bluuebunny/crossref_metadata_2025 huggingface dataset:
Asian American Representation in US History Textbooks
Authors: Lee Jacob , Cho Harrison , Orrego John
Published: March 2023

The US history curriculum is an indispensable part of American education and taught throughout all high schools. Though educational resources have diversified over the years, the most important tool used to inform the curriculum remains to be the textbook. Debates about the content and administration of the US history curriculum and textbook have been continuing for decades; a particularly popular debate is the portrayal of minorities within US history textbooks. Though the deficiency of representation is well documented within Native Americans, African Americans, and Hispanic Americans history, the representation of Asian Americans is severely lacking. Our paper will examine two US history textbooks, both used widely across the country in public school curriculums, and identify the patterns of Asian American representation. Our results show that Asian American representation is quite limited and passive. Accomplishments are stated vaguely, and important historical Asian American figures are completely absent. Textbook authors and history teachers throughout the US should recognize these limitations and work to reform the curriculum to create a more inclusive US history class.
An Intelligent System for Early Prediction of Cardiovascular Disease using Machine Learning
Authors: Kachhawa Aarush , Hitt Jeremy
Published: March 2023

Cardiovascular disease (CVD) remains the leading cause of death, responsible for 18.6 million deaths globally in 2019. Given the wide availability of several effective therapeutic treatment options, early diagnosis of CVD is critical for timely intervention and slowing down the progression of the disease. CVD is associated with a multitude of risk markers with non-linear interactions among them, making accurate diagnosis of CVD quite challenging, especially for non-specialized clinicians and under-resourced facilities in developing countries. In recent years, machine learning based computational techniques have shown great promise in becoming a great diagnostic tool. The goal of this research is to leverage multiple machine learning methods such as random forest, gradient boosting, logistic regression and artificial neural network and evaluate their prediction efficacy. This study also evaluates the feasibility of combining multiple UCI datasets in order to improve the prediction accuracy of the models. On a merged dataset of over 700 patients from the UCI machine learning repository, the most accurate model was found to be the random forest classifier, showing an accuracy and F1 score of 94% and AUC of 0.98. It was found that ensemble learning methodologies along with data optimization and hyperparameter tuning techniques were able to achieve higher accuracy relative to prior published studies on these datasets. Finally, this study also proposes how these machine learning workloads can be incorporated into a distributed cloud connected healthcare system to make them widely accessible to practicing doctors and enable them to assess CVD risk of their patients.
Understanding Human Thoughts, Behaviors, and Social Influence to Counter Racial Discrimination
Authors: Yoon Jayna
Published: March 2023

In light of the rising societal tension and critical importance of systemic response, such as the Black Lives Matter movement and the Stop AAPI Hate movement, many have reexamined the role that discrimination plays on their perception of others. This paper recounts how racial discrimination is currently affecting society holistically and how in particular it affects African American Youth. The paper will then utilize current literature on the psychological aspect of discrimination in order to solidify a clearer understanding of human behaviors and thoughts with regard to racial discrimination. Lastly, this paper will illustrate how public institutions can extend their preexisting diversifying efforts using the key concepts of Moscovici’s minority influence theory in order to combat this systematic problem.
Deep Learning for Automated Echocardiogram Analysis
Authors: Wang Samuel , Hu Ping
Published: March 2023

In the fight of heart disease (the #1 global killer), Left ventricular ejection fraction (EF) calculated via echocardiography plays crucial role for disease diagnosis and treatment because EF can distinguish heart failure from normal cardiac function. However, traditional EF calculation is a time-consuming manual process with high variability ‒ a single routine echocardiogram generates about 10–50 videos (~3,000 images), coupled with the limited capacity to analyze such large dataset by human experts often results in misdiagnosis. This project aimed to develop transparent and interpretable deep learning pipeline for automated echocardiogram analysis to calculate EF for quick assessment of cardiac function. Using the EchoNet dataset, five PyTorch deep learning models were trained to automatically calculate EF achieving mean error rates that ranged from 14-16%, comparable to that of expert cardiac sonographers, and significantly outperformed qualitative analysis by physicians (~30% error rate). Among the five models, MobileNet was identified as the best deep learning model for web application and portable devices; therefore, it was deployed as a web-based app through AWS, standalone PC, and Raspberry Pi, enabling upload and analyze echocardiogram videos and obtain EF calculation results within seconds. Such automated echocardiogram analysis can dramatically speed up image analysis, reduce the burden on cardiologists, eliminate inter-observer variability, hence democratize echocardiography by enabling non-experts to quickly and accurately assess cardiac functions at point of care even in cardiology expertise limited rural areas and developing countries. Future work includes improving these models with additional data and adapting the app for handheld ultrasound devices.
The Effects of bride price on the Chinese marriage market
Authors: Bairuiyang Bai
Published: March 2023

