-funded asthma medicine prescriptions were obtained from the Services Australia (government) website by searching under 'Pharmaceutical Benefits Scheme Item Reports' and using relevant item codes during the study and control periods. The medians for PM2.5 levels for the study period were significantly higher than those for the control period (p < 0.001). There were increases in the number of dispensed prescriptions of short-acting beta-2 agonists (SABA), inhaled corticosteroids, and long-acting beta-2 agonists combined with inhaled corticosteroids. The greatest difference was seen with the inhaled corticosteroids: a 138% increase. The increase in the number of dispensed asthma prescriptions during the bushfire season should be used to inform the stock holdings of these medicines in preparation for future events to ensure access to lifesaving asthma medicines.

V4D: Voxel for 4D Novel View Synthesis.

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the performance of the existing method is still limited by the capacity of the neural network, typically in a multilayer perceptron network (MLP). In this article, we utilize 3D Voxel to model the 4D neural radiance field, short as V4D, where the 3D voxel has two formats. The first one is to regularly model the 3D space and then use the sampled local 3D feature with the time index to model the density field and the texture field by a tiny MLP. The second one is in look-up tables (LUTs) format that is for the pixel-level refinement, where the pseudo-surface produced by the volume rendering is utilized as the guidance information to learn a 2D pixel-level refinement mapping. The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task. Moreover, we propose a more effective conditional positional encoding toward the 4D data that achieves performance gain with negligible computational burdens. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance at a low computational cost.

Comparison of different algorithms in Reversi AI

Minimax and alpha-beta pruning have been widely applied in AI for various strategic board games, the utilization of greedy algorithms in this context has received less attention. The Greedy algorithms aim to make locally optimal choices at each step, exploiting immediate gains. This research aims to reveal the potential benefits and limitations of applying greedy algorithms in Reversi gaming AI, specifically through a comparison with the Minimax algorithm. A series of AI versus AI matches were conducted to evaluate and compare the performance of the three different AI algorithms. The objective was to assess their gameplay strategies and decision-making abilities by measuring their average execution time and win rates. Relevant codes and experiments will be carried out in a C++ environment, and the shown codes in this article will only have pseudocode and comments. In conclusion, the findings of this study indicate that the Greedy Algorithm is not a superior alternative to the Minimax Algorithm in competitive scenarios, particularly with increased searching depth. However, greedy algorithms still have weak competitiveness with reduced computational time. For future research, concentrating on improving the performance of the Greedy Algorithm by incorporating more advanced heuristics or adaptive strategies maybe a good choice. Additionally, combining the strengths of both the Greedy Algorithm and the Minimax Algorithm could be a promising direction for further investigation.

Plasticity-based evaluation method for in-plane shear strength of steel-plate composite element

The steel-plate composite (SC) wall is considered an attractive structural type for the next generation nuclear power plants because of its efficient construction schedule, radiation shielding function, and excellent mechanical properties. During seismic action, the SC walls sustain large in-plane and out-of-plane loadings. So, it is essential for the evaluation of the in-plane and out-of-plane load carrying capacity of SC walls in design. Up to date, several equations that are developed based on elastic theory are proposed and adopted by relevant design codes. In the previous study by the authors, a lower bound equation was present for evaluation of the in-plane shear strength of SC elements based on the lower limit theorem of plastic limit theory. As a counterpart to that paper, an upper bound equation for evaluation of in-plane shear strength of SC elements based on the upper limit theorem of plastic limit theory was developed, and its minimum value solution which is the closest one to the exact solution was derived. Meanwhile, considering that concrete is not a perfectly plastic material, another equation is present to prevent the concrete between the steel faceplates from crushing prior to the steel yielding. A comparison between the results predicted by the proposed equation in this study and the test results of the SC panel specimens suggested the adequacy and accuracy of the proposed equation. In addition, comparisons of results evaluated by the proposed equations and the equations in codes JEAG 4618 and AISC N690 were also performed.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.

Improving dose-related adverse drug events among hospitalized older adults using a geriatric prescribing context.

