bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling oil price uncertainty effects on economic growth in Mexico: a sector-level analysis
Authors: Rodríguez-Benavides Domingo , Andrés-Rosales Roldán , del Río-Rama María de la Cruz , Irfan Muhammad 
Published: May 2022

AbstractThis paper analyzes the impact of international oil price uncertainty on the different economic sectors (primary, secondary, and tertiary) in Mexico in the period 1993:1–2020:4 through a bivariate structural vector autoregressive (VAR) model with a generalized autoregressive conditional heteroskedasticity (GARCH) in mean to capture the impact of oil volatility on economic growth at the sectoral level of economic activity. The results show that the uncertainty of the international price of oil has a differentiated effect on the different sectors of economic activity in Mexico since it does not influence the primary sector; it negatively impacts the secondary sector, and there is mixed evidence in the tertiary sector. Additionally, evidence is provided that both positive and negative shocks to the international oil price have asymmetric effects at the sectoral level in Mexico. The results highlight the need to implement public policies, at the country level, that help mitigate the effect of uncertainty in the oil market and promote economic stability at the sector level.
Mechanical assessment of energy wall in the long term
Authors: Dai Quanwei , Zhang Dongmei , Li Zili 
Published: May 2022

AbstractEmbedded retaining walls equipped with ground heat exchangers is one kind of energy geostructures, harvesting the shallow ground energy for heating and cooling buildings. However, past investigations mainly focus on the thermal assessment of energy walls, but pay limited attention to mechanical wall behavior and associated ground response. At present, there is no standard preliminary code for assessing the long-term mechanical performance of energy walls. In this study, a comprehensive literature review is conducted to summarize the existing studies on the long-term mechanical behavior of both conventional retaining walls and thermo-active ones, respectively. The review identifies three most critical mechanical factors for energy walls (i.e., horizontal wall movement, ground settlement, and basement heave), while their permissible values can be referred to standard preliminary design criteria of conventional retaining walls in the short-term and limited long-term field measurements. For elaborating the application of design criteria, a parametric study is implemented to evaluate the long-term mechanical performance of energy walls in stiff clay under various thermal conditions, including the influence of thermal solicitations, station temperature and asymmetrical operation mode. The vivid assessing results show that the thermal-induced mechanical movements due to geothermal operation may cause unfavorable serviceability issues (e.g., cracks), but are unlikely to bring critical damages to the structural performance. The identified critical mechanical factors and evaluation of thermal influences in this study provide some guidance for the mechanical assessment of long-term energy wall behavior, particularly in the light of limited field measurements and demonstration cases.
Cloud-Based Optimal Control of Individual Borehole Heat Exchangers in a Geothermal Field
Authors: Stoffel Phillip , Kümpel Alexander , Müller Dirk 
Published: May 2022

AbstractIntegrating renewable energy sources is a crucial component in reducing CO2 emissions in the building sector. In particular, shallow geothermal energy is expected to play a significant role in the regenerative energy supply of buildings. An effective control strategy for the geothermal field is crucial to reduce the overall energy consumption. This paper analyzes the benefits of controlling an existing field’s individual borehole heat exchangers (BHE) using nonlinear model predictive control (NMPC) and moving horizon estimation. The considered geothermal field consists of 41 BHEs and is used for heating and cooling. Each BHE is equipped with temperature sensors for in- and outflow and has individually controllable valves, while a central hydraulic pump feeds all BHEs. The sensor measurements are accessed through a cloud platform, enabling also set point writing for the pump speed and the valve positions. To control the BHEs individually, we propose a two-stage process. In the calibration stage, a moving horizon estimator estimates the actual borehole and ground temperatures for each BHE. In the second stage, first, a nonlinear model predictive controller optimizes the number of active BHEs necessary to meet the buildings’ energy demand. With the estimated ground temperatures as a basis, it is determined which BHEs shall be (de)-activated. The active BHEs are fed with a fixed volume flow of 24 L/min to ensure turbulent heat transfer. To reduce the power usage of the pumps, an optimal control problem based on a simple hydraulic model of the geothermal field is used. The methodology is analyzed through simulations first and then validated experimentally. The results show that half or more of the BHEs could be deactivated most of the time, leading to 67% savings in electricity consumption by the hydraulic pump.The experimental validation confirms the high energy saving potential of the proposed methodology, reducing the consumption of electrical energy by 71%. Additionally, the deactivated BHEs regenerate faster and improve the field’s long-term behavior. In conclusion, the proposed strategy improves the short and long-term performance of the geothermal field.
Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey
Authors: Zhang Xiao-Qing , Hu Yan , Xiao Zun-Jie , Fang Jian-Sheng , Higashita Risa , Liu Jiang 
Published: May 2022

AbstractCataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians’ diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.
Paradigm Shift in Natural Language Processing
Authors: Sun Tian-Xiang , Liu Xiang-Yang , Qiu Xi-Peng , Huang Xuan-Jing 
Published: May 2022

AbstractIn the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.
Evaluation of the Interaction of Sex Hormones and Cardiovascular Function and Health
Authors: Willemars Myrthe M. A. , Nabben Miranda , Verdonschot Job A. J. , Hoes Martijn F. 
Published: May 2022

AbstractPurpose of ReviewSex hormones drive development and function of reproductive organs or the development of secondary sex characteristics but their effects on the cardiovascular system are poorly understood. In this review, we identify the gaps in our understanding of the interaction between sex hormones and the cardiovascular system.Recent FindingsStudies are progressively elucidating molecular functions of sex hormones in specific cell types in parallel with the initiation of crucial large randomized controlled trials aimed at improving therapies for cardiovascular diseases (CVDs) associated with aberrant levels of sex hormones.SummaryIn contrast with historical assumptions, we now understand that men and women show different symptoms and progression of CVDs. Abnormal levels of sex hormones pose an independent risk for CVD, which is apparent in conditions like Klinefelter syndrome, androgen insensitivity syndrome, and menopause. Moreover, sex hormone–based therapies remain understudied and may not be beneficial for cardiovascular health.
Neural Control of Action Selection Among Innate Behaviors
Authors: Jiang Xinyu , Pan Yufeng 
Published: May 2022

AbstractNervous systems must not only generate specific adaptive behaviors, such as reproduction, aggression, feeding, and sleep, but also select a single behavior for execution at any given time, depending on both internal states and external environmental conditions. Despite their tremendous biological importance, the neural mechanisms of action selection remain poorly understood. In the past decade, studies in the model animalDrosophila melanogasterhave demonstrated valuable neural mechanisms underlying action selection of innate behaviors. In this review, we summarize circuit mechanisms with a particular focus on a small number of sexually dimorphic neurons in controlling action selection among sex, fight, feeding, and sleep behaviors in both sexes of flies. We also discuss potentially conserved circuit configurations and neuromodulation of action selection in both the fly and mouse models, aiming to provide insights into action selection and the sexually dimorphic prioritization of innate behaviors.
Social skills and sports: Pupils of an elite school of sports are more competitive and cooperative
Authors: Haas André , Wäsche Hagen , Wittelsberger Rita , Nieken Petra , Woll Alexander 
Published: May 2022

AbstractIt is frequently observed that individuals who actively practice sports also do well in other domains of life, e.g., the labor market. While the underlying mechanisms are not yet fully understood, it is often argued that sports participation is embedded in a setting which fosters qualities which prove advantageous beyond sports. In the present paper, we examine competitive and cooperative behavior in children aged 10–13 years. Rather than on individual levels of sports participation, the focus is on the institutional setting of an elite school of sports (ESS) as compared to regular upper secondary schools. To obtain measures for competitiveness and cooperativeness, we utilize experimental methods which are established in economics. Our results show that ESS students are both more competitive and more cooperative than their counterparts at regular schools.
Geometric algebra for sets with betweenness relations
Authors: Jost Jürgen , Wenzel Walter 
Published: May 2022

AbstractGiven a betweenness relation on a nonempty set E, a certain abelian group $${\mathbb {T}}= {{\mathbb {T}}}_E$$

T
=

T
E

given in terms of generators and relations is investigated. This group controls the given betweenness relation in an algebraic form. That is, the group structure algebraically unfolds geometric relations, and in turn allows us to read off geometric properties from algebraic relations emerging from them. The most important examples for betweenness relations arise from ordered sets on the one side and from intervals in metric spaces on the other side. The structure of $${\mathbb {T}}$$
T
will be determined completely in case of totally ordered sets as well as for several classes of metric spaces.
Boron-modified perhydropolysilazane towards facile synthesis of amorphous SiBN ceramic with excellent thermal stability
Authors: Zhan Ying , Li Wei , Jiang Tianshu , Fasel Claudia , Ricohermoso Emmanuel , Bernauer Jan , Yu Zhaoju , Wu Zhenghao , Müller-Plathe Florian , Molina-Luna Leopoldo , Grottenmüller Ralf , Riedel Ralf 
Published: May 2022