This paper will mainly focus on the marriage market of China and how bride price affects it through diverse methods like analyzing the amount of bride price in different parts of China, using the economics models including demand and supply curve and the indifference curves to figure out whether bride price will affect the marriage rate, and finally developing a theory to depict a females’ psychological and economics evaluation on whether a male is worth marrying. The author finds that if the bride price is too high, then it will negatively affect the marriage rate. The bride price will also affect some of the females’ decisions in choosing their lifestyles. However, for more educated women, the theory suggests that the bride price is no longer the most important factor in their marriage decision.
Nuclear Energy as a Highly Viable Source to Mitigate Rapid Warming
Authors: Zhang Huaxu , Morrissey Joel
Published: March 2023

The longstanding issue of global warming has taken its toll on countries around the world. Global warming has become one of the major issues that humans need to face together in the 21st century. A highly viable solution to this issue is adopting nuclear energy as one of the world’s major energy sources. Nuclear energy is more reliable and consistent than renewable energy and cost-efficient in energy generation in the long run, making it a suitable replacement for acquiring energy from traditional fossil fuels. In addition, nuclear power and renewable energy should both be considered as possible solutions for mitigating global warming, rather than as rivals contending for superiority. Currently, the two major challenges of using nuclear energy include the high short-term cost of building nuclear power plants and nuclear fear by the general public. At the current rate of warming, it is necessary to use every viable means to slow down the process of warming enough for better solutions to climate change to be devised.
Electric Potential Analysis of Contaminated Lands in Idaho for Utility-scale Solar Energy
Authors: Su Cindy , Stutzman Erin
Published: March 2023

In the state of Idaho, major electric utilities, such as Idaho Power, have shown a growing interest in solar energy. Because utility-scale solar energy (USSE) requires large land usage, this aspect may be especially problematic for indigenous communities and wildlife when siting USSE. Though less problematic, the proximity of transmission networks to a new USSE site may also be a drawback because of the cost associated with building new networks. However, the use of contaminated sites listed in EPA’s RE-Powering Initiative may be an effective solution for reducing the difficulties associated with siting USSE. Within Idaho borders, the estimated electric potential of such contaminated sites within Idaho Power’s service area was evaluated in this study. This was done through map overlays and simple mathematical calculations that consider factors that affect PV performance. The RE-Powering sites were found to be of use for providing energy to Idaho Power’s customers in Idaho and reducing greenhouse gas. Thus, this study may be of use in mitigating the challenges associated with siting USSE so that further solar energy adoption may be achieved.
Examining the predictive power of moving averages in the stock market
Authors: Chaddha Arjun , Yadav Shilpa
Published: March 2023

Moving averages are common technical analysis tools which investors use to generate buy and sell calls in the stock market. The purpose of this research paper is to analyse whether common moving average techniques can reliably predict stock market behaviour. Using hypothesis testing, this paper tests whether the percentage return yielded by using moving average combinations to trade stocks in the S&P 500 index was significantly higher than a) the percentage return yielded by randomly buying and selling the stocks and b) the market percentage return. The tests were conducted for the S&P 500 stocks in four different time frames to understand the performance of moving averages during different stock market trends (uptrend, sideways trend, downtrend). Moreover, the performance of three different buying and selling techniques which use moving averages were compared. The results of the paper indicate that an investor should not use moving averages to trade stocks owing to their limited predictive power. There were only a few moving average combinations which were significantly better than randomly buying and selling. Even those few combinations could not yield higher percentage returns than the market percentage return.
Bioartificial Liver Manufacturing Methodologies in Comparison to Hepatogenesis
Authors: Roy Aanya , Kethar Jothsna
Published: March 2023

