bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of phyllosphere Microorganism-Azolla Interaction: Insights into Incidence Rates and Metagenomic Analysis
Authors: Yang Yan-Qiu , Deng Sufang , Yang Youquan , Ying Zhao-Yang 
Published: August 2023

Abstract

Background: Azolla is a versatile aquatic fern that is rich in nutrients and possesses valuable antibacterial components, making it a useful green manure and medicinal raw plant material. However, the growth of Azolla is affected by microorganisms under different environmental conditions, the investigation on the interaction between microorganisms and Azolla is one of the crucial projects for Azollaexploration and application.
Results: In this study, we cultivated two different Azolla species, Azolla imbricata (Aim) and Azolla pinnata (Api), under identical condition to investigate their respective incidence rates. Metagenome analysis of phyllosphere microorganisms was performed to uncover the interaction between Azollaand microorganisms. Our results revealed significantly higher incidence rates in Aim compared to Api. The microbiological community taxonomy showed a predominance of Proteobacteria phylum and Burkholderiales order, with higher proportions in the Aim group. Conversely, the Api group had higher proportions of Cyanobacteria phylum, Nostocales order, Nostocaceae family, and Trichormusgenus. Moreover, pathogenic bacteria exhibited a higher relative abundance in the Aim group. We further analyzed significant differences in KEGG pathways between the two groups and identified the top 10 enriched pathways. Additionally, according to the resistance gene analysis results, five resistant genes showed different patterns between Api and Aim groups. Expect AAC6-IIa, the other five resistant genes had a higher abundance in Aim than in Api.
Conclusions: The greater disease susceptibility of Aim compared to Api may be linked to the microbial community structure of the two species. The variations in microbial community structure could be influenced by the antibacterial components present in Api, whereas Aim may lack or have lower levels of these antibacterial components.These findings provide insights into the microorganisms-Azolla interaction, aiding the development of strategies to enhance Azolla growth and utilization.
A survey of Reinforcement and Deep reinforcement learning for Coordination in Intelligent Traffic Light Control
Authors: Saadi Aicha , Abghour Noureddine , Chiba Zouhair , Moussaid Khalid , Ali Saadi 
Published: August 2023

Abstract
Intelligent traffic signal control is required for a transportation system to function properly. In contrast to existing traffic signals, where rules are typically developed manually, an intelligent traffic signal control system should dynamically adapt to real-time traffic. The use of reinforcement learning for intelligent traffic signal control is a growing trend, and recent studies have shown promising results. However, none of the current studies have tested actual traffic data yet. This paper presents the primary techniques learning and methods (RL, DL, DRL).The analysis of each technique, the learning of its strengths and limitations, in order to evaluate at which levels they satisfy the requirements of urban traffic. The paper also lines some of the simulators, which perform adaptive traffic.
Mechanical Response and Crack Evolution Law of Coal and Rock Mass Under Different True Triaxial Stress
Authors: Shi‧ Wenbao , Xu‧ Qingzhao , Miao‧ Zhuang , Qi‧ Chao , Chang‧ Jucai , li chuanming 
Published: August 2023

Abstract

In order to study the mechanical response and crack evolution law of surrounding rock under different stress environments in deep high ground stress mines, the true triaxial disturbed unloading rock test system was used to realize the original rock stress occurrence environment, and the mechanical properties of coal and rock mass with different intermediate principal stresses were studied. The intermediate principal stress deviatoric stress model was established to analyze the crack evolution law of coal and rock mass under different stress environments. The results show that when the intermediate principal stress is 3MPa, 6MPa, 10MPa and 14MPa, with the increase of intermediate principal stress, the deformation in this direction is further inhibited, and the elastic modulus and peak strength of coal and rock mass are enhanced, but the increase of coal is weaker than that of sandstone. The lateral expansion of rock mainly appears along the direction of minimum principal stress, and the development of cracks appears on the plane of σ1-σ3 and expands along the direction of σ2. From the proportion of RA-AF value, it is found that the increase of intermediate principal stress aggravates the development of shear cracks in coal and reduces the development of shear cracks in sandstone. Based on the acoustic emission signal (ringing count, cumulative energy), it is found that with the increase of intermediate principal stress, the acoustic emission signal of coal and rock mass gradually tends to be stable in the elastic stage, and the cumulative energy curve of sandstone shows ladder-like fluctuation. When the failure occurs, the acoustic emission signal of coal and rock mass increases with the increase of intermediate principal stress. The acoustic emission signal is very active when the coal and rock mass is close to failure, and the ringing count and cumulative energy increase suddenly, which indicates that the rock is about to fail. The acoustic emission activity can be used as a precursory information for judging the rock failure. The research results can provide an experimental basis for the sudden instability of deep high ground stress roadway.
Enhancing Eco-Friendly Natural Rubber Composites through the Incorporation of Spent Coffee Grounds
Authors: Somdee Patcharapon , Shettar Manjunath , Prasoetsopha Natkritta , Detsunhnoen Saksan , Matnok Suchat , Ansari Manauwar Ali 
Published: August 2023