AbstractSiBN ceramics are widely considered to be the most promising material for microwave-transparent applications in harsh environments owing to its excellent thermal stability and low dielectric constant. This work focuses on the synthesis and ceramization of single-source precursors for the preparation of SiBN ceramics as well as the investigation of the corresponding microstructural evolution at high temperatures including molecular dynamic simulations. Carbon- and chlorine-free perhydropolysilazanes were reacted with borane dimethyl sulfide complex at different molar ratios to synthesize single-source precursors, which were subsequently pyrolyzed and annealed under N2atmosphere (without ammonolysis) to prepare SiBN ceramics at 1100, 1200, and 1300 °C with high ceramic yield in contrast to previously widely-used ammonolysis synthesis process. The obtained amorphous SiBN ceramics were shown to have remarkably improved thermal stability and oxidation resistance compared to amorphous silicon nitride. Particularly, the experimental results have been combined with molecular dynamics simulation to further study the amorphous structure of SiBN and the atomic-scale diffusion behavior of Si, B, and N at 1300 °C. Incorporation of boron into the Si—N network is found to suppress the crystallization of the formed amorphous silicon nitride and hence improves its thermal stability in N2atmosphere.
Sex differences and associations between zinc deficiency and anemia among hospitalized adolescents and young adults with eating disorders
Authors: Nagata Jason M. , Bojorquez-Ramirez Paola , Nguyen Anthony , Ganson Kyle T. , McDonald Christine M. , Machen Vanessa I. , Downey Amanda , Buckelew Sara M. , Garber Andrea K. 
Published: May 2022

Abstract
Purpose
To determine sex differences in and associations between zinc deficiency and anemia among adolescents and young adults hospitalized for medical complications of eating disorders.

Methods
We retrospectively reviewed electronic medical records of 601 patients aged 9–25 years admitted to the University of California, San Francisco Eating Disorders Program for medical instability, between May 2012 and August 2020. Descriptive statistics, crude, and adjusted logistic regression models were used to assess the association between zinc deficiency (< 55 mcg/dL) and anemia (< 13.6 g/dL in males [M] and < 11.8 g/dL in females [F]).

Results
A total of 87 males and 450 females met eligibility criteria (age 15.98 ± 2.81, 59.4% anorexia nervosa; admission body mass index 17.49 ± 2.82). In unadjusted comparisons, plasma zinc in males and females were not statistically different (M 64.88 ± 14.89 mcg/dL vs F 63.81 ± 13.96 mcg/dL, p = 0.517); moreover, there were no differences in the percentage of males and females with zinc deficiency (M 24.14% vs F 24.89%). However, a greater percentage of males than females were anemic (M 50.00% vs F 17.61%, p < 0.001), with similar findings in the subgroup with anorexia nervosa. In logistic regression models stratified by sex and eating disorder diagnosis, zinc deficiency was significantly associated with anemia in males (AOR 3.43, 95% CI 1.16, 10.13), but not females (AOR 1.47, 95% CI 0.86, 2.54).

Conclusions
For the first time, we demonstrate that zinc deficiency is equally severe in males compared to females hospitalized with medical complications from eating disorders, with nearly a quarter of inpatients experiencing zinc deficiency. Anemia is more common in males than females hospitalized with eating disorders.

Level of evidence
Level V: descriptive cross-sectional study.
The effect of ageing on fat infiltration of thigh and paraspinal muscles in men
Authors: Engelke Klaus , Ghasemikaram Mansour , Chaudry Oliver , Uder Michael , Nagel Armin M. , Jakob Franz , Kemmler Wolfgang 
Published: May 2022

Abstract
Background
Myosteatosis, skeletal muscle fat infiltration, is associated with inflammation and fibrosis. The age-related increase of myosteatosis is an important characteristic of sarcopenia and contributes to fragility.

Aims
To investigate the impact of healthy aging on intermuscular adipose tissue (IMAT) and muscle fat fraction (FF) in the thigh and the paraspinal muscles in males.

Methods
In 54 healthy males (age 20–70), all active hobby golfers, magnetic resonance imaging was performed to determine volume of IMAT, volume of muscle tissue (MT) and of percentage of FF.