End-stage organ failure is a major global issue, with the liver being the second-highest transplanted organ due to lifestyle choices or other conditions. In the field of biomedical engineering, artificial organ manufacturing has been a possible alternative to organ transplants by aiming to achieve less immune rejection, more efficient production, and higher accessibility. Of the biological manufacturing methods today, two up-and-coming technologies for the liver include 3D bioprinting and decellularized organ regeneration. By analyzing general stages of 3D bioprinting and decellularization for liver development and comparing it to the cellular and genetic stages of hepatic development in the embryo, dilemmas and successes in bioartificial manufacturing can be identified. Overall, neither artificial methodology can replicate the genetic influence or non-liver based influence of hepatogenesis. 3D bioprinting is similar to hepatogenesis in cell development and construction, but contains shortcomings in vascularization, which can be addressed using the vasculogenesis and angiogenesis processes in hepatic development. Decellularization allows for cell differentiation, although it is unable to instill natural cellular distribution and gradual ECM development. This may cause blockages for clinical use in the future. Both methods have potential for bioartificial liver development and clinical application, but 3D bioprinting technology is more aligned with the stages of hepatic development, which has proven to induce fewer errors in the physiological nature of the manufactured liver. In the future, modification and research must be conducted on the specific stages of these methodologies for either to create an operational bioartificial liver for clinical use.
How a Reduction in Nursing Impacts Hospital Centers; A Scoping Review
Authors: Devireddy Nikhil
Published: March 2023

This article explores the relationship between the reduction in nurse staffing levels and the quality of patient care and hospital financial performance. The online database PubMed was searched by employing the MeSH terms “Nurse Staffing AND Hospitals AND (Patient Care OR Financial Performance)” and incorporating a set of inclusion (e.g., tracks trends in nurse staffing numbers and are conducted in adult medical units) and exclusion criteria (e.g., out-patient medical centers that aren’t affiliated with an academic institution). Through integrating a systematic search methodology into the format of a scoping literature review, this paper identifies that reducing nurse staffing correlates with higher rates of patient mortality and patient complications. Lower RN levels also correlated with higher costs in hospitals, thus a reduction in profit; though, this finding was inconsistent across included articles. The findings from this review contribute to the rising debate over the vitality of nurses in the healthcare industry. Along with the quantity of staffing, the quality of employment provided to nurses will help hospitals efficiently use their resources in providing patients with high value care—the best care possible.
Influence of Climate Change on High School Students' Attitudes Towards Electric Vehicles
Authors: Vitolo Kristen
Published: March 2023

Electric vehicles are gaining interest due to their increasing affordability, improved design, and longer range. Generation Z buyers currently comprise a small percentage of new electric vehicle purchases, but will dominate as the largest consumer group in the automotive industry during the next 10-20 years. To test the level of interest in electric vehicles by the high school subset of Generation Z, 56 anonymous individuals were surveyed to solicit their opinions. The survey asked for each participant's opinion on climate change and electric vehicles in an attempt to find a correlation between a person's attitude towards climate change and that person's interest in electric vehicles. I hypothesized there would be a positive correlation between a student's view on climate change and their perception of electric vehicles. Using a Pearson's correlation study, a positive, but weak, correlation was found between the two topics. Based on the data's p value, the correlation was determined to be statistically insignificant, rejecting my original hypothesis. Through an exploratory data analysis of the survey results, it was found that most high school students are aware of climate change, but their awareness alone may not influence their lifestyle choices to lessen the problem. I found that the respondents were more concerned about the price of electric vehicles than their environmental benefits. Despite observing a weak correlation, this study indicated that if price were not a factor, purchasing an electric vehicle would be more strongly considered by those who care about climate change than those who do not.
Predicting housing prices and analyzing real estate markets in the Chicago suburbs using machine learning
Authors: Xu Kevin
Published: March 2023

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models. The code and data files can be found at  https://github.com/ GeometricBison/HousePriceML.
The Empathic Potential of Video Games
Authors: Cho Bethany , Bainbridge Katie
Published: March 2023

Research suggests that video games can promote empathy, a vital skill for human interaction. The goal of the literature review was to consolidate and analyze game mechanics and storytelling techniques optimal for creating a game that promotes empathy as well as catalog commercial games that utilize these elements well. Additionally, practical applications for how video games that promote empathy can be used are explored.
Is Plant Protein Equally As Effective At Promoting Lean Muscle Mass Compared To Meat?
Authors: Hegde Aalaap , Roberts Aubrey
Published: March 2023