Adverse drug events (ADEs) during hospitalization are a serious, yet preventable concern for older adults. Our institution designed a Geriatric Prescribing Context (GPC) to adjust doses for the older adult population but its impact on ADEs was unknown. The goal of this study was to assess any differences in rates of ADEs before and after its implementation in July 2017. We used relevant ICD-10 codes followed by confirmatory chart review to identify dose-related ADEs from 10 commonly used medications at our institution. We assessed differences in the number of admissions with an ADE before and after the GPC implementation using a test of binomial proportions. The pre-period was from July 2016 through June 2017 and the post-period was from August 2017 through July 2018. We compared the rate of ADEs per 1000 patient days between periods with a Poisson rate test and further examined any differences in harm categories using a Fisher's exact test. The proportion of admissions with any dose-related ADEs significantly decreased from 0.0082 to 0.0037 after the GPC (p = 0.04). The rate of dose-related ADEs also declined from 2.5 per 1000 patient days to 1.1 per 1000 patient days (p = 0.001). Harm categories did not change significantly between time points (p = 0.30). Based on our list of relevant ICD-10 codes, the GPC was associated with lower dose-related ADEs for our selected medications among hospitalized older adults.

Rapid: Zero-shot Domain Adaptation for Code Search with Pre-trained Models

Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results in performance decreases when confronted with cross-domain scenarios including domain-specific or project-specific situations. This decline can be attributed to their limited ability to effectively capture the semantics associated with such scenarios. To tackle the aforementioned problem, we propose a ze R o-shot dom A in ada P tion with pre-tra I ned mo D els framework for code search named RAPID. The framework first generates synthetic data by pseudo labeling, then trains the CodeBERT with sampled synthetic data. To avoid the influence of noisy synthetic data and enhance the model performance, we propose a mixture sampling strategy to obtain hard negative samples during training. Specifically, the mixture sampling strategy considers both relevancy and diversity to select the data that are hard to be distinguished by the models. To validate the effectiveness of our approach in zero-shot settings, we conduct extensive experiments and find that RAPID outperforms the CoCoSoDa and UniXcoder model by an average of 15.7% and 10%, respectively, as measured by the MRR metric. When trained on full data, our approach results in an average improvement of 7.5% under the MRR metric using CodeBERT. We observe that as the model’s performance in zero-shot tasks improves, the impact of hard negatives diminishes. Our observation also indicates that fine-tuning CodeT5 for generating pseudo labels can enhance the performance of the code search model, and using only 100-shot samples can yield comparable results to the supervised baseline. Furthermore, we evaluate the effectiveness of RAPID in real-world code search tasks in three GitHub projects through both human and automated assessments. Our findings reveal RAPID exhibits superior performance, e.g., an average improvement of 18% under the MRR metric over the top-performing model.

OWNERSHIP STRUCTURE AND DIVIDEND POLICY OF LISTED OIL AND GAS COMPANIES IN NIGERIA

The study examines the effect of ownership structure on dividend policy of listed oil and gas companies in Nigeria. The independent variable was proxied using managerial ownership, government ownership and foreign ownership. On the other hand, dividend policy was proxied by dividend payout ratio. The study employed the ex-post facto research design in analyzing the panel data collected from eight (8) oil and gas companies sampled from a total population of twelve companies listed on the Nigerian Exchange Group (NGX). the study covered a period of ten years (2013 to 2022) while the purposive sampling technique was employed. The multiple regression technique of analysis was employed in analyzing the data and the results of the analysis revealed a positive and significant relationship between Managerial Ownership and Dividend policy. Thus, suggesting that, an increase in the proportion of shares held by management would results in increased dividend payment. On the contrary, government ownership and foreign ownership had no significant effect on dividend policy of listed oil and gas companies in Nigeria. The study therefore concludes that, managerial ownership would influence dividend payment. Hence it is recommended that, managerial ownership of oil and gas companies be monitored by regulatory agency to ensure compliance with relevant codes and provision.

Association between Endometriosis and the Risk of Ovarian, Endometrial, Cervical, and Breast Cancer: A Population-Based Study from the U.S. National Inpatient Sample 2016-2019.