Results
Between ages 20–70, at the thigh, IMAT/MT volume and MT FF increased annually by 2.9% and 1.3%, respectively. At the psoas IMAT/Psoas volume did not change with age. MT FF increased by 1.5% annually. At the erector spinae IMAT/Erector volume decreased by 0.3% and MT FF increased by 2.8% annually.

Discussion
With increasing age, in males, thigh muscle atrophied, muscle tissue was partly replaced by adipose tissue and remaining muscle tissue also contained more fat. Similar effects were observed in the erector spinae. The psoas muscle did not atrophy, although MT FF also increased with age. Overall correlations with age were weak to moderate with higher correlations observed in the paraspinal muscles.

Conclusions
Age-related increases of muscle fat infiltration were observed in the thigh and in the spine. Muscle atrophy did not occur in the psoas. In cross-sectional studies, an adjustment of volumetric parameters by muscle volume is advisable when comparing age-dependent results.
Racial Disparities in Climate Change-Related Health Effects in the United States
Authors: Berberian Alique G. , Gonzalez David J. X. , Cushing Lara J. 
Published: May 2022

Abstract
Purpose of Review
Climate change is causing warming over most parts of the USA and more extreme weather events. The health impacts of these changes are not experienced equally. We synthesize the recent evidence that climatic changes linked to global warming are having a disparate impact on the health of people of color, including children.

Recent Findings
Multiple studies of heat, extreme cold, hurricanes, flooding, and wildfires find evidence that people of color, including Black, Latinx, Native American, Pacific Islander, and Asian communities are at higher risk of climate-related health impacts than Whites, although this is not always the case. Studies of adults have found evidence of racial disparities related to climatic changes with respect to mortality, respiratory and cardiovascular disease, mental health, and heat-related illness. Children are particularly vulnerable to the health impacts of climate change, and infants and children of color have experienced adverse perinatal outcomes, occupational heat stress, and increases in emergency department visits associated with extreme weather.

Summary
The evidence strongly suggests climate change is an environmental injustice that is likely to exacerbate existing racial disparities across a broad range of health outcomes.
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Authors: Zhang Minghan , Gu Sai , Shi Yuhui 
Published: May 2022

AbstractConventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.
Multiple Andreev reflections effect spectroscopy of LiFeAs single crystals: three superconducting order parameters and their temperature evolution
Authors: Kuzmichev Svetoslav , Kuzmicheva Tatiana , Morozov Igor , Boltalin Alexander , Shilov Andrey 
Published: May 2022

AbstractThe structure of the superconducting order parameter of LiFeAs is studied by incoherent multiple Andreev reflections effect (IMARE) spectroscopy. The high transparent superconductor–thin normal metal–superconductor (SnS) contacts are created by a planar “break-junction” technique. Below $$T_c \approx 17.5$$

T
c

≈
17.5

K, the obtained I(V) and dI(V)/dV characteristics of SnS junctions show a presence of at least three bulk superconducting order parameters in LiFeAs. We directly determine the magnitudes, characteristic ratios, and temperature dependences of the superconducting gaps and discuss their symmetry.
Land use/land cover change analysis using geospatial techniques: a case of Geba watershed, western Ethiopia
Authors: Moisa Mitiku Badasa , Dejene Indale Niguse , Hinkosa Lachisa Busha , Gemeda Dessalegn Obsi 
Published: May 2022

AbstractLand use/land cover change is a global environmental problem that significantly affects sustainable development and environmental quality. Anthropogenic activities are one of the main driving forces of land use/land cover change in developing countries like Ethiopia. This study is conducted in the Geba watershed in southwestern parts of Ethiopia. This study aimed to analyze the land use/land cover change over the past thirty years using geospatial technologies. The present study uses Landsat TM 1990, Landsat ETM + 2003, and OLI/TIRS 2020 to examine the land use/land cover change between 1990 and 2020. Land use/land cover classes are classified using a supervised classification method with the maximum likelihood technique. Results show that the agricultural land increased significantly, from 1786.6 km2 (37.2%) in 1990 to 3163.2 km2 (65.8%) in 2020, representing an increased rate of 45.9 km2/year. On the other hand, dense forest has declined from 2129.2 km2 (44.3%) in 1990 to 1127.8 km2 (23.5%) in 2020, which declines by the rate of 33.4 km2/year. Our findings suggest that sustainable land use planning and management are critically important in the study area.