bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring secular variation of the gravitational constant from high-resolution quasar spectra
Authors: Le T. D. 
Published: July 2024

AbstractThe exploration of potential variations in fundamental physical constants is crucial for testing of Grand Unification Theories (GUTs), which aim to unify the fundamental forces of nature. This study utilizes direct observational tests to explore these variations, offering a deep-look into the universe's distant past. By analyzing high-resolution quasar spectra of HE 0515–4414* and comparing them with laboratory-calibrated Ritz wavelengths, we establish an upper limit on the possible cosmological deviation of the gravitational constant: $$\dot{\text{G}}/\text{G}=(0.918 \pm 2.830)\times {10}^{-15}{\text{ yr}}^{-1}$$

G
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over cosmic timescales. Our findings provide a novel tool for probing the physical implications of GUTs, contributing to our understanding of fundamental physics.
Exploring the impacts of risk factors on mortality patterns of global Alzheimer’s disease and related dementias from 1990 to 2021
Authors: Mobaderi Tofigh , Kazemnejad Anoshirvan , Salehi Masoud 
Published: July 2024

AbstractAlzheimer’s Disease and Related Dementias (ADRD) affect millions of people worldwide, with mortality rates influenced by several risk factors and exhibiting significant heterogeneity across geographical regions. This study aimed to investigate the impact of risk factors on global ADRD mortality patterns from 1990 to 2021, utilizing clustering and modeling techniques. Data on ADRD mortality rates, cardiovascular disease, and diabetes prevalence were obtained for 204 countries from the GBD platform. Additional variables such as HDI, life expectancy, alcohol consumption, and tobacco use prevalence were sourced from the UNDP and WHO. All the data were extracted for men, women, and the overall population. Longitudinal k-means clustering and generalized estimating equations were applied for data analysis. The findings revealed that cardiovascular disease had significant positive effects of 1.84, 3.94, and 4.70 on men, women, and the overall ADRD mortality rates, respectively. Tobacco showed positive effects of 0.92, 0.13, and 0.39, while alcohol consumption had negative effects of − 0.59, − 9.92, and − 2.32, on men, women, and the overall ADRD mortality rates, respectively. The countries were classified into five distinct subgroups. Overall, cardiovascular disease and tobacco use were associated with increased ADRD mortality rates, while moderate alcohol consumption exhibited a protective effect. Notably, tobacco use showed a protective effect in cluster A, as did alcohol consumption in cluster B. The effects of risk factors on ADRD mortality rates varied among the clusters, highlighting the need for further investigation into the underlying causal factors.
Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model
Authors: Yao Yafeng , Zhu Yan , Li Yongheng , Wang Wei , Zhang Zhemei 
Published: July 2024

AbstractTo address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.
DeepPIG: deep neural network architecture with pairwise connected layers and stochastic gates using knockoff frameworks for feature selection
Authors: Oh Euiyoung , Lee Hyunju 
Published: July 2024

AbstractSelecting relevant feature subsets is essential for machine learning applications. Among the feature selection techniques, the knockoff filter procedure proposes a unique framework that minimizes false discovery rates (FDR). However, employing a deep neural network architecture for a knockoff filter framework requires higher detection power. Using the knockoff filter framework, we present a Deep neural network with PaIrwise connected layers integrated with stochastic Gates (DeepPIG) for the feature selection model. DeepPIG exhibited better detection power in synthetic data than the baseline and recent models such as Deep feature selection using Paired-Input Nonlinear Knockoffs (DeepPINK), Stochastic Gates (STG), and SHapley Additive exPlanations (SHAP) while not violating the preselected FDR level, especially when the signal of the features were weak. The selected features determined by DeepPIG demonstrated superior classification performance compared with the baseline model in real-world data analyses, including the prediction of certain cancer prognosis and classification tasks using microbiome and single-cell datasets. In conclusion, DeepPIG is a robust feature selection approach even when the signals of features are weak. Source code is available at https://github.com/DMCB-GIST/DeepPIG.
Audio-visual modelling in a clinical setting
Authors: Jiao Jianbo , Alsharid Mohammad , Drukker Lior , Papageorghiou Aris T. , Zisserman Andrew , Noble J. Alison 
Published: July 2024

AbstractAuditory and visual signals are two primary perception modalities that are usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals—usually speech audio. In this study, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without relying on dense supervisory annotations from human experts for the model training. A simple yet effective multi-modal self-supervised learning framework is presented for this purpose. The proposed approach is able to help find standard anatomical planes, predict the focusing position of sonographer’s eyes, and localise anatomical regions of interest during ultrasound imaging. Experimental analysis on a large-scale clinical multi-modal ultrasound video dataset show that the proposed novel representation learning method provides good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions. Being able to learn such medical representations in a self-supervised manner will contribute to several aspects including a better understanding of obstetric imaging, training new sonographers, more effective assistive tools for human experts, and enhancement of the clinical workflow.
Reduced dynamic changes in pulmonary artery compliance during isometric handgrip exercise in patients with heart failure
Authors: Hashemi Djawid , Hou Xuewen , Doeblin Patrick , Weiß Jakob , Beyer Rebecca , Neye Marthe , Erley Jennifer , Bucius Paulius , Tanacli Radu , Kuehne Titus , Kelm Marcus , Blum Moritz , Edelmann Frank , Kuebler Wolfgang M. , Düngen Hans-Dirk , Schuster Andreas , Stoiber Lukas , Kelle Sebastian 
Published: July 2024