In the past few decades, there has been an increased emphasis on plant-based diets. While plant-based diets have many cardiovascular and environmental benefits, little is known about how plant-based diets specifically impact athletic performance and promote muscle growth. Many athletes are reluctant to adopt plant-based diets, fearing that they will lose lean muscle mass due to insufficient protein content. In this research review, we evaluate factors that differentiate plant proteins and meat in regard to optimal lean muscle synthesis. One of the more important differences between plant protein and meat is their amino acid composition. Since meats contain all 9 essential and many nonessential amino acids, they have a more “well-rounded” amino acid composition, thus being more effective at promoting lean muscle growth. A protein source’s amino acid composition, along with other factors such as cell structure, affects its bioavailability. Due to differences in protein bioavailability, different quantities of distinct protein sources may need to be consumed to reach the same anabolic effect. In addition to amino acid compositions, nutrient densities also differ between plant proteins and meats. Although there are some exceptions, plant-based proteins tend to offer a more balanced profile of minerals and nutrients, which also facilitate lean muscle synthesis on the chemical level. Overall, an omnivorous diet may be optimal for muscle growth, but introducing a variety of plant proteins with different amino acid and nutritional profiles can help overcome this variance.
How Gandhi Became Orientalist: The Misrepresentation of Mahatma Gandhi and Indian Decolonization in Gandhi
Authors: Ravindran Maya
Published: March 2023

Through examining the movie Gandhi directed in 1982 by Richard Attenborough, this essay aims to reflect on the pervasiveness of biased representations in Western cultural production and the legacy of imperialism in Western media. The inaccurate portrayal of Gandhi as a political figure extends to an incomplete narrative of Indian independence since India’s story of independence is strongly tied with his political identity. The movie Gandhi essentially serves as an introduction to India for the Western audience. Due to this, it is essential to reveal potentially misleading parts that the film depicts. Since the film primarily focuses on the characterization of Gandhi, each aspect discussed will show how the film has failed to adequately portray Gandhi’s character during the tumultuous period of the India’s decolonization process.
Is There a Correlation Between A Company’s Ethical Practices and Financial Performance?
Authors: Rangarajan Smriti , Cubra Chris
Published: March 2023

This paper examines thirty-nine different companies within and across various sectors to determine if a correlation exists between a company’s ethical business practices and financial success. To create such a comparison, a unique model of business ethics was created; one that was both quantifiable and took into account various aspects of the company. As a relatively new field, business ethics has traditionally been examined qualitatively and largely neglected. By examining business ethics directly with success, this project not only aims to raise awareness of business ethics but also determine whether companies have an incentive to follow through with ethical practices. After thorough research, nine factors comprised the ethics score, while five factors were totaled to obtain the success portion of the score; each score was worth 100 points. The calculations and graphing were done in Python using Google Colab. The results indicated that no correlation existed between a company’s ethical conduct and success, with companies in the Media sector scoring the highest on the ethical scale. These results point out the need for stricter policies on business ethics and more ethical training within companies.
Election Forecasting Using Macroeconomic and Social Indicators via Machine Learning
Authors: Guan Lucas , Mani Ganesh
Published: March 2023

A comparative analysis of machine learning models is executed for the forecasting of incumbent party losses during federal elections in democratic countries. A proprietary dataset that encompasses a wide array of potential economic and social factors affecting election outcomes is compiled, and the most significant factors are identified and evaluated. A myriad of the most popular machine learning models for supervised learning is applied to the dataset, utilizing them as classifiers to predict whether the incumbent party stays in power during federal elections for eleven of the world’s most populous and democratic countries: the United States, Canada, the United Kingdom, the Netherlands, Austria, Norway, Sweden, Denmark, Australia, India, and New Zealand. The results show that the most significant factors for election outcomes are inflation growth rate, unemployment growth rate, and voter turnout growth rate. Multilayer perceptron produces the most accurate classifications. Additionally, Gaussian models such as Gaussian process classifier and Gaussian naive Bayes have the poorest classification accuracy.
Impact of Immigration on transfer activity by English football clubs
Authors: Kapoor Keshav Malik
Published: March 2023

Rising immigration has led to a larger proportion of foreign players in each league. This paper aims to explore the relationship between rising immigration and average transfer fees. This paper also studies the impact of the Bosman Ruling in 1996 on the football transfer market. The variables are studied pre and post the ruling. In order to build the economic model, data of all teams from the English Premier League for the past 28 years is collected. The variables for which data are collected include the Relative Average Transfer Price (inclusive of inflation), Relative Broadcast Revenues earnt per club, and the league coefficient of the English Premier League. A multivariable linear regression is performed on these data with the Relative Average Transfer Price as the response variable and other variables as explanatory variables.Further, dummy variables are created for each team and each year to account for unaccounted changes. After analyzing the regression, the model is found to be relatively strong in its prediction power with an adjusted R Squared of 0.642 and a standard error of the regression of 21.528. The model’s results suggest that broadcast revenues and immigration numbers have the greatest impact on transfer fees, while the league coefficient is not as important as the general public’s view of it. However, it is seen that before the ruling it was the league coefficient that had a major impact on transfer fees. After the ruling in 1996, broadcast revenues and number of immigrants took precedence.
Biodiesel Production Innovation Based on Jatropha Curcas and Soybean Oil
Authors: Mayowa Jadesola
Published: March 2023