Abstract

This research aimed to enhance the physical properties of natural rubber (NR) by incorporating spent coffee grounds (SCGs) as a filler. SEM analysis displayed embedded and evenly dispersed SCG particles in the NR matrix, without any interaction due to the use of unmodified SCG particles. The addition of SCG resulted in a notable increase of up to 49.90 (~ 19% increment compared to unmodified sample) in Shore-A hardness of the composite material; however, the tensile strength and tear strength showed a decline with increasing SCG fraction. Optimal tear strength was achieved with 10 phr of SCG, attributed to the appropriate fraction and uniform dispersion of SCG in the NR matrix. The 300% modulus remained unaffected, indicating no chemical bonding between SCG and NR particles. The study also explored the odor of composites. Odor absorption efficiency (CO2 reduction) peaked at 2% compared to various SCG fractions (0–25 phr), and the highest increment of 69.2% in CO2 absorption observed at 25 phr compared to unmodified sample. Additionally, due to the unmodified surface of SCG influenced scorch and cure time when SCG content increased up to 20 phr. Scorch time increased (2.12% at 1 phr to 23.40% at 20 phr), and the cure time improved (3.12% at 1 phr to 76.82% at 20 phr).
Intraocular Complement Activation Is Independent of Systemic Complement Activation and Is Related to Macular Vascular Remodelling in Retinal Vein Occlusion
Authors: Liu Hengwei , Zhou Yufan , Qi Jinyan , Liang Shengnan , Guo Tingting , Chen Juan , Tan Huanhuan , Wang Jie , Xu Heping , Chen Zhongping 
Published: August 2023

Abstract

Background
Retinal vein occlusion (RVO) is a major cause of vision loss. The pathogenesis remains poorly defined although inflammation is known to play a critical role. In this study, we investigated the levels of complement proteins in the aqueous humour and plasma of RVO participants and the relationship between complement levels and retinal pathologies.
Methods
The plasma and aqueous humour were collected from 20 untreated RVO and 20 cataract patients. Retinal lesions were examined by fundus stereoscopy and optical coherence tomography angiography. The levels of C1q, C2, C4, C4b, C3, C3b/iC3b, C5, C5a, CFB, CFD, CFI, CFH, and MBL were measured using the Luminex® x MAP® multiplex assay.
Results
RVO patients had significantly higher levels of C4, C4b, C3b/iC3b, CFB, CFI, and CFH in the plasma and aqueous humour compared to controls. The aqueous levels of C1q, C2, C5, C5a, CFD and MBL were also significantly higher in RVO patients than in controls. C5a was below detectable limit in the plasma in 18 and 16 participants with cataract and RVO, respectively. The intraocular levels of C5a positively correlated with C2, C4, CFB, and CFH. The intraocular levels of C2, CFB, CFH, and MBL negatively correlated with the size of foveal avascular zone. The plasma levels of C4, C4b, C5, CFD positively correlated with their counterparts in the aqueous humour in cataract patients but not in RVO.
Conclusions
The intraocular complement activation in RVO is mediated by the classical and the alternative pathways and is largely independent of systemic complement activation. Intraocular complement activation is related to retinal oedema and vascular remodeling in RVO patients.
Consequence assessment and behavioral patterns of inhibition in decision-making: modelling its underlying mechanisms
Authors: Cecchini Gloria , DePass Michael , Baspinar Emre , Andujar Marta , Ramawat Surabhi , Pani Pierpaolo , Ferraina Stefano , Destexhe Alain , Moreno-Bote Ruben , Cos Ignasi 
Published: August 2023

Abstract
Learning to make decisions depends on exploring options, experiencing their consequence, and reassessing the strategy. Several studies have analyzed various aspects of value-based decision-making, focusing on cued and immediate gratification. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood.

We designed a decision-making task in which decisions altered future options. In the absence of any explicit performance feedback, participants had to test and internally assess specific criteria to make optimal decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates.

We formalized this operation mathematically by means of a multi-layered decision-making model. The first layer described the dynamics of two populations of neurons characterizing the decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting individual participants’ behavior and it faithfully predicted non-trivial patterns of decision-making.