We investigated the potential relationship between endometriosis and risk of ovarian, endometrial, cervical, and breast cancers using the National Inpatient Sample (NIS) database. We utilized the International Classification of Diseases (ICD-10) system to identify relevant codes from the NIS database (2016-2019). Univariate and multivariate regression analyses (adjusted for age, race, hospital region, hospital teaching status, income Zip score, smoking, alcohol use, and hormonal replacement therapy) were conducted to evaluate the association between endometriosis and gynecologic cancers and summarized as odds ratios (ORs) with 95% confidence intervals (CIs). In the examined dataset, there were 1164 and 225,323 gynecologic cancer patients with and without endometriosis, respectively. Univariate analysis showed endometriosis was significantly associated with a higher risk of ovarian (OR = 3.42, 95% CI: 3.05-3.84, p < 0.001) and endometrial (OR = 3.35, 95% CI: 2.97-3.79, p < 0.001) cancers. There was no significant association between endometriosis and cervical cancer (OR = 1.05, 95% CI: 0.85-1.28, p = 0.663). Interestingly, endometriosis was significantly associated with a low risk of breast cancer (OR = 0.12, 95% CI: 0.10-0.17, p < 0.001). Multivariate analysis after Bonferroni correction (p < 0.006) showed that endometriosis was significantly associated with a high risk of ovarian (adjusted OR = 3.34, 95% CI: 2.97-3.75, p < 0.001) and endometrial (adjusted OR = 3.61, 95% CI: 3.12-4.08, p < 0.001) cancers. Conversely, there was no significant association between endometriosis and cervical cancer (OR = 0.80, 95% CI: 0.65-0.99, p = 0.036). Patients with endometriosis exhibited unique gynecologic cancer risk profiles, with higher risks for ovarian and endometrial cancers, and no significant risk for cervical cancer. The observed connection between endometriosis and a reduced risk of breast cancer remains a perplexing phenomenon, which cannot be put into context to date.

Pharmacy Students’ Lived Experiences With Atopic Dermatitis Inform Perceptions of Learning in the Curriculum

ObjectiveTo explore the impact of lived experiences of pharmacy students with atopic dermatitis (AD) on perceptions of learning in pharmacy curriculum. MethodsAn exploratory qualitative study was conducted with pharmacy students in the United Kingdom to understand how their lived experiences affect their perception of AD in pharmacy curriculum. Semistructured interviews were conducted, and a thematic analysis method was followed. Firstly, codes were created, and then relevant codes were combined to identify themes. ResultsThirteen pharmacy students were interviewed. Study findings showed pharmacy students support teaching with a holistic approach to management and patient-centered care in AD in pharmacy curriculum. Although students had empathy and moral support for patients, they also described a need for teaching on the mental health effects of AD in pharmacy education. ConclusionThis brief report explores the role of lived experience of pharmacy students in considering the provision of holistic, patient-centered care in AD teaching in pharmacy education. Participants also suggest the need within the pharmacy curriculum for training to provide mental health advice to patients with AD.

Detection of Bowel and Mesenteric Injuries Using Deep Learning Computer Vision Models

Background/Objective:While only seen in 1-5% of patients who undergo a CT (computed tomography) scan, blunt bowel and mesenteric injuries (BMI) are associated with significantly increased morbidity and mortality. A significant cause of the increased morbidity of BMI is due to the difficulty of diagnosis from clinical and imaging information which leads to delay in diagnosis. Accurate and timely diagnosis is vital to reduce the morbidity of BMI.&#x0D; Methods:For this project, our primary objective is to create a binary prediction model that determines if a patient has BMI based on their abdominal CT scans. Due to the importance of the early and definitive diagnosis of BMI in trauma patients, an extension of this project will seek to introduce explainability into the model to highlight which features on the CT scan caused the model to make its prediction. The patients with BMI were sourced from a trauma registry that recorded trauma cases from IU Health with relevant diagnosis codes. The images from our search will be reduced to the relevant slices for diagnosis of BMI and then used to train an ML model to makea yes/no prediction from the image. Once the model is trained, testing data will be evaluated on the model and the gradient vectors from the model during inference will be used to create a heatmap with GRAD-CAM that illustrates what portions of the image were relevant for the decision made by the algorithm.&#x0D; Future Directions:Using the collected abdominal CTs, we can train our machine learning pipeline to detect BMI. Based on the performance of the model, we will determine if we need to collect more data. Then, we can evaluate the explainability of the model using GRAD-CAM and compareperformance of the ML model to the performance of expert and trainee radiologists.