Biodiesel Production Innovation Based on Jatropha Curcas and Soybean Oil
Jadesola Mayowa1
1University of Ibadan
Abstract
The study evaluated the potential of Jatropha curcas and soybean oil as feedstocks for biodiesel production and compared the performance of the biodiesels produced. The biodiesels were characterized and their performance was tested in terms of density, viscosity, engine efficiency, power output, and emissions. The results showed that both biodiesels had similar properties and performance compared to conventional diesel fuel, making them suitable alternatives. However, further research and optimization may be required to improve the properties of the biodiesels and ensure their sustainability as feedstocks for biodiesel production. The study provides valuable information for the development of sustainable and eco-friendly energy solutions based on Jatropha curcas and soybean oil as feedstocks for biodiesel production.
An Islamic Ethico-legal Framework for Articular Cartilage Tissue Engineering Research
Authors: Ahmad Radzi Muhammad Aa’zamuddin , Sha’ban Munirah , Mohd. Azharuddin Nur Syamimi , Azhim Azran , Abdulahi Hashi Abdurezak
Published: March 2023

The available studies showed a connection between mental health features of anxiety, depression, and stress with osteoarthritic patients. The joint disease can be treated with articular cartilage tissue engineering (ACTE), which has undergone various research and development. However, the ethical and legal concerns of such practices, including cell sources, biomaterials, signalling factors, and animal study, have yet to be addressed systematically. The study aims to formulate an Islamic ethical and legal framework to facilitate both the scientific and social progress of ACTE research in the Malaysian context. The present work used content analysis of relevant journal articles obtained through systematic online searches. The study then utilised the existing documents on Malaysian laws, regulations, and guidelines; and fatwā related to the ACTE research to build a framework. Based on the documents, the ethical and legal framework was formulated. The framework was proposed to include six steps in handling the ethical and legal issues in ACTE research. There are (1) to select research design and strategy, (2) to identify the potential issues, (3) to review relevant laws, regulations and guidelines, (4) to review the relevant sharīʿah rulings and opinions, (5) to consider possible alternatives and its consequences, and (6) to decide and implement the best course of action. These six steps are aligned based on the concept of Maqāṣid al-Sharīʿah (the objectives of Islamic law) and al-Qawāʿid al-Fiqhiyyah (Islamic Legal Maxim). The framework can serve as a guide to conduct safe and sound ACTE research based on the Islamic perspective.
Data Cultures: a scoping literature review
Authors: Oliver Gillian , Cranefield Jocelyn , Lilley Spencer , Lewellen Matthew
Published: March 2023

Introduction. This paper reports the outcomes from a literature review of the concept of data cultures as the first step in extending the body of knowledge relating to information culture in the information science domain and raising awareness of the need for further research. Method. A scoping literature review was conducted of English language peer reviewed publications indexed in SCOPUS as well as specialist information and computer science databases. Results. There is a rich and growing body of research concerned with the concept of data cultures, spread across a wide range of disciplines but without any evidence of a universally adopted shared definition or understanding. Information science expertise does not appear to be widely recognised as providing any essential insight into this problem space. Conclusion. The knowledge and understanding of information cultures gained from information science research does not seem to be being applied to data dominated environments. Expertise relating to information behaviour, digital literacies, information management, digital sustainability and preservation are unique and essential perspectives to understand issues relating to data cultures.
Internet use and e-health literacy among tuberculosis patients in the Directly Observed Therapy Centre, Lagos State, Nigeria
Authors: Olayemi Olalekan Moses , Shola Abolarinwa Timothy
Published: March 2023