AbstractExercise intolerance is a debilitating symptom in heart failure (HF), adversely affecting both quality of life and long-term prognosis. Emerging evidence suggests that pulmonary artery (PA) compliance may be a contributing factor. This study aims to non-invasively assess PA compliance and its dynamic properties during isometric handgrip (HG) exercise in HF patients and healthy controls, using cardiovascular magnetic resonance (CMR). We prospectively enrolled 36 subjects, comprising 17 HF patients (NYHA class II and III) and 19 healthy controls. Participants performed an HG test, and we assessed changes in PA compliance and hemodynamic flow parameters using advanced CMR techniques. We also explored the relationship between CMR-derived PA compliance metrics and established clinical indicators, ensuring the validity of our findings through intra- and interobserver agreements. HF patients had significantly lower resting PA compliance compared to controls (28.9% vs. 50.1%, p < 0.01). During HG exercise, HF patients exhibited a dampened adaptability in PA compliance. Hemodynamic responses, including heart rate and blood pressure, were not significantly different between the groups. Further analyses revealed a significant correlation between changes in PA compliance and functional capacity, and an inverse relationship with NYHA class. Our study demonstrates a marked difference in PA vascular responses during HG exercise between HF patients and healthy controls. The compromised adaptability in PA compliance in HF patients is correlated with diminished functional capacity. These findings have significant clinical implications and may guide future interventional strategies in HF management.
Positive public attitudes towards agricultural robots
Authors: Zeddies Hendrik Hilmar , Busch Gesa , Qaim Matin 
Published: July 2024

AbstractRobot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies—such as plant genetic engineering—shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany—a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.
A recirculating device of cooling water powered by solar energy for the laboratory
Authors: He Jiahui , Deng Wenhao , Zhu Maochun , Máille Gearóid M. Ó. , Wu Zihang , Wang Longsheng , Wei Yongge 
Published: July 2024

AbstractAimed at energy conservation and water saving for the lab, we have designed and constructed one kind of lab-scale small recirculating device of cooling water utilizing a water recirculator coupled to a solar energy system via a self-made multifunctional voltage regulator, which is equipped with an active heat radiator and powered by a solar energy system. It can provide cooling water for 1–3 sets of ordinary refluxing setups in series without additional consumption of water and electricity. The temperature difference between the water in the bucket and the environment is less than 4 °C for eight common solvents in single refluxing set-up or three combined refluxing setups with different solvents in series. In the performance assessment experiments for the refluxing of eight common solvents with different boiling point, the largest solvent loss is less than 6% if the condenser is open to the air in the refluxing time of 8 h, but none obvious solvent loss are found if the condensers were equipped with an oil bubbler. Control experiments indicates that the preparation of bromoethane/ethyl acetate/propyl hexanoate using our water recirculator can achieve almost unanimous yields in relative to those reactions using tap water as cooling water.
Electrical integrity and week-long oscillation in fungal mycelia
Authors: Fukasawa Yu , Akai Daisuke , Takehi Takayuki , Osada Yutaka 
Published: July 2024

AbstractThe electrical potential of the mycelia of a cord-forming wood decay fungus, Pholiota brunnescens, was monitored for over 100 days on a plain agar plate during the colonization onto a wood bait. Causality analyses of the electrical potential at different locations of the mycelium revealed a clear and stable causal relationship with the directional flow of the electrical potential from the hyphae at the bait location to other parts of the mycelium. However, this causality disappeared after 60 days of incubation, coinciding with the onset of slow electrical oscillation at the bait location, which occurred over one week per oscillation cycle. We speculated that the hyphae that initially colonized the bait may act as a temporary activity center, which generates electrical signals to other parts of the mycelium, thereby facilitating the colonization of the entire mycelial body to the bait. The week-long electrical oscillation represents the longest oscillation period ever recorded in fungi and warrants further investigation to elucidate its function and stability in response to environmental stimuli.
Reasons why life on Earth rarely makes fluorine-containing compounds and their implications for the search for life beyond Earth
Authors: Petkowski Janusz J. , Seager Sara , Bains William 
Published: July 2024