These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.
The association of diet quality scores with the adverse pregnancy outcomes
Authors: He Jin , Li Yan , Chi Yang , Meng Yizi , Mo Yanxiang , Chen Chen , Qu Bo 
Published: August 2023

Abstract
Gestational weight gain (GWG) and gestational diabetes mellitus (GDM), as two major adverse pregnancy outcomes, could be affected by diet patterns, and GWG also influenced GDM. Therefore, we aimed to explore the four diet quality scores and two adverse pregnancy outcomes in a more macroscopic way. 667 women for GWG part and 333 women for GDM part who were pregnant from the National Health and Nutrition Examination Survey (NHANES), aged 20 to 44 years, were involved in this study, respectively. Four diet quality scores including dietary inflammatory index (DII), dietary Approaches to Stop Hypertension (DASH), Healthy Eating In-dex-2015 (HEI-2015), and Alternative Healthy Eating Index–2010 (AHEI-2010) were chosen in this study. We found that higher HEI-2015 and AHEI-2010 were associated with lower risk of GWG, especially for advanced maternal age. Lower DII and higher DASH were associated with lower risk of GDM. These associations were robust after excluding the diabetic patients. For pregnant women with GWG, DASH was negatively associated with the risk of GDM. Summarily, adherence of healthy dietary pattern associated with decreased risk of adverse pregnancy outcomes. We recommended advanced maternal age women to adherence of HEI-2015 and AHEI-2010 to prevent GWG. For pregnant women with GWG, adherence of DASH was benefit to GDM.
Reliable and Adaptive Computation offload strategy with load and cost coordination for Edge computing
Authors: Tang Weicheng , Gao Donghui , Yu Siyu , Lu Jianbo , Wei zhiyong , Li Zhanrong , Chen Ningjiang 
Published: August 2023

Abstract

There are several important factors to consider in edge computing systems, including latency, reliability, power consumption and queue load. Task replication requires additional energy costs in the mobile edge offloading scenario based on master-slave replication for fault tolerance. Excessive task offloading may lead to sharp increase in the total energy consumption of the system including replication costs. On the contrary, new tasks cannot enter the waiting queue and are lost, resulting in unreliable problems. This paper proposes propose an adaptive task offloading strategy for balancing the edge node queue load and offloading cost (Lyapunov and Differential Evolution based Offloading schedule strategy, LDEO). The LDEO strategy customizes the Lyapunov drift-plus-penalty function by incorporating replication redundancy offloading costs to establish a balance model between the queue load and offloading cost. By integrating a low-complexity differential evolution method, the LDEO strategy computes the optimal offloading decisions with dynamic adjustment characteristics, aiming to find the optimal balance point that minimizes the offloading cost while maintaining reliability performance. Experimental results show that compared with the existing strategies, the LDEO strategy effectively reduces the redundancy and the growth rate of task completion time, with similar task execution completion rate. By ensuring that the queue length remains stable within a reasonable range, controlling both task waiting time and loss rate, and reducing the additional energy consumption associated with replication redundancy, the LDEO strategy effectively achieves optimal balancing under multiple conditions.
Examining the Impact of Health Insurance and Socioeconomic Factors on Children's Hospitalization in Indonesia: An Empirical Analysis
Authors: Rahmawati Yessi 
Published: August 2023

Abstract
The purpose of the present research is to study the impact of healthcare and insurance on the health of Indonesian youth. This study uses logistic regression, which is a statistical analysis method that can examine the relationships between variables and predict outcomes. The study was conducted through a national household survey (SUSENAS 2020), which is a data collection method that gathers information from individuals within a household. The study found that children without health insurance were significantly more likely to be hospitalized than those with insurance. Private insurance and employer-sponsored insurance were not found to significantly reduce hospitalization frequency. Other significant factors affecting children's health included age, sex, parent's education, smoking behavior, and house location. The results showed that older children were generally healthier, children of well-educated and non-smoking parents were less likely to be hospitalized, and those living in urban areas had a higher likelihood of hospitalization than those in rural areas. The study emphasizes the importance of improving access to health insurance and promoting parental education and non-smoking behavior to enhance children's overall health. Neglecting these issues could pose significant challenges for the future, given that the current young generation will eventually become the nation's leaders.
Climate Penalty on Air Pollution Abated by Anthropogenic Emission Reductions in the United States
Authors: Liu Pengfei , Yin Lifei , Bai Bin , Zhang Bingqing , Zhu Qiao , Di Qian , Réquia Weeberb , Schwartz Joel , Shi Liuhua 
Published: August 2023