Introduction. This study investigated Internet use and e-health literacy among tuberculosis patients attending Directly Observed Therapy Centre in Lagos State, Nigeria. Method. The study population comprised registered adult tuberculosis patients on active treatment and assessing healthcare in the designated Directly Observed Therapy Centre. Data were collected using a questionnaire and e-health literacy was measured using the 8-item e-health literacy scale. Analysis. Descriptive statistics and Pearson correlation coefficient was used for the analysis. Results. The findings of the study revealed that a majority (73.3%) of the respondents had mobile phone devices for accessing e-health information and acknowledged that the Internet is very useful to their health. However, many of them did not have the skills to evaluate the health resources found on the Internet and cannot differentiate high quality from low quality health resources. Hence many of the patients did not feel confident in using information sourced from the Internet. The result of the hypotheses shows that educational level was significantly associated with usefulness and perceived importance of online health information (r= 0.189, p=0.010). Conclusion. The results showed the need to inculcate e-health literacy into the educational program of tuberculosis patients in the country, and how to evaluate online information as a reliable source of medical information.
Social Media by Proxy: how older adults work within their ‘social networks’ to engage with social media
Authors: Webster Gemma , Ryan Frances VC
Published: March 2023

Introduction. This paper reports an exploratory qualitative study investigating the ways in which older adults are supported by social media proxies defined as 'an individual who uses a social media account for or supports the use of a social media account by another person'. Method. Interviews, a diary study, and a focus group were conducted to explore the motivations for undertaking a proxy role; formal or informal agreements between proxies and account holders; and collaborative proxy practices that exist between the individuals providing or receiving proxy support. Analysis. A reflective thematic analysis of all three data sources was undertaken. The coding structure was developed from the interview, diary and focus group guides as a way of categorising the data into themes. Results. Social media proxy relationships exist, even if proxy roles are not clearly defined, and that older adults engage with their ‘social networks’ to identify proxy support and it is likely that without this support the older adult would be unable to fully access or engage with social media or other online accounts. Conclusion. This research highlights the need for more in-depth investigations related to social media proxies, especially as the use of social media and other online platform is increasing steadily across all age groups.
Green Business Sustainability: SMES Strategy to Increase Business Performance and Save the Environment
Authors: PURBAWATI Dinalestari , WIDIARTANTO Widiartanto , Jumpakita PINEM Robetmi
Published: March 2023

This study aims to test the variables of green marketing, innovation, and ethics towards business performance. This study employs a quantitative approach with a survey method, with the population of the study consisting of all visitors who had visited ecotourism in Indonesia prior to the COVID-19 pandemic.
Endoscopic management of buried bumper syndrome using the Balloon Dilation Pull (BDP) technique: a multicenter analysis
Authors: Maly M , Bronswijk M , Christiaens P , Snauwaert C
Published: March 2023

Background and study aims: Buried bumper syndrome (BBS) may complicate percutaneous endoscopic gastrostomy placement. In these patients, endoscopic treatment ought to be considered. Various approaches have been published, ranging from dissectionbased techniques to novel dedicated devices, although the evidence supporting the use of the Balloon Dilation Pull (BDP) technique has been limited to single case reports. The aim of this paper is to assess the feasibility, efficacy and safety of the systematic use of the BDPtechnique for the endoscopic treatment of BBS.

Patients and methods: We performed a retrospective multicenter analysis of prospectively collected data from all patients treated with the BDP-technique between January 2011 and November 2021.

Results: In total, 26 patients were identified (median age 72 (SD ± 13) years, 74% male, 84.6% underlying neurological disease). Technical success was achieved in 92.3%, with a median procedure time of 17.5 minutes (range 5-27). Adverse events were identified in 3.8% of patients (N=1, aspiration, ASGE lexicon severity grade: moderate).

Conclusions: Our experience suggests that the BDP-technique is highly efficacious and safe, using accessories readily available in every endoscopic unit. Given the limited procedure time and tools required, this procedure has the potential to further optimize patient care in the context of BBS.
Novel insights in the pathophysiology and management of functional dyspepsia
Authors: Vanuytsel T
Published: March 2023