AbstractLife on Earth is known to rarely make fluorinated carbon compounds, as compared to other halocarbons. We quantify this rarity, based on our exhaustive natural products database curated from available literature. We build on explanations for the scarcity of fluorine chemistry in life on Earth, namely that the exclusion of the C–F bond stems from the unique physico-chemical properties of fluorine, predominantly its extreme electronegativity and strong hydration shell. We further show that the C–F bond is very hard to synthesize and when it is made by life its potential biological functions can be readily provided by alternative functional groups that are much less costly to incorporate into existing biochemistry. As a result, the overall evolutionary cost-to-benefit balance of incorporation of the C–F bond into the chemical repertoire of life is not favorable. We argue that the limitations of organofluorine chemistry are likely universal in that they do not exclusively apply to specifics of Earth’s biochemistry. C–F bonds, therefore, will be rare in life beyond Earth no matter its chemical makeup.
Shoreline barriers may amplify coastal groundwater hazards with sea-level rise
Authors: Su Xin , Befus Kevin M. , Hummel Michelle A. 
Published: July 2024

AbstractSubsurface barriers have been proposed to protect coastal aquifers from sea-level rise induced seawater intrusion, but the potential for groundwater emergence near subsurface barriers remains unknown. Here, we investigated how emergence changes groundwater flow conditions and influences the protective performance of subsurface barriers with sea-level rise. We tested the subterranean consequences of sea-level rise for cutoff walls and subsurface dams with cross-shore groundwater flow and salt transport models, investigating how barrier design, aquifer properties, and hydrological conditions control the potential for emergence, groundwater partitioning at the barrier, and seawater intrusion with sea-level rise. We find that most subsurface infrastructure cannot prevent seawater intrusion and emergence simultaneously. Subsurface dams spanning more than half of the aquifer thickness created emergence hazards and subsequent groundwater partitioning for all scenarios tested. Cutoff walls were less effective at reducing seawater intrusion for all opening sizes but could reduce the emergence potential compared to similarly sized subsurface dams. Our results demonstrate the challenging trade-offs in mitigating the coastal groundwater hazards of seawater intrusion and emergence with sea-level rise, where groundwater flooding inland of protective infrastructure would require combinations of subsurface impoundments and other mitigation techniques, such as pumping or drains.
Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI
Authors: Gomez Daniela , Selvaraj Michael Gomez , Casas Jorge , Mathiyazhagan Kavino , Rodriguez Michael , Assefa Teshale , Mlaki Anna , Nyakunga Goodluck , Kato Fred , Mukankusi Clare , Girma Ellena , Mosquera Gloria , Arredondo Victoria , Espitia Ernesto 
Published: July 2024

AbstractCommon beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers’ ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.
Modeling of scour hole characteristics under turbulent wall jets using machine learning
Authors: Khuntia Jnana Ranjan , Devi Kamalini , Mumtaz Mohd Aamir 
Published: July 2024

AbstractThe novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.
The mediating role of ego depletion in the relationship between state anxiety and academic procrastination among University students
Authors: Fan Junqiang , Cheng Yongjie , Tang Ming , Huang Yuxin , Yu Jingjing 
Published: July 2024

AbstractThe issue of academic procrastination is highly prevalent among university students. It not only has a deterimental effect on students' academic performance but also poses a risk to their physical and mental well-being. Anxiety, as a negative emotion, has attracted researchers’ attention in relation to academic procrastination. Research indicates a correlation between state anxiety and academic procrastination, but the underlying mechanisms that drive this association remain unclear. When individuals experience ego-depletion, it can lead to psychological exhaustion, subsequently leading to procrastination. Gender role conceptions, shaped by sociocultural and psychological mechanisms, have profound implications on individuals’ cognition, emotions, and behaviors. This study primarily aims to explore the relationship between state anxiety and academic procrastination among university students, with a particularly focus on the mediating role of ego-depletion and the moderating role of gender. A survey using the State Anxiety Scale, Ego-Depletion Scale, and Irrational Procrastination Scale was administered to 3370 undergraduates. State anxiety shows positive correlations with ego depletion and academic procrastination (r = 0.665, p < 0.01; r = 0.491, p < 0.01), while ego depletion is also positively linked to academic procrastination (r = 0.500, p < 0.01). State anxiety serves as a positive predictor of academic procrastination, with a confidence interval of 95% [0.626, 0.696]; additionally, ego depletion partially mediates the relationship between state anxiety and academic procrastination, with a confidence interval of 95% [0.168, 0.251]. Gender acts as a moderator in directly predicting the impact of state anxiety on academic procrastination and in the latter stage of mediating the effect of ego depletion. State anxiety can significantly and positively predict academic procrastination among university students. Ego-depletion partially mediates the relationship between state anxiety and academic procrastination. The direct predictive effect of state anxiety on academic procrastination, as well as the mediating role of ego-depletion, is moderated by gender. This provides educators and university students themselves with reference for addressing the issue of academic procrastination.