Abstract
Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, how a warmer temperature deteriorates air quality, known as the “climate penalty” effect, remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US and their decadal changes using high-resolution datasets generated by machine learning models. Our findings demonstrate that, in the eastern US, efficient emission control strategies have significantly reduced the climate penalty effects on PM2.5 and O3, lowering the associated population exposure. In contrast, summer and annual PM2.5 in the western US became more sensitive to temperature, highlighting the urgent need for the management and mitigation of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change.
Enhanced Stock Market Forecasting: A Hybrid Multivariate Approach Incorporating CEEMDAN and Deep Recursive Neural Networks
Authors: Ghamgosar Ali , Alizadeh Sasan H 
Published: August 2023

Abstract
Forecasting the future of the stock market is a crucial challenge for companies, financial analysts, policymakers, and other stakeholders. The primary objective is to develop a stable model that can effectively predict stock prices in the face of a volatile financial environment. However, financial time-series forecasting is inherently difficult due to its nonstationary, non-linear, and noisy nature. While various statistical and deep learning models are commonly used for stock price prediction, there is a noticeable lack of research on the effectiveness of different feature pre-processing methods and their combinations for diverse time series. To address this challenge, we propose a hybrid method that incorporates multiple perspectives to achieve optimal results. Our study focuses on financial time-series forecasting in the context of crude oil price fluctuations. The method involves several stages, including feature pre-processing, signal processing, and deep recursive neural networks. Anomalies, representing financial noises, are detected using an unsupervised Autoencoder. Additionally, a data enrichment phase refines the time series using CEEMDAN decomposition and Fourier Transform. Finally, a bi-directional LSTM model is utilized to extract latent patterns and forecast future trends in the time series. This approach aids in the progression of stock market forecasting, imparting valuable perspectives for decision-making among businesses, financial analysts, policymakers, and other individuals with a vested interest.
Evaluating the BERTScore of synthetic text and its sentiment analysis
Authors: Gupta Pranjali 
Published: August 2023

Abstract
Text generation, which combines artificial intelligence as well as computational linguistics to write new text, is a subfield of Natural Language Processing (NLP). Significant advancements in text generation have been accomplished recently, producing human-like text. The most recent text generation models like LSTM, GPT, and BART are changing the field. It has recently become quite popular across a variety of industries, including news, reviews, social networks, and poetry composition, to mention a few. In our proposed work, the process of generating automatic text includes training a model that takes input data and generates fresh content related to the subject of the input data. We used BERTSCORE, an assessment metric for language generation evaluation metric, it calculates the similarity between two phrases, the input and the generated text, as the sum of the cosine similarities between their token embeddings. In comparison to previous metrics, BERTSCORE has a higher correlation with human assessments and offers superior model selection performance. The generated text is being preprocessed and we use the cleaned data for sentiment classification. Natural language processing's significant role in sentiment analysis has drawn a lot of attention recently. Models like BERT have demonstrated tremendous effectiveness in capturing the contextual details of text since the rise of deep learning techniques. In this paper, we explore the application of BERT for sentiment analysis on text generated by a language model. We test different methods for optimizing BERT and assess the performance of our models using news datasets. Our findings show that BERT can accurately categorize sentiment in generated text, the experimental results for the sentiment classification task for the GPT-2 generated text are 94%, and for BART is 96%.
Finite Element Analysis of Ground Floor level of Old Unreinforced Masonry Senate Hall building using macro modelling approach
Authors: Kumar Ambareesh , Pallav Kumar 
Published: August 2023

Abstract

Finite element (FE) macro 3D modelling of the unreinforced masonry (URM) wall of ground floor (GF) is evaluated for static and dynamic behavior and compared with an actual in-situ condition of the building. The Senate Hall (SH) building is built in 1915 (108 years old), designed from Indo-Saracenic style of architecture. In-situ survey and geometrical drawing of the building is used for entire modelling the unreinforced masonry load bearing (thickness 1.07m) and the partition walls (0.91m to 0.31m) on the ground floor level. A site visit of the building has been answered several unknown features such as geometrical plan, construction techniques, mechanical properties, architecture style, damaging maps (cracks, failures, damages, collapse, etc.), strengthening, renovation, retrofitting and actual conditions of the SH building. The major cracks and material deformities are observed in load bearing walls and arches. Most of the construction material deteriorated due to water seepage, moisture, and atmospheric behavior, etc. The finite element technique has been used for built 3D model of entire GF level of the SH building using macro modelling approach. The mechanical properties have been evaluated from non-destructive tests performed on the masonry and stone materials of the building after used approximate values. Static analysis results show the maximum stress and deformation response of the GF level for its self-weight and live load. Dynamic modal analysis results are also shown. Finally, simulation results of the GF level have been compared with the actual in-situ survey condition of the SH building.