Functional dyspepsia is a common functional gastrointestinal disorder with bothersome symptoms in the upper abdomen without an organic lesion that is likely to explain the complaints. Traditionally, changes in gastric physiology were held responsible for the symptoms, including delayed gastric emptying, impaired gastric accommodation and hypersensitivity to distension. However, gastric sensorimotor disturbances correlated only poorly to symptom severity and treatments targeting these abnormalities are not very effective. In the last decade, the duodenum has been identified as a key integrator in the pathophysiology of functional dyspepsia with an impaired barrier function and immune activation with a particular role for eosinophils and mast cells. Moreover, changes in the duodenal microbiota were associated to dyspeptic symptoms and eosinophil counts. PPIs – still the first line treatment for functional dyspepsia – have been shown to reduce symptoms through anti-inflammatory effects in the duodenum, similar to their effect in eosinophilic esophagitis. Finally, specific probiotic strains were effective in improvement of postprandial symptoms, most likely through an anti-inflammatory effect as demonstrated by reduced Th17 signaling. These novel insights in pathophysiology and treatment provide novel hope for patients with this challenging condition.
A retrieval of xCO2 from ground-based mid-infrared NDACC solar absorption spectra and comparison to TCCON
Authors: Chiarella Rafaella , Buschmann Matthias , Laughner Joshua , Morino Isamu , Notholt Justus , Petri Christof , Toon Geoffrey , Velazco Voltaire A. , Warneke Thorsten
Published: March 2023

Abstract. Two global networks of ground-based Fourier transform spectrometers are measuring abundances of atmospheric trace gases that absorb in the near and mid infrared, Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON). The first lacks a CO2 product, therefore this study focuses on developing a xCO2 retrieval method for NDACC from a spectral window in the 4800 cm−1 region. This retrieval will allow to extend ground-based measurements back in time, which we will demonstrate with historical data available from Ny-Ålesund. This region is covered by both TCCON and NDACC, which is an advantage for collocated comparisons where available. The results are compared with collocated TCCON measurements of column-averaged dry-air mole fractions of CO2 (denoted by xCO2) in Ny-Ålesund, Svalbard and only TCCON in Burgos, Philippines. We found that it is possible to retrieve xCO2 from NDACC spectra with a precision from 0.2 % . The comparison between the new retrieval to TCCON showed that the sensitivity of the new retrieval is high in the troposphere and lower in the upper stratosphere, similar to TCCON, and that the seasonality is well captured. We determined an optimal retrieval setup covered in section 7.
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Authors: Dong Bo , Bannister Ross , Chen Yumeng , Fowler Alison , Haines Keith
Published: March 2023

Abstract. The paper presents a simplification to the Kalman smoother that can be run as a post processing step using only minimal stored information from a Kalman filter analysis, which is intended for use with large model products such as reanalyses of Earth system variability. A simple decay assumption is applied to cross time error covariances and we show how the resulting equations relate formally to the fixed-lag Kalman smoother, and how they can be solved to give a smoother analysis along with an uncertainty estimate. The method is demonstrated in the Lorenz 1963 idealised system, being applied with both an extended Kalman smoother and an Ensemble Kalman smoother. In each case the root mean square errors (RMSE) against truth, for both assimilated and unassimilated (independent) data, of the new smoother analyses are substantially smaller than for the original filter analyses, while being larger than for the full smoother solution. Typically 60 % of the full smoother error reduction with respect to the filter, is achieved. The uncertainties derived for the new smoother also agree remarkably well with the actual RMSE values throughout the assimilation period. The ability to run this smoother very efficiently as a post processor should allow it to be useful for real large model reanalysis products, especially ensemble products, that are already being developed by various operational centres.
Effects of ion composition on escape and morphology at Mars
Authors: Zhang Qi , Holmström Mats , Wang Xiao-Dong
Published: March 2023

Abstract. We refine a recently presented method to estimate ion escape from non-magnetized planets and apply it to Mars. The method combines in-situ observations and a hybrid plasma model (ions as particles, electrons as a fluid). We use measurements from the Mars Atmosphere and Volatile Evolution (MAVEN) mission and Mars Express (MEX) for one orbit on 2015-03-01. Observed upstream solar wind conditions are used as input to the model. We then vary the total ionospheric ion upflux until the solution fits the observed bow shock location. This solution is a self-consistent approximation of the global Mars-solar wind interaction at this moment, for the given upstream conditions. We can then study global properties, such as the heavy ion escape rate. Here we investigate the effects on escape estimates of assumed ionospheric ion composition, solar wind alpha particle concentration and temperature, solar wind velocity aberration, and solar wind electron temperature. We also study the amount of escape in the ion plume and in the tail of the planet. Here we find that estimates of total heavy ion escape are not very sensitive to the composition of the heavy ions, or the amount and temperature of the solar wind alpha particles. We also find that velocity aberration has a minor influence on escape, but that it is sensitive to the solar wind electron temperature. The plume escape is found to contribute 29 % of the total heavy ion escape, in agreement with observations. Heavier ions have a larger fraction of escape in the plume compared to the tail. We also find that the escape estimates scales inversely with the square root of the atomic mass of the escaping ion specie.
The Teddy-Tool v1.0: temporal disaggregation of daily climate model data for climate impact analysis
Authors: Zabel Florian , Poschlod Benjamin
Published: March 2023

Abstract. Climate models provide required input data for global or regional climate impact analysis in aggregated form, often on a daily basis to save space on data servers. Today, many impact models work with daily data, however, sub-daily climate information is getting increasingly important for more and more models from different sectors, such as the agricultural, the water, and the energy sector. Therefore, the open source Teddy-Tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values for temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure and wind speed. Thereby, mass and energy are strictly preserved by the Teddy-Tool to exactly reproduce the daily values from the climate models. Here, we describe and document the temporal disaggregation, which is based on globally available bias-corrected hourly reanalysis WFDE5 data from 1980–2019 to take specific local and seasonal features of the diurnal course empirically into account. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation, autocorrelation and extreme value analysis for 30 globally distributed samples around the world, representing different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). Consequently, sub-daily data provided by the Teddy-Tool could make climate impact assessments more robust and reliable.
An evaluation of the LLC4320 global ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
Authors: Gallmeier Katharina Martha , Prochaska J. Xavier , Cornillon Peter , Menemenlis Dimitris , Kelm Madolyn
Published: March 2023

Abstract. We have assembled 2,851,702 nearly cloud-free cutout images (sized 144×144 km2) of Sea Surface Temperature (SST) data from the entire 2012–2020 Level-2 Visible Infrared Imaging Radiometer Suite (VIIRS) dataset to perform a quantitative comparison to the ocean model output from the MIT general circulation model (MITgcm). Specifically, we evaluate outputs from the LCC4320 global-ocean simulation for a one-year period starting on November 17, 2011 but otherwise matched in geography and day-of-year to the VIIRS observations. In lieu of simple (e.g., mean, standard deviation) or complex (e.g., power spectrum) statistics, we analyze the cutouts of SST anomalies with an unsupervised Probabilistic AutoEncoder (PAE) trained to learn the distribution of structures in SST anomaly (SSTa) on ~10-to-80-km scales (i.e., submesoscale-to-mesoscale). A principal finding is that the LLC4320 simulation reproduces well, over a large fraction of the ocean, the observed distribution of SST patterns, both globally and regionally. Globally, the medians of the structure distributions match to within 2σ for 65 % of the ocean, despite a modest, latitude-dependent offset. Regionally, the model outputs reproduce mesoscale variations in SSTa patterns revealed by the PAE in the VIIRS data, including subtle features imprinted by variations in bathymetry. We also identify significant differences in the distribution of SSTa patterns in several regions: (1) in the vicinity of the point at which western boundary currents separate from the continental margin, (2) in the Antarctic Circumpolar Current (ACC), especially in the eastern half of the Indian Ocean, and (3) in an equatorial band equatorward of 15°. It is clear that (1) is a result of premature separation in the simulated western boundary currents. The model output in (2), the Southern Indian Ocean, tends to predict more structure than observed, perhaps arising from a misrepresentation of the mixed layer or of energy dissipation and stirring in the simulation. The differences in (3), the equatorial band, are also likely due to model errors, perhaps arising from the shortness of the simulation or from the lack of high-frequency/wavenumber atmospheric forcing. Although we do not yet know the exact causes for these model-data SSTa differences, we expect that this type of comparison will help guide future developments of high-resolution global-ocean simulations.
Data space inversion for efficient uncertainty quantification using an integrated surface and subsurface hydrologic model
Authors: Delottier Hugo , Doherty John , Brunner Philip
Published: March 2023

Abstract. Hydrological numerical modelling is generally designed to provide predictions of uncertain quantities in a decision-support context. In the implementation of decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data Space Inversion (DSI) provides an alternative (and highly model-run-efficient) method for predictive uncertainty quantification that avoids the need for parameter estimation. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By focusing on the direct relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using such as Integrated Surface and Subsurface Hydrologic Models (ISSHMs). These models are characterised by long run times, a penchant for numerical instability and/or complex parameterisation schemes that are designed to maintain geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions, at the same time as it provides a basis for complementary linear analyses that can explore issues such as data worth. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic stream-aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and nonlinear estimates of prediction uncertainty based on DSI are validated against a more traditional approach to prediction uncertainty quantification which requires adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. This demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify prediction uncertainty and support data worth analysis in complex hydrogeological environments.